4 Ways Banks Can Improve Their Fraud-Fighting Efforts
Adopt A Layered Approach Leveraging Multiple Analytical Techniques
There has been an increase in both frequency and complexity in bank fraud. Opportunistic fraudsters are taking advantage of financial institutions’ customer-centric programs, while organized fraudsters are becoming more and more sophisticated in multi-dimensional attacks. Today's fraud exposure is growing with more advanced plays involving everything from cyber to organizational and logistical capabilities to attack banks in multiple locations at once.
We believe that banks need to look at layered approaches to predict fraud and protect the organization on multiple levels. Organized fraudsters are smart and know how to defeat your models and your rules, but they leave trails. Banks need the ability to identify these trails, and uncover how the fraudsters mask their identities. On the other hand, opportunistic fraudsters do not leave trails, but can be caught with more sophisticated predictive analytics.
Banks also must widen their observation space, which defines the areas and sources of data that they can analyze and observe behavior. The richer and broader that you can make this space, the more likely you’ll be able to disrupt and defeat the more sophisticated fraudsters.
There’s no magic for balancing fraud protection with customer convenience, but a layered approach can go a long way for financial firms. Banks cannot separate fraud from other customer-centric activities. Launching a customer-focused enterprise or doing a digital transformation and other customer-focused initiatives create avenues and opportunities for fraudsters.
To protect themselves from both organized and opportunistic fraudsters, banks need to be able to model behaviors using predictive analytics and have the ability to recognize and understand history and relationships. Otherwise, if an individual exhibits a behavioral tendency that indicates that he or she is a fraudster, but if a bank doesn’t connect that information to the individual’s identify or relationships, the institution could make a big mistake in flagging the activity as fraudulent. On the flipside, today’s sophisticated analytics may be able to uncover hidden patterns and relationships that can help banks to contain fraud and better manage risk while improving customer relationships.
The normal approach is to use pattern recognition and behavioral tendency, but if you don’t couple that with identity detection and relationship analysis, you could come up with many false positives. It all goes back to the essential layered approach that leverages multiple advanced analytical techniques.
-- Rick Hoehne, Global Leader for Fraud Solutions for IBM Global Business Services, IBM