Overview
The rapid adoption of AI in the financial services sector is much more organic than most other sectors owing to the fact that analytics and statistics have long remained intrinsic to product and process intelligence choices made by financial institutions. Large volumes of historical data on banking, insurance, mortgages, and financial trading have been integrated with deep learning algorithms to automate routine tasks, mitigate risk, prevent frauds, and generate new insights. Generative AI combined with machine learning is now enabling financial institutions to deliver increasingly personalized services while streamlining more routine tasks. In a survey conducted by Nvidia in February 2024, involving more than 400 financial services professionals, it was found that 91% of financial services companies are evaluating or have already implemented AI to enhance operational efficiency.
Figure 7: Financial Services & Insurance: AI market spending in North America in US$ billion
Source: AgileIntel
Trends
Personalized Wealth Management Solutions
Even though the rapid growth of robo-advisors is a good example of the increased penetration of AI in the wealth management industry, it represents only a fraction of the technology’s potential. Robo-advisors have simple and rule-based algorithms that select exchange-traded funds (ETFs) based on information such as age, risk appetite, income, etc. However, the next-generation of AI-powered wealth management solutions will also have the ability to self-learn and therefore offer higher degrees of personalized advice for each consumer. Australian bank ANZ was among the first to explore the potential of AI when it started using IBM’s Watson supercomputer to aid financial advisors. Since then, many other firms, such as Goldman Sachs, BlackRock, UBS, Deutsche Bank, and Bridgewater Associates, either have built their own AI engines or are investing in third-party developers.
- In 2023, Bridgewater Associates, the world’s largest hedge fund, launched a new strategy geared towards enhancing returns, boosting profitability, and cultivating fresh revenue streams by increasing its investments in AI and machine learning. One step in this direction was its partnership with software maker Elemental Cognition, to leverage its AI-enabled software-as-a-service application, Cora, to bolster its investment research capabilities.
- BlackRock’ AI solution Aladdin is based on open-source technology and uses neuro-linguistic programming (NLP) to analyze large volumes of data from documents such as news stories and broker reports. For example, it analyzes data on trade activity in order to detect complex patterns and predict the transactions most likely to fail. It can also gather satellite images to see how full a retailer's parking lot is and then correlate that data to the company's revenue and stock price. In December 2023, BlackRock launched it first generative AI powered "co-pilot“, designed to transform users' written queries into personalized data visualizations.
- S&P Global’s solution Kensho uses machine learning to help find correlations responsible for movements in stock and currency prices. The engine answers questions such as “How do defense stocks react to terrorism incidents in Europe?” or “How do populist votes affect local currencies?”.
- Wells Fargo, BlackRock, UBS, and Deutsche Bank developed a solution called Sqreem that uses deep learning to analyze data on people’s digital activity to predict which products and services they are most likely to want. It also protects companies against financial crimes through algorithms that can detect anomalies relating to illicit behavior. In March 2024, Quantum AI, an AI advisory firm, initiated a collaborative partnership with Sqreem to provide the latter's technology to Qantm AI's clientele.
Fraud Detection
The proliferation of connected devices and mass digitization of companies has increased the risk of fraud, hacking, data compromise, and other cyber-vulnerabilities. According to an estimation by the United States Agency for International Development, the worldwide expense of cybercrime surpassed US$8 trillion in 2023. In order to combat this and detect patterns of anomalies, many financial institutions are turning to machine learning techniques such as logistic regression, decision tree, random forest, neural networks, and clustering. These AI techniques help financial institutions to study the buying behavior of each customer and then compare it to other indicators to build a complete picture of a transaction.
Almost all major banks also use AI algorithms to ascertain patterns and evaluate creditworthiness more accurately. Such applications have led to fewer loan defaults, improved profit margins and in turn, enhanced risk management. Financial institutions, especially in the Fintech and the Digital Banking market, are now committed to developing much more advanced models and solutions, and the use of AI in credit risk management is only expected to grow rapidly.
Advanced AI components are also being added to existing systems to facilitate identification of yet undetected transactional methods, data abnormalities and dubious relationships between individuals and entities. Allowing for a more proactive stance, such AI applications are now in place to prevent fraud before it ever happens as opposed to the traditional reactive approach. For instance, JPMC claims AI has significantly reduced fraud by improving payment validation screening, leading to a 20% reduction in account validation rejection rates and significant cost savings.
Detecting fraud is especially important in areas such as online shopping, online payment, and credit card usage, and there are many examples of companies in those areas.
- One good example is PayPal, which has used machine learning to bring its fraud rate down to just 0.32% as compared to the industry average of 1.32%. It applies machine learning to study users’ purchase history and detect patterns, which can then be used to implement new rules that prevent scams being repeated.
- The unicorn Darktrace is another example of a company offering AI-powered cybersecurity services. Its machine learning platform understands the normal patterns of behavior of each user and device connected to a corporate network.
- CO-OP Financial Services has partnered with Feedzai, a provider of AI-powered payment applications, to develop a machine-learning-based risk management tool. The company works across all payment types, including cards, vouchers, prepaid card tokens, or bitcoin.
Chatbots and Customer Relationship Management
AI powered chatbots have had a significant impact on query response time, thus in turn enabling the customer service department in these financial institutions to handle and resolve a much larger volume of queries than was ever possible before. AI enabled Chatbots utilize Natural Language Processing (NLP), to interact with customers 24/7 and personalize online conversations. These chatbots have now evolved from simple query solvers to almost teller like services like opening new accounts and directing complaints to appropriate customer service units, amongst others.
AI solutions can also identify new business opportunities and optimize marketing campaigns, potentially boosting revenue streams. An example is, Bank of America (BoA) which uses AI to recommend personalized investment strategies, potentially increasing customer engagement and product adoption. AI enabled facial recognition and voice command features used in financial applications have also worked to increase customer confidence in security and information privacy. A growing number of financial institutions, especially in consumer banking, are also applying AI to study customer behavior patterns for enhanced customer segmentation. This allows for targeted marketing and in turn, improved customer experience and interaction.
AI powered claims processing and insurance purchase
Much like its banking counterpart, the insurance industry has been utilizing AI for in a variety of use cases ranging from customer service centers, risk modeling, data forecasting and most recently, claims handling. In recent times, a significantly large number of claims activities have been replaced by AI enabled automation with algorithms handling initial claims routing and loss evaluation. For example, in case of an automobile accident, a policy bearer can take a video of the damaged vehicle, which is then translated by AI into loss descriptions and estimate amounts. For major catastrophe claims, insurance companies monitor residences and vehicles in real time using integrated IoT, telematics, and mobile phone data, all of which enable AI algorithms to assess or predict damages.
AI applications also expedite the purchasing process making simpler for the customer to engage with less active involvement on the part of the insurer. With enough data points known about customer behavior, AI algorithms can create risk profiles that drastically reduce cycle times associated with the purchase of an auto, commercial, or life policy.