Rippleshot Blog

How Banks and Credit Unions Can Benefit from Machine Learning

Posted by Anna Lothson on 23, Jun, 17

Artificial intelligence has secured its spot in the FinTech ecosystem — making machine learning the chief AI advancement for companies to watch, particularly as it relates to cybersecurity and fraud mitigation efforts for financial institutions.

Need more proof? Just follow the money.

Companies are spending capital hand over fist on researching, developing, and implementing AI and machine learning technology. In 2016, $5 billion in venture capital investments went toward machine learning alone, and corporate investment in AI overall is predicted to triple in 2017. Not keeping up? Now may be the time to invest in machine learning tech. 

What Is Machine Learning and Why It Matters For Financial Institutions

At its core, machine learning is a type of AI that uses algorithm-based data analysis to draw conclusions, make predictions, and/or learn about potential additional programming. Essentially, the technology "learns" about customers based on their patterns of behavior — what they buy, where they buy it, what time they typically make certain types of purchases, etc.

How Are Businesses Using Machine Learning and AI?

  • Cost Reduction
  • Improved Customer Service
  • Automated Financial Processing

How Can Machine Learning Specifically Impact Banks and Credit Unions?

For banks and credit unions, properly integrating machine learning technology into their business practices can help them proactively distinguish fraudulent activity more quickly and efficiently, and also detect fraud by analyzing data patterns and flagging suspicious financial patterns.

In addition to the aforementioned general business benefits machine learning offers, there are two major advantages that are of special interest to banks and credit unions:

1. Enhanced Fraud Monitoring & Mitigation Strategies: Machine learning technology can identify potentially fraudulent spending activity with incredible speed and accuracy. In a nutshell, the software sifts through decades of customer spending activity data as it compares each new transaction with a customer's typical spending profile. The instant a card appears to be compromised, it alerts the financial institution of the event. For comprehensive details on how machine learning technology can significantly strengthen financial institutions' fraud monitoring efforts, review how Rippleshot Sonar automates card compromise detection and the reissuance process for issuers.

Leveraging the power of AI and machine learning, Sonar automatically detects compromised merchants and reviews every single card in an issuer’s portfolio daily, assessing its total exposure from all breached merchants to provide a Fraud Forecast™score, indicating the probability that it will become fraudulent — and equipping issuers with an automated, strategic action plan to meet their fraud loss and reissue goals while balancing customer impact.

2. Focused Account Holder Targeting: Think you know who your most valuable account holders are? Think again. Wells Fargo utilized machine learning to confirm its best customers were those with large balances and large loans. To its surprise, the software found that a group of stay-at-home moms in Florida with large social media presences were actually their most influential customers in terms of referrals. The computer picked up on patterns that humans had missed until that point. As a result, the bank was better able to target those key influencers.

And that's just one example.

Machine Learning: From Cutting-Edge to Table Stakes in No Time Flat

As those within the financial industry know, FinTech moves at the speed of light. An innovation is introduced and, in the blink of an eye, it becomes old news as another disruptive technology comes into play — often not to take its place, but to build on the most recent industry improvement.

This is why it's especially important for banks and credit unions to find new ways (and new partners) to help them implement machine learning into their systems in order to take a proactive approach in managing and mitigating fraud. 

Topics: Machine Learning