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Updated November 17th Related Posts Link to automotive form, business form

Discover how token-based authentication works, its types, and why businesses trust it to secure sensitive data.

Discover how data and analytics can enhance deposit growth strategies, improve customer engagement, and drive financial wellness for banks and credit unions.

By: Kari Michel In August, consumer bankruptcy filings were up by 24 percent over the past year and are expected to increase to 1.4 million this year.  “Consumers continue to turn to bankruptcy as a shield from the sustained financial pressures of today’s economy,” said American Bankruptcy Institute’s Executive Director Samuel J. Gerdano. What are lenders doing to protect themselves from bankruptcy losses? In my last blog, I talked about the differences and advantage of using both risk and bankruptcy scores. Many lenders are mitigating and managing bankruptcy losses by including bankruptcy scores into their standard account management programs. Here are some ways lenders are using bankruptcy scores: • Incorporating them into existing internal segmentation schemes for enhanced separation and treatment assessment of high risk accounts; • Developing improved strategies to act on high-bankruptcy-risk accounts • In order to manage at-risk consumers proactively and • Assessing low-risk customers for up-sell opportunities. Implementation of a bankruptcy score is recommended given the economic conditions and expected rise in consumer bankruptcy. When conducting model validations/assessments, we recommend that you use the model that best rank orders bankruptcy or pushes more bankruptcies into the lowest scoring ranges.  In validating our Experian/Visa BankruptcyPredict score, results showed BankruptcyPredict was able to identify 18 to 30 percent more bankruptcy compared to other bankruptcy models.  It also identified 12 to 33 percent more bankruptcy compared to risk scores in the lowest five percent of the score range.  This supports the need to have distinct bankruptcy scores in addition to risk scores.  

By: Kennis Wong As I said in my last post, when consumers and the media talk about fraud and fraud risk, they are usually referring to third-party frauds. When financial institutions or other organizations talk about fraud and fraud best practices, they usually refer to both first- and third-party frauds. The lesser-known fraud cousin, first-party fraud, does not involve stolen identities. As a result, first-party fraud is sometimes called victimless fraud. However, being victimless can’t be further from the truth. The true victims of these frauds are the financial institutions that lose millions of dollars to people who intentionally defraud the system. First-party frauds happen when someone uses his/her own identity or a fictitious identity to apply for credit without the intention to fulfill their payment obligation. As you can imagine, fraud detection of this type is very difficult. Since fraudsters are mostly who they say they are, you can’t check the inconsistencies of identities in their applications. The third-party fraud models and authentication tools will have no effect on first-party frauds. Moreover, the line between first-party fraud and regular credit risk is very fuzzy. According to Wikipedia, credit risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. Doesn’t the definition sound similar to first-party fraud? In practice, the distinction is even blurrier. That’s why many financial institutions are putting first-party frauds in the risk bucket. But there is one subtle difference: that is the intent of the debtor.  Are the applicants planning not to pay when they apply or use the credit?  If not, that’s first-party fraud. To effectively detect frauds of this type, fraud models need to look into the intention of the applicants.

Vintage analysis, specifically vintage pools, present numerous useful opportunities to further understand the risks within specific portfolios.