The next phase in the financial crime risk lifecycle involves identifying and responding to threats that are active or ongoing. Effectively handling these types of risks requires both accuracy and speed, as a few seconds mean the difference between stopping a criminal act or suffering the effects of a financial or data loss, compliance violation, or major customer disruption. Financial services organizations (e.g. banks, investment banks, insurance companies, credit card companies and stock brokerages) have acquired and built a myriad of systems and sensors to monitor for specific events and types of behavior; however, the many technologies lack the wider scope needed to see incidents in context. That lack of greater visibility often allows complex, cross-channel schemes to go undetected as criminals exploit the gaps between detection systems.
The challenge of detecting complex risks within financial services organization (e.g. banks, investment banks, insurance companies, credit card companies and stock brokerages)includes:
(a) Identifying deviations and anomalies or unexpected changes for a single user across all the devices, channels, and activities with which they interact with the institution.
(b) Monitoring such changes and anomalies in customer behavior, account usage, suspicious patterns, behavior, financial or non-financial transactions, and seeing the interconnectivity between activities as well as how certain sequences increase risk.
(c) Comparing how user behavior shifts over time as compared to prior behavior, prior account usage, peer groups within that individual account or organization in a way that is fast enough to react to events, but scalable across all of the institution’s customers.
Detecting a suspicious incident among millions or even billions of transactions and non monetary activities requires broad coverage against various avenues of risk as well as the intelligence to correlate patterns from across the organization into a single, context-driven decision mechanism. While financial services organizations (e.g. banks, investment banks, insurance companies, credit card companies and stock brokerages) often have greater context when detecting risk than when preventing risk, the challenge is no less difficult as patterns continuously shift. This means that the detection methodology and technology that is effective today may not be as useful in the near future.
Maintaining effective financial crime risk detection is an ongoing activity. Financial services organizations (e.g. banks, investment banks, insurance companies, credit card companies and stock brokerages) have found success by developing programs that both learn continuously from customer activity and preserve more traditional model and precedentbased detection logic, all the while adapting to new patterns of risk. By constantly looking at these patterns and understanding the nature of threats and vulnerabilities, institutions can separate anomalous, but low-risk activity from truly suspicious acts. This has the additional benefit of not overloading staff with alerts and not unnecessarily disrupting customer experience.
