Identifying and Addressing Bias in Machine Learning Models Used in Banking

Banks are increasingly relying on Machine Learning models as decision support systems in various areas such as fraud detection, credit scoring, and optimal order execution. When a model makes a decision on a client application, it is important to ensure that the decision is unbiased and explainable, both from regulatory and moral standpoint. This talk will focus on relevant regulations and some of the ways in which these biases can be identified and addressed.

Key Takeaways:
1. Talk will present the types of applications of ML models where ‘biases’ could influence model decisions
2. Applicable regulations in banking domain
3. Some statistical techniques that will help identify the biases and possible course of actions to address the biases.

Kishore Karra, Executive Director, Model Review Group at J.P Morgan

Kishore Karra is the lead reviewer of models used in Anti-Money Laundering at JP Morgan Chase. In this role, he assesses and mitigates risk posed by models used for the purposes of Sanctions Screening and Transaction Monitoring. Kishore holds a Master's degree in Mathematics from Rutgers University and an MBA from the Indian School of Business.

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