Financial Service Applications

Risk Management

Risk management models aid financial institutions in assessing, quantifying, and mitigating potential risks. However, these models face significant challenges when used in production, such as updating model inputs, evolving consumer behaviors, and explaining the decision-making process of the models.

  • Enable real-time anomaly detection to promptly identify deviations from expected feature distributions and predictions
  • Detect and troubleshoot model performance issues promptly to prevent any negative impact on customers
  • Mitigate the impact of algorithmic harm by employing explainability and bias tracing, which provides insight into the model’s decision-making processes
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Fraud Detection

Effective fraud detection models need to continuously adapt to the changing tactics of fraudsters. However, because they leverage a constant stream of new data, these models often encounter issues such as missing ground truth data, imbalanced class distribution between fraudulent and non-fraudulent transactions, and the complexity of custom feature generation, which makes evaluating performance a challenge.

  • Safeguard against monetary loss by monitoring for drifting dimensions, which can help to catch new and evolving fraudulent behaviors
  • Detect suspicious activities in real-time, enhance customer satisfaction, and reduce the rates of false positives and negatives
  • Save time with in-depth troubleshooting, drill down to the root cause of an issue – such as a specific merchant – with performance tracing
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Credit Scoring

A credit scoring model evaluates an individual’s creditworthiness to predict their likelihood of repaying debts and managing credit. However, deployed credit scoring models often face significant problems with predictive accuracy due to regulatory requirements, the need to address biased and discriminatory predictions, and meeting customer expectations.

  • Gain a comprehensive view of model performance and drift to fully understand the features that negatively impact model outcomes
  • Proactively evaluate how models behave using bias tracing with native support for fairness metrics such as recall parity
  • Understand how your model performance impacts overarching business goals with custom metrics to calculate customer retention and churn
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