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  • TRACK G

    10G Social Network Analysis and Data Science for Fraud Detection

    Tuesday, June 19 | 3:35-4:50 PM


    Level: Intermediate

    Recommended Prerequisite: Knowledge of analytics (general knowledge or applied)

    Field of Study: Auditing

    This session will explore how Elder Research data scientists applied machine learning techniques and graph database technology at numerous federal agencies including the Internal Revenue Service (IRS) to identify networks of tax preparers working together based on information appearing on tax returns. By applying these techniques, investigators and analysts dramatically reduced the time required to identify the most egregious and suspicious actors in a network while also creating a force multiplier for efficient targeting of due diligence treatment resources.

    You Will Learn How To:

    • Examine innovative analytics techniques for enhanced fraud detection
    • Analyze the science and technology behind social network analysis
    • Recall how numerous federal agencies applied analytics algorithms and technologies for enhanced fraud detection
    • Recognize how data science algorithms and technologies can aid fraud detection and fraud investigations
    rhan headshot
    Robert Han, PMP

    Managing Director, Fraud and Risk Analytics Practice Lead, Elder Research Inc.

    Robert Han is managing director at Elder Research. He oversees Elder Research's Washington, D.C. office and its Fraud and Risk Analytics practice. At Elder Research, he has led advanced data science teams that have tackled some of the most challenging fraud and risk analytics problems in both the federal government and private sector. Some examples include applying machine learning techniques to identify and prioritize highly suspicious actors (financial market players, medical providers, unemployment insurance recipients and workers compensation beneficiaries) for investigators and analysts; building text mining models that ingest large amounts of documents to build risk profiles from unstructured text; and leveraging network analysis and graph modeling techniques to detect highly suspicious social network behaviors and potential collusion between highly connected actors amidst the opioid crisis.

See all speakers
  • rhan headshot
    Robert Han, PMP

    Managing Director, Fraud and Risk Analytics Practice Lead, Elder Research Inc.

    Robert Han is managing director at Elder Research. He oversees Elder Research's Washington, D.C. office and its Fraud and Risk Analytics practice. At Elder Research, he has led advanced data science teams that have tackled some of the most challenging fraud and risk analytics problems in both the federal government and private sector. Some examples include applying machine learning techniques to identify and prioritize highly suspicious actors (financial market players, medical providers, unemployment insurance recipients and workers compensation beneficiaries) for investigators and analysts; building text mining models that ingest large amounts of documents to build risk profiles from unstructured text; and leveraging network analysis and graph modeling techniques to detect highly suspicious social network behaviors and potential collusion between highly connected actors amidst the opioid crisis.

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