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 is an Executive Director in the Model Review Group at JP Morgan Chase. In this role, he assesses and mitigates risks posed by Machine Learning models used in different areas of the bank. Kishore holds a Master’s degree in Mathematics from Rutgers University and an MBA from the Indian School of Business.