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Conducting a validation on historical data is a good way to evaluate fraud models; however, fraud best practices dictate that a proper validation uses properly defined fraud tags. Before you can determine if a fraud model or fraud analytics tool would have helped minimize fraud losses, you need to know what you are looking for in this category. Many organizations have difficulty differentiating credit losses from fraud losses. Usually, fraud losses end up lumped-in with credit losses. When this happens, the analysis either has too few “known frauds” to create a business case for change, or the analysis includes a large target population of credit losses that result in poor results. By planning carefully, you can avoid this pitfall and ensure that your validation gives you the best chance to improve your business and minimize fraud losses. As a fraud best practice for validations, consider using a target population that errs on the side of including credit losses; however, be sure to include additional variables in your sample that will allow you and your fraud analytics provider to apply various segmentations to the results. Suggested elements to include in your sample are; delinquency status, first delinquency date, date of last valid payment, date of last bad payment and indicator of whether the account was reviewed for fraud prior to booking. Starting with a larger population, and giving yourself the flexibility to narrow the target later will help you see the full value of the solutions you evaluate and reduce the likelihood of having to do an analysis over again.

By: Tom Hannagan This blog has often discussed many aspects of risk-adjusted pricing for loans. Loans, with their inherent credit risk, certainly deserve a lot of attention when it comes to risk management in banking. But, that doesn’t mean you should ignore the risk management implications found in the other product lines. Enterprise risk management needs to consider all of the lines of business, and all of the products of the organization. This would include the deposit services arena. Deposits make up roughly 65 percent to 75 percent of the liability side of the balance sheet for most financial institutions, representing the lion’s share of their funding source. This is a major source of operational expense and also represents most of the bank’s interest expense. The deposit activity has operational risk, and this large funding source plays a huge role in market risk – including both interest rate risk and liquidity risk. It stands to reason that such risks are considered when pricing deposit services. Unfortunately it is not always the case. Okay, to be honest, it’s too rarely the case. This raises serious entity governance questions. How can such a large operational undertaking, not withstanding the criticality of the funding implications, not be subjected to risk-based pricing considerations? We have seen warnings already that the current low interest rate environment will not last forever. When the economy improves and rates head upwards, banks need to understand the bottom line profit implications. Deposit rate sensitivity across the various deposit types is a huge portion of the impact on net interest income. Risk-based pricing of these services should be considered before committing to provide them. Even without the credit risk implications found on the loan side of the balance sheet, there is still plenty of operational and market risk impact that needs to be taken into account from the liability side. When risk management is not considered and mitigated as part of the day-to-day management of the deposit line of business, the bank is leaving these risks completely to chance. This unmitigated risk increases the portion of overall risk that is then considered to be “unexpected” in nature and thereby increases the equity capital required to support the bank.

In a previous blog, we shared ideas for expanding the “gain” to create a successful ROI to adopt new fraud best practices to improve. In this post, we’ll look more closely at the “cost” side of the ROI equation. The cost of the investment- The costs of fraud analytics and tools that support fraud best practices go beyond the fees charged by the solution provider. While the marketplace is aware of these costs, they often aren’t considered by the solution providers. Achieving consensus on an ROI to move forward with new technology requires both parties to account for these costs. A more robust ROI should these areas: • Labor costs- If a tool increases fraud referral rates, those costs must be taken into account. • Integration costs- Many organizations have strict requirements for recovering integration costs. This can place an additional burden on a successful ROI. • Contractual obligations- As customers look to reduce the cost of other tools, they must be mindful of any obligations to use those tools. • Opportunity costs- Organizations do need to account for the potential impact of their fraud best practices on good customers. Barring a true champion/challenger evaluation, a good way to do this is to remain as neutral as possible with respect to the total number of fraud alerts that are generated using new fraud tools compared to the legacy process As you can see, the challenge of creating a compelling ROI can be much more complicated than the basic equation suggests. It is critical in many industries to begin exploring ways to augment the ROI equation. This will ensure that our industries evolve and thrive without becoming complacent or unable to stay on top of dynamic fraud trends.
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