Finding the Right Fit: A Fraud Strategy without Compromise

by Kevin King

Kevin King

Businesses today are seeing a greater volume of fraud attacks at account opening than ever before,1 and with valid personally identifiable information readily available from data breaches, application fraud techniques are becoming more sophisticated and widespread.2 This puts lenders and service providers in a precarious position. They must do all they can to identify fraud during new account opening, while being mindful that overly aggressive or inefficient fraud detection strategies may also present a risk to their organization.

Every business wants to stop the bad guys—but at what cost? Fraud management is about more than controlling losses. Inefficient fraud strategies can drive up operational expenses, while intrusive fraud defenses can introduce friction that leads to customer abandonment.3 Efficient fraud strategies that manage these pain points can help balance financial losses with operational budgets and manage customer friction.

When it comes to setting your organizational thresholds for fraud, there typically isn’t a one-size-fits-all strategy, thresholds will vary based on business priorities.

A lender offering unsecured loans might be willing to incur significant operational expenses and increased abandonment rates by placing a higher percentage of applicants through fraud remediation, or by implementing more stringent remediation techniques, because their potential financial loss is high if the consumer defaults. On the other hand, a retail card issuer may prioritize a seamless customer experience and opt to perform less stringent remediation on applicants, recognizing that a high-friction consumer experience could drive business away.

An efficient fraud management strategy:

  • Isolates risk in a small population to limit screenings of valid applicants, allowing more legitimate consumers to automatically proceed toward account opening
  • Balances fraud prevention, operational expenses, and the customer experience based on the strategic priorities of the business
  • Meets the organization’s compliance and regulatory review needs


It is a difficult balance to strike—which is why it is essential that lenders utilize fraud solutions that can help minimize trade-offs and compromises.

Highly-predictive application fraud tools can help lenders and service providers identify and minimize potential fraud attacks, while reducing the number of potential customers placed into remediation, resulting in decreased operational expenses and improved customer experience.

ID Analytics is committed to helping our clients drive more effective, efficient fraud strategies. For more than a decade, we’ve worked with leading lenders and service providers to help them minimize identity fraud losses at the point of origination.

As our clients compete in increasingly dynamic markets and navigate the proliferation of compromised identities from numerous data breaches, we strive to continually improve the predictiveness of our flagship fraud solution. We recently launched the latest version of our application fraud solution, ID Score®9.5, which offers a 20 percent improvement in fraud detection over the previous release.4 ID Score 9.5 applies machine learning technology to a cross-industry network of application and identity data, adapting to changing fraud trends to better identify high-risk applications.

With the busy retail season rapidly approaching, it is important to consider how large spikes in application volume may challenge your existing fraud strategy, and your ability to manage losses, expenses, and friction.  Will your prioritization of these pain points shift during these critical months?

Please join us on November 8, from 11:00 am – 12:00 pm PST for our webinar, Protecting the Peak: Developing Fraud Strategies for Peak Retail Days. We’ll share new research regarding fraud trends during peak retail days (e.g., Black Friday) and discuss best practices to minimize trade-offs in fraud strategy when anticipating higher volumes of applications.


Kevin King is Director, Product Marketing at ID Analytics


1. 2017 Identity Fraud Study, Javelin Strategy & Research

2. American Banker, (accessed October 11, 2017).

3. Forbes, (accessed October 31, 2017).

4. 2017 Data Study, ID Analytics