Data-Driven Agent Allocation in a Process using Machine Learning and Optimization in SAS®

Data-driven agent allocation provides an immense opportunity in improving the efficiency of any process. Cost effective system can be designed using machine learning and optimization in SAS®. In this paper, the application of machine learning and optimization for agent allocation in two-stage job processing is described using a case study of a Business Process Outsourcing (BPO) organization. This organization handles verification and underwriting processes for credit card applications of large banks. The paper provides a brief overview of unsupervised machine learning algorithms for agent and application profiling. A Cluster of application and agents are created and are used in the agent allocation optimization problem. Optimization model framework is extensively discussed to solve the problem of skill-based agent allocation for credit card application processing based on its complexities, which has two stages of processing, application verification, and underwriting. A mixed integer optimization problem is modeled and is solved using the OPTMODEL procedure. The design for the end-to-end process to implement optimization for agent allocation is also discussed in this paper.