Utilities Q&A Perspective Series: Navigating the Utilities Collections Ecosystem

by Laura Burrows 5 min read May 26, 2020

The COVID-19 pandemic has created unprecedented challenges for the utilities industry. This includes the need to plan for – and be prepared to respond to – changing behaviors and a sudden uptick in collections activities.

As part of our recently launched Q&A perspective series, Mark Soffietti, Experian’s Senior Manager of Analytics Consulting and Tom Hanson, Senior Energy Consultant, provided insight on how utility providers can evolve and refine their collections and recovery processes. Check out what they had to say:

Q: How has COVID-19 impacted payment behavior and debt collections?

TH: Consumer payment behavior is changing. For example, those who paid as agreed, may not currently have the means to pay and are now distressed borrowers. Or those who were sloppy payers before the pandemic may now be defaulting on a more consistent basis.

MS: As we saw with the last recession when faced with economic stress, consumer and commercial payment behavior changes based on their needs and current cash flow. For example, people prioritize their car, as they need it to get to and from work, so they’ll likely pay their auto bills on time. The same goes for their credit cards, which they need to make ends meet. We expect this will also be true with COVID-19. The commercial segment will face more dramatic and challenging circumstances, where complete or partial business closures and lack of federal relief could have severe ramifications.

Q: What new restrictions have been put in place surrounding debt collection efforts and outbound calls?

TH: To protect consumers who may be experiencing financial distress, most states have imposed new, stringent restrictions to prevent utilities from engaging in certain collections activities. Utilities are currently not charging any late payment fees and are instead structuring payment plans. Additionally, all outbound collections efforts have been suspended and there is fieldwork being executed of services for both commercial and consumer properties. As of now, consumer and commercial fieldwork will likely not commence until after the first year or when the winter moratorium concludes.

MS: The new restrictions imposed upon collections activities will likely drive consumer payment behavior. If consumers know that their utilities (i.e. energy and water) will not be shut off if they miss a payment, they will make these bills less of a priority. This will dramatically increase the amount owed when these restrictions are lifted next year.

Q: Can we predict how the utilities industry will fare post-COVID-19?

TH: The volume of accounts in collections and eligible for disconnect will be overwhelming. Many utility providers fear the unpaid balances consumers and commercial entities accumulate will be nearly impossible to fit into a repayment schedule. Both analyzing internal payment segments and overlaying external factors may be the best way to optimize the most critical go-forward plan.

MS: The amount of people who fall into collections is going to greatly increase and utility providers need to start planning for it now to weather the storm. They will need to use data, analytics and tools to help them optimize their tasks, so they can be more efficient with their resources. Like many other industries, the utilities sector will look to increasing digitalization of their processes and having less social interaction where possible. This could mean the need and drive for expediting current smart meter programs where possible to enable remote fieldwork to assist in managing this unprecedented level of activity that is sure to overwhelm field operations (where allowed by state regulators).

Q: What should utility providers be doing to plan for an uptick in collections activities post-COVID-19?

TH: With regulatory mandated suspensions of collections activities for utility providers and self-selected reductions due to stay at home orders and staff protection, the backlog of payments, calls and inquiries once business resumes as normal is set to overwhelm existing capacity. More than ever, self-service options (text/web), Q&A and alternative communication methods will be needed to shepherd consumers through the collections process and minimize the strain on call center agents. Many utility providers are asking for external data points to segment their consumers by industry or by those whose employment would have been adversely impacted by COVID-19.

MS: Utility providers should be monitoring consumer data in order to prepare for when they are able to collect. This will help them strategize the number of resources they will need in their call centers and out in the field performing shut off activities. Given that the rise in cases will be more volume than their call centers can handle, they will need to use their resources wisely and plan to use them efficiently when they are able to resume collections.

Q: How can Experian help utility providers reduce collections costs and maximize recovery?

TH: Experian can help revise collections tactics and segmentation strategies by providing insight on how consumers are paying other creditors and identifying new segmentation opportunities as we emerge from the freeze on collections activities. Collections cases will be complex, and many factors and constraints will need to balanced against changing goals, making optimization key.

MS: Utilizing Experian’s credit data and models can help ensure that resources are being used efficiently (i.e. making successful calls). There is also a need to leverage ability to pay models as well as prioritization models. By using these models and tools, utility providers can optimize their treatment strategies, reduce costs and maximize dollars collected.

Learn more

About our Experts:

Tom Hanson, Senior Energy Consultant, Experian CEM, North America

Tom is a Senior Consultant within the Energy Vertical at Experian, supporting regulated energy companies throughout the U.S. He brings over 25 years of experience in the energy field and supports his clients throughout the customer lifecycle, providing expertise in ID verification, account treatment, fraud solutions, analytics, consulting and final bill/field optimization strategies and techniques.

Mark Soffietti, Analytics Consulting Senior Manager, Experian Decision Analytics, North America

Mark has over 15 years of experience transforming data into actionable knowledge for effective decision management. Mark’s expertise includes solution development for consumer and commercial lending across the credit spectrum – from marketing to collections.

Related Posts

Who’s Driving What? How Fuel Loyalty and Generational Preferences are Shaping the Vehicle Market

Take a look around at any road, parking lot, or highway, and you’ll see just how diverse today’s vehicle landscape has become. Within that evolving mix, electric vehicles (EVs) have seen years of rapid growth, and while the market is seemingly entering a new phase, interest remains. So, with several vehicles and fuel types to choose from, what keeps drivers coming back to electrified vehicles? Experian Automotive’s Automotive Market Trends Report: Q2 2026 found that among EV owners who returned to the market in the last 12 months, majority (72.2%) replaced their EV with another EV, while 18.5% switched to a gasoline vehicle. Hybrid buyers also showed considerable loyalty to electrification, with 55.7% of gas-hybrid owners staying with the same fuel type when replacing their vehicle, and 32.7% swapping for a gasoline vehicle. Consumers are seemingly remaining loyal to EVs and hybrids because they are attracted to the benefits that fit their everyday lifestyle, such as lower fuel or charging costs amid the elevated gas prices. For some, it could also be tied to convenience, as drivers who have found a reliable charging routine or appreciate the efficiency of a hybrid may have little reason to switch back to a traditional gasoline vehicle. Generations are taking different paths to electrification Generational differences also influence hybrid and EV loyalty, with younger consumers generally showing greater openness to alternative fuel types. While older generations tend to have greater familiarity with traditional gasoline vehicles, hybrid and EV adoption is increasing across all age groups as these options become more accessible and mainstream. Millennials, in particular, showed the strongest inclination toward electrified vehicles. Through Q2 2026, they accounted for the highest EV share at 8.2%, compared with Gen X at 5.5%, Gen Z (4.7%), and Baby Boomers (4.7%). The difference becomes even more pronounced when hybrids are in the mix, as 23.1% of Millennial registrations were gas-electric hybrids or plug-in hybrids, versus 16.7% for Gen X, 16.8% for Gen Z, and 18.0% for Baby Boomers. For automotive professionals, these differences make understanding who is driving what, and what they may choose next, increasingly important. The future of the automotive market may be less about consumers choosing one vehicle type over the other and more about understanding the distinct patterns and preferences of each generation. As the market continues to evolve, those insights can help automotive professionals better meet consumers where they are. To learn more about vehicle market trends, view the full Automotive Market Trends Report: Q2 2026 presentation on demand.

September 24, 2026 by John Howard
New Data Available for MBS Investors: Current Credit Score 

In a previous post, we described how every mortgage borrower’s financial situation and credit profile evolve over time.  After a borrower opens a loan, their financial status evolves—jobs are gained and lost; incomes can rise or fall, and financially stressful situations or windfalls can occur. These effects are often reflected in the consumer’s evolving credit score, which changes with the consumer’s payment behavior on open loans, credit inquiry activity, credit card utilization, and other revolving lines, among other things.    Even though MBS, whole loan, and MSR investors ultimately bear borrower credit risk, they may have access to less current borrower credit information than other participants in the mortgage ecosystem.   In securitized markets (both agency MBS and private-label MBS), updated scores are not provided in disclosure to bondholders, even as loans age year over year.  In whole loan and MSR markets, a single origination credit score is often provided at the time of bid, and after a successful bid, the investor may have a permissible purpose to pull individual scores on an owned portfolio. But until recently, there was no single loan-level dataset that included continuously refreshed credit scores across the U.S. mortgage market—the type of foundational dataset needed to build and tune credit and prepayment models.  A monthly-refreshed Current Credit Score field meets our three-pronged materiality standard for new data delivery to MBS markets:  New: Provides information not available in existing datasets (i.e., orthogonal to currently available data). Neither private-label MBS nor agency MBS standard market data includes a monthly-refreshed borrower credit score.  Material: Impacts a sizeable portion of the MBS universe. For the vast majority of loans in MBS, borrowers credit scores are available.  Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions.  A current credit score wraps all of a borrower’s credit-related behaviors into a single numerical value and has historically been associated with a borrower’s likelihood of becoming 60+ days past due on any obligation within the subsequent 24 months.    In fact, a current credit score is among the most informative indicators of near-term mortgage default risk, as shown in the image below, which depicts 30+ DPD rates by current credit score bands for the entire U.S. mortgage market, controlling for origination score <=650.    Without access to current credit scores, investors are limited to the score at origination—causing the four distinct performance trends shown here to appear as a single averaged line. In reality, score migration since origination reveals significant divergence in credit risk, with the lowest current-score bucket exhibiting a nearly 10 times higher 30+ DPD rate than the highest-score bucket in the latest period shown.  Source:  Experian Mortgage Loan Performance (MLP) dataset hosted on IVolatility DataDriven Platform  In this article, we’ll take a quick look at how score migration acts as an early predictor of a performing loan’s first roll into 30-day delinquent status.    MBS Investors’ Current Credit Score Blindspot: Solved   An MBS investor relying on standard market data and securitization remittance reports sees no sign of borrower stress until the subject mortgage loan in the securitization misses a payment and is reported at 30 days delinquent. Of course, in the vast majority of cases, a borrower begins struggling financially well before missing a mortgage payment:  The borrower may miss payments on other types of loans (credit card, auto loan, personal unsecured, or payday loans) as they prioritize their home and mortgage.  Outstanding balances on credit cards may grow as the borrower begins to make only minimum payments on revolvers.  The borrower may apply for additional credit cards, personal or payday loans   The borrower may apply to increase limits on existing credit cards as outstanding balance nears spending limit  All these stress-indicative behaviors result in a decreasing credit score, many months before the borrower misses their first mortgage payment. An MBS investor with access to each borrower’s current credit score, refreshed each month, can predict increased likelihood of default many months before the first missed mortgage payment—and is therefore at a major information advantage relative to the market generally.  Experian’s Mortgage Loan Performance (MLP) dataset contains thousands of fields describing mortgage performance from each borrower, loan, and property perspective, all refreshed monthly (including, amongst other things, new credit scores and refinance inquiry activity, loan performance on all types of debt, filed junior liens, and AVM values).   MLP is much more comprehensive than loan-level data provided by Freddie Mac, Fannie Mae, Ginnie Mae, and PLS data vendors in several ways:   Standard market datasets may not contain certain data elements that some market participants consider useful when evaluating mortgage prepayment or credit performance. Basic, critical fields such as the borrower’s current credit score and the current junior lien balance on the property are missing.    MLP contains borrower, loan, and property data fields spanning a broad portion of the mortgage universe, including Agency, Non-Agency, and Esoteric mortgage products (CES, HELOC, Reverse), including both securitized and non-securitized loans.   MLP enables full three-dimensional (borrower + loan + property) tracking with persistent keys for borrower (before and after refinancing), loan (in securities/deals even after exit due to payoffs or buyouts, including before and after MSR sales), and property.  This enables end-to-end analysis of each borrower’s (and property’s) mortgage experience throughout their credit lifecycle.  Is Downward-Trending Credit Score a Signal for Impending Delinquency?  MLP contains thousands of fields describing each loan, borrower, and property across all U.S. mortgages.  It allows for virtually unlimited segmentation and granular analysis.   For purposes of this illustrative article, we’ll take a high-level look at the entire U.S. mortgage market and perform a quick analysis to confirm intuition that a declining credit score provides a signal for higher likelihood of near-term mortgage delinquency.  Figure 1 illustrates the current pay status (as of 6/30) for the entire U.S. mortgage market, as contained in the MLP dataset, along with count, UPB and UPB-weighted Vantage 4.0 credit score for each bucket.  Figure 1  Source:  Experian Mortgage Loan Performance dataset  As illustrated in Figure 1, approximately 772,000 individual mortgage loans were reported to Experian as 30 days delinquent as of 6/30/2026.  Of the 772,000 30d delinquent loans in the June snapshot, approximately 426,000 were current in the prior (May) snapshot.  Some of these 426,000 loans were reperformers which had been bouncing from 30 DPD to current over the prior few snapshots. To remove reperformance score noise, we further parsed out the population which: 1) had rolled from current to 30 DPD from May to June; and 2) was consistently current for a full year prior to the 6/30 missed payment.  The population meeting both conditions totaled approximately 123,000 loans.  Figure 2 below shows, for this population of 123,000 “clean current” loans, the UPB-weighted average Vantage4 credit score for each of the 12 months leading up to the June missed payment, as well as the impact of the missed payment on the 6/30 score.  Figure 2  Source:  Experian Mortgage Loan Performance Dataset  Figure 2 reveals a rather slow and steady ~20-point deterioration of score in the 12 months prior to first missed payment – as well as the 80-point drop once the missed payment hits.  When we compare this cohort’s Vantage 4.0 score trend to the broader Current population across the entire dataset in Figure 3, we see a marked difference in both absolute value and trend:  Figure 3  Source:  Experian Mortgage Loan Performance Dataset  Not only is the cohort’s starting Vantage 4.0 score lower than the broader current population, but it also displays a dropping trend (with a notable 2 to 3x acceleration in monthly score drop the month before the first missed mortgage payment) while the broader Current population’s score (of which the isolated cohort is a subset) remains rock steady.  Lastly, we present Figure 4, a histogram comparing the distribution of at-origination and as-of 5/30 (i.e., the period just before the missed June mortgage payment) credit scores for the clean current population. The distribution appears to shift toward lower credit scores. To the extent credit scores are correlated with credit risk, this shift may indicate elevated credit risk relative to origination. Since this degradation occurs during a period of perfect mortgage pay performance, it is invisible to MBS investors who lack access to current borrower credit scores. Experian MLP provides monthly refreshed credit scores for mortgage borrowers contained within the MLP database.  Figure 4  Source:  Experian Mortgage Loan Performance Dataset 

September 22, 2026 by Michael Pyatski, Perry DeFelice
Ask the Expert: The Future of Lending Starts With Identity With Shawn Rife and Brian Cardona

Identity intelligence and alternative data can help lenders validate consumers and support more informed decisions across the customer lifecycle.

September 16, 2026 by Julie Lee

Subscribe to our Newsletter

Enter your name and email for the latest updates.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe to our Newsletter

Don't miss out on the latest industry trends and insights!
Subscribe