Data-driven approach for identifying the factors related to debt collector performance
The company's success and growth heavily rely on its workforce's performance, yet the evaluation of employees has been only partially and inconclusively executed so far. The primary goal of this research is to build an open innovation framework for analyzing the performance of the debt col...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Open Innovation: Technology, Market and Complexity |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2199853124001793 |
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| author | Keerthana Sivamayilvelan Elakkiya Rajasekar Santhi Balachandran Ketan Kotecha Subramaniyaswamy Vairavasundaram |
| author_facet | Keerthana Sivamayilvelan Elakkiya Rajasekar Santhi Balachandran Ketan Kotecha Subramaniyaswamy Vairavasundaram |
| author_sort | Keerthana Sivamayilvelan |
| collection | DOAJ |
| description | The company's success and growth heavily rely on its workforce's performance, yet the evaluation of employees has been only partially and inconclusively executed so far. The primary goal of this research is to build an open innovation framework for analyzing the performance of the debt collector. We have developed the Reinforcement Learning based Continual Learning (RLC) approach for evaluating the performance by analyzing the metrics such as visit patterns and collection percentage. We have used the private debt collection dataset to assess the debt collector's performance. We formulated hypotheses derived from insights gained during exploratory data analysis and subsequently validated them through statistical testing. Whether there are noticeable distinctions among debt collectors in terms of visitation frequency, collection rates, and collection modes. This proposed open innovation framework for analyzing the debt collector performance provides significant variation in terms of collection rate. The proposed EDQN-CL achieved a 13.56 % higher classification rate than the existing algorithm for categorizing the debt collector performance. |
| format | Article |
| id | doaj-art-de157c1c4c234d71ab79880d468426df |
| institution | OA Journals |
| issn | 2199-8531 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Open Innovation: Technology, Market and Complexity |
| spelling | doaj-art-de157c1c4c234d71ab79880d468426df2025-08-20T02:30:42ZengElsevierJournal of Open Innovation: Technology, Market and Complexity2199-85312024-12-0110410038510.1016/j.joitmc.2024.100385Data-driven approach for identifying the factors related to debt collector performanceKeerthana Sivamayilvelan0Elakkiya Rajasekar1Santhi Balachandran2Ketan Kotecha3Subramaniyaswamy Vairavasundaram4School of Computing, SASTRA Deemed University, Thanjavur 613401, IndiaxPERT Research Group, Department of Computer Science, Birla Institute of Technology & Science, Pilani - Dubai Campus, Dubai 345055, United Arab EmiratesSchool of Arts, Sciences, Humanities & Education (SASHE), SASTRA Deemed University, Thanjavur 613401, IndiaSymbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Pune, India; Corresponding authors.School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India; Corresponding authors.The company's success and growth heavily rely on its workforce's performance, yet the evaluation of employees has been only partially and inconclusively executed so far. The primary goal of this research is to build an open innovation framework for analyzing the performance of the debt collector. We have developed the Reinforcement Learning based Continual Learning (RLC) approach for evaluating the performance by analyzing the metrics such as visit patterns and collection percentage. We have used the private debt collection dataset to assess the debt collector's performance. We formulated hypotheses derived from insights gained during exploratory data analysis and subsequently validated them through statistical testing. Whether there are noticeable distinctions among debt collectors in terms of visitation frequency, collection rates, and collection modes. This proposed open innovation framework for analyzing the debt collector performance provides significant variation in terms of collection rate. The proposed EDQN-CL achieved a 13.56 % higher classification rate than the existing algorithm for categorizing the debt collector performance.http://www.sciencedirect.com/science/article/pii/S2199853124001793Continual learningDebt collector performanceDebt collectionDeep reinforcement learningStatistical analysisAnd machine learning |
| spellingShingle | Keerthana Sivamayilvelan Elakkiya Rajasekar Santhi Balachandran Ketan Kotecha Subramaniyaswamy Vairavasundaram Data-driven approach for identifying the factors related to debt collector performance Journal of Open Innovation: Technology, Market and Complexity Continual learning Debt collector performance Debt collection Deep reinforcement learning Statistical analysis And machine learning |
| title | Data-driven approach for identifying the factors related to debt collector performance |
| title_full | Data-driven approach for identifying the factors related to debt collector performance |
| title_fullStr | Data-driven approach for identifying the factors related to debt collector performance |
| title_full_unstemmed | Data-driven approach for identifying the factors related to debt collector performance |
| title_short | Data-driven approach for identifying the factors related to debt collector performance |
| title_sort | data driven approach for identifying the factors related to debt collector performance |
| topic | Continual learning Debt collector performance Debt collection Deep reinforcement learning Statistical analysis And machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2199853124001793 |
| work_keys_str_mv | AT keerthanasivamayilvelan datadrivenapproachforidentifyingthefactorsrelatedtodebtcollectorperformance AT elakkiyarajasekar datadrivenapproachforidentifyingthefactorsrelatedtodebtcollectorperformance AT santhibalachandran datadrivenapproachforidentifyingthefactorsrelatedtodebtcollectorperformance AT ketankotecha datadrivenapproachforidentifyingthefactorsrelatedtodebtcollectorperformance AT subramaniyaswamyvairavasundaram datadrivenapproachforidentifyingthefactorsrelatedtodebtcollectorperformance |