In a broader application of inferring identity, FISC enhanced the capability to include fuzzy identity matching—where only partial details were available. This draws on both free-text content, as well as any structured data, to identify and connect partial references and make a previously unknown connection.
Data disclosures, tip-offs and other intelligence information are generally in free-format and arrive sporadically. In order to utilise this valuable data, it takes hours of laborious manual work to identify entities and the relationship between them. This enables a systematic view of behaviour patterns and identification of emerging topics. The lack of consistent identifying detail has hampered efforts to connect these data sources together, to enable a richer analysis of a situation, individual or trend.
FISC enhanced its identity recognition analytical models to incorporate fuzzy matching. This is particularly useful when only partial details are available. It can match a document that mentions J Citizen with a database entry of Citizen, John because of associated context or language.
The feature is utilised in a power tool to cross reference identifiers (for individuals, companies or other firms) between separate data disclosures and sources, to identify relationships and behaviour patterns. Similar to the Risk and Intelligence department, investigators could now see all the useful information about clients in one place and could identify relationships and behaviours of interest that would have previously been undetected.
- It enables connection of intelligence where identity matching is not perfect.
- It enriches the view of clients and relationships, and empowers proactive identification of high-risk individuals and enablers.