Machine learning-powered risk scoring: ML algorithms that had been trained on historical data related to tax fraud were deployed to identify relevant risk factors. Based on the criteria identified by the ML algorithms, the organization’s analysts configured business rules to flag and prioritize important risk factors that were previously not considered. NEXYTE then automatically scored and prioritized audit candidates based on risk. Investigation workspace: NEXYTE presented discrepancies between reported and actual income to surface audit candidates. AI engines were then applied to extract and analyze data from images and texts in seller postings of these audit candidates and infer relationships between sellers in the same business. These sellers reported income separately to evade taxes. A visual link analysis graph was automatically generated to enable investigators to identify suspicious seller networks. Unstructured data analysis: NEXYTE surfaced online posts made on multiple online platforms and analyzed structured and unstructured data, including videos. NEXYTE’s video analytics engine was applied to extract sales indications from recorded live streams to flag potential unreported sales. Information from videos further provided indications regarding potential discrepancies between reported and actual sales. NEXYTE allowed investigators to proactively surface sellers and link between them and their tax declarations, as well as to detect new online shops more easily. NEXYTE’s investigation workspace enabled intuitive data exploration using visual widgets, filters, and tags. NEXYTE –Business intelligence dashboard NEXYTE – Risk scoring NEXYTE Investigation Dashboard NEXYTE – Entity extraction from text NEXYTE – Link analysis