Anti-Fraud and Anti-Money Laundry: A key application of graph base
The construction of Neo4J and Graph IQ databases enables the adaptation of data structures to changing requirements (e.g. anti-fraud tactics).
Financial crimes are getting harder to detect. Fraudsters create:
- artificial identities – new identities are created based on real data from various sources,
- networks of connections, both small and large. The greater the number of related persons or companies, the greater the scam.
Standard fraud detection mechanisms (including those based on Machine Learning) do not detect the above situations to a large extent. This is because data is being analyzed, not relationships. If we connect people using the same ID number or PESEL number, people who have the same telephone number or e-mail address or people who have the same addresses of residence, it turns out that these people will obtain the status of related (although in conventional databases are alien to each other). Algorithms embedded in the graph database allow for the detection of links and groups of information, and this in turn allows for the detection of crime chains.