Fraud Analytics for Open Banking: Behavioral Profiling

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Digital channels are increasingly popular, and of customers is vital in preventing new types of . The banking revolution makes understanding each customer’s behavior even more important in preventing fraud by considering all the aspects of transactions. Transactions in the world of banking contain data not previously seen in the payments ecosystem.

Behavioral profiling approaches are extremely important in tackling fraud that happens when banks share financial data with third parties through application programming interfaces. The behavioral profiling in the FICO Falcon Platform leverages historical details to track a customer’s patterns, including:

  • typical spending velocity
  • the hours and days when they tend to transact
  • which foreign countries they have transferred to before
  • favorite beneficiaries

Transaction Profiles

Transaction profiles enable FICO Falcon Platform to detect subtle, yet anomalous changes in behavior and elevate the score on the transaction. Each profile is a continuous learning cognitive “mini-model” that uses machine learning to interpret behavior in real-time.

Profiles compactly summarise each customer’s transactional history, which is too big to be retrieved when a decision has to be made in milliseconds (Figure 1). This is why we require streaming .

FICO chart

Transaction profiling, applying Kalman filter principles, creates a profile for each customer. This is updated in real time, with each transaction, to account for behavioral changes.

Profiles are:

  • Recursively updated; computing the estimate for the current profile state only requires the estimated profile state from the previous transaction and the information connected with the current transaction.
  • Composed of numerous monetary and non-monetary parameters that are continuously updated to enable adaptive behavioral profiling.
  • Memory-efficient and do not require extensive storage space.

In practice, when a transaction enters the FICO Falcon Platform, the system pulls a profile connected with that transaction. The system updates the variables stored in that profile, and uses the updated profile to produce the final score, which indicates the likelihood of fraud.

Understanding Recurring Behavior

People form habits, and by looking at their transactional history we can learn their frequent behaviors. Generally, customers use the same devices, such as computer or mobile phones, go to the same online merchants and transfer money to repeated beneficiaries. These recurrences can be analysed and understood to shine further light on normal behavior, and thus on fraud.

To understand recurrences, FICO Falcon Platform maintains behavior-sorted lists or B-LISTS, which enable the system to create a real-time ranking of features associated with each customer’s most frequent behaviors.

By using machine learning, the system makes sure that only the activities that keep recurring remain in each customer’s B-LIST. Frequent activities have higher ranks and are less likely to be fraudulent.

FICO chart

In Figure 2, money transfers to the same beneficiaries have higher weights in a customer’s B-LIST and are less likely to be fraudulent. On the other hand, money transfers to destinations that are not included in the customer’s B-LIST are substantially riskier. FICO’s B-LIST technology is a powerful facet of the transaction profile.

The open banking changes, specifically the need to fight fraud and keep genuine customers happy, means that behavioral profiling at the individual customer level is crucial. Each customer’s profile, including transaction profiling and B-LIST technology, is a “mini-model” that uses machine learning in order to learn highly detailed behavioral patterns of that customer in real time.

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