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By: Kari Michel On March 18th 2011 the Federal Reserve Board approved a rule amending Regulation Z (Truth in Lending) to clarify portions of the final rules implementing the Credit CARD Act of 2009. Specific to ability to pay requirements, the new rule states that credit card applications generally cannot request a consumer's "household income" because that term is too vague to allow issuers to properly evaluate the consumer's ability to pay. Instead, issuers must consider the consumer's individual income or salary. The new ruling will be effective October 2011. Given the new direction outlined in the latest rules, we've been hard at work on developing 2 income models to support these regulatory obligations and enhance the underwriting and risk assessment process – Income InsightSM and Income Insight W2SM. Both income models estimate an individual’s income based on an individual credit report and can be used in acquisition strategies, account management review and collection processes. Why two models? Income InsightSM estimates the consumer’s total income, including wages, investments, rentals and other income. Income Insight W2SM estimates wages only. Check them out – and let us know what you think! We want to hear from you.

By: Kennis Wong When we think about fraud prevention, naturally we think about mininizing fraud at application. We want to ensure that the identities used in the application truly belong to the person who applies for credit, and not identity theft. But the reality is that some fraudsters do successfully get through the defense at application. In fact, according to Javelin’s 2011 Identity Fraud Survey Report, 2.5 million accounts were opened fraudulently using stolen identities in 2010, costing lenders and consumers $17 billion. And these numbers do not even include other existing account fraud like account takeover and impersonation (limited misusing of account like credit/debit card and balance transfer, etc.). This type of existing account fraud affected 5.5 million accounts in 2010, costing another $20 billion. So although it may seem like a no brainer, it’s worth emphasizing that we need to have fraud account management system and continue to detect fraud for new and established accounts. Existing account fraud is unlikely to go away any time soon. Lending activities have changed significantly in the last couple of years. Origination rate in 2010 is still less than half of the volume in 2008, and booked accounts become riskier. In this type of environment, when regular consumers are having hard time getting new credits, fraudsters are also having hard time getting credit. So they will switch their focus to something more profitable like account takeover. In addition to application fraud, does your organization have appropriate tools and decisioning strategy to minimize fraud loss from existing account fraud?

While the majority of your customers may be consumers, most telecommunications companies also work with a number of business accounts. Understanding business credit scores — and what attributes have the most impact on them — can go a long way in helping you identify good customers as well as better manage risk. The following article was originally posted by Peter Bolin on the Experian Business Credit blog. There are a number of factors that impact business credit risk scores. Keep in mind that most risk models are built using multivariate statistical methods that not only look at each attribute, but also look for the interaction between the attributes. However, there are three general factors that will impact a business score. Recency: How recently has the business been delinquent? Events that have happened recently tend to be most predictive of business behavior in the near future. For example being days beyond credit terms (DBT) in the past 30, 60, and 90 days will tend to negatively impact, on average, a business’s credit score versus those that are current. Frequency: How frequently is the business delinquent or applying for credit? If a business has multiple beyond terms events then the algorithm will reflect this behavior and will tend to impact the score to the low side. In addition, if a business is frequently applying for credit (called inquires) then this will also negatively impact the score. Monetary/Usage: How large is the debt burden? Businesses that carry large balances in relation to credit limits tend to be more risky than those that carry lower balances in relation to credit limits. This is called the utilization ratio or balance-to-limit ratio. As the debt burden increases interest payments also grow placing more stress on cash flows. This tends to negatively impact a business’ risk score. Please comment on this post to let me know of specific topics you want to hear more about.


