Accounts Receivable Seamless Prediction for Companies by Using Multiclass Data Mining Model

Ferry Irawan, Febriliyan Samopa

Abstract


Most companies find themselves in highly competitive markets nowadays. As a result, many companies struggle to manage their financial obligation to pay their supplier on time. Delayed payments to suppliers can create all kinds of issue with the supplier's cash flow. Finding a way to reduce or avoid any potential losses because of this delay is needed. Currently, data mining techniques have been widely applied to the assessment or prediction of credit scores for customers in the banking industry (credit scoring), and the most commonly used method is classification. Based on previous studies, research has been conducted to develop a data mining model to produce the best classification model to predict a customer’s payment capabilities. With the application of data mining approaches using oversampling, feature selection (FS) algorithm, including Relief, Information Gain Ratio, PCA, and multiclass algorithm, including Random Forest, SVM, One-versus-all, All-versus-all and Error Correcting Output Coding (ECOC), is expected to produce good accuracy to predict the ability of these payments. As a result of this research, the model proposed can provide the best classification model with 84.24% accuracy and AUC value of 95.3% using sample dataset of manufacturing industry within three years period

Keywords


credit scoring; data mining; payment; prediction; receivables

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References


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DOI: http://dx.doi.org/10.12962/j23546026.y2019i1.5096

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