Risk Modeling

CHAPTER 8Optimizing Parameters for Machine Learning Models and Decisions in Production

We can define optimization as the act, process, or method that is employed to find the “best” element from a set of alternatives based on the objectives, such as maximizing yield, while satisfying all the constraints. Within the context of a current business problem, this “best” element can be far-reaching: from a system, decision, or technical design. “Problems” can include but are not limited to:

• Capital allocation across portfolios to maximize yield
• Efficient use of resources to minimize waste
• Optimal decision path generation with the least amount of information

For example, to optimize capital across portfolios, the allocation of credit risk mitigants to credit risk exposures under different regulatory regimes can be done with a specific objective in mind. Here, the allocation is optimized in a way that allows a firm to reduce their regulatory capital requirements within the context of a regulatory framework. Here, the regulatory requirements are the constraints within the optimization problem.

In mathematical terms, optimization refers to the act of minimizing or maximizing a value function subjected to constraints as an expression. The function and constraints can be linear or nonlinear. Mathematical optimization is integral and extensively used in AI and machine learning (e.g., minimizing a “loss function” subject to constraints). This is achieved by applying an optimization routine ...