Rizz or AI - Which is your word of the year?

Today (4th December) the Oxford University Press announced their word of the year for 2023 as “Rizz”.  The word, a shortened version of “Charisma” is defined as “style, charm or attractiveness; the ability to attract a romantic or sexual partner”.  Coming from someone from the Tom Holland, “I have no rizz whatsoever” school, the Collins Dictionary word of the year is a more fitting “artificial intelligence”

Artificial Intelligence (AI) has been a universal topic across all sectors– with financial services being one of the largest proponents for AI.  At a recent EY event, we asked how many times people were using AI tools such as ChatGP, Bard, Dall-E or Microsoft Designer.  The responses – an average of a couple of times a week – were perhaps lower than I expected but speaks to the potential as more and more of us get used to embedding the tools in our everyday.

The application of AI in financial services is not a novelty, but it has come a long way from its rudimentary forms used back in the 1980s. While AI can provide significant benefits, financial institutions must stay cautious and approach its adoption into their practices with vigilance and great responsibility.

Let’s have a look at the key opportunities and risks AI brings to financial services companies.

The AI revolution – making processes better, faster, cheaper

Enhanced efficiency and productivity

According to EY’s survey on Generative AI in Retail and Commercial Banking, 78% of banks see productivity and efficiency as primary drivers for utilising AI in their practice.  Financial institutions can automate tasks such as data collection, data entry and analysis, customer service queries, and fraud detection. This can help free up time, allowing people to focus on higher-level tasks and strategic decision-making. It also elevates accuracy by decreasing the chance for human error in certain processes.

The use of AI-powered chatbots is an immediate area banks are focusing on.  Got right, it can enhance communication and response times with customers, allow immediate access to internal policies and procedures and reduce friction.

In a recent case study EY showed that a bank could use its unstructured data to “gain a better understanding of what top performers do and apply these learnings to improve training, coaching and overall performance trajectory.” 

Security and fraud detection

AI also has the potential to significantly improve fraud detection in the financial sector by analysing and identifying patterns in big data, identifying anomalies, and flagging suspicious activities. Fraud detection systems powered by AI can use algorithms to scan customer data to detect potentially fraudulent behaviors, such as unauthorized access to accounts or large and unusual transactions.

AI-powered fraud detection systems can learn from past fraud incidents, enabling them to refine and enhance their detection capabilities. This allows financial institutions to prevent fraudulent activities before significant losses occur.

A world of hyper-personalisation

Hyper-personalization will leverage AI to go further than current customer segmentations, enabling the tailoring of messages and to the creation of experiences and unique touchpoints that are personalised to the individual.  This will create a different type of dialogue with customers.

Refining economic and monetary predictions

By processing vast datasets, detecting patterns and incorporating multiple variables, AI can refine economic and monetary predictions for indicators such as inflation, GDP growth, and unemployment rates. These enhanced models can inform policy choices, improving decisions on interest rates, liquidity management, and macroprudential measures.

Revolutionizing financial transactions and investing

The advent of AI-driven trading systems will result in unmatched speed, precision, and innovation. By analyzing vast datasets, these systems can identify hidden correlations and execute high-frequency trades with ease. The adaptability and complexity of AI, exhibited through tools such as artificial neural networks and sentiment analysis will result in new trading strategies.  For example, by analysing “unstructured data” from social media and other public sources to gauge public sentiment on relevant issues could shaping investment strategies.   This is something that regulators will pay close attention to; the risk of amplifying biases and intensifying volatility.

Challenges as well as opportunity

Ethical concerns

The use of AI in finance raises ethical concerns regarding job losses, privacy, and fairness in decision-making. During a conversation between Rishi Sunak and Elon Musk, Musk told Sunak he thought AI was "the most disruptive force in history", speculating the technology would be able to "do everything" and make employment as we know it today a thing of the past. "I don't know if that makes people comfortable or uncomfortable,"

I agree with Jonathan Sears, an EY global people advisory services global technology leader who said that “AI might present a first draft of a piece of work, but it’s well-trained and trusted people who make final decisions.” To avoid falling victim to disseminating or using low-quality or false information, companies must put humans at the center of AI transformation. I encourage you to read the whole article written by Jonathan: How AI can augment a people-centered workforce.

Data privacy at risk?

While AI has the ability to scan social media and process “alternative data,” there are privacy concerns around the technology. This raises fundamental questions. For example, should people have their social media used for such purposes? Also, can AI draw the line between using data for picturing public opinion and invading people’s privacy?

In addition there are bias concerns. Certain demographics are underrepresented in public datasets and even the best AI technology is only as good as the database that it is trained on. It is essential to ensure that AI is ethically responsible, transparent, and accountable to its users to prevent the creation of black boxes.

The price of progress

Integrating AI in the financial sector requires significant investments in technology, infrastructure, and workforce re-skilling.  Significant resources of time and money need to be invested into developing an AI system and training your staff to use it. In some cases, even whole new teams or AI departments AI must be established.  There will also be some misses as well as hits with such a new technology where the use cases are still being discovered.

Security challenges

The increased use of AI in finance also poses certain security risks, including increased potential for cyber-attacks and data breaches. As with any other technology, AI can also be used for malicious purposes. Financial institutions must stay ahead in the game and adopt rigorous security measures to protect their data against such risks.

Regulatory challenges

In such a fast-moving space it is somewhat inevitable that regulators will be playing catch up with new developments.  However, there is a recognition of the need for a level playing field, and that there does need to be independent regulation.  At the recent Bletchley Park AI Summit, the final declaration referenced “that countries should consider the importance of a pro-innovation and proportionate governance and regulatory approach that maximises the benefits and takes into account the risks associated with AI.”

Transformative…over time!

AI is set to transform the finance sector, bringing in numerous opportunities ranging from increased productivity and accuracy to new strategies. However, this is not without its challenges, with issues such as ethical concerns, economic barriers, and security risks needing attention.  It will also take time and experimentation to find out where AI has the greatest impact.  As a firm EY has invested heavily in EY.ai and is conscious that AI will inevitably shakeup the profession.  But as a firm that is we are well placed to advice on the creation of AI policy and frameworks, how to connect an AI ecosystem, how to land initial use cases and then scale them. 

So, whilst rizz may come and go, AI looks like it will last a whole lot longer and change the world around us.