TY - GEN
T1 - Mining bad credit card accounts from OLAP and OLTP
AU - Islam, Sheikh Rabiul
AU - Eberle, William
AU - Ghafoor, Sheikh Khaled
N1 - Publisher Copyright: © 2017 Association for Computing Machinery.
PY - 2017/5/19
Y1 - 2017/5/19
N2 - Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as "good" when it is actually bad, could lead to loss of revenue - and marking an account as "bad" when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification.
AB - Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as "good" when it is actually bad, could lead to loss of revenue - and marking an account as "bad" when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification.
KW - Classifier
KW - Data warehouse
KW - OLTP
KW - Risk probability
KW - WEKA
UR - https://www.scopus.com/pages/publications/85030091454
UR - https://www.scopus.com/pages/publications/85030091454#tab=citedBy
U2 - 10.1145/3093241.3093279
DO - 10.1145/3093241.3093279
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 129
EP - 137
BT - Proceedings of 2017 International Conference on Compute and Data Analysis, ICCDA 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Compute and Data Analysis, ICCDA 2017
Y2 - 19 May 2017 through 23 May 2017
ER -