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Optimize Your Credit Risk Management Process

How the forward-thinking credit manager leverages analytics

We caught up with Dun & Bradstreet Senior Finance Leader Chris Rios, an expert on finance operations, to talk about how innovative credit teams can expand their influence on the success of their company with smart analytics. Here's what he had to say.

You travel around the country speaking about using predictive analytics in your risk management workflow. In general, you focus on three key tenets to this relationship: educated decisioning, collection management strategy, and portfolio management. To begin, could you explain what educated decisioning really is when we’re talking about using analytics in the risk management process?

Credit and risk professionals are attempting to move from the conventional credit professional into the moderate, middle-line business partner into the forward-thinking finance leader.
Chris Rios, Senior Finance Leader, Dun & Bradstreet
 

Historically, the credit function has been thought of as “the Department of No.” If you didn’t have all your t’s crossed and i’s dotted, you weren’t going to get credit approval. It was perceived as a difficult area of finance to deal with. However, over the last 20 years, we’ve seen an evolution in the credit management function. Credit and risk professionals are attempting to move from the conventional credit professional into the moderate, middle-line business partner into the forward-thinking finance leader. This forward-thinking finance leader happens to own the credit function but understands that decisions around credit and risk management will have broader organizational implications downstream in the sales cycle. The concept of “educated decisioning” attempts to provide more tools to the repertoire of the credit professional.

Financials will always provide the greatest amount of insight into a company’s ability to pay their bills, remain a going concern, deliver shareholder value and provide investment opportunities. At the end of the day, I don’t know too many companies where revenue growth isn’t at the top of their strategic plan. So instead of setting yourself up to be a perceived barrier, broaden your view of the credit professional’s role. You have a tremendous amount of information at your disposal in your quote-to-cash process. This information goes far beyond just saying “yes” or “no” with some rationale to a new sales opportunity.

 

It’s an opportunity to provide proactive information to your sales and marketing organization. Where are the upsell opportunities? Where are the cross-sell opportunities? Where is the inherent risk? Even beyond creating opportunities, sometimes being able to quickly ascertain information and make decisions is equally important.

When Hurricane Katrina devastated Louisiana and the Southern states in 2005, we were able to quickly evaluate our exposure in those markets. We had approximately $200,000 outstanding with various businesses. Our ability to evaluate our exposure within minutes allowed us to make key business decisions. One of which I’m proud to say was to forgive all debt to those devastated businesses. As these companies endeavored to rebuild, we wanted to make sure we were there to provide them the right credit reporting so that when they applied for loans to re-establish themselves, banks would have a D&B report available.

While this example doesn’t necessarily expound upon the benefits of predictive analytics and metrics, it is an example of how to quickly ascertain information and make decisions.

What is the essence of educated decisioning as it relates to the credit professional?

Essentially I’m talking about educating credit/finance professionals to help them drive organizational optimization to deliver on financial strategy execution and enable sales. Once you’ve achieved organizational optimization, financial strategy execution becomes easier. While credit managers and analysts aren’t tasked with selling they can certainly contribute to the sales process by reducing sales cycle time.

Moving on to the second component, what is collection management strategy and why is it important to the credit professional?

Accounts receivable is arguably the largest account on any company’s balance sheet. So it is critical that you’re in a position to turn those receivables into working capital. That translates into a company’s ability to reinvest in its infrastructure. Finance as a whole is being asked to do more with less, and we have an opportunity to utilize analytics and models within our data to automate certain processes.

When you apply predictive analytics to the portfolio, you find there’s always a subset of customers that make up the largest portion of your exposure who will pay you. Your time and energy are better spent focusing on the high-risk portion of your portfolio. For instance, if you’re able to capture 70% of your risk within 20% of your portfolio, you can reallocate your resources. If I know certain customers are going to pay me, I’d rather keep in touch with them through an automated process so I can focus on that 20% of the portfolio that is higher risk.

Once you pinpoint that risk, your resource allocation doesn’t need to be what it may have been one month or one year prior. You can take your top talent in your credit and collection management space and focus on this high-risk portion of the portfolio. When you’ve automated the low-risk part of your quote-to-cash process, you can begin to pull down open requisitions, avoid the cost of hiring new people, and reallocate existing resources to maintain your talent.

So the collection management strategy is really the next step from educated decisioning. This is really what the forward-thinking credit professional is actually doing every day.

Absolutely. When you start freeing up time in the heavily transactional, somewhat redundant processes of the quote-to-cash cycle, you start tapping into the talent. You’re not bogged down with day-to-day issues.

How does modeling help these credit professionals actually accomplish this?

Every credit professional has probably 99.9% of the same information they require to make a decision. But each is going to put a different weight or value on that information. Someone reviewing financials may say, “Balance sheet ratios are the most important to me,” while someone else may say, “Nope, cash flow statement analysis is the most important to me.” They both agree that financial information is important, but they apply checks and balances differently on each component. When you combine that financial information with D&B data and other public information, you’re essentially building a model.

The “judgmental” modeler uses external, seemingly objective data to come up with a recommendation. The predictive modeler takes the same information but adds his own portfolio data. Now you’re able to view customer behavior over time. What did your portfolio look like in October versus September versus August? By adding your portfolio information, you can begin to predict future outcomes.

Portfolio management is the third component of modeling and analytics. What is portfolio management and how does it affect the downstream operations of today’s credit professional?

There’s an art and a science to credit. The science is in the facts. What you do with that information is the art. With portfolio management, you’re looking at your overall exposure, your overall customer base and the behavioral patterns of that customer base. Many companies like D&B segment their portfolios into strategic, commercial, middle market, small business and key accounts, among others. When the credit professional takes a step back from the individual transactional line items, she can use the same methodology to analyze one account on the entire portfolio. This macro view provides insight into who is underutilizing credit lines, who is consistently over credit lines, and where the inherent risk resides within the portfolio.

For example, suppose I discover a small fast food restaurant in Austin, Texas, that is high risk. It wouldn’t make sense for me to assume all small fast food restaurants are high risk. The macro view says, “This size business in this particular industry within this segment of my portfolio is actually more likely to be low risk, so I’m proposing to sales that this is an industry that should be targeted for opportunity.” That one small business in Austin may just be in a bad neighborhood or set up incorrectly or ill managed, but I’m not focused on that one unless that particular store wants to order a product or service from me. I’m focused on the real risk.

That’s the bigger picture of a particular segment of your portfolio. This is where the credit professional takes the methodology she has used on the transactional level and applies it to the portfolio. It’s about examining the portfolio to reveal such things as propensity for disputes or carrying severely delinquent receivables. The ability to segment your portfolio and provide specific analysis on that segment for sales reps 60 days before they go into that market to sell new business or renew business is very powerful.

Credit and risk professionals need to understand that the work they perform can and should go beyond the transactional level. There is an inherent opportunity to add value to the organization.

Is the judgmental and the predictive modeling approach also applicable in this more macro sense to portfolio management?

I think it is. Both lend a certain amount of insight and predictability. Equally important is it helps credit professionals achieve consistency in their process which will help when it comes to the control environment and remaining Sarbanes-Oxley compliant. Again, financial statement analysis is typically what gives credit professionals the most comfort when performing their analysis, but an analyst isn’t an analyst because he can do the simple math associated with balance sheet ratio analysis. Those are important elements to understand, but what’s more important is how he uses the output to make a decision, articulate that decision, and defend the rationale behind the action he’s taking.

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To learn more about how modeling and predictive analytics can help you optimize your credit management processes, you can contact Chris Rios at riosc@dnb.com.

 

 

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