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Heuristic strategies for solving the combinatorial optimization problem in real-world credit risk assessment

Published: 19 July 2022 Publication History

Abstract

Credit risk assessment plays an important role in financial institutions and banks. A common method of predicting credit risks is to build an interpretable rule-set model to distinguish high-risk and non-risk users. This further assists experts and analysts to make critical decisions. In a rule-set model, the quality of selected rules dramatically influences the prediction accuracy. It is challenging to automatically select rules for a rule-set learning task.
The rule selection problem in credit risk assessment is considered as a combinatorial optimization problem: the goal is to find a subset of rules that maximize the coverage of risky users under given constraints. In this paper, we employed four methods on top of traditional heuristic strategies, including the Simulated Annealing (SA), Beam Search (BS), Roll-In Roll-Out (RIRO), and Dynamic-step Search (DS). Experiments on real-world datasets show that the proposed heuristics based on SA, BS, and RIRO can detect the most high-risk users in different datasets.

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Cited By

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  • (2024)Machine Learning Prediction of Multiclass Credit Score ClassificationPractical Statistical Learning and Data Science Methods10.1007/978-3-031-72215-8_18(413-433)Online publication date: 28-Dec-2024
  • (2023)A Rule-based Decision System for Financial Applications2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00270(3535-3548)Online publication date: Apr-2023
  • (2022)A Comparison Analysis of Constraint-Handling Techniques on Rule Selection Problem in Credit Risk Assessment: An Industrial View2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C57518.2022.00099(621-625)Online publication date: Dec-2022

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  1. Heuristic strategies for solving the combinatorial optimization problem in real-world credit risk assessment

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      cover image ACM Conferences
      GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2022
      2395 pages
      ISBN:9781450392686
      DOI:10.1145/3520304
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 19 July 2022

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      Author Tags

      1. beam search
      2. combinatorial optimization
      3. heuristic strategies
      4. simulated annealing

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      View all
      • (2024)Machine Learning Prediction of Multiclass Credit Score ClassificationPractical Statistical Learning and Data Science Methods10.1007/978-3-031-72215-8_18(413-433)Online publication date: 28-Dec-2024
      • (2023)A Rule-based Decision System for Financial Applications2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00270(3535-3548)Online publication date: Apr-2023
      • (2022)A Comparison Analysis of Constraint-Handling Techniques on Rule Selection Problem in Credit Risk Assessment: An Industrial View2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C57518.2022.00099(621-625)Online publication date: Dec-2022

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