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Sifting through the noise around first party fraud

first party fraud

We all know that first party fraud is a problem.

No one can seem to agree on the definitions of first party fraud and who is on the hook to find it, absorb the losses and mitigate the risk going forward. More often than not, first-party fraud cases and associated losses are simply combined with the relatively big “bucket” of credit losses. More importantly, the means of quickly detecting potential first-party fraud, properly segmenting it (as either true credit risk or malicious behavior) and mitigating losses associated with it usually lies within more general credit policies instead of with unique, targeted strategies designed to combat this type of fraud.

In order to create a frame of reference, it’s helpful to have some quick — and yes, arguable — definitions:

  • Synthetic identity: the fabrication of an identity with the intention of perpetrating fraudulent applications for, and access to, credit or other financial services
  • Bust-out: the substantive building of positive credit history, followed by the intentional, high-velocity opening of several new accounts with subsequent line utilization and “never payment”
  • Default payment: intentionally allowing credit lines to default to avoid payments
  • Straight-roller: an account opened with immediate utilization followed by default without any attempt to make a payment
  • Never pay: a form of straight-roller that becomes delinquent within the first few months of opening the account

So what’s a risk manager to do?

In my opinion, the best methods to consider in the fight against first-party fraud include analytical solutions that take multiple data points into consideration and focus on a risk-based approach. For my money, the four most important are:

  • Models and scores developed with the proper set of identity and credit risk attributes derived from current and historic identity and account usage patterns (in other words, ANALYTICS) — Used at both the account opening and account management phases of the Customer Life Cycle, such analytics can be customized for each addressable market and specific first-party fraud threat
  • The monitoring of individual identity elements at a portfolio level and beyond — This type of monitoring and LINK ANALYSIS allows organizations to detect the creation of synthetic identities
  • Reasonable (e.g., one-to-one) identity and device associations over time versus a cluster of devices or coordinated attacks stemming from a single device — Knowing a customer’s device profile and behavioral usage with DEVICE INTELLIGENCE provides assurance that applications and account access are conducted legitimately
  • Leveraging industry experts who have worked with other institutions to design and implement effective first-party fraud detection and loss-mitigation strategies — This kind of OPERATIONAL CONSULTING can save time and money in the long run and afford an opportunity to avoid mistakes

By active use of these methods, you are applying a risk-based approach that will allow you to realize substantial savings in the forms of loss reduction and operational efficiencies associated with non-acquisition of high-risk first-party fraud applications, more effective credit line management of potentially high-risk accounts, better segmentation of treatment strategies and associated spend against high-risk identities, and removal of first-party fraud accounts from traditional collections processes that will prove futile.

Download our recent White Paper, Data confidence realized: Leveraging customer intelligence in the age of mass data compromise, to understand how data and technology are needed to strengthen fraud risk strategies through comprehensive customer intelligence.