Many aspects of Fraud, Audit, Enforcement, Prevention and Investigation systems depend upon data about the names, addresses and other identification attributes of people and organizations.

All such identification data suffers from unavoidable variation and error. Often the data is out of date or incomplete. Often the entity committing the fraud or perpetrating the crime is in fact trying to defeat existing matching algorithms, by subjecting the identification data to deliberate, abnormal or extreme variation.

In systems which support intelligence and investigation work, databases of potentially relevant incidents and known perpetrators are maintained such that suspicious activity or new incidents can be linked or matched against them, or new patterns discovered.

Such databases require sophisticated indexing and search techniques that cope well with poor quality data, and provide timely and accurate results.