IBM > 实例探究 > 使用预测分析增加收入：加州特许经营税委员会案例研究

Predictive Analytics Boosts Revenue Collection for California Franchise Tax Board

技术

挑战

当面对超过三百万拖欠或未申报纳税人的名单时，很难知道从哪里开始收集。加州特许经营税委员会 (FTB) 依靠经验和轶事证据不再奏效，州收入下降。是时候缩小董事会收取的款项与所欠款项之间数十亿美元的差距了。

关于客户

加州特许经营税委员会 (FTB) 是加州政府运营局的一部分，负责征收加州的州个人所得税和企业所得税。它由加州审计长、加州财政部长和加州均衡委员会主席组成。

解决方案

该部门开发了一种高级分析解决方案，该解决方案使用来自州和联邦来源的信息来识别成功解决的案例中最常见的属性和属性组合。然后，该解决方案使用复杂的算法根据这些属性对数百万个案例进行评分，根据每个案例产生付款的可能性以及付款的金额来确定部门工作量的优先级。

运营影响

The implementation of the advanced analytics solution has revolutionized the way the California FTB operates. The ability to prioritize cases based on their likelihood of payment and potential payment amount has significantly improved the efficiency of the department. The staff can now focus on the cases with the highest potential for revenue reclamation, which has not only increased revenue but also improved the success rate of contacting business-entity nonfilers. The solution has effectively bridged the gap between the amount the board collected and what it was owed, thereby significantly boosting state revenue.

数量效益

Increased revenue by more than USD 400 million in the first two years after implementation
Achieved a 300 percent improvement in the number of successful attempts to contact business-entity nonfilers
Maximized efficiency for its audit, nonfiler, and collections departments by focusing on cases with the highest potential for revenue reclamation

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