The insurance industry remains under continuing pressure to increase operating efficiency through better data utilization. At the same time, insurers must also manage a wide range of risks. By using social network analysis (SNA) to root out fraud and criminal behavior, carriers can increase efficiency and manage risk simultaneously.

In recent years, a new brand of criminal has emerged that is immune to conventional risk scoring. Traditional data and record-matching techniques struggle with poor data quality, missing data and an inability to root out deliberate attempts by criminals to hide identities. Legacy systems mostly resort to inexact or fuzzy matching, which generates reams of false positives that few institutions have the staff to thoroughly investigate.

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