Getting to Your Ideal Level in Fraud Prevention
Analytics ferret bank fraudsters while protecting customer relationships
Bank fraud is not a new problem. What makes it more difficult than ever to fight is an increasing reliance on electronically transferred funds and growing networks of organized fraudsters intent on exploiting weak links in the evolving banking infrastructure.
Financial institutions need to build a fraud framework that allows for a sophisticated analytical approach to not just finding fraud – but heading it off before it occurs. By using social networking analytics and predictive modeling, financial institutions can get one step ahead of the fraudsters.
Start with a self-assessment
Many financial institution fraud experts know they need to do more, but as many companies have already poured money into simple solutions it is difficult to explain to executives where the institution stands and what it needs to do next. Many companies can't even measure how successful their efforts are using the tools they do deploy. A simple five-level assessment framework can get the conversation started.
Level 1 companies aren't using any tools and can't measure the sources of fraud or the magnitude or direction of payments risk.
Level 2 companies have processes and tools, but still base investigations on intuition, tend to keep information siloed, and may not properly apply or understand the technology outside the immediate project team. The debit card side of the house often doesn't know about the customer's checking account habits. And the rich demographic data the marketing side uses to acquire and retain customers isn't used to help understand the customer from a risk perspective. Measuring success isn't common.
Level 3 companies have defined processes and use tools to acquire data and assess fraud and risk. However, the ad hoc tools used to acquire data and manage fraud and risk may not be integrated with one another to make cross-channel communication and measurement effective.
Level 4 companies have a managed and measured approach. These institutions can benchmark themselves against industry performance, have processes in place to understand and root out new types of fraud and consider fraud potential prior to entering new businesses.
Level 5 companies are optimized. A portfolio approach is used to aggregate enterprise-level, cross-channel payment risk. These companies are completely up to date with regulatory compliance and relevant regulatory guidance.
Very few financial institutions have reached Level 5 unless they rely extensively on very expensive, large investigative staffs. Those financial institutions that reach this level do so for the same reason – to alleviate the risk of not knowing how much fraud risk the institution might encounter and reduce costs associated with fraud while protecting their reputations. Government rules such as Sarbanes-Oxley, Basel II and even the Patriot Act require financial institutions to control and manage risk. However, controlling and managing risk at a holistic level by fully optimizing resources of people, processes, data and analytics will more than justify the ROI of keeping your company in business and your customers happy.
Why knowing the customer is key to beating fraud
When financial institutions look for fraud by first understanding the customer, it's easier to pick out aberrant behavior. A rules-based program might flag every check cashed in Eastern Europe or China from a US-based checking account. But that will unnecessarily clog the investigative wheels by flagging millions of legitimate remittances made by companies or individuals with ties to those countries. Another rules-based program might simply set a dollar limit to begin investigating questionable ACH transfers. But again, that doesn't look at the customers who are transferring funds and what normal behavior is for them.
An analytic approach starts with what is known about the customer (has never written a check to Eastern Europe), what is known about customers in a similar demographic (they do travel overseas) and builds models that allow potential individual and group fraud patterns to emerge. Particularly rich analytics incorporate social networking strategies that build visual patterns among identities, addresses and users to ferret out suspicious group activity and leverage analytics and alert management to ensure the most suspicious and risky alerts get to the investigators most able to handle them
Financial institutions that lack robust analytical approaches and rely on large investigative teams are working at a decided disadvantage. Fraudsters are a moving target. They are also extremely resourceful, constantly inventing new ways to exploit, deceive and steal billions every year. They defraud consumers, businesses and government. The perpetrators range from one-time check fraudsters to organized teams of international information thieves. Today's economy provides an environment especially conducive to payments fraud.
The business case for using multiple analytical approaches across all organizational transactions will not only give you better monitoring of fraudulent activities, but more accurate behavior profiles that result in incremental detection and reduced false positive rates. This will keep your customers safe from financial harm and protect your financial institution's reputation.