Why the Chief Risk Officer Needs AI

October 8, 2018
· 2 min read

“Confusion may be the right term to describe the state of artificial intelligence (AI) in business today. Nobody really seems to agree on what AI is, let alone how it should be implemented in an enterprise like a bank.” – 5 AI Solutions Every Chief Risk Officer Needs

The statement above is why we we’ve developed a white paper tailored specifically for today’s Chief Risk Officer detailing five critical AI solutions. Here are two examples discussed in the white paper:

1. Anti-Money Laundering and Know Your Customer

Banks spend millions of dollars to detect, investigate, and report on potential money laundering. Assessing alerts for potential money laundering is a time-consuming and manual process that can lead to false-negative results. Machine learning models can save time, cut down costs and resources, and can reduce the rate of false negatives down to nearly zero. for banks predicting money laundering efforts. 

Learn more about Anti-Money Laundering and Know Your Customer by reading Strengthen Your Anti-Money Laundering Program with Automated Machine Learning.

2. Fraud Detection/Prevention

Losses due to fraud increase every year, with some estimates claiming worldwide losses to fraud as high as $200B in 2017.

Many banks are equipped either with outdated, rule-based tools or expensive black-box vendor models when attempting to predict and prevent fraud. Machine learning models use the bank’s data to learn from the bank’s own experience.  These models can then be used to block fraud before it happens.

Learn more about fraud detection (and how AI can help!) by listening to this  on-demand webinar, Advances in Fraud Detection with Automated Machine Learning.

Want to learn more? Download the full white paper below. New call-to-action


About the author
Greg Michaelson
Meet Greg Michaelson
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