Data Dialogue: DLL Reduces Bad Debt

Auto-decisioning helps DLL optimize risk and reward

As a global financial services organization, DLL Financial Services Group knows the importance of automated decisioning in addressing compliance and regulatory issues, getting a handle on the myriad of data that's now available out there, and driving operational efficiencies.

 

"The world of data is evolving," says Michael Ludwig, Regional Team Lead for Scorecard Management at the company. "There's a whole new world of data out there, and sorting out the data to optimize the risk/reward balance within the organization is a crucial part of the equation."

For DLL, it all comes down to identifying which information is most valuable to them and using that information in the most efficient way possible to support the growth initiatives of the company.

We caught up with Ludwig to talk about how they're doing it.

Who is DLL and what is your role in the organization?

DLL is a global financial solutions company that operates in more than 35 countries and nine industries. As Regional Team Lead for Scorecard Management, I'm responsible for everything related to automated credit scoring, such as scorecards, decision trees, cut-off rules and how and when to apply those elements in different verticals.

How do you make sense of "Big Data?"

With so much information out there, it's all about finding what's most valuable for your organization and for your business, what's most predictive and what combination of the elements is most predictive. It's a continual challenge. It's not something that you identify one day, then you're good for the next 10 years. Macroeconomics change, industries grow or decline, new data elements are introduced. It's always evolving.

When you say "predictive," what are you looking to predict?

We're looking to predict performance of our customers so that we can minimize bad debt. We're taking all the data attributes available to us and matching them up with our own internal customer performance to determine what combination is most predictive. So when we receive more applications from these companies, we know where they stand as far as credit risk.

What are your priorities for the year ahead?

As a global organization operating in over 30 countries, we really need to standardize our credit-scoring operations. There are many different bureaus in different countries and varying legalities and regulations around those. As a result, it becomes challenging to implement credit scoring in each one of those countries. So we're looking to standardize as much as we can, which in some cases may require custom solutions in different places.

Another key challenge are the compliance regulations in our environment. This means more pressure to make sure that we know who our customer is, where they come from, and if they are who they say they are.

What are you doing to address the compliance challenge?

We are partnering with Dun & Bradstreet to create an automated credit scoring environment so, at the time of credit underwriting, we can take into account certain factors before we auto-approve. This will tell us, for example, whether or not a customer can be validated. This is a crucial piece for us.

How has Dun & Bradstreet helped with the advances you've been making?

We've relied on Dun & Bradstreet in our credit scoring operations and have gotten comfortable with the data, what it means and the quality of that data. There's been pressure recently from the commercial side of our organization to increase auto-approval rates, and we've been working with Dun & Bradstreet's analytics team on the scorecard development process. To be honest, we feel D&B knows their data better than anyone, and enlisting them in the process gives us the confidence that we'll end up with extremely powerful scorecards.

Why is auto-decisioning important to your business?

Ultimately, we'd like faster turnaround times with fewer manual underwritings. This would serve as a key competitive advantage for us; a simpler, quicker approval process could mean the difference between winning and losing a customer who has multiple lenders interested in them.

Fewer manual underwritings also translates to greater operational efficiencies, as risk managers would be able to reallocate some of those resources to other areas of the organization, such as the compliance regulatory piece, for example.

As a user of Dun & Bradstreet's SBRI, how to you plan to leverage the solution moving forward?

We've been accessing Small Business Risk Insight (SBRI) data since for five or six years now. When we first started with the solution, we were not automated in our scorecards, primarily because we were still developing them at the time. But we're looking to redevelop our scorecards now, and we expect SBRI play a key role in the automation. The real value for us is the predictiveness of that data, as it's very close to our customer base.

Parting Words

At the end of the day, it's all about knowing who the customer is. With an understanding of the customer, who they are, where they came from and the risk associated with them, DLL can make more profitable decisions while adhering to varying compliance requirements. By automating the process by which the firm gathers those insights, they can create operational efficiencies within the organization and strengthen their competitive position in the market.

For more stories of how Dun & Bradstreet works with credit and risk teams at successful companies, visit this case study collection.

 

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