Modeling and Application of Credit Scoring Based on A Multi-Objective Approach to Debtor Data in PT. Bank Riau Kepri

- Sugianto - Politeknik Caltex Riau, Pekanbaru, 28265, Indonesia
Yohana Dewi Lulu Widyasari - Politeknik Caltex Riau, Pekanbaru, 28265, Indonesia
Kartina Diah Kusuma Wardhani - Politeknik Caltex Riau, Pekanbaru, 28265, Indonesia

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The development of information technology in Indonesia, marked by the start of Industry 4.0, is very rapid. With the development of technology, many companies use technology to develop their business, one of which is banking, which analyses the process of prospective customers. New employees find it challenging to interpret and tend to agree more easily with prospective customers because they only see the fulfillment of general requirements. This research aims to find an overview of the primary and additional factors to analyze prospective credit customers using The Cross-Industry Standard Process for Data Mining (CRISP-DM). Develop a model in this study using data variables of prospective customers in health insurance as a moderating variable. This model tested the Decision Tree algorithm with an accuracy value of 92.49%, the Random Forest with an accuracy value of 81.72%, the Support Vector Machine (SVM) with an accuracy value of 91.25%, and K-Nearest Neighbor (K-NN) with an accuracy value. 90.58%, Gradient Boosting with an accuracy value of 90.69%, and XGBoost with an accuracy value of 93.27%. The algorithm uses a cross-validation technique at the validation stage by changing the K value to 2, 4, 6, 8, and 10. The results show that the XGBoost Algorithm accuracy is 93.27% with a K value of 8. As the highest model accuracy, this model was implemented using the XGBoost Algorithm.


Supervised learning; credit scoring; algorithm; XGBoost; application

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