TruEra

Integrate TruEra with the credit scoring ML model.

Define fairness metrics and protected attributes (e.g., age, gender, race).

TruEra continuously monitors the model's predictions for disparate impact and bias across demographic groups.

Upon detecting bias, TruEra identifies the contributing features or data segments using XAI.

Analysts adjust model features or retraining data to mitigate bias, and TruEra verifies the effectiveness of these changes.

Deploy TruEra's LLM Observability to monitor chatbot interactions in real-time.

Track key metrics like response accuracy, coherence, sentiment, and safety violations.

TruEra alerts on spikes in negative sentiment, repeated escalations, or detected hallucinations.

Engineers use TruEra's diagnostics to identify problematic prompts, model configurations, or data causing the issues.

Iteratively improve the LLM model or prompt engineering based on TruEra's insights to enhance chatbot quality and customer satisfaction.

Connect TruEra to the production fraud detection model and its incoming transaction data.

Set up continuous monitoring for model performance metrics (e.g., precision, recall) and data drift.

TruEra detects a sudden drop in precision or a shift in key feature distributions indicating potential model degradation.

The platform automatically triggers an alert and provides diagnostics on the root cause (e.g., new types of transaction data not seen during training).

Data scientists can then quickly retrain or update the model with new data, ensuring the fraud detection remains effective and minimizes financial exposure.