New machine learning tools and processes are helping the financial services firm to improve its modeling capabilities and drive better business decision-making. AI and machine learning (ML) technologies are helping financial services firm Morgan Stanley use decades of data to supplement human insight with accurate models for fraud detection and prevention, sales and marketing automation, and personalized wealth management, among others.
With an AI practice that’s poised to grow, the firm is leveraging MLOps principles to scale AI and ML.
“We need to be able to scale from hundreds of models to thousands,” says Shailesh Gavankar, who heads the analytics and machine learning practice in Morgan Stanley’s Wealth Management Technology department. “There are limitations to doing everything manually as long as data scientists and data analysts are working on their own ‘island’ without the ability to collaborate or share data.”
Currently, the practice is using common platforms for managing data and developing, deploying, and monitoring ML models. To build and test models, people created a sandbox with access to a centralized data lake that contains a copy of the data used in the production system—a technique that makes it easier to bring models from development into production.
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In the development environment, data scientists, business analysts, and data engineers across the practice can access the same standardized data in near-real time, enabling them to efficiently and collaboratively explore, prototype, build, test, and deliver ML models. Advanced techniques mask personally identifiable information so the teams can generate insights without exposing sensitive data.
“Across our AI practice, processes are built around data accuracy and privacy,” Gavankar says. “Applying the highest standards to the training system ensures that we meet data compliance and regulatory requirements.”
For good model governance, transparency, and accountability, an independent, in-house model risk management team was established. With years of experience deploying trading models, the team is responsible for assessing risk and validating the quality of ML models before they go to production. The team evaluates the accuracy of the models and works to identify sources of bias or other unintended consequences. It also reviews data lineage as well as plans for production monitoring and intervention should the model start to drift.
As its AI practice evolves, Morgan Stanley Wealth Management will be focusing on continuing to improve speed to market by further automating the model risk management process and integrating the sandbox and production systems. “As MLOps tools and processes enable us to operationalize models more efficiently, we can continue to increase the number of models in production and more fully leverage AI’s ability to drive better business decisions,” Gavankar says.[wsj-responsive-related-content id=”0″]