AI & ML ARE POWERING THE FUTURE OF FINANCIAL SERVICES
HOW AI & ML ARE POWERING THE FUTURE OF FINANCIAL SERVICES
2021 was a year marked by the implementation of the rapid digital transformations that first sprouted when the coronavirus pandemic hit the world in 2020. Fintech firms and other businesses around the world invested heavily in transforming to meet the needs of the new normal — remote working, social distancing, and a business world changed perhaps forever. Now we usher into 2022 industry standards and there is a reinvigorated focus on consumer-friendly tech. Logically, AI and ML are at the core of this and have the potential to become an industry that is predicted to be worth over$17billion with a CAGR of 17.9% by 2027.
In the context of the above, the prime concern of any organization is determining how to best allocate precious resources to reorganize and be well equipped to overcome the present crises that emerged due to COVID-19, but also thrive and remain competitive. The world of financial services has entered the era of artificial intelligence and machine learning. The number of uses of ML in finance is constantly rising. The technology is beginning to play a significant role in various accessible machine learning tools, a variety of algorithms, and decent computing capacity will only increase the number of interactions between machine learning and custom software products development, in Banking, FIs, Fintech, etc. to remain in sync with the trend.
Data is the blood of financial institutions. It is neither analytics nor decision-making. But some of the business entities manage to integrate these three elements into one efficient system. a “data-driven organization,” and only 28% have a “data culture.” Precisely, AI is a kind of suit of technologies underpinning it, as numerous inherent capabilities, making it a strong competitor for decision optimization:
Accurate pattern recognition, Ability to create good rules, Blazing-fast data processing speed, Ability to anticipate future events, A way to communicate with others (people or systems). The crank is that AI can’t always anticipate the full spectrum of weird and wonderful things humans do. As history (and behavioral economics) teaches us, a strategy based purely on mathematical rationality will either fail or underperform. That’s why we still need a human to make the final decision. Decision intelligence is the discipline of turning information into better actions at any scale.
AI and ML technologies are increasingly becoming integral part of daily life of human being of the world and every spheres which affects the life of human being. AI and ML are set to change the future of almost every industry.
FIs require to deploy these technologies at scale to remain relevant. Success requires a holistic transformation spanning multiple layers of the organization. Most of FIs have observed and convinced that significant increase of revenue growth prospects and pruning of operating expenses, and automate manually to improve bottom-line by at least 20% on conservative estimates.
The advance towards application of AI depend up on the nature of financial firms, such as, for Fintech, Investment firms, choose the most mentioned AI applications were algorithmic trading, fraud detection, and the best effective use of Portfolio. This replicates a special emphasis on defending and capitalize on client returns. However, Banks and other FIs envisaging most usefulness in fraud detection, recommender systems, and sales and marketing efficiency as their maximum AI use cases. On the other hand, Consumer or personal loan providing NBFC not only given attention to fraud detection and prevention, but also construct AI-enabled applications for customer acquisition and retention along with offering a better related product and improved versions of customized products and services.
Capital Market to consumer finance to Fintech, AI is empowering the future of finance. Traders are using AI and high-performance computing (HPC) to accelerate algorithmic trading and backtesting while meeting industry regulations through explainable models. Fintech and traditional banks are reforming the distribution of financial services across services and products—such as banking, lending, insurance, and payments—with AI-enabled solutions. It is cultivating productivity for financial institutions through virtual agents in call centers and automated analysis of lengthy financial documents.
BUILDING THE AI-POWERED BANK
AI is assisting the top management of FIs to offer smarter and more secure services to their clients and customers. The Royal Bank of Scotland (RBC) assembled a private AI cloud for banking to run thousands of simulations, train AI models, and analyze millions of data points in a fraction of the time compared to before. The private AI cloud has helped reduce client calls and brought sooner delivery of new applications for RBC clients. Consequently, RBC expects to transform the customer banking experience with a new generation of AI-enabled smart applications. Firms are also using AI solutions to make robust fraud detection and prevention systems, quicken risk calculations, and fraud detection. BNY Mellon, one of the world's largest cross-border payments service providers that processes more than $1 trillion daily, built a collaborative fraud detection outline that runs Inpher” secure multi-party computation which protects third party data. The bank’s ML and AI models were proficient on over 100 million data samples, and improved fraud prediction accuracy by 20%, while preservative the privacy and residency of the input training data. As we can see AI and banking go hand-in-hand because of the multiple benefits that this technology offers. According to Forbes, 65% of senior financial management expects positive changes from the use of AI and ML in banking. Thus, all banking institutions must invest in AI solutions to offer novel experiences.
The financial services sector is undergoing digital transformation, and the driving force behind it is machine learning (ML). ML provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. As the finance sector operates on tons of personal data and billions of critical transactions every second, it becomes especially expedient to make a automated regulating any adverse situation.
ACCELERATED COMPUTING FOR TRADERS
Market data volumes have poured with the advent of new instruments, data types, and venues. To stay competitive, financial institutions are bringing the power of AI and HPC to adapt to real-time market conditions and shortened trading windows. Successful trade execution is often measured in nanoseconds, and faster computing results in smarter trade strategies and increased opportunities for profit.
Integrating an end-to-end trading infrastructure that joins enterprise AI with high-speed networking is key to providing the lowest latency and highest bandwidth trading. Trading firms are scaling out with Ethernet switches, adapters, and messaging accelerators to accelerate every point in the trading cycle. Discretionary and systematic traders can be enhanced with teams of AI assistants to squash more intelligence out of target windows to maximize trading.
PROTECTING PAYMENTS WITH AI
Payments influence the global economy, it may be transferring money to family and friends, paying utility bills or shopping products online, or using the phone to check out in-store. FIs are taking the help of AI to improve security and transparency in systems for payments fraud detection and prevention, and additionally for identity verification to meet regulatory requirements associated with Anti-Money Laundering (AML), and Know-Your-Customer (KYC).
The Global company American Express has utilized AI fraud algorithms to supervise each transaction on their platform in real-time for more than $1.2 trillion taking place annually. The financial giant deployed deep-learning-based models to detect fraud and generate decisions in milliseconds.
CREATING ACCURATE INSURANCE POLICIES
AI-powered applications are remarkably affecting the insurance industry as well as insurers move beyond traditional claims management and hold digital workflows that employ a fully analytics-driven approach. This includes using AI to automate claims processing, to identify fraudulent claims, and create new digital services to increase customer satisfaction.
Cape Analytics is a computer vision startup that transforms data relating to or denoting that is associated with a particular location into actionable insights for insurers to write better policies and provide suggestions for homeowners to protect their property against wildfire damage. The startup uses AI to produce detailed data on the vegetation density, roof material, and proximity to surrounding structures along with a calculated risk that homeowners can use to take preventative action. Cape Analytics trains its models on servers and uses them for live inferencing, with geospatial data converted into actionable structured data in seconds.
FINTECHS USE AI FOR DISRUPTIVE INNOVATION
Fintechs are generating more intuitive and tailored interactions between customers and their finances using recommendation engines, conversational AI, and deep learning fraud detection models. Some of the Fintechs are enumerated below:
1. NerdWallet, a fintech-focused on personal finance, uses ML in its approval engine to match its customers with the best-fit financial products, such as mortgages and insurance. The fintech’s models learn how profile features including credit scores, outstanding balances, and credit utilization are getting members approved or declined. As their models become more familiar with underwriting procedures, they improve their ability to match NerdWallet’s members with suitable products.
2. Square, a financial services and digital payments fintech uses conversational AI to power its virtual assistant that understands and provides help for 75% of customer’s questions and reduces appointment no-shows from potential customers with sales teams by 10%. Their team uses a mix of small, medium, and large NLP models, and is working towards a general-purpose NLP model in the long term. As Square Assistant expands from dozens to thousands of tasks, its neural network models expand to handle more requests from small business customers.
3. AI is helping financial institutions drive the future of finance for their customers and clients. Ultimately, financial institutions will AI-enable hundreds, if not thousands, of applications. Those banks that invest in enterprise AI transformation stand to garner more market share, improve customer satisfaction and increase their financial performance who still operating in the traditional way.
EMBEDDED FINANCE IS NOT A NEW CONCEPT BUT FLOURISHING IN FRANCE
AlphaGo, a machine, defeated 18-time world champion Lee Sedol at the game of Go, a complex board game necessitating intuition, imagination, and strategic thinking—abilities long considered distinctly human. Since then, AI technologies have advanced even further, and their transformative impact is increasingly evident across industries. AI-powered machines are adapting recommendations of digital content to individual tastes and preferences, designing clothing lines for fashion retailers, and even beginning to surpass experienced doctors in detecting signs of cancer. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year.
Many banks, however, have fought to move from experimentation around select use cases to scaling AI technologies across the organization. Reasons are the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that impede collaboration between business and technology teams. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are deciding to enter financial services as the next adjacency. To compete positively and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences.
ADOPTING AI IS NOW A NECESSITY OF THE BANK NOT AN OPTION
Banking Industry has been changing itself in tandem with the First Industrial Revolution advancing like any other Industry. In fact, Banks have been playing a key role from 1st IR to the ongoing 4th Industrial Revolution since the beginning. As Industrial revolution has been categorized in line with the advancement in the field of science and technology. Banking Industry has been always played a key role in this revolution itself and buttressing other industries.
As per the latest technology innovations progressing Banking Industry had not been a laggard and immune from this revolution to reconsidering their relation with customers pertaining with them. The changes can be seen in the introduction of ATMs in the 1960s and card-based payments in the ’70s. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based banking so swift and convenient to operate fast in the 2010s. Surprisingly the pace of growth after 2010 in general and from March 2020 especially during the COVID period has been exceptionally fast.
Undoubtedly, we are not saying only the digital era but more apt if we denote the cyber or virtual world and in the AI-powered digital age, facilitated by reducing costs for data storage and processing, increasingly reaching and interconnecting with others, and unbelievable pace with AI technologies are making advancement. These technologies can lead to higher automation and, when deployed after monitoring for risks, may increase more accurate prediction than human decision making and much fast too. as per various research papers, the possibility of value creation the largest across industries, which has pruned the cost of providing services to the customers may open the probability of $1 trillion annual incremental value for banks.
Disruptive AI technologies can meaningfully improve banks’ ability to achieve four key results: higher profits, at-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles. Banks that fail to make AI central to their core strategy and operations as becoming “AI-first” will keep abstaining from the race and lose the confidence of their customers. This risk is further heightened by the following trends:
The expectation of customers can only be met by proactively identifying customers' preferences which have accelerated the change as per expectation hopes as adoption of digital banking increases. Initially, the COVID-19 pandemic, use of online and mobile banking channels globally has increased manifold up to 20 to 50% and is predictably to continue at this higher level once the pandemic subsides. Across diverse global markets, between 15 and 45 % of consumers demand to use digital platforms than entering the branch. Their delight in using al banking growing more expectation, especially when compared to the standards they are accustomed to from leading consumer-internet companies. Meanwhile, these digital experience leaders continuously improve the lift of more personalization, to such a point when it anticipates customer needs before awareness of the customer and offers highly-personalized services at the right time, through the right channel.
Top FIs’ use of advanced AI technologies is continually progressing. As per a survey report, more than 50 % of financial-services sector respondents have accepted that embedded at least one AI capability. The most commonly used AI technologies are: robotic process automation (36%) for structured operational tasks; virtual assistants or conversational interfaces (32 % ) for customer service divisions; and ML techniques (25%) to detect fraud and support underwriting and risk management. While for many FSs firms, the use of AI is episodic and focused on specific use cases, an increasing number of banking leaders are taking a comprehensive approach to deploy advanced AI, and embedding it across the full lifecycle, from the front- to the back-office.
Digital ecosystems are disrupting traditional financial services by dint of enabling a common access point to a may pack of services, digital ecosystems have metamorphosed the manner consumers have to discover, evaluate, and purchase goods and services. Considering As WeChat users in China can use the same app not only to exchange messages, but also to book a cab, order food, schedule a massage, play games, send money to a contact, and access a personal line of credit. Similarly, across countries, NBFC and “super apps” are embedding financial services and products in spreading growth, performing compelling experiences for customers, and disrupting traditional methods for identifying banking products and services. As a result, banks will need to rethink how they participate in digital ecosystems, and use AI to harness the full power of data available from these new sources.
Technology giants are entering financial services as the next contiguousness to their Core Banking Solution(CBS). Globally, top technology giants have built extraordinary market advantages: a large and engaged customer network; a store of valuable data, empowering a robust and precise understanding of individual customers; natural strengths in developing and scaling innovative technologies (including AI); and access to low-cost capital. Previously, tech giants have as a response entered into adjacent businesses in search of new revenue streams and to keep customers' attention with a fresh stream of offerings.
2. THE AI-BANK OF THE FUTURE LLANDSTEPS
To meet customers’ rising expectations and beat competitive threats in the AI-powered digital era, the AI-first bank will offer propositions and experiences that are intelligent (that is, recommending actions, anticipating and automating key decisions or tasks), personalized (that is, relevant and timely, and based on a detailed understanding of customers’ past behavior and context), and truly omnichannel (seamlessly spanning the physical and online contexts across multiple devices, and delivering a consistent experience) and that combines relevant products and services beyond banking. Exhibit 3 illustrates how such a bank could engage a retail customer throughout the day. Exhibit 4 shows an example of the banking experience of a small-business owner or the treasurer of a medium-sized enterprise.
Internally, the AI-first institution will be optimized for operational efficacy through extreme automation of manual tasks (a “zero-ops” mindset) and the replacement or augmentation of human decisions by advanced diagnostic engines in diverse areas of bank operations. These gains in operational performance will flow from the broad application of traditional and leading-edge AI technologies, such as machine learning and facial recognition, to analyze large and complex reserves of customer data in (near) real-time. The AI-first bank of the future will also be first ensuing the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets.
OBSTACLES PREVENTING THE BANKS FROM DEPLOYING AI & ML CAPABILITIES AR LARGE SCALE
The top-ranking giant banks face two sets of objectives, which at first glance appear to be at odds. On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization. Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.6 Two additional challenges for many banks are, first, weak core technology and data backbone and, second, an outmoded operating model and talent strategy.
Built for stability, banks’ core technology systems have performed well, particularly in supporting traditional payments and lending operations. However, banks must address the weaknesses inherent to legacy systems before they can deploy AI technologies at scale. The prime reason, these systems often lack the capacity and flexibility required to support the variable computing requirements, data-processing needs, and real-time analysis that closed-loop AI applications require. Core systems are also difficult to change, and their maintenance requires significant resources. What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases. Without a centralized data backbone, it is practically impossible to analyze the relevant data and generate an intelligent recommendation or offer at the right moment. If data constitute the bank’s fundamental raw material, the data must be governed and made available securely in a manner that enables analysis of data from internal and external sources at scale for millions of customers, in (near) real-time, at the “point of decision” across the organization. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way.
Banks’ traditional operating models further impede their efforts to meet the need for continuous innovation. Most traditional banks are organized around distinct business lines, with centralized technology and analytics teams structured as cost centers. Business owners define goals unilaterally, and alignment with the enterprise’s technology and analytics strategy (where it exists) is often weak or inadequate. Siloed working teams and “waterfall” implementation processes invariably lead to delays, cost overruns, and suboptimal performance. Additionally, organizations lack a test-and-learn mindset and robust feedback loops that promote rapid experimentation and iterative improvement. Often unsatisfied with the performance of past projects and experiments, business executives tend to rely on third-party technology providers for critical functionalities, starving capabilities, and talent that should ideally be developed in-house to ensure competitive differentiation.
BENEFIT OF BECOMING A BANK AI_ FIRST.
For the task that impedes the organization-wide deployment of AI technologies, banks must take a holistic approach with many angles. To become AI-first, banks must invest in transforming capabilities across all four layers of the integrated capability stack the engagement layer, the AI-powered decision layer, the core technology, and data layer, and the operating model.
As we will explain, when these interdependent layers work in unison, they enable a bank to provide customers with distinctive omnichannel experiences, support at-scale personalization, and drive the rapid innovation cycles critical to remaining competitive in today’s world. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise. The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack.
Layer 1: Reimagining the customer engagement layer
Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts.
First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.”8 This requires embedding personalization decisions (what to offer, when to offer, which channel to offer) in the core customer journeys and designing value propositions that go beyond the core banking product and include intelligence that automates decisions and activities on behalf of the customer. Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. The fintech’s customers can solve several pain points—including decisions about which card to pay first (tailored to the forecast of their monthly income and expenses), when to pay, and how much to pay (minimum balance versus retiring principal)—a complex set of tasks that are often not done well by customers themselves.
The second necessary shift is to embed customer journeys seamlessly in partner ecosystems and platforms so that banks engage customers at the point of end-use and in the process take advantage of partners’ data and channel platforms to increase higher engagement and usage. ICICI Bank in India embedded basic banking services on WhatsApp (a popular messaging platform in India) and scaled up to one million users within three months of launch.9 In a world where consumers and businesses rely increasingly on digital ecosystems, banks should decide on the posture they would like to adopt across multiple ecosystems—that is, to build, orchestrate, or partner—and adapt the capabilities of their engagement layer accordingly.
Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension. Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up.
Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners. Banks will need to adopt a design-thinking lens as they build experiences within and beyond the bank’s platform, engineering engagement interfaces for flexibility to enable tailoring and personalization for customers, reengineering back-end processes, and ensuring that data-capture funnels (e.g., clickstream) are granularly embedded in the bank’s engagement layer. All of this aims to provide a granular understanding of journeys and enable continuous improvement.10
Layer 2: Building the AI-powered decision-making layer
Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real-time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. Across domains within the bank, AI techniques can either fully replace or augment human judgment to produce significantly better outcomes (e.g., higher accuracy and speed), enhanced experience for customers (e.g., more personalized interaction and offerings), actionable insights for employees (e.g., which customer to contact first with next-best-action recommendations), and stronger risk management (e.g., earlier detection of the likelihood of default and fraudulent activities).
To establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying advanced-analytics (AA)/machine learning (ML) models of knowledge (e.g., repositories) across teams. Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in the business-as-usual processes. These Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish.
Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. While many banks may lack both the talent and the requisite investment appetite to develop these technologies themselves, they need at minimum to be able to procure and integrate these emerging capabilities from specialist providers at rapid speed through an architecture enabled by an application programming interface (API), promote continuous experimentation with these technologies in sandbox environments to test and refine applications and evaluate potential risks, and subsequently decide which technologies to deploy at scale.
To deliver these decisions and capabilities and to engage customers across the full life cycle, from acquisition to upsell and cross-sell to retention and win-back, banks will need to establish enterprise-wide digital marketing machinery. This machinery is critical for translating decisions and insights generated in the decision-making layer into a set of coordinated interventions delivered through the bank’s engagement layer. This machinery has several critical elements, which include:
Data-ingestion pipelines that capture a range of data from multiple sources both within the bank (e.g., clickstream data from apps) and beyond (e.g., third-party partnerships with telco providers)
Data platforms that aggregate, develop, and maintain a 360-degree view of customers and enable AA/ML models to run and execute in near real-time
Campaign platforms that track past actions and coordinate forward-looking interventions across the range of channels in the engagement layer.
Layer 3: Strengthening the core technology and data infrastructure
Deploying AI capabilities across the organization requires a scalable, resilient, and adaptable set of core-technology components
robust core-technology backbone, starved of the investments needed for modernization, can dramatically reduce the effectiveness of the decision-making and engagement layers. The core-technology-and-data layer has six key elements:
Tech-forward strategy. Banks should have a unified technology strategy that is tightly aligned to business strategy and outlines strategic choices on which elements, skill sets, and talent the bank will keep in-house and those it will source through partnerships or vendor relationships. In addition, the tech strategy needs to articulate how each component of the target architecture will both support the bank’s vision to be an AI-first institution and interact with each bank’s data management must ensure data liquidity—that is, the ability to access, ingest, and manipulate the data that serve as the foundation for all insights and decisions generated in the decision-making layer. Data liquidity increases with the removal of functional silos and allows multiple divisions to operate off the same data, with increased coordination. The data value chain begins with the seamless sourcing of data from all relevant internal systems and external platforms. This includes ingesting data into a lake, cleaning and labeling the data required for diverse use cases (e.g., regulatory reporting, business intelligence at scale, AI/ML diagnostics), segregating incoming data (from both existing and prospective customers) to be made available for immediate analysis from data to be wiped off and cleaned and labeled for future analysis. Furthermore, as banks design and build their centralized data-management infrastructure, they should develop additional controls and monitoring tools to ensure data security, privacy, and regulatory compliance—for example, timely and role-appropriate access across the organization for various use cases.
APIs are the connective tissue enabling controlled access to services, products, and data, both within the bank and beyond. Within the bank, APIs reduce the need for silos, increase the reusability of technology assets, and promote flexibility in the technology architecture. Beyond the bank, APIs speed up the ability to share externally, open for unlocking new business opportunities, and enhance customer experiences. While APIs can provide significant value, it is critical to start by defining where they are to be used and establish centralized governance to support their development and curation\Intelligent infrastructure for diversity. As companies in diverse industries increase the share of workload handled on public and private cloud infrastructure, there is ample evidence that cloud-based platforms allow for the higher scalability and resilience crucial to an AI-first strategy. Additionally, cloud-based infrastructure reduces costs for IT maintenance and enables self-serve models for development teams, which enable rapid innovation cycles by providing managed services (e.g., setting up new environments in minutes instead of days).
TRANSITIONING TO THE PLATFORM OPERATING MODEL
The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. While most banks are transitioning their technology platforms and assets to become more modular and flexible, working teams within the bank continue to operate in functional silos under suboptimal collaboration models and often lack alignment of goals and priorities.
The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. Each platform team controls its own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank. In the target state, the bank could end up with three archetypes of platform teams. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance. And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture.
By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise.
The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack. Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals.
A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank. Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent. They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams.
Equally important is the design of an execution approach that is tailored to the organization. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every quarter with an iterative build of long-term institutional capabilities. Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists.
For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative. Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success.
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Algorithms were the first form of technology in the financial services sector. In 1986, APEX (Applied Expert Systems) introduced PlanPower, a commercially applied AI financial technology that was used to create financial plans for those with an annual income over US$75,000 per year.
Now, AI is a critical part of the fintech space in terms of collecting data, analyzing information, safeguarding and facilitating transactions, creating customer-centric products, and streamlining processes.
But with great technology comes great responsibility and the application of AI and data collection in financial services is one that raises many questions in terms of management, security and regulation. The European Union recently introduced rules that will begin to shape the way AI is used, with a particular focus on the financial services sector. Shawn Tan is chief executive of AI ecosystem builder Skymind, a machine intelligence startup company supporting the open-source deep learning framework Deeplearning4j and the JVM-based scientific computing library ND4J.
AI DATA AND DIVERSITY
Diversity is a hot-button topic in terms of AI usage, along with the emergence of bias. Many researchers are working on social aspects of AI, algorithms, platforms, and privacy. The regulatory aspect of AI in the financial sector will likely include more thorough audits of training data and algorithms to identify areas where bias is treating people unfairly or blocking people from certain products.
“We have seen that in advertising as well, where certain groups (often women or Black people) don’t see Facebook adverts for better financial products like mortgages or job opportunities. The whole point of algorithmic decision-making is to discriminate – to judge people according to certain criteria like where they live, their age, their occupation. But we can design the algorithms and AI to support people or to make existing social biases worse. The future of regulation is looking to address some of these concerns.” there has been a change in thinking, and organisations are starting to embrace the use of machine learning and AI in compliance.
“You cannot underestimate the levels of criminal activity within the finance sector. Those who have prospered from illegal activity have an unlimited budget, an appetite to continue, and access to sophisticated technology.
“Compare that to your average AML (anti-money laundering) officer, and they are completely outgunned. The only way that you can begin to solve this is through the application of technology,” Dixon says.Some experts also predict the permanent shift towards digital banking and contactless payments will lead to great levels of fraud - accelerating the trend towards using AI to track and identify malicious activity such as card payments and identity theft.
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THE FUTURE OF AI CHATBOTS IN FINANCIAL SERVICES
According to Juniper Research, chatbots are the future of fintech customer servicing as they handle a multitude of requests from customers that can be managed by AI technology rather than human call handlers which can be deployed to deal with more complicated queries. Research shows that:
Successful banking-related chatbot interactions will grow 3,1505% between 2019-2023. 826 million hours will be saved by banks through chatbot interactions in 2023. 79% of successful chatbot interactions will be through mobile banking apps in 2023.
TRENDS IN FINANCIAL SERVICES AI
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Process control and optimization (PCO) by the way of process mining and management advanced tools have been boosting companies improve business processes more coherent, quick and enhanced general productivity. Customer Experience refinements employing virtual or Robo assistant chatbots powered with AI and ML will respond within seconds. Fast customer delivery is now must to sustain and an edge on competition be competitive in their business/operation. The majority of currently-used credit scoring systems are being well structured. To reach in conclusion for taking better decision is hinged on an assumed supposed customer base, including demographics, age, marital status, possible preferences. AI and ML usage for well apt decision making, compliance, and proactive and skilled customer marketing have adopted to reduce stirring and excellent customer experience. The increased use of AI by cyber defense tech companies has been assisting proactive mechanisms for detection and prevention attacks and safe guarding valuable data from hackers community of banking and fintech executives.
Artificial intelligence in finance is transforming the way we interact with money. AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management.
CREDIT DECISIONS
Credit is base of any commercial base. Now more than 80% of transactions are digital and online, few still prefer cash with ease in easier payment options isn't the only reason the availability of credit is important to consumers. Having good credit scores in receiving favorable financing options, landing jobs, and renting an apartment, to name a few examples. With so many of life's important necessities hinging on credit history, the approval process for loans and cards is more important than ever.AI solutions are empowering banks and credit lenders to make smarter underwriting decisions by utilizing a variety of factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process. Here are a few examples of companies helping the financial industry rethink the underwriting process.
ENOVA
This product is the an inventor of the Colossus platform, which utilizes AI and ML to provide advanced analytics and technology to both non-prime consumers, businesses and banks in order to facilitate responsible lending .The Colossus problem helps customers solve real-life problems, such as emergency costs for consumers and bank loans for small businesses, without putting either the lender or recipient in an unmanageable situation.
Oculus This software product finds decisions to ensure businesses, organizations and individuals have the funding they need to reach their potential. The machine learning-enabled platform analyzes bank statements, pay stubs, tax documents, mortgage forms, invoices, and more to determine loan eligibility, with areas of focus including mortgage lending, business lending, consumer lending, credit scoring, and KYC. It easier and
SCIENTIFIC
It provides normally, it is also ion to other financial-based services, Scienaptic Systemolutions through its platform. The company's platform leverages a unique predictor library to improve credit underwriting by providing an underwriting platform that gives banks and credit institutions more transparency while cutting losses. Currently scoring over 100 million customers, Scienaptic's Ether connects myriad unstructured and structured data, smartly transforms the data, learns from each interaction, and offers contextual underwriting intelligence. Adopted in one major credit card company, It made a positive impact to save in just three weeks.$15 million.
ZestFinance
ZestFinance is the maker of the Zest Automated Machine Learning (ZAML) platform, an AI-powered underwriting solution that helps companies assess borrowers with little to no credit information or history.
The platform utilizes thousands of data points and provides transparency that other underwriting systems cannot, which helps lenders better assess populations traditionally considered "at risk." ZAML is an end-to-end platform that institutions can implement and scale quickly. Auto lenders using ML underwriting by 23% annually, more accurately predicted risk and reduced losses by more than 25%, according to ZestFinance.
It analyzes thousands of data points from credit bureau sources to assess credit risk for consumer and small business loan applicants.
The platform acquires portfolio data and applies machine learning to find patterns and determine good and bad applications. Because of its accuracy, Underwriter.ai claims it can reduce defaults by 25-50%. Since working with Underwriter.ai in 2015, a major online lender providing dental financing reduced its default rate from 17.8% to 5.4%, according to a case study has cited on the company's website.
MANAGING RISK
Time is key essence in the finance world, but the risk can be deadly if not given the proper attention. Accurate forecasts predictions are crucial to both the speed and protection of many businesses. Financial markets are turning more and more to machine learning, a subset of AI, to create more exacting, nimble models. These forecast input warnings to pinpoint trends, identify risks, conserve manpower and ensure better information for future planning.
Some of the below-mentioned companies are just a few examples of how AI is helping financial and banking institutions improve predictions and manage risk.
KENSHO TECHNOLOGIES
Kensho provides machine intelligence and data analytics to leading financial institutions like J.P. Morgan, Bank of America, Morgan Stanley, and S&P Global. Kensho’s software offers analytical solutions using a combination of cloud computing and natural language processing (NLP). The company's systems can provide answers to complex financial questions in plain English. Traders with access to Kensho's AI-powered database in the days following Brexit used the information to quickly predict an extended drop in the British pound, according to a 2017 Forbes article. In March 2018, S&P Global announced a deal to acquire Kensho for roughly $550 million.
This Ayasdi creates cloud-based and on-premise machine intelligence solutions for enterprises and organizations to solve complex challenges.
For companies in the fintech space, Ayasdi is deployed to understand and manage risk, anticipate the needs of customers and even aid in anti-money laundering processes. Ayasdi is helping banks combat money laundering with its anti-money laundering (AML) detection solutions. The sheer volume of investigations has been a major strain on financial institutions. Using the company's AML solution, one major bank saw a 20% reduction in veg920% reduction in investigative volume, according to Ayasdi.
QUANTITATIVE TRADING
Quantitative trading is the process of taking into consideration using large data sets to identify patterns that can be used to make strategic trades. Artificial intelligence is especially useful in this type of trading. AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. The resulting algorithmic trading processes automate trades and save valuable time.
The following companies are just a few examples of how AI-infused technology is helping financial institutions make better trades.
This assures that alternate investment data can be collected and extracted efficiently, utilizing APIs, AI and advanced data science capabilities to ingest, validate and deliver crucial information. The first-of-its-kind technology modernizes data workflows and is infinitely scalable to serve customers of all sizes.
An AI-powered search engine for the finance industry, serves clients like banks, investment firms and Fortune 500 companies. The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research and news to discover changes and trends in financial markets. This is valuable to a variety of financial professionals, organizations and companies and specifically, the platform is very helpful for brokers. The search engine provides brokers and traders with access to SEC and global filings, earning call transcripts, press releases and information on both private and public companies.
This uses machine learning and quantitative analysis to process huge sets of unstructured data and identify real-time patterns in financial markets.
One of Kavout's solutions is the Kai Score, an AI-powered stock ranker. The Kai Score analyzes massive amounts of data, such as SEC filings and price patterns, then condenses the information into a numerical rank for stocks. The higher the Kai Score, the more likely the stock will outperform the market. A latest analysis "top picks portfolio" boasts a 21.9% compound annual growth rate (CAGR) since 2012, vastly outperforming the S&P 500's 13.3% CAGR.
This assembles proprietary deep learning technology and high-speed data storage to provide short and long-term forecasting applications. Besides, technology identifies patterns in market price-changes and translates its findings into multi-market dashboards. The company recently partnered with financial news giant Bloomberg to provide users with its "AlpacaForecast AI Prediction Market." The program predicts short-term forecasts in real-time for major markets.
PERSONALIZED BANKING
Traditional banking isn't cutting it with today's digital savvy consumers. Accenture studies reveal that of some 33,000 banking customers found 54% want tools to help them monitor their budget and make real-time spending adjustments. Additionally, 41% are "very willing" to use computer-generated banking advice. AI assistants, such as chatbots, use artificial intelligence to generate personalized financial advice and natural language processing to provide instant, self-help customer service.
Here are a few examples of companies using AI to learn from customers and create a better banking experience.
This is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. It helps banks reduce call center volume by providing customers with self-service options and solutions. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions. TD Bank Group announced plans to integrate Kasisto technology into their mobile app, providing customers with real-time support and spending insights.
This is a virtual financial assistant that integrates with Google Home, SMS, Facebook, Amazon Alexa, web, and mobile to provide customers with more convenient banking. The assistant provides services ranging from simple knowledge and support requests to personal financial management and conversational banking. In 2016 Abe released its small financial chatbot for Slack. The app helps users with budgeting, savings goals, and expense tracking.
Trim is a money-saving assistant that connects to user accounts and analyzes spending. The smart app can cancel money-wasting subscriptions, find better options for services like insurance, and even negotiate bills. Trim has saved $6.3 million for more than 50,000 people.
CYBERSECURITY & FRAUD DETECTION
The massive digital transactions take place daily as users transfer money, pay bills, deposit checks, trade stocks and more via online accounts and smart phone applications. The need to augment cybersecurity and fraud detection efforts is now a necessity for any bank or financial institution, and AI and ML are playing a key role in improving the security of online finance. Open banking, combined with a set of new players and the shift towards payment initiation and digital wallets, is also opening new doors for all types of financial crime, such as the increased risk to consumers from authorized push payments (APP) scams across payment networks, globally. Payment providers that help merchants and their customers move money across borders might also enable sanctions evasion and money laundering.
· These are analytical services with ML and AI capabilities to identify authorized payment fraud. They encompass the necessary speed and processing capabilities that are required to analyze data in real-time. Risk scoring tools use statistical models to identify possible fraudulent transactions. Risk scoring allocates a probability of fraud using evolving criteria. Mule accounts (those set up by a real customer but with fraudulent papers or identity to enable criminal use) can be targeted using modeling tools that find behavior patterns in anonymous crowdsourced intelligence from millions of daily consumer activities. The pandemic’s effect in driving increased e-commerce provided an opening for fraudsters, with the average value of attempted fraudulent purchases rising by 70% in 2022, compared with the previous year, according to a report by digital fraud prevention company shift. Detecting Digital Frauds
As per a CNBC report, “digital fraud attacks against financial services companies increased 109% in the U.S during the first four months of 2021.”Fraudsters target online transactions frequently. Machine Learning (ML) and AI tools can prevent this in claims, AML, BSA, KYC frauds by analyzing transactions, determining trends, detecting frauds in real-time, rejecting duplicate applications, and rejecting fraud applicants. The digital footprint and browsing pattern of devices such as cell phones can also identify fraudulent applications. Using a combination of OCR and AI, fraudulent documents can be identified by detecting typography discrepancies.
The following are some of the companies s providing AI-based cybersecurity solutions for this:
This company is behind Cognito, an AI-powered cyber-threat detection and hunting solution. Its platform automates threat detection, reveals hidden attackers specifically targeting financial institutions, accelerates investigations after incidents, and even identifies compromised information. In a case study it was observed that provides an overview of its work to help a prominent securities exchange prevent malware attacks. Cognito immediately identified a misconfiguration in the exchange's authentication systems that would have otherwise gone unnoticed.
SHAPE SECURITY
Adopted by some leading banks in the U.S., it curbs credit application fraud, credential stuffing, scraping and gift card cracking by pinpointing fake users. The company's ML models are trained on billions of requests, allowing the software to effectively distinguish between real consumers and bots. Shape Security's Blackfish network also uses AI-enabled bots to detect compromised login credentials, alerting both customers and companies to security breaches instantly. Shape's solutions have helped one major bank protect customers from account high jacking and detected one million credential stuffing attacks in the first week of use.
DARKTRACE
This creates cybersecurity solutions for a variety of industries and financial institutions are no exception. The company's machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms. In an underlined case study on the company's website, global financial software firm Ipreo deployed Darktrace to protect its customers from sophisticated cyber attacks. Ipreo saw immediate results in real-time threat detection and defense against internal and external threats.
TQ TEZOS
This leverages blockchain technology to create new tools on Tezos blockchain, working with global partners to launch organizations and software designed for public use. TQ Tezos ensures that organizations have the tools they need to bring extraordinary ideas to life across industries like fintech, healthcare, and more.
SHAPESHIFT
This is a decentralized digital crypto wallet and marketplace that supports more than 750 cryptocurrencies across eleven blockchains. The platform is home to 500,000 wallets and sees 150,000 active users per month. ShapeShift has also introduced the FOX Token, a new cryptocurrency that features several variable rewards for users.
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AI-driven decision analytics.
Cassie Kozyrkov, Chief Decision Scientist at Google
A decision intelligence framework leverages AI/ML superpowers to help you make real business decisions.
AI helps you collect the information you need to make the right decision without settling for a suboptimal choice due to lack of time, facts, or the ability to quickly access data. We can’t know everything, but AI has a much larger capacity than humans to rummage through every bit of data at its disposal.
Here’s a simple analogy from finance to illustrate how artificial intelligence and decision-making works:
· AI research can help you build a mobile banking app.
· Applied AI is using that mobile app.
· Decision intelligence is using a mobile banking app effectively to meet your goals and switching to another app when you need to do something else.
In essence, decision intelligence is all about finding the right means and establishing the right process for a streamlined flow of insights.
· r a customer segment, a personalized portfolio allocation scheme, or the optimal email marketing sequence for a targeted demographic
· Scoring algorithms to prioritize the most profitable leads and make custom credit scoring model.
The role of decision intelligence is to model different outcomes and scenarios based on the available data.
For example, an AI algorithm may determine that a particular client can maximize their returns if they purchase more stocks and drop some of their bonds. But from personal conversations, you may know that the client prefers a safer investment strategy.
Or the decision algorithm may suggest that now is the best time to invest in an aggressive push notification campaign, but you’d prefer not to undermine your brand image by annoying the heck out of your user base.
The point is that AI is great at suggesting various means to an end so that you can cherry-pick the best options using your judgment.
ASSET AND INVESTMENT MANAGEMENT
This field is particularly ripe for the adoption of decision intelligence! Here are the top use cases worth looking into:
· Analysis of alternative data for investment decisions — weather forecasts, online company sentiment, media coverage, etc. — to improve hedging strategies
· Intelligent client outreach based on recent behavior patterns both online and in-person
· Real-time access to automated insights on individual customers’ portfolios
Morgan Stanley WealthDesk is an apt example of decision intelligence in action. The WealthDesk platform allows Morgan Stanley advisors to run advanced scenario analysis for their clients in real-time and propose an array of viable investment strategies.
WealthDesk also has a predictive Next Best Action (NBA) tool, powered by machine learning, that can make highly accurate predictions for Morgan Stanley customers based on recent life events. For instance, if a customer recently had a child, the system may suggest the optimal time to set up a college fund and offer a series of other financial management tips.
Retail banking
Retail banks also have a lot to gain from advanced analytics, especially in light of rising competition from digital banks.
Here are several main value opportunities:
Improved pricing strategies. One US bank used machine learning to analyze discounts private bankers offered their customers. What they found was that their bankers doled out unnecessary discounts too often. After correcting the strategy, the bank’s revenue within several months.
Data-driven product marketing. After deploying a group-wide analytical ecosystem, Lloyds Banking Group attributed 24% of new leads directly to the new analytics solution as it enabled the bank to market its products with higher precision at the right price point.
Advanced segmentation and personalization. An Asian bank fed several datasets to its proprietary decision engine: customer demographic data, credit card statements and transactions, POS data, online and mobile payments, credit bureau data. After churning through all those insights, the system identified over 15,000 micro customer segments. As a next step, the bank developed a next-product-to-buy model that suggested the right products to these segments. As a result, the likelihood of a sale increased threefold..
Beyond making decisions, AI engines can do all sorts of lead scoring tasks and optimize backend operations for lending as a service such as lo?-termine if it’s legitimate or fraudulent. Their algorithm has been perfected on hundreds of thousands of datasets and has an incredibly low rate of false positives.Similarly, intelligent decision models can be deployed to perform automated due diligence for large transfers(including cross-border ones) to speed up clearance. The “three-day good funds model” is no longer good enough for most customers.
SOME SPECIAL CHARACTERISTICS OF AL AND MLBottom of Form
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1, ML algorithms used in finance work best for pattern identification. They detect correlations among tons of sequences and events, extracting valuable information that’s disguised among vast data sets. Such patterns are often overlooked or could not be compared with humans intelligent . The ability of ML to learn and predict enables FinTech providers to recognize new business opportunities and work out coherent strategies.
2. FinTech companies that are exploring ML in banking and Finance can expect higher interest from venture funds. Venture Scanner examined funding by AI tech categories and concluded that machine learning platforms and ML applications not only led the sector in Q2 2018 funding but dominate the industry in all-time funding.
But what makes banking and finance one of the most targeted business segments for ML? It’s definitely the tremendous volume of data and the nearly infinite size of this segment worldwide. There are many MLS, including for banking and credit offerings, payments and remittances, asset management, personal finance, and regulatory and compliance services. One of the main benefits of ML in banking is volumes of data — including accurate accounting records and other numbers — that have been saved by financial companies for years can now be turned into effective business drivers.
3, Interest in peer-to-peer lending has skyrocketed both on the part of borrowers and investors. Along with P2P lenders, traditional banks are also looking for new mechanisms to improve market share without additional risk. Credit scoring is one of the most useful applications of machine learning in FinTech.
Mls: use cases in finance give lenders better insights into a borrower’s ability to pay by working with far more data and more complex calculations than conventional models. Machine learning processes more layers of data and isn’t limited to FICO scores and income data. Such applications of machine learning in finance open alternative data sources to lenders. Thousands of factors, such as data from social profiles, telecommunications companies, utilities, rent payments, and even health checkup records will now count. Ml algorithms compare aggregated data points with those of thousands of other customers to generate an accurate risk score. If a risk score is under the threshold set by the lender, a loan will be approved automatically.
THE BENEFITS OF MACHINE LEARNING IN BANKING CREDIT SCORING.
More loan approvals attract borrowers who were previously overlooked.
Trustworthy credit scores with fewer lending risks.
credit score or need to rebuild their credit.
Intellias has extensive experience in FinTech solutions. They’ve assisted a US-based SaaS lending provider with developing an ML-enabled credit score calculator and microservices software architecture. It runs with the help of ML algorithms and a custom-built AWS-based fault-tolerant database to get the most data about borrowers and their businesses.
Machine learning in FinTech can evaluate enormous data sets of simultaneous transactions in real-time. Moreover, the ability to learn from results and update models minimizes human input. Using machine learning techniques, FinTech providers can label historical data as fraudulent or not fraudulent. By running ML algorithms, the system will learn to recognize activity that looks suspicious. ML models can detect unusual activity, for instance in the course of an online transaction.
Feedzai is a startup that offers one of the most mature machine learning engines, which is quick at taking advanced fraud prevention measures.
Biotech combines behavioral biometrics with machine learning to recognize and prevent human and non-human cybersecurity threats mainly in banking, payments, and insurance.
Ravelin is a London-based company that uses machine learning to prevent and stop fraud in online payments.
Machine learning in banking and finance helps companies comply with ever-changing regulations
The role of machine learning in regulatory compliance
As if billions of dollars spent on regulatory compliance were not enough for financial firms, the majority still have to deal with more new rules and regulations. Focusing on regulatory issues in FinTech and banking requires lots of time and money. Even so, this investment can’t guarantee that all new rules are followed in a timely manner.
Among top ML use cases in finance are applications under the category of Regulatory Technology (RegTech). Because ML algorithms can read and learn from a pile of regulatory documents, they can detect correlations between guidelines. Cloud platforms with incorporated machine learning algorithms used in finance can automatically track and monitor regulatory changes as they appear. Banking institutions can also monitor transaction data to identify anomalies automatically. This way, machine learning can ensure that customer transactions comply with regulatory What requirements. learning
Banking organizations can more successfully conform with applicable regulations, laws, and supervisory expectations.
Time-consuming and often tricky tasks can be performed by machines instead of humans.
Regulatory work can be done faster with minimized risks of non-compliance, reducing multiple manual tasks.
Here are several providers worth mentioning in this category:
Pender Systems is a FinTech company that works with unstructured data to streamline the compliance process for its clients.
Compare.ai is a Silicon Valley startup that uses adaptive machine learning models in FinTech to automate research and track financial regulatory content and regulatory updates in a single platform.
ComplyAdvntage is a US-based startup that uses machine learning to accelerate FinTech compliance and enable online fraud prevention tools.
Providers enrich the customer experience using machine learning in customer service
There are several reasons why people choose FinTech services over traditional ones. With machine learning’s ability to delve into petabytes of data to find out exactly what matters to a particular customer, financial institutions can create personalized offers. Even better, machine learning algorithms in banking and finance can analyze customer data and return predictions about a user’s preferences. This way, companies can know what services or offerings a particular client is likely to appreciate.
AI and ML platforms in the framework of customer service infrastructure
ple of a rewarding machine learning use cases in banking is a chatbot. Machine learning supports a new generation of chatbots that are more intelligent, human-like, and client-oriented. As chatbots learn from each interaction, the conversations they hold become more helpful and personalized. Less need to build or expand customer service departments is another great benefit, especially for small and mid-sized financial operators.
Chatbots will be behind 85% of all the customer service interactions by the year 2020.
What are the benefits of ML for customer service infrastructure?
· Increased revenue thanks to improved user experiences and better productivity.
· Companies that use machine learning for advanced customer service are perceived as something more in touch.
· Clients appreciate innovation-led FinTech businesses that simplify their lives and add real value.
Here are several providers worth mentioning:
Kassito uses AI and ML algorithms to power omnichannel virtual assistants.
Wells Fargo was the first US bank to launch an AI-driven customer chat experience for Facebook Messenger.
Bank of America Erica, an AI-based virtual assistant, was launched in March 2018 and helped more than 1 million users in the first three months.
Machine learning is the new superpower on the stock market
The vast volumes of trading operations result in tons of historical data — an unlimited potential for learning. Still, historical data is only the grounds on which predictions are made. ML algorithms monitor data sources available in real time, such as news and trade results, to pinpoint patterns indicating stock market dynamics. The task left to traders is to determine which ML algorithms to include in their strategies, make a trading forecast, and choose a behavioral pattern.
DATA ANALYTICS BECOMES MORE THAN JUST STATISTICS.
Over the last few years, the value of data has been growing and forecasted that by 2025, more than 180 zettabytes of data will be in existence. Two years later, by 2027, the data market is set to be worth at over $103 billion. It means that companies will likely invest heavily in comprehending and the value of the data and its use in making smart business decisions. So not quantity is However, it’s not just the quantity of data that matters, it’s the quality of the analysis that counts. Investments in consumer behavioral analysis are set to rise, and there is a renewed focus on gaining a deeper understanding of the current market.
CONVERSATIONAL AI ENHANCES CLIENT ENGAGEMENT.
Nowadays, consumers expect response times to be faster and more convenient to them, 24/7 communication is the new normal for many. However, for many businesses, it’s almost impossible to ensure round-the-clock communications, and this is where conversation AI is coming in.
With an estimated3150% in terms of successful chatbot interactions between 2019 and 2023 and an estimated 862 million hours saved for businesses in the future, it’s clear that chatbots will continue to impact how business communication is done in the future. Conversational AI is transforming chatbots from a stopgap in consumer communications to a genuinely useful tool to help consumers, and this is something we are likely to see more of in coming years as AI techniques are able to make chats more “human.”
MODULE-BASED SOLUTIONS GAINING TRACTION.
Now, low- and no-code module-based solutions are gaining popularity due to their potential to offer clients the ability to customize software without having to develop a fully tailored solution. With a predicted revenue generation of$18n by 2030, giving it a CAGR of 31.1%, Gartner estimates that over 65% of application development activity will be low/no-code in the future, which will speed up development processes, increase time-to-market and make adapting to industry changes so much faster.
Influence of the metaverse (Web 3.0).
Ever since Facebook changed its name this month to Meta, the metaverse is all the world can talk about, and it’s not without good reason. While by and large, leaders are unsure precisely how the metaverse, a shared virtual space, will look in 2022 and beyond, there are some things that fintech firms should watch out for. Crypto, NFTs and digital tokens are taking on a whole new life, and the way finance is done online is changing. Facebook’s name change could prove more than just a rebranding but instead suggests a much bigger development is at hand.
What areas of fintech should companies focus on in 2022?
As the world moves ahead and leaders plan for 2022 and the future, it’s essential to start planning digital transformations now before it’s too late to catch up to competitors.
To choose the technologies that will reinforce your business in the future, the best thing to do is start strategically planning how this technology will fit in your overall business plan. Analyze your business processes and use smart big data to discover how you can improve and meet your consumer’s needs. The future will no doubt be data-driven, so this is a good starting point for any business seeking to digitally transform.
CHATBOTS
Undoubtedly, chatbots are one of the best examples of practical applications of artificial intelligence in banking. Once deployed, they can work 24*7, unlike humans who have fixed working hours.
Additionally, they keep on learning about the usage pattern of a particular customer. It helps them understand the requirements of a user in an efficient manner. One of the best examples of AI chatbot in banking apps is Erica, a virtual assistant from the Bank of America. This AI chatbot can handle tasks like credit card debt reduction and card security updates. Erica managed over 50 million client requests in 2019.
TRACKING MARKET TRENDS
AI in financial services helps banks to process large volumes of data and predict the latest market trends, currencies, and stocks. Advanced machine learning techniques help evaluate market sentiments and suggest investment options.AI for banking also suggests the best time to invest in stocks and warns when there is a potential risk. Due to its high data processing capacity, this emerging technology also helps speed up decision-making and makes trading convenient for both banks and their clients.
Data collection and ANALYSIS
Banking and finance institutions record millions of transactions every single day. Since the volume of information generated is enormous, its collection and registration turn into an overwhelming task for employees. Structuring and recording such a huge amount of data without any error becomes impossible.
In such scenarios, AI-based innovative solutions can help inefficient data collection and analysis.. This, in turn, improves the overall user experience. The information can also be used for detecting fraud or making credit decisions.
CUSTOMER EXPERIENCE
Customers are constantly looking for a better experience and convenience. For example, ATMs were a success because customers could avail themselves of essential services of depositing and withdrawing money even when banks were closed. This level of convenience has only inspired more innovation. Customers can now open bank accounts from the comfort of their homes using their smartphones. Integrating artificial intelligence in banking and finance services will further enhance consumer service and increase the level of convenience for users. AI technology reduces the time taken or credit gets automated using AI, which means clients can eliminate the hassle of going through the entire process manually. In addition, AI-based software can reduce approval times for facilities such as loan disbursement.
AI banking also helps to accurately capture client information to set up accounts without any error, ensuring a smooth experience for the customers. have serious impacts on banking and financial industries. During such volatile times, it’s crucial to take business decisions extra cautiously. AI-driven analytics can give a reasonably clear picture of what is to come and help you stay prepared and make timely decisions.
AI also helps find risky applications by evaluating the probability of a client failing to pay back a loan. It predicts this future behavior by analyzing past behavioral patterns and smartphone data.
REGULATORY COMPLIANCE
Banking is one of the highly regulated sectors of the economy worldwide. Governments use their regulatory authority to ensure that banking customers are not using banks to perpetrate financial crimes and that banks have acceptable risk profiles to avoid large-scale defaults.
In most cases, banks maintain an internal compliance team to deal with these problems, but these processes take a lot more time and require huge investment when done manually. The compliance regulations are also subject to frequent change, and banks need to update their processes and workflows following these regulations constantly.
AI uses deep learning and NLP to read new compliance requirements for financial institutions and improve their decision-making process. Even though AI banking can’t replace a compliance analyst, it can make their operations faster and more efficient.
PREDICTIVE ANALYTICS
One of AI’s most common use cases includes general-purpose semantic and natural language applications and broadly applied predictive analytics. AI can detect specific patterns and correlations in the data, which traditional technology could not previously detect.
These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.
PROCESS AUTOMATION
RPA algorithms increase operational efficiency and accuracy and reduce costs by automating time-consuming repetitive tasks. This also allows users to focus on more complex processes requiring human involvement.
As of today, banking institutions successfully leverage RPA to boost transaction speed and increase efficiency. For example, JPMorgan Chase’s CoiN technology reviews documents and derives data from them much faster than humans can..
Real-world examples of artificial intelligence in banking
A few big banks have already started leveraging AI technologies. To improve their quality of service, detect fraud and cybersecurity threats, and enhance customer experience.
JPMorgan Chase: Researchers at JPMorgan Chase have developed an early warning system using AI and deep learning techniques to detect malware, Trojans, and phishing campaigns. Researchers say it takes around 101 days for a Trojan to compromise company networks. The early warning system would provide ample warning before the actual attack takes place.
It can also send alerts to the bank’s cybersecurity team as hackers prepare to send malicious emails to employees to infect the network.
Capital One: Capital One’s Eno, the intelligent virtual assistant, is the best example of AI in personal banking. Besides Eno, Capital One is also using virtual card numbers to prevent credit card fraud. Meanwhile, they are working on computational creativity that trains computers to be creative and explainable.
Apart from commercial banks, a number of investment banks such as Goldman Sachs and Merrill Lynch have also integrated analytical AI-based tools in their routine operations. Many banks have also started utilizing Alphasense, an AI-based search engine, that uses natural language processing to discover market trends and analyze keyword searches.
Now that we have looked into the real-world examples of artificial intelligence in banking, let’s dive into the challenges that exist for banks using this emerging technology.
Challenges in the wider adoption of AI in finance and banking
The wide implementation of high-end technology like AI is not going to be without challenges. From the lack of credible and quality data to security issues, a number of challenges exist for the banks using AI technologies.
So, without further ado, let’s take a look at them one-by-one:
1. Data security: One of the key challenges of AI in banking is the amount of data collected that contains sensitive information requires additional security measures to be implemented. So, it’s important to look for the right technology partner who will offer a variety of security options to ensure your customer data is appropriately handled.
2. Lack of quality data: Banks need structured and quality data for training and validation before deploying a full-scale AI-based banking solution. Good quality data is required to ensure that the algorithm applies to real-life situations. Also, if data is not in a machine-readable format, it may lead to unexpected AI model behavior. So, banks accelerating towards the adoption of AI need to modify their data policies to mitigate all privacy and compliance risks.
3. Lack of explain ability: AI-based systems are widely applicable in decision-making processes as they eliminate errors and save time. However, they may follow biases learned from previous cases of poor human judgment. Minor inconsistencies in AI systems do not take much time to escalate and create large-scale problems, thereby risking the bank’s reputation and functioning.
To avoid calamities, banks should offer an appropriate level of explain ability for all decisions and recommendations presented by AI models. Banks need to understand, validate, and explain how the model makes decisions.
· Augmenting Decision-making Process
Predictive analytics based on information influx augments the decision-making process for various scenarios, such as eligibility calculation for lending or policy issuance. It evaluates all new applications, identifies the tagged data, does real-time validation with third-party systems, and forwards them to straight-through-processing if all required criteria are met. Failures are sent for review.
· Improving Risk Accuracy
As per Deloitte, “40% of the health insurance customers, 38% of home insurance customers, and 48% of motor insurance customers are willing to track their behavior and share this data with insurers for a more accurate premium.” An AI-enabled underwriting engine analyzes existing historical databases, scans through customers’ current policy data, tracks customers’ behavior, determines the risk, and decides the accurate premium. FIs can effectively leverage AI to evaluate first-time applicants with no credit score by calculating their creditworthiness based on online transactions.
· Delivering Hyper-personalized Products and Services
AI solutions can analyze the browsing patterns to decide the visitor’s purpose. This enables financial institutions to improve a visitor’s propensity to become a customer by sending personalized offers, increasing the conversion rate. Current customers’ transaction details, real-time location, and even social groups can be used as input to send personalized and contextual offers.
Insurance carriers can similarly leverage machine learning and mathematical models to analyze customer data such as exercise, nutrition, working patterns, and medicinal usage, to provide customized life, health, and specialized insurance policies.
· Reducing Manual Intervention
A McKinsey survey of U.S retail banking customers found that at the banks with the highest degree of reported customer satisfaction, deposits grew 84 percent faster than at the banks with the lowest satisfaction ratings.
AI-enabled tools like chatbots and voice-AI can significantly improve customer care services with 24X7 availability, no waiting time, and a better customer experience. Voice AI can stimulate a conversation in natural language, and chatbots can use customer data based on account information, social media interaction, and past customer interactions to deliver contextual responses. Human agents can utilize their time in more value-added services.
According to The Economist Intelligence Unit, “banks and insurance companies expect an 86% increase in AI-related investments into technology by 2025”.
communist Party invented the Five-Year Plan to exploit electric power. Indeed, it wouldn’t be an exaggeration to say that modern planning practices originated with Lenin’s plan for the electrification of the Soviet Union. To appreciate the importance of electrification, it is worth reading Lenin’s short Report on the Work of the Council of People’s Commissars.
Today, the most serious practitioner of Soviet-style planning is the Chinese Communist Party. In 2015, it announced the $1.68 trillion Made in China 2025 plan, to do with AI as Lenin had done for electric power. The plan is to transform the Chinese economy and dominate global manufacturing by 2030. China has neither the entrepreneurial nimbleness of America nor the capable public finance systems of Western Europe, but it is putting a lot of money into digital dominance. The question is whether this will be enough.
The last two decades witnessed the rise of China as an economic power; the next 10 years will decide whether it will eventually become a superpower. For now, President Xi’s approach could be summed up much as Lenin’s strategy was in 1920: State capitalism is the People’s Party plus artificial intelligence.
The story goes that in 2018, President Donald Trump complained to President Xi Jinping that Made in China 2025 was insulting to the U.S. because it aimed to make China the global leader in technology.
The key role digitization plays in the financial lives of more and more of the world’s population, electronic payments are at the epicenter of this transformation. Payments are becoming increasingly cashless, and the industry’s role in fostering inclusion has become a significant priority. Payments also are supporting the develop me of digital economies and are driving innovation — all while functioning as a stable backbone for our economies.
head to 2025, what do you expect to be your organization’s top 3 challenges over the next 5 years in the order of priority for an organization as such, Digital transformation Impact of new technologies(21%), Regulatory compliance (20%), Increasing frequency of cyber threats (19%), Attracting new customers (19%), Low or zero interest rate environment (17%), Attracting and retaining talented employees(17%), Increasing profitability of customers(16%)
Geopolitical uncertainty(15%),Climate change and environmental issues e.g. ESG(15%), Retaining existing customers(15%)Pressure on Fees(15%).New and digital only market entrants (14%).Crisis response preparedness(14%) Customers loss of trust in their financial institutions(13%), Investor demands (12%),Product development(11%),Increasing inequality (9%),Inadequacy of basic infrastructure (8%),Don't kno(1%),Other (please specify)(0%).
Digital payments— a shift that might ultimately lead to a cashless global society. Global cashless payment volumes are set to increase by more than 80% from 2020 to 2025, from about 1tn transactions to almost 1.9tn, and to almost triple by 2030, according to an analysis by PwC and Strategy&.
Asia-Pacific will grow fastest, with cashless transaction volume growing by 109% until 2025 and then by 76% percent from 2025 to 2030, followed by Africa (78%, 64%) and Europe (64%, 39%). Latin America comes next (52%, 48%), with the US and Canada growing least rapidly (43%, 35%).
This means that by 2030 the number of cashless transactions will be about double to triple the current level, across regions.
During COVID-19 lockdowns, many people adopted digital behaviors, accelerating the proliferation of mobile-first digital economies and rendering cash even less relevant to daily life than it already was (although in less developed economies, cash remained essential). In our latest global survey of banking, fintech, and payments organizations, 89% of respondents agreed that the shift towards e-commerce would continue to increase, requiring significant investment in online payment solutions. Not only that, but they agreed (97%) that there will be a shift towards more real-time payments.
Underneath the shift to cashless lies a larger, more profound change. Not only are traditional ways of paying for goods and services — including the humble paper check and analogue invoices — set for radical transformation, but the entire infrastructure of payments is being reshaped, with new business models emerging.
That reshaping involves two parallel trends: an evolution of the front- and back-end parts of the payment system (instant payments; bill payments and request to pay; and plastic cards and digital wallets); and a revolution involving huge structural changes to the payment mix and ecosystem (emergence of so-called “buy now, pay later” offerings; cryptocurrencies; and work underway on central bank digital currencies).
Both evolution and revolution are sweeping the globe, but in different ways and at different paces, creating a complex payments matrix. Many organisations are trying to figure out where to play — and win — in that matrix, as evidenced by the intense level of merger and acquisition (M&A) activity since 2017.
The key asset in all of this is data. Payments generate roughly 90% of banks’ useful customer data — information about who is buying what, how much, and when. This is creating new revenue streams for payments businesses that can monetise that data, yet also exposes them to issues and risks related to data privacy. How the payments matrix develops will be determined by the response of banks, technology companies, regulators, governments and consumers to arguably the most profound change in how money moves — even what defines money in our society — for decades to come.
CONCLUSION
Six macro trends — driven by a combination of consumer preference, technology, regulation and M&A – will define how the next five years play out. We believe leadership teams need to understand each of these trends in order to properly plan for their future.3. D
1. Inclusion and trust
In 2014, the World Bank set a goal under its Universal Financial Access program that by 2020, adults who were not part of the formal financial system would be able to have access to a transaction account to store money and send and receive payments. That goal is still some way off from being achieved, but there’s a growing number of initiatives to address, like Thailand’s PromptPay which enables users to make and receive payments using bank accounts or digital wallets linked to their national ID, mobile phone number or email address. By 2019,it had attached 43 million subscribers, in a country with a population at the time of 69.5 million.
In developing countries, financial inclusion will continue to be driven by mobile devices and providing access to affordable, convenient payment mechanisms. By 2025,smartphone penetration is estimated to reach 80% globally driven by uptake in emerging markets like Indonesia, Pakistan, and Mexico. Trust in these systems, particularly as central banks consider the feasibility of CBDCs, puts new emphasis on the role of supervisors to ensure data privacy and traceability for consumers and businesses.
2. Digital currencies
CBDCs — digital tokens or electronic records that represent the virtual form of a nation’s currency — along with private sector cryptocurrencies are predicted to have the biggest disruptive impact over the next 20 years (see Figure 4). In our survey, financial services organisations in Europe, the Middle East and Africa with more than US$5bn in revenues cited “market uncertainty and potential disruption,” such as the introduction of CBDCs, within their top three concerns.
Prominent private sector examples like the Diem, proposed in 2019 by Facebook as a form cryptocurrency that would be backed by a basket of sovereign currencies, could replace account-based payments with a tokenised system of non-sovereign payment systems.
Scepticism within central banks about the potential of private sector cryptocurrencies to undermine the conduct of monetary policy may begin to shift, as some players have recently said they’re prepared to facilitate use of such digital assets.
3. Digital wallets
Digital wallets allow consumers to load and store payment methods and access funding sources, such as cards or accounts, on their mobile devices. These wallets will be increasingly pivotal as a payment “front end,” as exemplified by Apple Pay, the relaunched Google Pay and the rise of super-apps WeChat Pay and Alipay in China. The use of digital-wallet-based transactions grew globally by 7% in 2020, according to a report by FIS, a financial services technology group, which predicts that digital wallets will account for more than half of all e-commerce payments worldwide by 2024, as consumers shift from card-based to account- and QR code-based transactions.
In response, banks and card companies have been partnering with or investing in digital wallet businesses to create payments platforms with scale, such as Standard Chartered bank’s venture with TOSS, the largest payments company in South Korea, operated by Viva Republica.
Looking ahead, as many as 86% of our survey respondents agreed with the prediction that traditional payments providers will collaborate with Fintechs and technology providers for innovation. 45% of respondents “strongly agreed” that there will be increased investment in mobile technology beyond retail payments to support business-to-business (B2B) payments and the digitalization of supply chains. traditional accounts to digital wallets and as regulators force the industry to strengthen, or build up, domestic infrastructure for payments.