Link Analysis to Fraud Detection
Link analysis is a technique to analyze the relationship between data and entities by investigating how they are connected. Because of its sophisticated characteristics, it was considered for detecting the identity fraud, which relies on linking requests, such as IP address or Smart ID, together with their corresponding entities.
By linking requests and entities together, we can identify malicious entities and spot any transactions executed by these entities that were not marked as fraud. This is how it works. When a new account opens and there is one device associated with multiple fraudulent new account opening requests elsewhere on the global network. Any future new account opening requests performed by this device are highly likely to be fraud — even if the attributes presented at the time of that event look legitimate. Even though there’s a chance for it to be legitimate, it should definitely be flagged as high-risk.
In the case of link analysis, we need to match the entities to their associated events, whether it be login, account opening, or payment. For each entity, some of the events will be fraudulent and some will be non-fraudulent. Therefore, the visualization and probability of identifying the frauds become important. As a responsible fraud-detector, he/she also need to calculate the mathematics behind all the phenomenon and events to increase the accuracy. Furthermore, it could also be dangerous if he/she lets the frauds go.
(Example of Graph Visualization of the Links)
In the future, smart analysis, which delivers real-time pattern recognition through advanced behavioral analytics will be much valued. By moving away from heavy reliance and manual processes across multiple software, fraud modeling exports can be supported by real-time behavioral analytics to reduce rates and streamline policy development.