Top Machine Learning Use Cases in the Financial Industry
As machine learning becomes increasingly popular, we’re keeping track of the way it is used across industries. These types of algorithms are especially useful for applications that need classification or prediction based on complex factors spanning thousands of data points. We’ve already covered some of the implications of machine learning and we looked at its application in the healthcare industry.
Today, we’ll look at some of the ways machine learning is impacting the financial industry, including examples of how the top companies in the space are leveraging machine learning for security, speed, and automation.
Automate Manual Data Summarization
One area where machine learning excels is summarization and categorization of records across a large dataset. In the context of the financial industry this is useful for a variety of applications that consume publicly available information about the financial markets. Combined with internal data that’s unique to each institution, machine learning algorithms can help financial leaders make decisions in hours instead of months.
For instance, manual review of contracts, credit agreements, balance sheets of publicly held companies, and more would take hundreds of thousands of man-hours. However, machine learning algorithms, once trained, can consume this data and provide predictions–along with a confidence level–in a matter of hours.
Even if the algorithm produces a less detailed analysis than a human reviewer would, these time savings are significant enough to make up the difference. In addition, the first reviews by machine learning algorithms can help institutions better decide where to allocate human analysts’ time, leading to higher efficiency and faster decisions. In a market where prices, interest rates, and market makeup change regularly, the value of this speed can’t be underestimated.
Fraud Detection & Account Security
Another huge area for machine learning in finance is fraud detection. Machine learning excels at pattern recognition. As a cardholder, you likely visit the same types of stores regularly and are confined to a certain geographic area. When a purchase is made across the country using your card for something you wouldn’t usually buy, that’s likely fraud.
Machine learning algorithms can detect these types of transactions across millions of cardholders simultaneously, flagging suspicious ones. Banks that leverage this technology have lower fraud rates, process fewer chargebacks, and see boosted consumer confidence from their proactive efforts.
The same goes for breaches of security for online banking and credit applications. Machine learning algorithms can consume the application logs and immediately identify anomalies in access patterns. By freezing accounts until customer support representatives can investigate, these algorithms can prevent unauthorized account access and even loss of funds.
Credit Scoring, Underwriting, & Lending Risk
Recently credit providers and lenders have begun using machine learning algorithms to provide immediate evaluations of credit worthiness on applications. When you apply for a new credit card, mortgage, or personal loan, chances are some advanced algorithm will review your credit history as well as any other publicly available data in order to determine how likely you are to repay your balance.
These algorithms require less staff time from bank employees to review applications while also being more accurate in assessing lending risk. Often, they can provide an immediate decision on the loan or line of credit in real-time to the consumer without input from bank staff.
When trained well on unbiased data, these algorithms are also more likely to lend fairly to underrepresented communities.
Machine Learning in Finance
We’ve covered three major branches of machine learning in finance, but the potential applications go well beyond these few examples. Financial institutions have vast amounts of data about their customers, the markets, and the communities they lend to. Combined with well-developed algorithms, this data has the potential to unlock even more value for financial institutions.
From assistance with regulatory compliance, automated investing, robo-advisors, to underwriting, and more, machine learning has the potential to significantly impact the financial industry over the coming years.
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