AI Concepts for Finance Professionals

Interactive demonstrations of AI implications in your daily work

AI Bias in Credit Scoring

See how AI models can inadvertently discriminate and learn mitigation strategies.

Model Interpretability

Understand the trade-off between model complexity and explainability.

Linear Regression

Accuracy: 75% Interpretability: 95%

Random Forest

Accuracy: 87% Interpretability: 60%

Deep Neural Network

Accuracy: 94% Interpretability: 15%

AI Risk Monitoring

Live simulation of AI model drift and performance degradation.

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Adversarial Attacks on AI

See how small changes can fool AI systems and learn defense strategies.

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Original Prediction

Low Risk Client 98% confident

After Attack

Low Risk Client 98% confident

AI Regulatory Compliance

Check your AI system against major financial regulations.

Select AI Use Case:

Feature Importance in AI Models

Understand which factors drive your AI's decisions.

Adjust Features:

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Key Takeaways

🎯 Bias Mitigation

AI systems can perpetuate historical biases. Regular auditing and diverse training data are essential.

🔍 Transparency Requirements

Financial regulations increasingly demand explainable AI. Balance accuracy with interpretability.

📊 Continuous Monitoring

Model drift is inevitable. Implement real-time monitoring and retraining pipelines.

🛡️ Security First

Adversarial attacks are real threats. Build robust models with security in mind.