Jacob Rosen

Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood

We detect tax law abuse by simulating the co-evolution of tax evasion schemes and their discovery through audits. Tax evasion accounts for billions of dollars of lost income each year. When the IRS pursues a tax evasion scheme and changes the tax law or audit procedures, the tax evasion schemes evolve and change into undetectable forms. The arms race between tax evasion schemes and tax authorities presents a serious compliance challenge. Tax evasion schemes are sequences of transactions where each transaction is individually compliant. However, when all transactions are combined they have no other purpose than to evade tax and are thus non-compliant. Our method consists of an ownership network and a sequence of transactions, which outputs the likelihood of conducting an audit, and requires no prior tax return or audit data. We adjust audit procedures for a new generation of evolved tax evasion schemes by simulating the gradual change of tax evasion schemes and audit points, i.e. methods used for detecting non-compliance. Additionally, we identify, for a given audit scoring procedure, which tax evasion schemes will likely escape auditing. The approach is demonstrated in the context of partnership tax law and the Installment Bogus Optional Basis tax evasion scheme. The experiments show the oscillatory behavior of a co-adapting system and that it can model the co-evolution of tax evasion schemes and their detection.

Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion

We investigate the application of a version of Genetic Programming with grammars, called Grammatical Evolution, and a multi-population competitive coevolutionary algorithm for anticipating tax evasion in the domain of U.S. Partnership tax regulations. A problem in tax auditing is that as soon as one evasion scheme is detected a new, slightly mutated, variant of that scheme appears. Multi-population competitive coevolutionary algorithms are disposed to explore adversarial problems, such as the arms-race between tax evader and auditor. In addition, we use Genetic Programming and grammars to represent and search the transactions of tax evaders and tax audit policies. Grammars are helpful for representing and biasing the search space. The feasibility of the method is studied with an example of adversarial coevolution in tax evasion. We study the dynamics and the solutions of the competing populations in this scenario, and note that we are able to replicate some of the expected behavior.