UTILIZING SCIENCE DATA TO INCREASING THE NUMBER MSME DEBTORS AT PT.BANK CENTRAL ASIA.TBK (CASE STUDY OF PT. BANK CENTRAL ASIA.TBK KCU TEBING TINGGI)

Effan Budiawan, Meilita Tryana Sembiring, Nazaruddin
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Abstract

This study aims to increase the number of MSME debtors at the BCA Tebing Tinggi Branch. Since the enactment of Bank Indonesia Regulation Number 23/13/PBI/2021 concerning the Macroprudential Inclusive Financing Ratio (RPIM) for Conventional Commercial Banks, Sharia Commercial Banks, and Sharia Business Units. So Commercial Banks began to adjust the percentage of the use of funds that would be used to finance MSMEs and PBR. BCA Tebing Tinggi Branch is committed to meeting the increase in the percentage of RPIM. One way that can be used to explore Potential Funding is by Utilizing Data Science. Data science studies data, especially quantitative data, with the aim of finding hidden patterns in the data. Researchers will study profile information and transaction patterns in accounts to find MSME customers who are given the right financing. This study processes data using the Machine Learning method with the Random Forest algorithm.
利用科学数据增加中亚pt.bank的中小微债务人数量。TBK (pt. bank central asia个案研究)。TBK kcu(香港)
本研究旨在增加BCA特兵亭吉分行中小微企业债务人的数量。自印度尼西亚银行第23/13/PBI/2021号法规颁布以来,该法规涉及传统商业银行、伊斯兰商业银行和伊斯兰商业部门的宏观审慎包容性融资比率(RPIM)。因此,商业银行开始调整用于中小微企业和PBR融资的资金使用比例。BCA特兵亭吉分公司致力于满足RPIM百分比的增长。探索潜在资金的一种方法是利用数据科学。数据科学研究数据,特别是定量数据,目的是发现数据中隐藏的模式。研究人员将研究账户中的个人资料信息和交易模式,以找到获得适当融资的中小微企业客户。本研究使用随机森林算法的机器学习方法处理数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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