The primary objective of an analytical Fraud Detection project is to summarise, through a series of specific scores and alerts, the key variables that can anticipate fraudulent events. The analysis goal is to provide the Management with a series of tools for the preventive (and continuous) fraud management.

There are many possible fraud patterns and they change rapidly and continuously. In particular, there are two points that need to be examined with the help of statistical models:

• Anomaly detection: the identification of patterns and any anomalies using channels for banking activities is fundamental for the bank; it would be useful to model and assign a score to customers and/or accounts that presents changes or anomalies in the pattern of use of banking services.
• Mule account prediction: these are Current Accounts used to raise funds from fraudulent activities; to sum up, these accounts are used for the last step of the fund subtraction. It is therefore essential to identify the current accounts that are likely to be Mule Accounts;

The implementation of fraud forecasting models, assigning to each customer/current account the probability of being connected to fraudulent activities, is carried out through statistical processing of particular behaviors and characteristics (anomalous movements and/or presence of characteristics detected as risk factors, etc.). These behaviors of individual customers become manifest in a predetermined period of time, before the prediction moment.