Credit Scoring Models and Applications Based on Personality Predictions Using Twitter Data and Debtor Big Data at PT. Bank Riau Kepri - sugianto; Dadang Syarif Sihabudin Sahid; Juni Nurma Sari; Yohana Dewi Lulu Widyasari (International ABEC, 2021)
The development of social media in Indonesia is very fast, even the latest data shows that social media users in Indonesia have increased from year to year. The total active social media is 160 million or 59% of the total Indonesian population aged 16 to 64 years. 99% of social media users via mobile. The most widely used social media is the Twitter platform. Rapid development, many business lines are starting to use social media analysis to see the personality of users. This phenomenon is called personality analysis by utilizing Big Data. In the internal of Bank Riau Kepri itself, there are no tools that can be used to analyze a person, including the analysis of prospective debtors. Therefore, debtor data in the Core Banking System at Bank Riau Kepri internal and tweet data on the twitter platform will be analyzed using Big Data using a machine learning model with the application of the Decision Tree and Random Forest algorithms. This analysis aims to see the personality of prospective debtors by utilizing the Twitter platform social data media combined with big data from Bank Riau Kepri debtors to see the character, capacity, and capital. After the analysis is done, testing is done on the model built by performing Split validation and cross validation to determine the level of model accuracy. The end result will help to see the credit analysis of the prospective debtor, which will be visualized in the application in the form of credit scoring. Credit scoring using algorithms combined with Big Data shows a very good level of accuracy, as evidenced by several previous studies.