Introduction
Tax assures government budgets, community services and quality, and society healthy development, etc. Tax analytics is to analyze, predict and intervene risks, problems and improvements associated with taxpayers, tax lodgments, debt collections, taxation operations, and client services, etc. for taxation fairness, transparency, integrity and efficiency.
Research and Practical Problems
Data-driven discovery can be applied to taxation for various research and practical problem-solving, including but being not limited to:
- Individual and institutional taxpayer behavior analysis
- Tax lodgment analysis
- Tax debt detection, recovery and prevention
- Tax fraud detection
- Overclaim and debt intervention strategies and next-best actions
- Taxable income prediction
- Improving client services
- Compliance and risk analytics associated with tax staff and services
Relevant Projects
Over years we were engaged with the taxation office on developing advanced tax analytics for the government. Our work addressed various tax problems and challenges, including:
- Debt profiling
- Risk factor analysis
- Modeling taxpayers’ response to government intervention actions
- Detecting and recommending debt self-finalizing strategies and taxpayers
- Detecting debt non-self-finalizers and policy changes
- Recommending tailored SMS to debt taxpayers
- Evaluating and recommending intervention actions
- Recommending next-best actions for debt interventions
Our work led to significant social and economic benefits, including discovering behavior insights, chasing billions of dollars of habitual debt, improving client communications through active and tailored client engagement, and optimizing policies and processes. Outcomes were mentioned in some of government’s annual reports and OECD report.
References
[1] Longbing Cao. Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management. KDD2017, 2017. BibTeX
[2] Zhigang Zheng, Wei Wei, Chunming Liu, Wei Cao, Longbing Cao, Maninder Bhatia. An effective contrast sequential pattern mining approach to taxpayer behavior analysis, World Wide Web 19(4): 633-651 (2016). BibTeX
