Social network analysis is built upon database analysis techniques such as collapsing and matching, which seek common denominators (such as names, addresses, birth dates, phone numbers, bank accounts, prior insurance claims, cars, etc.) and closely bound behavior (patterns of activity that seem related) that link what appear to be, on the surface, unrelated individuals, companies, account holders, clients, transactions, insurance claims, etc.
Some particularly important applications of social network analysis are to help uncover patterns of criminal behavior, such as insurance claim fraud (such as repeated staged car crashes) or money laundering. Familiarity with the basics of social network analysis thus is important for insurance investigators and insurance claims examiners, and can be useful for people in various compliance roles.
For a detailed discussion of social network analysis, its applications, and the growing career opportunities in the field, especially within insurance and banking, see “A web of fraudulent details,” Financial Times, 8/12/2010.
Social network analysis, especially in its use of collapsing and matching methods, is akin to the process of householding long used for benign purposes by financial services firms.Also Known As: Collapsing, Matching, Householding, Closely Bound BehaviorExamples: Social network analysis often uncovers a common mistake made by people who set up fake identities, or who operate under multiple aliases: using the same birth date to reduce the number of facts that they must recall when questioned.