Overdue invoice forecasting and data mining
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2016
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| Online Access: | http://hdl.handle.net/1721.1/104327 |
| _version_ | 1826205151590875136 |
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| author | Hu, Weikun |
| author2 | David Simchi-Levi. |
| author_facet | David Simchi-Levi. Hu, Weikun |
| author_sort | Hu, Weikun |
| collection | MIT |
| description | Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016. |
| first_indexed | 2024-09-23T13:08:13Z |
| format | Thesis |
| id | mit-1721.1/104327 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:08:13Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1043272019-04-12T16:24:28Z Overdue invoice forecasting and data mining Hu, Weikun David Simchi-Levi. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Civil and Environmental Engineering. Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 64-67). The account receivable is one of the main challenges in the business operation. With poor management of invoice to cash collection process, the over due invoice may pile up, and the increasing amount of unpaid invoice may lead to cash flow problems. In this thesis, I addressed the proactive approach to improving account receivable management using predictive modeling. To complete the task, I built supervised learning models to identity the delayed invoices in advance and made recommendations on improving performance of order to cash collection process. The main procedures of the research work are data cleaning and processing, statistical analysis, building machine learning models and evaluating model performance. The analytical and modeling of the study are based on the real-world invoice data from a Fortune 500 company. The thesis also discussed approaches of dealing with imbalanced data, which includes sampling techniques, performance measurements and ensemble algorithms. The invoice data used in this thesis is imbalanced, because on-time invoice and delayed invoice classes are not approximately equally represented. The cost sensitivity learning techniques demonstrates favorable improvement on classification results. The results of the thesis reveal that the supervised machine learning models can predict the potential late payment of invoice with high accuracy. by Weikun Hu. S.M. in Transportation 2016-09-13T19:25:55Z 2016-09-13T19:25:55Z 2016 2016 Thesis http://hdl.handle.net/1721.1/104327 958280271 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 67 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Civil and Environmental Engineering. Hu, Weikun Overdue invoice forecasting and data mining |
| title | Overdue invoice forecasting and data mining |
| title_full | Overdue invoice forecasting and data mining |
| title_fullStr | Overdue invoice forecasting and data mining |
| title_full_unstemmed | Overdue invoice forecasting and data mining |
| title_short | Overdue invoice forecasting and data mining |
| title_sort | overdue invoice forecasting and data mining |
| topic | Civil and Environmental Engineering. |
| url | http://hdl.handle.net/1721.1/104327 |
| work_keys_str_mv | AT huweikun overdueinvoiceforecastinganddatamining |