Data Quality Assurance Improvement for Commercial Segment at PT Bank XYZ (The Indonesian Journal of Business Administration, 2021)
Bank XYZ Wholesale Credit Operations Bandung (WCOB) are faced with the challenge of dealing with ever-increasing amounts of data. Provision of quality data is essential to support business decisions and reports to SLIK (Sistem Layanan Informasi Keuangan) by OJK as regulators. This research will focus on Commercial segment which is 65% of the total bank wide portfolio. With such a large portfolio, of course accompanied by a very large risk of errors in data. Error data can cause various types of loss for Bank XYZ both in Operational risk, legal risk, reputation risk, and financial risk (fines sanction by OJK). Currently, there are still a lot of Debtor data errors that arise at Bank XYZ, this has the potential to result in substantial fines from the OJK, which will harm Bank XYZ. The root cause of the problem is identified by using CRT. The Data Quality Assurance (DQA) error rate is caused by the internal system which not yet qualified and the Employee lack of knowledge. The important part which affects the appearance of data errors is the first input of data in the system. If the PIC did not input data accurately, and complete, or even did not filled in the system, then it will cause an error data. This problem caused by the PIC who still doesn’t have a risk awareness, and that is because they still lack of knowledge. Thus, if the system not yet qualified; no validation system, which can detect blank data, and ‘anomaly’ data, it will also cause a potential error data. With the ‘Done Right at the First Time’ approach, the author proposed several solutions; for the system is upgrade internal system (IPS) by implement Poka Yoke method, and for the employee is through employee training and development, and implement reward and punishment system. By implement these solutions, using pessimistic scenario it is estimated that the error data will decrease by 27% and also estimated to decrease in potential fine sanction as much as IDR 112 million, and by optimistic scenario it is expected to decrease error data by 78% in the future and it is estimated to decrease fine sanction as much as IDR 226 million.Keywords: Data Quality Assurance, Banking Data Reporting, Current Reality Tree, Poka Yoke.