3. Fraud Detection
As data breaches become a more common occurrence, fraud has become a major problem for banks and credit card companies. U.S. consumers lost an estimated $16 billion to fraud and identity theft in 2016.
With hundreds of millions of bank accounts and credit cards, trying to find instances of fraud manually is plainly impossible. The only way that financial companies have any hope of fighting fraud is to use machine learning algorithms to detect unusual transactions.
By feeding the algorithm millions of data points about real and fraudulent activities, machine learning models can make better guesses about which transactions are most likely to be suspicious. Some of the most common methods of detecting fraud are identifying amounts, locations, and times that are out of the ordinary.
4. Loans and Insurance Underwriting
Banks and credit card companies have traditionally used only basic heuristics about their customers when making financial decisions. First, a human analyst might look at half a dozen pieces of information such as the customer’s age, credit score, location, and occupation. The company then decides whether or not to provide a loan or open a new credit card account, and if so with what rates and terms.
In today’s world, however, financial companies have access to more information about their customers than any human being could possibly retain—perhaps hundreds of thousands of data points for each person. Only machine learning algorithms are able to process all of this information and use it to help companies make decisions that maximize their profitability.
Similarly, insurance underwriters want to limit the amount of money that an insurance company will have to pay out to its policyholders. With so many different kinds of insurance now available—life insurance, health insurance, property insurance, disaster insurance, and more—companies need AI and machine learning models to use all the information about their customers that’s at their fingertips.
5. Digital Assistants
You’ve heard of Apple’s Siri and Amazon’s Alexa, but digital assistants are invading the world of financial services as well. Automated phone systems that rely on machine learning can help route callers to the right department within a company, providing good-quality customer service without the need for human employees.
AI and machine learning techniques are used for speech recognition and natural language processing in order to understand what customers want and connect them with a human agent if necessary. For example, recurrent neural networks (RNNs) are used to parse the individual phonemes of a voice clip and assemble them into words.