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Case Study: A Spark-based Distributed Simulation Optimization Architecture for Portfolio Optimization in Retail Banking

Strata Data Conference NYC 2018

Kaushik Deka, Director & CTO, Novantas
Ingrid Liu, Senior Software Engineer, Novantas

In retail banking, product managers have to regularly optimize their consumer portfolio across products, markets, customer segments, and other dimensions for a range of objective functions. These range from maximizing total revenue over N months across the entire portfolio with the least interest expense to adjusting front and back book pricing to narrowly defined regional and product-level targets. In all use cases, the unit of optimization is the most granular pricing cell where rate is a variable, and the optimization scope can easily involve hundreds of thousands of such pricing cells across multiple geographies, products, and channels. What makes it even more complicated are real-world constraints on those pricing cells that make them inter-dependent (such as price ordering, lock-step behavior, “frozen” cells, and more).

In this session we will delve into a series of challenges and solutions critical to solving the problem at hand.

For more information, contact Novantas Marketing

+1 (212) 901-2772


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