Fraud Detection
Stopping Fraud in its Tracks with AI
Challenges
Fraud is a huge problem in the banking industry. In 2016, the top 10 fraud types including wire fraud, card fraud, and loan fraud accounted for $181 Billion in annual losses, and the numbers are only increasing, according to fraud expert Frank McKenna. Detecting and preventing fraud is a huge challenge for banks given the large variety of fraud types and the volume of transactions that need to be reviewed and manual or rules-based systems can’t keep up.
Opportunity
AI can be used to analyze large volumes of transactions to find fraud patterns and then use those patterns to identify fraud as it happens in real-time. When fraud is suspected, AI models can be used to reject transactions outright or flag transactions for investigation and can even score the likelihood of fraud, so investigators can prioritize their work on the most promising cases. The AI model can also provide reason codes for the decision to flag the transaction. These reason codes tell the investigator where they might look to uncover the issues and help to streamline the investigative process. AI can also learn from the investigators as they review and clear suspicious transactions and automatically reinforce the AI model’s understanding to avoid patterns that don’t lead to fraudulent activities.
Why H2O.ai
The mission at H2O.ai is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. The financial services industry is a key focus for the company with strong partnerships with leading brands like Wells Fargo, Citigroup, Capital One, PayPal, Discover, Dun & Bradstreet and Equifax helping to drive significant product innovation. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models. Driverless AI includes innovative features of particular interest to financial services including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.
Resources
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