TheCreditapplication.com’s Post

The #bankingindustry often deals with massive amounts of #data. However, humans lack the ability to interpret such large data accurately. As a result, #banks are turning to #AI to evaluate them and find crucial insights that might help them decrease #risk and improve the #customerexperience.     Have you used AI in your bank? Tell us in the comments.  If you still haven’t, feel free to contact us: https://lnkd.in/eed776aA  #aitobi #artificialintelligence #bankingsector #banking #finance #finance #frauddetection

To avoid payment disruptions, it's crucial to have a credit application that safeguards your business from delinquent accounts, obligation defaults, and nonpayments. To ensure that your credit application is effective, consider including the following essential elements in the form. #B2Bdigitalcredit #financialsolutions #creditmanagement #seamlessintegration #digitalcreditapplication #AI #innovation #creditapplication Thomas Dobis, Veena Gundavelli, Terry Jordan, Dexter Peavy, Brian Shappell, CBF, Chris Mann, Raghavendra Shankar, Qualikh S A https://lnkd.in/gTFhJUg

💰 Your data, their interest. The hidden cost of AI 🤖 We all trust banks with our money, right? When we deposit it, we assume it’s still ours, right? In reality, the bank takes control, invests it, and in return, we get a small reward, interest. 👉 Now, replace 'money' with 'data', and replace 'bank' with 'AI provider'. Companies deposit their data into AI systems, believing it will boost innovation, efficiency, and growth. Just like a bank, the AI provider takes full control. In return, you get the 'interest', a smarter, faster, more capable system. But here’s the kicker, who benefits the most? AI models improve by learning from the data you provide. Your input makes the system smarter, but you’re not the only one profiting from it. The AI provider owns the model, refines it with your data, and possibly uses it in ways you never intended. Just like money in a bank, once your data is inside an AI system, it’s no longer fully in your hands. When you take out a loan, you read the terms and conditions and you ask... ▪️ What’s the interest rate? ▪️ How long does it last? ▪️ What happens if I miss a payment? But do companies ask the same critical questions when handing over their data to AI providers? 👉 Do you really understand what happens to your data? 👉 Who truly owns the value created by it? 👉 Are the benefits worth the trade-off? Banks give us interest, AI gives us innovation, but at what cost? Before companies rush into AI, they should think about their data deposits, because without control, you’re not really the owner, you’re just a depositor, earning a small return while someone else makes the real profit. 🤔 What do you think? Should companies be more cautious with their AI deposits? Let’s discuss! 👇 #AI #dataownership #artificialintelligence #innovation #dataprivacy #responsibleAI #risk #datatradeoff #AIgovernance #compliance #termsandconditions #IPR

Beware finance bros: AI is coming for banking before any other kinds of - https://lnkd.in/grFe-PWA Bad news for the one percent: According to a new exhaustive report from Citigroup, finance is on track to bear the brunt of AI-related displacement. AI will “profoundly change money,” the 124-page report declares in its introductory paragraph. Sure, it creates “new opportunities for growth and innovation, often improving our

We have been talking with a lot of banks and financial institutions about adopting AI in their credit assessments, and I find a common misconception surrounding AI in credit scoring. While AI can significantly expedite financial inclusion initiatives when assessing unbanked customers, it is actually incredibly powerful when assessing existing and banked customers as well. Traditional credit scoring models often treat customers as static clusters. For instance, if you’re evaluating a lawyer with a certain salary, the model assigns the same weight to their salary as it does for all lawyers, regardless of other factors like family members, car ownership, phone type, or where they live. Not to mention, that traditional models, often fall into survivorship bias, where both non-performing and good-performing customers are often above a certain credit threshold. In our example, this lawyer could get unfavorable terms, such as bad pricing, or a low limit, making them take their business elsewhere, even though they could have been a profitable customer for the bank. This inability to personalize the offering to their exact context means losing out on a potentially loyal customer without actually increasing risk. Here comes AI.. By dynamically adjusting variable weights, based on various contexts and treating the complex relationships between these different variables, AI ensures highly tailored, responsible, and competitive terms that attract and retain high-value customers without increasing risk. AI enables a much deeper, more granular, and truly individualized assessment than traditional methods. AI creates customer loyalty, optimizes risk management, bringing the cost and time to decision significantly lower, and creates really differentiated financial products. Certainly, the unbanked or underbanked use case for AI is definitely impactful, but the agility it brings to any financial product cannot be overstated. #ai #credit_scoring #decisioning #banks #finanical_institution #bnpl

Discover InferIQ’s AI-driven platform for seamless bank statement extraction. Reduce costs by 75% and save over 99% time compared to manual extraction while boosting accuracy. Read more: https://bit.ly/3LAsde8 #Banking #FinTech #AI #BankStatementExtraction #DataExtraction #FinancialServices

You must have realized how it feels to see that financial services have recently been so customized. Be it in the form of bespoke investment advice to you or through warnings against unusual spending, it is AI at the backend that makes things tick. AI is making a difference in finance everywhere: • Personalized Investment Advice: You may have heard about those robo-advisors, like Betterment and Wealthfront. Using AI, they provide investment advice in a very customized way for your goals and risk tolerance—all with just the click of a button. • Fraud Detection: AI is involved each time your bank informs you about some suspicious activity. It monitors transactions in real-time and detects abnormalities that might point to fraud, thus helping to protect your hard-earned money. • Customer Service: Have you ever chatted with any bank's virtual assistant? 24/7 inquiries can be handled by AI-driven chatbots that efficiently and quickly resolve issues, hence making banking more accessible and convenient. AI is not a fad; it's a game-changer in finance. It runs through volumes of data to help institutions make better decisions, manage their risks, and offer services that are more attuned to our needs. The future of finance has arrived, and it is powered by AI. But still, one may ask: how much can we really trust AI regarding our financial affairs? How do you think AI is going to change how we operate our money? Share in the comments below! ???? #AIinfinance #FinancialAI #Technology

"Financial institutions must develop internal guidelines to operationalise the use of AI systems in a manner compliant with applicable regulation" 🏦 AI is well-established in financial services due to benefits such as automation of repetitive tasks and cost reduction, but it is important that it is governed in the right way, including through internal policies on issues such as risk management and lifecycle management To find out more about how AI is used in syndicated learning and the safeguards needed to minimize harm, check out Charles Kerrigan's blog on the Holistic AI feed below 👇 #ethicalai #fintech #financialservices #aiethics #responsibleai #AIgovernance

Treasury issues request for information on use of AI in financial services Interesting article by By Peter D. Hardy & Nathaniel Botwinick on the U.S. Department of the Treasury (“Treasury”) released Request for Information on the Uses, Opportunities, and Risks of Artificial Intelligence (“AI”) in the Financial Services Sector (“RFI”). AI is a broad topic and the term is sometimes used indiscriminately; as the RFI suggests, most AI systems being used or contemplated in the financial services sector involve machine learning, which is a subset of AI. The RFI implicitly concedes that Treasury is playing “catch up” and quickly needs to learn more about AI and how industry is using it. The RFI discusses a vast array of complex issues, including anti-money laundering (“AML”) and anti-fraud compliance, as well as fair lending and consumer protection concerns – particularly those pertaining to bias. In the press release for the RFI, Under Secretary for Domestic Finance Nellie Liang stated that Treasury is seeking to understand . . . . how AI is being used within the financial services sector and the opportunities and risks presented by developments and applications of AI within the sector, including potential obstacles for facilitating responsible use of AI within financial institutions, the extent of impact on consumers, investors, financial institutions, businesses, regulators, end-users, and any other entity impacted by financial institutions’ use of AI, and recommendations for enhancements to legislative, regulatory, and supervisory frameworks applicable to AI in financial services. Treasury is seeking a broad range of perspectives on this topic and is particularly interested in understanding how AI innovations can help promote a financial system that delivers inclusive and equitable access to financial services. Continue reading the the full Article below: https://lnkd.in/e9uUAeS2 #FintechNews #Finance #ai #DigitalBanking #payments #BankingTechnology #fintech #fusionalgo #banking #temenos #p2pfintech #thinkers360

AI systems can analyze transaction patterns in real time, identifying anomalies that may indicate fraudulent activity with a speed and accuracy beyond human capabilities. #fraud #banking #finance #ai #algorithms