Case Study: California Franchise Tax Board increases revenue by using IBM predictive analytics

Predictive analytics used to increase revenue by prioritizing nonpayment cases

The California Franchise Tax Board (FTB), responsible for collecting state income taxes, was struggling with a multibillion-dollar revenue gap from millions of delinquent taxpayers. Relying on outdated methods, they needed a smarter approach to prioritize their collections and audit efforts. To address this challenge, the FTB partnered with vendor IBM to implement an advanced analytics solution using IBM SPSS Modeler and IBM SPSS Collaboration and Deployment Services.

IBM's solution used sophisticated algorithms to score cases based on historical data, allowing the FTB to focus on delinquent accounts most likely to result in payment. This implementation by IBM delivered substantial real-world results, helping the agency increase revenue by over $400 million in the first two years. The solution also improved the success rate of contacting business nonfilers by 300% and significantly increased operational efficiency for the tax board's staff.

California Franchise Tax Board

Jeff McTygue

Manager, Business Intelligence and Data Services Section