Detecting Tax Fraud
Reviewing tax returns is a labor-intensive process. Tax agents need to perform these reviews efficiently and effectively to maximize tax revenue and enforce compliance with tax law. Identifying as many fraudulent tax returns as possible is hence especially important.
The digitization of many government services yields a wealth of information. Machine Learning can unlock the information contained in these datasets. Using historical tax data, a ML model can be trained to classify tax returns as either fraudulent or compliant with the law. These models can be used as a decision support system to allow tax agents to classify tax returns.
Modulos AutoML makes the creation of effective ML models easy and accessible to domain experts. The platform and the ML models can furthermore also be deployed on-premise, making them well suited for the analysis of potentially sensitive data.