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Machine Learning in Banking
Banks are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from risk analysis and fraud detection to marketing, in order to make data-driven decisions that lead to increased profitability.

Use cases in Banking

Blockchain

As a relatively new financial system, blockchain is particularly vulnerable to security threats. Build and deploy machine learning algorithms that can detect anomalous behavior anywhere along the chain.

See the use case

Credit Card Fraudulent Transactions

The cost of credit card fraud is billions of dollars per year. By accurately predicting which transactions are likely fraudulent, banks can significantly reduce these illegal transactions while providing card holders an excellent customer experience.

See the use case

Credit Default Rates

Individuals or businesses often need loans. Making accurate judgments on the likelihood of default is the difference between a successful and unsuccessful loan portfolio.

See the use case

Digital Wealth Management

See the use case

Who Is DataRobot?
We are the leader in enterprise AI, delivering trusted AI technology and ROI enablement services to global enterprises and individual users.
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