Artificial intelligence (AI) and machine learning have been getting a considerable amount of press lately, but what are they and what do they mean for banking?
AI refers to the general ability of computers to reason, learn, and make decisions like human beings and perform human-like tasks in the real world. Machine learning is the specific capability of an AI system to identify patterns in large amounts of data, form models, make decisions, and confirm or adjust its models (learn) as it receives new data.
AI and machine learning in finance can benefit banks in a number of important ways. They can help streamline operations, assess risk and prevent fraud, enhance customer service, and provide personalized advice and product recommendations.
One of the most significant uses of artificial intelligence in finance is to provide smarter lending solutions. In the current economic climate of higher interest rates, market uncertainty and heightened business competition, many financial businesses face the challenge of finding more high-quality customers while effectively managing risk and profitability. An AI lending platform can be an effective tool in accomplishing these goals.
Harnessing AI and Machine Learning in Lending
Digital lending platforms took the first step beyond traditional lending systems, providing more convenience and responsiveness for consumers. AI lending goes further, with its ability to crunch far more data than a human could ever evaluate and produce quick, accurate lending decisions.
Prospective borrowers can go on an AI lending platform and fill out a fairly simple application. Then, instead of waiting for a human underwriter to sift through reams of complex paperwork, AI accesses hundreds of millions of traditional and alternative data points, analyzes that data, and quickly produces an automated lending decision.
The purpose of AI’s analysis is the same as that of a human underwriter – to evaluate a potential borrower’s financial patterns and capacity for repayment, and to assess the risk of late payments or default. Risk assessment is a critical part of formulating loan decisions. AI’s ability to do it with speed and accuracy can help banks that incorporate this technology to expand their business without compromising risk management.
AI lending can also upend the old one-size-fits-all model of credit and enable lenders to offer potential and existing customers personalized access to products and services suited to their needs. The data AI gathered on individual customers, lifestyle and financial behavior can help lenders design loans and other products that are favorable both for customers and the business.
Benefits of AI and Machine Learning in Lending
Automation is a significant advantage for banks, reducing the amount of staff time spent on repetitive standard tasks and freeing them for more creative and growth-oriented work. However, there are more benefits to AI lending.
AL can make loan decisions that are more accurate, due to the amount of data involved. Traditional loan decisions rely substantially on credit history. The underwriter looks through a credit report for clues to how well an applicant handled car loans, student loans and other lines of credit. These clues are intended to help them predict a borrower’s ability to pay back what they borrow. If a credit history is too brief or there have been late payments or defaults in the past, the applicant will likely not be approved.
However, the traditional credit history is limited in the clues it contains. For one thing, it speaks almost exclusively to past behavior. What about the consumer who has turned their financial life around after making a few early mistakes? Or the young person who has demonstrated responsibility in their spending and saving patterns, but hasn’t yet had the opportunity to handle credit?
The “big data” collected by AI gives lenders a detailed view of an individual’s purchasing and lifestyle patterns, social media activity, professional licenses, responsibility level, potential major expenses and risk of default. On a micro level, it can give lenders insights into the daily patterns of individual consumers while, on a macro level, it can take current market trends into account as well. Analysis of this vast amount of data can boost the accuracy of default probabilities and minimize potential human bias in decision-making.
In today’s economic environment, millions of individuals are unable to find the credit they want. As consumers have battled inflation, rising interest rates and the reinstatement of student loan payments, their spending power and savings rates have diminished – leading to elevated loan balances and a rise in overdue payments.
Lenders have responded by tightening their lending standards, further limiting consumer access to needed credit products. Nearly 60% of households found securing credit more challenging, while a staggering 42% of consumers acknowledged they had been denied or hadn’t received the full credit they required.
This problem hurts families, communities and lenders. Families without credit access miss out on opportunities to improve their lives, while their lack of participation and buying power hurts their communities. At the same time, lenders are missing out on potential customers and critical business growth. The economy as a whole suffers.
AI can help banks access and incorporate big data to make more loans safely to more customers and provide those loans more efficiently and far more quickly. The loan processing that takes a human underwriter days, weeks or months can be accomplished by AI in milliseconds. Families get the funds they need quickly and conveniently, while banks save time and money on operations and improve their bottom line.
Automating more processes effectively, with less human intervention, can make it easier and less costly to run the day-to-day operations of any kind of business, and banking – with its complex and critical operations – is no exception. AI is the natural next development of the digital revolution and its applications are revolutionizing the back office.
The Future of Artificial Intelligence in Finance
In the face of greater regulatory oversight and surging consumer demand for innovative banking solutions, 2024 is projected to be the year many banks invest in the future of their business with more AI.
According to International Data Corp. (IDC), global AI spending is anticipated to be approximately $450 billion by 2027, with banking contributing around 13%. This investment could yield between $200 billion and $340 billion in value annually, representing a 9%- 15% increase in banks’ operating profits, according to a 2023 report by McKinsey & Co.
Given the logarithmic pace of recent technological progress, it’s extremely likely we’ll see even more advancements in AI-driven lending technologies over the next few years. While few lenders have the resources to build their own AI solutions, partnering with an innovative AI solutions provider can be an effective and cost-efficient option.
The Federal Reserve Bank recently published a research paper on the expanding role of bank-fintech partnerships that have allowed banks to access more information on consumers through data aggregation, artificial intelligence/machine learning, and other tools. They found that fintech partnerships result in banks being more likely to offer credit cards, personal loans and mortgage loans to the credit invisible and below-prime consumers — and also more likely to grant larger credit limits to those consumers.
Successful fintech-bank partnerships have the potential to actually strengthen the banking industry and move us toward a more inclusive financial system.
