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ExperianThis is the citation

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By: Kari Michel This blog completes my discussion on monitoring new account decisions with a final focus: scorecard monitoring and performance. It is imperative to validate acquisitions scorecards regularly to measure how well a model is able to distinguish good accounts from bad accounts. With a sufficient number of aged accounts, performance charts can be used to: • Validate the predictive power of a credit scoring model; • Determine if the model effectively ranks risk; and • Identify the delinquency rate of recently booked accounts at various intervals above and below the primary cutoff score. To summarize, successful lenders maximize their scoring investment by incorporating a number of best practices into their account acquisitions processes: 1. They keep a close watch on their scores, policies, and strategies to improve portfolio strength. 2. They create monthly reports to look at population stability, decision management, scoring models and scorecard performance. 3. They update their strategies to meet their organization’s profitability goals through sound acquisition strategies, scorecard monitoring and scorecard management.

By: Wendy Greenawalt The combined impact of rising unemployment, increasing consumer debt burdens and decreasing home values have caused lenders to shift resources away from prospecting and acquisitions to collection and recovery activities. As delinquencies and charge-off rates continue to increase, the likelihood of collecting on delinquent accounts decreases — because outstanding debts mount for consumers and their ability to pay declines. Integrating optimized decisions into a collection strategy enables a lenders to assign appropriate collection treatments by assessing the level of risk associated with a consumer while considering a customer’s responsiveness to particular treatment options. Specifically, collections optimization uses mathematical algorithms to maximize organizational goals while applying constraints such as budget and call center capacity — providing explicit treatment strategies at the consumer level — while producing the highest probability of collecting outstanding dollars. Optimization can be integrated into a real-time call center environment by targeting the right consumers for outbound calls and assigning resources to consumers most likely to pay. It can also be integrated into traditional lettering campaigns to determine the number and frequency of letters, and the tone of each correspondence. The options for account treatment are virtually limitless and, unlike other techniques, optimization will determine the most profitable strategy while meeting operational and business constraints without simplification of the problem. By incorporating optimization into a collection strategy that includes a predictive model or score and advanced segmentation, an organization can maximize collected dollars, minimize the costs of collection efforts, improve collections efficiency, and determine which accounts to sell off – all while maximizing organizational profits.

There are a lot of areas covered in your comment: efficiency; credit quality (human side or character in an impersonal environment); and policy adherence. We define efficiency and effectiveness using these metrics: • Turnaround time from application submission to decision; • Resulting delinquencies based upon type of underwriting (centralized vs. decentralized); • Production levels between centralized and decentralized; • Performance of the portfolio based upon type of underwriting; and • Turnaround time from application submission to decision Due to the nature of Experian’s technology, we are able to capture start and stop times of the typical activities related to loan origination. After analyzing the data from 160+ financial institutions of all sizes, Experian publishes an annual small business benchmark report that documents loan origination process efficiencies and inefficiencies, benchmarking these as industry standards. Turnaround Time From the benchmark report, we’ve seen that institutions that are centralized have consistently had a turnaround time that is half of those with decentralized environments. Interestingly, turnaround time is also much faster for the larger institutions than for smaller. This is confusing because the smaller community banks tend to promote the close relationship they have with their clients and their communities. Yet, when it comes to actually making a loan decision, it tends to take longer. In addition to speed, another aspect of turnaround is consistency. We all can think of situations where we were able to beat the stated turnaround times of the larger or the centralized institutions. Unfortunately, these tend to be isolated instances versus the consistent performance that is delivered in the centralized environment. Resulting delinquencies based upon type of underwriting/Performance of the portfolio based upon type of underwriting Again, referring to the annual small business lending benchmark report, delinquencies in a centralized environment are 50% of those in a decentralized environment. I have worked with a number of institutions that allow the loan officer/relationship manager to “reverse the decision” made by a centralized underwriting group. The thinking is that the human aspect is otherwise missing in centralized underwriting. When the data is collected, though, the incremental business/portfolio that is approved by the loan officer (who is close to the client and knows the human side) is not profitable from a credit quality perspective. Specifically, this incremental portfolio typically has a net charge-off rate that exceeds the net interest margin — and this is before we even consider the non-interest expense incurred. Your choice: is the incremental business critical to your success…or could you more fruitfully direct your relationship officer’s attention elsewhere? Production levels between centralized and decentralized Not to beat a dead horse, but the multiple of two comes into play here too. As one looks at the throughput of each role (data entry, underwriter, relationship manager/lender), the production levels of a centralized environment are typically double that of a decentralized. It’s clear that the data point to the efficiency and effectiveness of a centralized environment
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