Mapping Cases with Graphs
Considering the case as a graph opens a whole new avenue for detection. Once the data has been sourced, investigators can view all entities, customer, account, address, external entity and financial institution, and the various relationships in the form of an intuitive graph representation.
A graph database can help map the connections and make sense of the data. Graph technology naturally indexes data by relationships for detection and investigation and generates red flags in an entity network relating to all involved parties. The individual event scores are then consolidated to draw an overall case score.
Graph similarity relates similarities between two graphs. With graph similarity, the machine learning model is not limited to specific point in time risk indicators (such as transaction volume, transaction count, risk level, and number of parties) but expands to overall money movement pattern and the customer network. This is a more efficient way of comparing new versus previous cases leading to a better outcome.