Software that can solve problems and make decisions without human input can be hugely beneficial in banking and other financial services. Artificial intelligence (AI) brings challenges as well as opportunities to those interested in finance, AI, data science, or machine learning. With the right training the future of AI in finance is yours to shape!
A Brief Overview of AI in Finance
Financial institutions have turned to machine learning, predictive analytics, neural networks, and natural language processing to solve various business problems. As with so many business innovations, the goal is often to save money, find new sources of profit, or manage risks. Software that simulates the human ability to see patterns, make decisions, and even carry on simple conversations are valuable because so many functions in finance are complex and fraught with risk. Think about making a huge loan to a business or putting together an investment portfolio for a wealthy individual.
An estimated 70% of financial institutions use machine learning for credit scoring, fraud detection, and so on. Machine learning is the sub-discipline of AI concerned with “teaching” a computer program to recognize such things as faces, risk factors for a heart attack, and evidence of fraudulent behavior on some bank account. A neural network—that is, computer code that attempts to simulate how a human or animal brain works—can be useful for sales forecasting and evaluating loan applications.
Most functions that a bank or investment firm might perform, from trying to attract new customers to preventing data loss and developing new products, can in theory be aided with artificial intelligence and some advanced statistical modeling. For example, an investment bank may use machine learning to evaluate an investing tactic or to analyze thousands of signals from stock markets, foreign currency exchanges, and other sources. A mortgage lender may use dozens of variables and hundreds of thousands of data points to determine whether to make a mortgage loan.
Here are some specific application areas and examples of how a financial services firm might use AI in each.
Process automation – One of the main processes banks can streamline is learning about their customers. Banks need to confirm a customer’s identity. Neural network software can review and validate a photo ID as part of the Know Your Customer protocols that most financial institutions must follow.
Fraud detection and prevention – Fraud and other financial crimes including money laundering are constant problems in banking and investing. Financial institutions have traditionally used rules written by experts, which are included in their business regulations or coded into software. Those hard-coded rules have been useful in terms of efficiency. Criminals, however, can figure out how to cheat those systems. AI may create a system that “learns” what suspicious behavior looks like without relying on a strict set of rules.
Loan decisions – Loan decisions hinge on measures of creditworthiness, but manual reviews take time, introduce subjective judgments, and can be biased due to the human element. A big mortgage lender might use their massive databases to construct a model for making a home loan versus using the applicant’s credit score and income.
Stock trading – In 2020, 60% to 70% of equity trades in the United States were carried out by an AI-supported system. Statistical analysis titan SAS offers Real Time Decision Manager to help financial institutions manage financial risk and make revenue forecasts. Similarly, other types of trading, like options and foreign currency, could be less risky and more profitable for the investment firm that makes good use of AI.
Portfolio planning – Computerized advisory services can be just as capable as human financial advisors at rebalancing a portfolio or constructing one with an investment goal in mind. Financial advisory services can take advantage of machine learning algorithms to evaluate investment strategies.
Customer service – Chatbots in particular have become much more effective thanks to natural language processing. Neural networks can recognize handwritten signatures and numbers, which may speed up loan applications and other processes.
Credit ratings – Machine learning algorithms can increase both speed and accuracy when it comes to determining whether someone should get a loan or how much credit to extend to a business.
AI applications offer many potential opportunities to save money, reduce risk, and develop new products or services. With those opportunities come some challenges.
Challenges and Opportunities
Artificial intelligence applications do introduce some potential challenges, particularly if the data are of questionable quality. Bias is probably the challenge that comes up most. AI software must be trained on data which may contain significant errors. Perhaps the data set excludes an important variable. Maybe the data leave out a certain common type of business or group of people. The exact process a program uses to take a data set and produce a loan decision, for example, may be a “black box” that can’t be properly evaluated. Finally, financial institutions have regulations they must follow, plus a vested interest in keeping their financial records secure.
Another challenge is a bit harder to explain, but it involves complex data sets. Computers are great at processing vast numbers of observations—loan refusals, stock prices, credit scores, etc. Where they have trouble is processing data with dozens or hundreds of variables. This complexity may need to be reduced before an AI stock trader or portfolio manager could be counted on to make smart decisions. A human expert may be able to reduce 50 variables down to 5 factors that are just as useful in making predictions.
Those challenges also introduce opportunities for software developers, data scientists, and mathematical statisticians. Bias is one potential issue, especially when it comes to making loan decisions. The advantages of AI include higher speed and accuracy, lack of bias, and lower risk of loss due to fraud. Artificial intelligence may save customers money on things like financial advisory services.
A sufficiently advanced AI bot can give solid financial planning advice at modest cost, which a savvy advisory service would pass along to customers.
Here are some real opportunities for applying statistical analysis, programming skills, and specific training in predictive analytics or machine learning:
- Extracting meaning from text and speech
- Image processing
- Making AI-powered processes more understandable
- Building effective algorithms that can make good trade decisions
Keep an eye on state and federal regulations regarding AI. In early 2023, a wide range of rules have been proposed at the state level, but AI applications in finance are unregulated now, although current rules on data privacy and fair lending still apply.
Demand for AI Professionals in Finance
The aforementioned opportunities and challenges make way for exciting career avenues. The most obvious path in terms of education is in data science, where you can learn to process huge, diverse, rapidly evolving data sets. Mathematical statisticians will also find their training in demand for creating and testing algorithms. Machine learning specialists will be needed as well to train those algorithms and improve natural language processing.