How should I expect my stock portfolio to perform over the next few months? Is it worth investing in this up-and-coming company? While it might seem impossible to tell, Colby University professor Ekaterina Seregina has created an AI system that might be able to provide the answers. Her algorithm analyzes world events and then predicts how they will impact the finance sector, providing useful insights for enterprises, investors, retailers, and consumers alike.
Imagine what financial institutions could do with data that tells them which stocks and investment portfolios are most likely to under- or overperform given certain circumstances. A machine learning model could flag all portfolios likely to suffer due to a global crisis—while at the same time identifying others that would be more resilient. Even though this kind of predictive modeling falls short of providing a definitive crystal ball, it illustrates the unique power of AI in finance.
5 Applications of AI in Finance to Increase Efficiency & Drive Results
AI can process data and provide insights in seconds, making it a powerful tool when it comes to driving revenue, streamlining operations, cutting costs, and managing risk.
This plays out in dozens of ways for financial organizations every day—here are some of the most powerful applications and use cases for AI in finance.
1. Identifying Market Shifts
It can be difficult for a human to analyze price fluctuations in the market quickly enough to take advantage of trends and reversals. But, because AI can identify patterns minor shifts, and major anomalies, and then make decisions based on the insights found, it’s an effective tool for automated trading.
For example, the Quantopian algorithm can automatically decide when to buy and sell stocks. It uses the principle of mean reversion, which is based on the idea that when prices spike or dip, they’re bound to return to average sooner or later. Using this concept, the Quantopian algorithm identifies drastic price movements and then buys or sells stocks, timing the trade and adjusting quantities based on the equity’s trend data.
In addition, an AI system can use machine learning to analyze different markets and use those results to train a more advanced version of an existing machine learning model, delivering insights in moments that could take humans weeks to discern.
2. Preventing Fraud
An AI-powered fraud prevention system can save countless hours for employees processing credit card applications and managing mountains of personal data. AI systems designed to prevent fraud can detect anomalies in account transactions and flag suspicious activity, catching fraud before it can harm an organization.
In addition, AI can study other factors, such as when and from where someone submits information, correlating it with “normal,” safe behavior. If something is out of the ordinary, the system can elicit warning signal for a human worker to investigate the activity.
3. Operating Chatbots
Chatbots powered by AI can conduct a realistic, helpful conversation with a customer, allowing a financial firm to save time and human resources. A chatbot can answer basic questions regarding a customer’s account, as well as simple inquiries about the market or world events. For example, the Jenny chatbot uses natural language processing and understanding to ascertain customer intent. It then provides the answers customers are looking for, easing the burden on human service reps. The potential of an AI-enabled chatbot is limited only by its programming.
Another advantage of chatbots is they not only do the talking for you, but they can also be used to collect data regarding what customers want, how they feel, and the strengths and weaknesses of a financial company’s services. For instance, a chatbot can use natural language processing to listen to what people say and keep a log of specific words and phrases that may indicate how they feel about a certain product. Management can then take this data and use it to:
- Track customer satisfaction
- Decide which services should be phased out and which should be emulated
- Troubleshoot the issues that customers most frequently mention
4. Providing Robo-Advisory Services
It’s nearly impossible for a human to observe and manage all of the data needed to provide a comprehensive recommendation for an individual’s financial portfolio. But, with an AI-powered system, you can leave the data-crunching up to artificial intelligence, enabling a human advisor to make essential judgment calls.
A robo-advisor could act as an assistant to a financial advisor that has a range of clients with different investment goals. Some may want a more aggressive portfolio with riskier investments, while others might be more risk-averse. Regardless of each investor’s risk profile, the robo-advisor can figure out the best way to allocate their assets according to their preferences.
Further, a robo-advisor can even handle portfolio management on a client’s behalf. For instance, an investor could tell the robo-advisor the kinds of investments they’re interested in, as well as their short- and long-term goals. The advisor can then take this information, as well as reams of data about different investments, and decide how to allocate the client’s funds. This alleviates the burden of account management on human advisors, freeing them up for business-critical projects or focusing on higher-value accounts.
5. Automating Processes
In the finance space, there are several tedious, time-consuming processes that an AI system can do on behalf of human workers. For example, when customers submit applications consisting of many forms and data fields, you can eliminate the need for a human to thumb or scroll through endless pages by having an AI system do it instead.
AI can also be used to verify people’s IDs by scanning their identification credentials and checking for incomplete details or even signs of fraud. This can save considerable time and energy for human employees. At the same time, because any issues can be sent to a real person for analysis, an organization doesn’t have to eliminate the human factor. The result is a more streamlined, yet still personalized, experience for customers.
Bringing Data Science to Financial Services
Ready to start reinventing how you do business, saving your employees countless hours while still providing a top-notch experience for your customers? These are just some of the most impactful applications of AI that can help your financial institution combat the growing threat of fraud and cyberattacks, meet your customers’ rising expectations for flexibility, and maximize your profitability.
Data science isn’t always easy. For more tactical advice about how to kick-start your organization’s AI initiative, check out A Human’s Guide to Machine Learning Projects—we’ll break it down for you.