Data Analytics Driven Quadratic Optimization for Agent-Debtor Assignment In Debt Recovery Problem

Toygun Karabaş

Late payments and irrecoverable debts are rising in today’s economy, and proper management is crucial for financial stability. An effective debt collection process begins with the analysis of historical customer data to identify payment trends. The key challenge is to assign the most suitable agents to debtors for recovery. This work proposes a novel hybrid optimization framework which is composed of two phases. In the first phase, a machine learning-based predictive analytics pipeline is proposed to predict the debt recovery rate of all agent-debtor pairs. The second phase addresses the problem of grouping agents and debtors simultaneously and matching those groups with each other in a way that maximizes recovery efficiency. This problem is formulated as a constrained binary optimization problem that proposes quadratic and linear mathematical models. To solve these mathematical models in a reasonable time, three optimization approaches are proposed. This hybrid optimization framework is tested with synthetic and mimicked data. (This is a joint work with Dr. Cem Iyigun).

Short Bio

Toygun Karabaş received his BSc and MSc in Industrial Engineering from Middle East Technical University (METU) in 2022 and 2025, respectively. He currently works as a Machine Learning Engineer and has prior experience as a Data Scientist and Business Intelligence Developer. His academic work focuses on data-driven decision-making and optimization, with particular interest in combining predictive analytics and quadratic optimization for real-world operational and financial problems.

Venue

Friday, December 5th, 2025, 4:00 pm

Online

The link for the seminar is: https://teams.microsoft.com/meet/38013014973679?p=Ayz3TNsUBCY19sMvzo

Meeting ID: 380 130 149 736 79

Passcode: Gy9gN9v8