Trustworthy AI in banking
Explain, analyze, and monitor ML models.
Improve quality. Build trust. Scale up.
Scaling AI in banking is hard
Artificial Intelligence and Machine Learning (AI/ML) are transforming banking, and is now a key battleground between incumbents and challengers. However, in most banks, uptake remains slow and impact limited.
One key barrier – for business leaders, control functions, regulators and customers – is the quality and trustworthiness of AI/ML models, particularly where the stakes are high.
TruEra’s AI Quality solutions help explain, analyze and monitor AI/ML models – to ensure quality, build trust and enable impact at scale.
TruEra helps banks capture real business value from AI and ML at scale
Use cases in banking
- Help customers understand why their loan application was rejected
- Assess if the credit model is unfairly discriminating against particular groups
Financial Crime Compliance
- Convince regulators that the system to automate investigations is reliable
- Help investigators understand why the system has flagged a particular transaction as suspicious
- Monitor the performance and stability of a fraud-detection algorithm over time
- Explain how customers can reduce the risk of their transactions getting blocked erroneously
- Assess if the marketing engine is mis-selling products to customers (e.g. loan to a vulnerable customer)
- Explain to a Relationship Manager why they are being advised to promote a particular product
- Demonstrate that a trading algorithm does not pose a systemic market risk
- Get buy-in for a new (ML) pricing model by providing meaningful comparison with existing (non-ML) model
- Provide early warning when data drift is likely to impact the accuracy of models used to automate operational processes
- Assess the reliability of model to determine the appropriate level of human supervision