Optimizing Cross-Channel Fraud Detection, Prevention, and Analytics using a Single, Unified Model.
The client, a Fortune 500 company, is an American financial, retirement, investment, and insurance company.
The client was looking for a partner who could help them with a single unified model to provide more overage to uncover more fraud patterns. SLK worked with the client to optimize the existing fraud-detection model spread across various channels, and co-innovated to convert it to a single, centralized model. This led to $11 million in yearly savings, with a 15% improvement in model performance and 30% savings in infrastructure costs.
The insurance client employed individual fraud-detection models across various channels, such as web, mobile, and others. The existing siloed models were prone to runtime performance issues and were not efficient in identifying the complete patterns of fraud possibilities. Managing and maintaining these individual models added to extraneous costs. Therefore, the client was looking for a strong data partner who could unify these channel-specific legacy models, optimize the infrastructure, and improve the model performance and accuracy
The SLK team built a new data-science model to identify fraud with parameters that covered multiple channels. The new model, built by the SLK team, considered data variables such as Variety, Velocity, Veracity, and Volume, uncovering all possible patterns for modeling. This model:
Savings per year
Reduction in runtime
The optimized cross-channel fraud-detection model led to $11 million in savings in the first year, with 15% improvement in model performance and 30% savings in infrastructure costs. The unified model also cut down the process runtime by 500% to just an hour.
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