Key Energy Services, Inc., an onshore, rig-based well servicing contractor, will introduce predictive analytics, a risk-based collections methodology available in SunGard’s GETPAID, to drive prioritization of collections activity for improved cash flow and reduced bad debt expense.
Ross Guthrie, vice president, order-to-cash, at Key Energy explains, “Our decision to upgrade to GETPAID 8 was based on the availability of predictive analytics and risk modeling that is now a part of the GETPAID solution. I used predictive analytics at a previous company and understand the value it brings to the credit risk and collections processes. Despite the fact that we are 87% current, we could further improve our collections processes and our customer service by contacting customers who are actually at risk. With SunGard, we will also be able to score our portfolios on a monthly basis with quicker access to more accurate data around our at-risk customers.”
Risk analytics allow companies to run models on their existing A/R portfolio to determine the likelihood of specific accounts going ‘bad’ over time. The models are based on over thirty different variables and are calibrated to account for nuances specific to industries, geographies and customer types. By using the risk grade output, companies can then prioritize their collections efforts and integrate into their credit risk policies for improved effectiveness as evidenced by increased cash flow and reduced bad debt expense.
“A company’s largest current asset is usually their accounts receivables. Our philosophy is that this portfolio should be proactively monitored for both risk and opportunity. Not only can our risk models identify high collection risk customers, we can also help to find the low risk customers that are not exhausting their credit lines. Those customers represent opportunity for sales. We are helping our customers to more strategically manage this asset,” explains C.J. Wimley, chief operating officer for AvantGard Receivables, SunGard’s corporate liquidity business.
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