By: Wendy Greenawalt Large financial institutions have acknowledged for some time that taking a more consumer-centric versus product-centric approach can be a successful strategy for an organization. However, implementing such a strategy can be difficult, because inherently organizations want to promote a specific product for one reason or another. With the current economic unrest, organizations are looking for ways to improve customer loyalty with their most profitable and lowest risk customers. They are also looking for ways to improve offers to consumers to provide segment of one decisioning, while satisfying organizational goals. Customer management, and specifically cross-sell or up-sell strategies, are a great example of where organizations can implement what I call “segment of one decisioning”.  In essence, this refers to identifying the best possible decision or outcome for a specific consumer when given multiple offers, scenarios and objectives. Marketers strive to identify the best strategies to maximize decision-making, while minimizing costs. For many, this takes the form of models and complex strategy trees or spreadsheets to identify the ideal offering for a segment of consumers. While this approach is effective, algorithm-based decisioning processes exist that can help organizations identify the optimal decisioning strategies, while considering all possible options at a consumers level. By leveraging an optimization tool, organizations can expand the decision process by considering all variables and all alternatives to find the most cost effective, most-likely-to-be-successful strategies. By optimizing decisions, marketers can determine the ideal offer, while quantifying the ROI and adhering to budgetary or other campaign constraints. Many organizations are once again focusing on account growth and building strategies to implement in the near future. With the limited pool of qualified candidates and increased competition, it is more important than ever that each consumer offer be the best to increase response rates, achieve portfolio growth goals and build a profitable portfolio.

By: Kennis Wong In the last entry, I mentioned that consumers’ participation in protecting their own identity information is an important aspect of an identity theft prevention program to minimize fraud loss.  Large financial institutions are starting to take charge in educating their customers, but others are having a hard time investing in such initiatives. I do understand that it is difficult to establish a direct linkage of revenue and positive return on investment for this type of activities. Business may view customer education of identity protection as a public service but not a necessity. After all, if my customer loses his identity information, it doesn’t necessarily mean that identity fraud will happen to my very own organization. But educating customers about identity protection and fraud trends can be a marketing tool and can increase customer loyalty, in additions to actual fraud prevention. Although consumers may not be aware of all the precautions they can take to protect their identity, undoubtedly identity theft is a hot topic in the media today. If there are two banks providing about the same service, but one of them goes an extra mile to provide me education on preventing identity theft, I would go with that bank. Also, as a financial institution, if my customers understand identity protection more, they would understand why I am putting some procedure in place and would be glad to comply with them. For example, they would be more patient when spending another minute in answering knowledge-based authentication questions, so that for their own protection, the bank can assure they are the true identity owners. Consumers can also actively monitor their credit report, whether through the bank or through other third party vendors. When consumers receive fraud alert from activities that could be a result of identity theft, they can actively contact the financial institutions about the situation. The sooner the identity fraud is discovered, the better off for both the consumers and the businesses.

By: Kari Michel How are your generic or custom models performing? As a result of the volatile economy, consumer behavior has changed significantly over the last several years and may have impacted the predictiveness of your models. Credit models need to monitored regularly and updated periodically in order to remain predictive. Let’s take a look at VantageScore, it was recently redeveloped using consumer behavioral data reflecting the volatile economic environment of the last few years. The development sample was compiled using two performance timeframes: 2006 – 2008, and 2007 – 2009, with each contributing 50% of the development sample. This is a unique approach and is unlike traditional score development methodology, which typically uses a single, two year time window. Developing models with data over an extended window reduces algorithm sensitivity to highly volatile behavior in a single timeframe. Additionally, the model is more stable as the development is built on a broader range of consumer behaviors. The validation results show VantageScore 2.0 outperforms VantageScore 1.0 by 3% for new accounts and 2% for existing accounts overall. To illustrate the differences that were seen in consumer behavior, the following chart and table show the consumer characteristics that contribute to a consumer’s score and compare the characteristic contributions of VantageScore 2.0 vs VantageScore 1.0. Payment History Utilization Balances Length of Credit Recent Credit Available Credit Vantage Score 2.0 28% 23% 9% 8% 30% 1% Vantage Score 1.0 32% 23% 15% 13% 10% 7% As we expect ‘payment history’ is a large portion driving the score, 28% for VantageScore 2.0 and 32% for VantageScore 1.0. What is interesting to see is the ‘recent credit’ contribution has increased significantly to 30% from 10%. There also is a shift with lower emphases on balances, 9% versus 15% as well as ‘length of credit’, 8% versus 13%. As you can see, consumer behavior changes over time and it is imperative to monitor and validate your scorecards in order to assess if they are producing the results you expect. If they are not, you may need to redevelop or switch to a newer version of a generic model.