Print Email Facebook Twitter Late payment prediction of invoices through graph features Title Late payment prediction of invoices through graph features Author Hovanesyan, Arthur (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Wang, Huijuan (mentor) Redi, Judith (mentor) Cesar Garcia, Pablo (graduation committee) Lofi, Christoph (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2019-12-18 Abstract Keeping a steady cash flow is one of the biggest if not the biggest problem that Small to Medium Enterprises (SMEs) deal with daily. Within the different types of cash flow, Accounts Receivable (AR) classifies the balance of money that needs to be paid by the company's customers. In the most typical case, after receiving goods or services, the customer receives an invoice with the amount that is owed to the supplier. However, this often does not happen before the aforementioned date, meaning that the invoice is often paid late. Intervention requires resources and over-intervention could cause unwanted customer dissatisfaction. Knowing whether an invoice is going to be paid late can be vital information. Current methods of late payment prediction focus only on the history between the seller and the buyer and are unusable when this history is not present. Intuitively, one's business depends on the relationships and transactions that it has with its neighbors. Suggesting that neighbor behavior could be useful when predicting the cash flow of a company. Unfortunately, this type of information is not always given and needs to be data mining from non-relational data. This work presents a method for building a relational network of SMEs using entity resolution and improving the current state of the art of late payment prediction using features extracted from the graph. Subject Machine LearningFeature EngineeringGraph EmbeddingEntity Resolution To reference this document use: http://resolver.tudelft.nl/uuid:1b482e0f-6e06-465a-8f7d-ec680d80ac0b Embargo date 2020-07-01 Part of collection Student theses Document type master thesis Rights © 2019 Arthur Hovanesyan Files PDF _Final_Version_Msc_Thesis ... iction.pdf 3.4 MB Close viewer /islandora/object/uuid:1b482e0f-6e06-465a-8f7d-ec680d80ac0b/datastream/OBJ/view