Alongside increased risk associated with lending, banks have witnessed growing fraudulent behavior. This behavior may be internal (by undisciplined staff) or external (by fraudulent customers). In the insurance market, the incidence of fraudulent events has grown, especially in certain geographical areas.
Overt fraud is known to be low, but suspect cases and claims that are resolved, for example, by settlement between the counterparties, are significantly higher. Lack of control over such events can lead to over time and (sometimes sizeable) losses. Businesses do not have the right information needed to tackle a variety of fraudulent situations. It is crucial for fraud managers to have as much information as possible to spot fraudulent and new abnormal behavior early on, and to identify possible fraudulent networks of people among counterparties, dealers, and other parties involved in the business.
Fraud Detection is designed to help organizations reduce fraud-related costs. The application’s predictive capabilities combine different techniques, mixing in a single risk score the business user experience with predictive modeling techniques and anomaly detection models.