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Accurately identify and prioritize tax returns that have a high likelihood of underreporting and a high magnitude of potential collections

More sophisticated state government revenue departments are accurately identifying and prioritizing tax returns that have a high likelihood of under-reporting and a high magnitude of potential collection by augmenting their rules-based tax auditing systems with scores generated by analytical modeling – improving audit success by as much as 20 to 50 percent. This paper describes the benefits of advanced, non-linear modeling over traditional rules-based approaches, for more precision and better rank ordering, fewer false positives, higher revenues and faster ROI.

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