Using data from IntelliViewSM, Credit.com recently compiled a list of states with the highest average bankcard utilization rates. Alaska took first place, with an average utilization ratio of 27.73 percent. This should come as no surprise since Alaska has recently topped lists for highest credit card balances and highest revolving debt.

By: Maria Moynihan Government organizations that handle debt collection have similar business challenges regardless of agency focus and mission. Let’s face it, debtors can be elusive. They are often hard to find and even more difficult to collect from when information and processes are lacking. To accelerate debt recovery, governments must focus on optimization–particularly, streamlining how resources get used in the debt collection process.  While the perception may be that it’s difficult to implement change given limited budgets, staffing constraints or archaic systems, minimal investment in improved data, tools and technology can make a big difference. Governments most often express the below as their top concerns in debt collection: Difficulty in finding debtors to collect on late tax submissions, fines or fees. Prioritizing collection activities–outbound letters, phone calls, and added steps in decisioning. Difficulty in incorporating new tools or technology to reduce backlogs or accelerate current processes. By simply utilizing right party contact data and tools for improved decisioning, agencies can immediately expose areas of greater possible ROI over others. Credit and demographic data elements like address, income models, assets, and past payment behavior can all be brought together to create a holistic view of an individual or business at a point in time or over time. Collections tools for improved monitoring, segmentation and scoring could be incorporated into current systems to improve resource allotment. Staffing can then be better allocated to not only focus on which accounts to pursue by size, but by likelihood to make contact and payment. Find additional best practices to optimize debt recovery in this guide to Maximizing Revenue Potential in the Public Sector. Be sure to check out our other blog posts on debt collection.

I don’t know about your neighborhood this past Fourth of July, but mine contained an interesting mix of different types of fireworks. From our front porch, we watched a variety of displays simultaneously: an organized professional fireworks show several miles away, our next-door neighbor setting off the “Safe and Sane” variety and the guy at the end of the street with clearly illegal ones. This made me think about how our local police approach this night. There’s no way they can investigate every report or observance of illegal fireworks as well as all of the other increased activity that occurs on a holiday. So it must come down to prioritization, resources and risk assessment. When it comes to fraud prevention, compliance and risk, businesses — much the same as the police — have a lot of ground to cover and limited resources. Consider the bureau alerts (aka high-risk conditions) on a credit report. They’re an easy, quick tool that can help mitigate risk and save money cost-effectively. When considering bureau alerts, clients commonly ask the following questions: How do I investigate all of the alerts with the limited resources I have? How should I prioritize the ones I am able to review? I usually recommend that, if possible, they incorporate a fraud risk score into their evaluation process. The job of the fraud risk score is to take a very large amount of data and put it into an easy-to-understand and actionable form. It is built to evaluate negative or risky information (at Experian, this includes bureau alerts and many other items) as well as positive or low-risk information (analysis of address, Social Security number, date of birth, and other current and historical personal information). The result is a holistic assessment rather than a binary flag, which can be tuned to resource levels, risk tolerance or other drivers. That’s always where I start. If a fraud score is not an option, then I suggest prioritizing the alerts by the most risk and the frequency of occurrence. With some light analysis, you’ll typically see that the frequency of the most risky alerts is often low, so you can be sure to review each one — or as many as possible. As the frequency of occurrence increases, you then can make decisions about which ones to review or how many of them you can handle. For example, I worked with a client recently to prioritize high-risk but low-frequency alerts. Almost all involved the Social Security number (SSN): The inquiry SSN was recorded as deceased The report contained a security statement There was a high probability that the SSN belongs to another person The best on-file SSN was recorded as deceased  I would expect other organizations to have a similar prioritized risk-to-frequency ratio. However, it’s always good (and pretty easy) to make sure your data backs this up. That way, you’re making the most of your limited resources and your tools.