Only companies who can quickly respond to unpredictable shifts in the market and consumer behavior can readily support their customers’ financial wellbeing.
Adapt to unpredictable market shifts in real-time
Provide equal opportunities with transparent and ethical predictions
Augment business ROI with high performing models
The Fiddler AI Observability platform empowers you to increase confidence and mitigate model bias in lending and trading. Safeguard your customers by improving model prediction accuracy and reducing fraudulent behavior.
Prevent fraudulent behavior that can cost millions of dollars with early detection.
Get real-time model performance alerts and surface data anomalies in your models that signal potential fraud. Monitor models with imbalanced datasets to detect the slightest data change and avoid malicious intents before they impact your business and customers.
Improve the accuracy in credit and underwriting assessments.
Obtain actionable insights on the performance of your credit and underwriting models. Perform root cause analysis to understand which features contributed to drift, and use Fiddler’s ‘Slice and Explain’ capability to further analyze segments of your dataset, including feature impact, correlation, and distribution.
Increase confidence in automated lending decisions.
Discover why certain loans are approved or denied with explanation methods, like Shapley Values and Fiddler SHAP. Drill down on local and global-level explanations to understand how each feature contributed to loan decisions. Perform ‘what if’ analyses to see which values affect prediction outcomes.
Approve credit cards ethically while reducing risks on payment defaults.
Adopt model and dataset fairness to ensure credit card approvals are fair and ethical. Eliminate multi-dimensional and algorithmic bias, and track out-of-the-box metrics, including disparate impact, group benefit, equal opportunity, and demographic parity.
Boost investor value with accurate financial advice and recommendations.
Improve robo-advisor predictions by detecting model drift caused by unpredictable forces, such as market volatility or asset class performance. Adjust data shifts to provide up-to-date and accurate recommendations.