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May 14th, 2019

3 Excellent Use Cases for AI in the Financial Sector

Money changes everything, especially when it comes to artificial intelligence. Consider the typical financial professional: whether they’re a banker, broker or insurance agent, they have a strong vested interest in protecting customers’ financial well-being and finding productive investments.

If AI algorithms make their jobs easier, they’ll invest in them.

Stories on the financial press often mention “black box” algorithms designed to drive investment outcomes in stocks, debt and other financial instruments. While that’s a crucial use case for AI in finance, there’s a lot more happening in this space beyond Wall Street.

For instance, AI and machine-learning algorithms can search for patterns in financial transactions to help us avoid fraud, optimize our investments and streamline B2B operations. A quick review of these use cases can be helpful if you’re new to the idea of implementing AI in your company.

Anti-Fraud

When you buy coffee and a blueberry muffin with your debit card, your bank needs to know if that’s really you making the purchase. A PIN offers one layer of protection that a determined crook can easily overcome, so your bank needs more data. Most likely, it’ll run your transaction through an approval algorithm that can scan recent purchases and make an educated guess about your identity.

Today’s anti-fraud algorithms use simple rules, such as scanning only for previous purchases made with a single debit or credit card. In the years to come, AI will deploy complex rules that pull in data from increasing numbers of variables — essentially creating a behavioral signature that’s extremely difficult for a criminal to fake.

Soon, AI will be able to analyze data accurately enough to predict not only what we would do, but also what we wouldn’t do. That insight will throw up more roadblocks for would-be fraudsters.

Consumer Finance

We’re already seeing a burst of financial-planning apps. Robo-advisors use AI algorithms to help consumers pick profitable investments. Websites like Intel’s Mint.com allow users to import data from their financial accounts to track their wealth and manage spending.

Ideally, the data from these apps could be correlated to analyze your financial health the same way a fitness tracker extrapolates your physical health from your heartbeats, step counts and other real-time data. Today’s financial apps supply many pieces of the holistic puzzle of financial health, but it’s incomplete.

Every AI project eventually bumps up against limitations in its data sources, and personal finance is no exception. For AI to reach its full potential, financial companies will have to find secure, private methods to share data and remove silos.

Business to Business

AI and ML algorithms can scan enough patterns over enough time to predict future outcomes. While marketers in the consumer sector crave this kind of data to project sales and product demand, there’s also broad B2B potential for predictive AI.

Managing cash-flow challenges such as payroll, receivables and vendor payments boils down to optimizing revenues and expenses. Predictive algorithms can help companies do a better job of making sure they always have enough cash on hand for day-to-day operations while setting aside more retained earnings for reinvestment when they have excess capital.

Digital applications already produce substantial volumes of data to feed into AI algorithms. Sophisticated data-science projects can pull in external data on factors such as weather, demographic shifts and commodities prices to better predict future financial risks and opportunities.

Expect More AI innovations in Finance

Finance is an especially tricky arena for AI because of privacy regulations and companies’ natural desire to control their legal liabilities. And while it’s easy to extol the potential of learning algorithms, it’s difficult to make them work well. Data comes from multiple sources in many formats. We have to assess whether the data is accurate or producing false-positives. If inaccurate data pollutes useful data, we have to scrub out the inaccuracies.

For all these challenges, the financial industry and its customers have strong incentives to fight fraud, build wealth and optimize cash flow. These incentives will keep finance on the front lines of AI development.

– Niraj Patel, SVP IoT

Tags: AI analytics artificial intelligence Digital finance

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