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How Wells Fargo builds a world class data science team and keeps machine learning interpretable in a heavily regulated industry.
Where does the data come from? How does it all fit together? Do you understand what the data means? How do you train a model? Do you have the infrastructure? How's it going to sit in production? Do my data sources in training match what's going to be there at the time of scoring? There's so many factors to be worried about.
“In order to be able to create something awesome like a great data science team, we need to be able to take those chances organizationally with people so as to produce those great outcomes we have been looking for.”Krish SwamySenior Vice President - Enterprise Analytics and Data Science, Wells Fargo
Transparency, accountability, and trustworthiness of data-driven decision support systems based on AI and machine learning, or more traditional statistical or rule-based approaches, are serious regulatory mandates in banking, insurance, healthcare, and other industries. From pertinent regulations to increasing customer trust, data scientists and business decision-makers need to understand the challenges and opportunities with explainable AI.
“Managing machine learning model risk is of the utmost importance in heavily regulated industries such as finance; in particular, to manage potential risks due to bias/fairness, conceptual soundness, implementation, and model change control”Agus SudjiantoEVP, Head of Corporate Model Risk