Modern banking involves banking services beyond retail banking and commercial banking services, including insurances, financial planning, and financial innovations. Banking analytics take a data-driven approach to assure efficient money flow and enterprise objectives, analyze, detect, predict and intervene risks, problems and compliance, improve customer experience and services, optimize operations, planning and innovations, etc.
Research and Practical Problems
Banking businesses and services that can significantly benefit from banking analytics include but are not limited to:
- Detecting risk, fraud in banking businesses and services
- Real-time and proactive online banking risk management and surveillance
- Credit card risk and fraud detection, card limit estimation, new card customer identification
- Optimizing financial planning for personalized customer plan generation, detecting and predicting risks
- Improving customer experience, satisfaction, care and communications
- Developing tailored corporate banking services and products
- Evaluating pricing, loan credit, credit limit, etc.
- Optimizing and automating operational, review, accounting, auditing and legal services
- Evaluating risk in financial innovations eg blockchain, bitcoins, and e-payments
Over years, we’ve committed to several banks’ analytics. Our work involved retail banking risk analytics and optimization, customer relationship analysis and optimization, risk management of online banking, cheque risk analysis, general machine learning for whole-of-bank analytics, and specific banking problem-solving.
 Chengzhang Zhu, Qi Zhang, Longbing Cao and Arman Abrahamyan. Mix2Vec: Unsupervised Mixed Data Representation, DSAA’2020, Research Track
 Jinjiu Li, Can Wang, Longbing Cao, Philip S. Yu. Efficient Selection of Globally Optimal Rules on Large Imbalanced Data Based on Rule Coverage Relationship Analysis, SDM 2013. BibTeX
 Wei Wei, Jinjiu Li, Longbing Cao, Yuming Ou, Jiahang Chen. Effective detection of sophisticated online banking fraud on extremely imbalanced data, World Wide Web, 2012. BibTeX