Industries

Below is our 5 Results for Dealers and Agencies Using DPS infographic.

  The auto industry has been riding a wave of prosperity for the past seven years, bouncing back nicely from the 2008 market collapse. But, it looks like rising sales of the past 10 years, are, well...a thing of the past. According to Alix Partners, 2016 sales of 17.5 million units might be the high-water sales mark, at least through 2022. Alix Partners says the next five years sales will range between 15.6 million to 16.8 million annually. Suddenly, it will be challenging for dealers to stay in strong growth mode. How can dealers best react to the tightening market? The Experian white paper “Data Tools Evolve to Give Dealers an Edge in a Tight Sales Market” takes a look at how new and improved data and analytic tools can provide deeper insights to help automotive retailers unlock sales. The paper reviews current market sales statistics, historical sales trends and how dealers reacted during similar market conditions in the past. In addition, the paper provides a look at the challenges faced by automotive retailers, in terms of shrinking gross profit, higher advertising expenses and increased competition. Automotive retailers also will find information on the importance of customer conquesting and a look at technology tools to help provide a deeper understanding and actionable intelligence about local markets. Data and analytics are no longer the private purview of large mega-dealers. The Experian white paper outlines today’s data tools that can be implemented quickly and cost effectively by dealers of any size. To learn more about these trends, download the paper here: https://www.experian.com/automotive/dealerwhitepaper.html

You’ve been tasked with developing a new model or enhancing an existing one, but the available data doesn’t include performance across the entire population of prospective customers. Sound familiar? A standard practice is to infer customer performance by using reject inference, but how can you improve your reject inference design? Reject inference is a technique used to classify the performance outcome of prospective customers within the declined or nonbooked population so this population’s performance reflects its performance had it been booked. A common method is to develop a parceling model using credit bureau attributes pulled at the time of application. This type of data, known as pre-diction data, can be used to predict the outcome of the customer prospect based on a data sample containing observations with known performance. Since the objective of a reject inference model is to classify, not necessarily predict, the outcome of the nonbooked population, data pulled at the end of the performance window can be used to develop the model, provided the accounts being classified are excluded from the attributes used to build the model. This type of data is known as post-diction data. Reject inference parceling models built using post-diction data generally have much higher model performance metrics, such as the KS statistic, also known as the Kolmogorov-Smirnov test, or the Gini coefficient, compared with reject inference parceling models built using pre-diction data. Use of post-diction data within a reject inference model design can boost the reliability of the nonbooked population performance classification. The additional lift in performance of the reject inference model can translate into improvements within the final model design. Post-diction credit bureau data can be easily obtained from Experian along with pre-diction data typically used for predictive model development. The Experian Decision Analytics team can help get you started.

Early reports suggest the 2017 holiday season was a good one for retailers. Consumers were in the mood to spend, and as such, Americans’ total credit card debt continued to climb. Americans planned to spend $862 on gifts for the season, a huge jump from the $752 they planned on spending in 2016. And the numbers were significantly higher than their estimate in any November since 2007 -- just before the 2007-2009 recession. 29% of Americans said they planned to spend more than $1,000. What does this mean for card portfolios? Well, business is booming, but they should also prepare for the time of year when consumers are most apt to seek out debt consolidation and transfer options. A recent NerdWallet analysis revealed the average household that’s carrying credit card debt has a balance of roughly $15,654. Dig deeper into retail card specifically and reports indicate Americans are carrying $1,841 in retail debt. “There is seasonality to consumer credit card behavior,” said Denise McKendall, a credit card and trended data specialist for Experian. “As we roll into the late winter months and early spring, consumers often seek ways to transfer card debt to lower interest rate options, consolidate debt from multiple cards and perhaps even pull out personal loans. This makes it an ideal time for card portfolio managers to leverage data to anticipate consumer behaviors and be able to offer the best rates and options to retain cardholders and grow.” Card portfolio managers should consider these questions: What is my portfolio risk? Did some of my consumers overextend themselves? Do I have collections triggers on my accounts to mitigate risk and manage delinquencies? Which consumers in my portfolio will be looking to consolidate debt? Should I reassess credit line limits? Which of my consumers show a high propensity to make a balance transfer? Do I have opportunities to grow my portfolio by offering attractive rates to new customers? Which customers will leave after low introductory rates expire? Can I use this time of year to become the first credit card consumers’ consistently use, rather than the second or third card they pull from wallet? At first glance, it might appear challenging to answer many of these questions, but with the right data and analytics, a card manager can easily establish a game plan to conquest new business, mitigate risk and retain existing, high-value consumers. The robust holiday season was a boom for the economy. Now card companies need to ready themselves for the aftermath.

The U.S. Senate Banking Committee passed a financial regulatory relief bill (S. 2155) in December 2017 aimed at reducing regulatory burdens on community banks, credit unions and smaller regional banks.  Committee Chairman Senator Mike Crapo (R-ID), sponsored the bill, which has strong bipartisan support, with 23 cosponsors (11 Republicans and 12 Democrats and an independent). The package is likely to be considered by the full Senate in early 2018. The legislation includes two provisions related to consumer credit reporting.  Both were adopted, in part, in reaction to the Equifax data breach. As the bill moves through the legislative process during 2018, it will be important for all participants in the consumer credit ecosystem to be aware of the potential changes in law. One provision deals with fraud alerts and credit freezes for consumers and the other deals with how medical debt is processed for veterans who seek medical treatment outside the VA system. Credit Freezes The bill amends the Fair Credit Reporting Act to provide consumers with the ability to freeze/unfreeze credit files maintained by nationwide credit reporting agencies at no cost, and would extend the time period for initial fraud alerts from 90 days to one year. The credit freeze provisions would also establish a process for parents and guardians to place a freeze on the file of a minor at no cost. The bill would require the nationwide credit reporting agencies to create webpages with information on credit freezes, fraud alerts, active duty alerts and pre-screen opt-outs and these pages would be linked to the FTC’s existing website, www.IdentityTheft.gov.  The credit freeze and minor freeze provisions would preempt State laws and create a national standard. Protections for Veterans The bill also incorporates a provision that would prohibit credit bureaus from including debt for health-care related services that the veteran received through the Department of Veterans Affairs’ Choice Program. The provision would cover debt that the veteran incurred in the previous year, as well as any delinquent debt that was fully paid or settled. The legislation would require a consumer reporting agency to delete medical debt if it receives information from either the veteran or the VA that the debt was incurred through the Veteran’s Choice Program. What’s next The bill now awaits consideration before the full Senate. Senate Majority Leader Mitch McConnell has said that the bill is a “candidate for early consideration” in 2018, but the exact timing of floor debate has yet to be scheduled. Once the package passes the Senate, it will need to be reconciled with the regulatory relief package that was passed by the House last spring.

The nation’s economic recovery is continuing in a positive upward trend with consumer credit scores coming exceptionally close to pre-recession numbers—the healthiest in a decade. Experian’s 8th annual State of Credit report reveals the nation’s average credit score is up two points year-over-year to 675, and is just four points shy of the 2007 average of 679. “The trend line we are seeing is quite promising,” said Michele Raneri, Experian vice president of analytics and new business development. “With employment and consumer confidence on the rise, the data is indicating that we have made great progress as a country since the recession. The economy is expected to expand at a healthy pace this year and we believe that credit will continue to rebound. All of the factors point towards a good year for credit in 2018.” The study also revealed that year-over-year: Personal loan and auto loan originations increased 11 percent and 6 percent, respectively. The average number of retail cards remains at 2.5 per consumer, while the average retail debt increased $73 to $1,841 per consumer. The average number of bankcards increased slightly from 3.03 to 3.06, with the average card balance also increasing by $166 to $6,354. Instances of late payments (includes bankcard and retail) remained about the same at under 1 percent. And importantly, consumers have a positive outlook with consumer confidence up 25 percent. Top of the credit charts As part of the annual study, Experian analyzed consumer credit habits in U.S. cities. As in previous years, Minnesota continues to stand out with three of its cities — Minneapolis, Rochester and Mankato—leading the way with credit scores of 709, 708 and 708, respectively. Wausau, Wis. (706), Green Bay, Wis. (705) round out the top five. Again, Greenwood, Miss., and Albany, Ga., ranked the lowest with scores of 624 and 626. While still at the bottom of the list, Greenwood and Abany residents did improve their scores by two points. Riverside, Calif.,—fifth on the list—improved its score by four points—the greatest increase of any city in the bottom 10. Generational divide Taking the research further, Experian analyzed consumer credit information by generation, and found: Generation Z (born 1996 and later) is building credit through different methods than the generations before them, with heavier student loan debts and fewer credit cards and department store cards. And they are keeping debts low and managing them well. Generation Y/Millennials (born 1977-1995) have seen their scores climb four points over the past year. They’ve also decreased their overall average debt by nearly eight percent, but have added six percent in mortgage debt. Generation X (born 1965-1976) has a credit score of 658, the highest mortgage debt of all generations, and a high instance of late payments compared to the national average. Their scores have improved, so they are managing their debts better than in the past,. Baby Boomers (born 1946-1964) continue to carry quite a bit of mortgage debt, and have the lowest late payment instances of all the generations. The Silent Generation (born 1945 and before) has quite a bit of mortgage debt, but are keeping other debts low and making payments on time. At 729, they have the best credit score of all generations and the fewest late payments of any generation. To review findings from Experian’s 2017 State of Credit report, join WiseBread’s chat Jan. 18. To register, go to www.wisebread.com and follow #wbchat. To chat with Experian live, and learn more about credit, join #CreditChat hosted by @Experian_US on Twitter every Wednesday at Noon PT/3 p.m. ET.

As soon as the holiday decorations are packed away and Americans reign in the New Year, the advertisements shift to two of our favorite themes – weight loss and taxes. No wonder the “blues” kick in during February. While taxes aren’t due until April 17, the months of January, February and March have consumers prepping to file. Coincidentally, it is also a big season for lenders to collect after the high-spending months of October through December. “Knowing which of your customers may receive a refund is critical,” said Colleen Rose, an Experian product manager specializing in the collections industry. “This information can help lenders create a strategy to capitalize on this important segment during the compressed collections window.” The industry has become more familiar with trended data and its ability to predict how consumers are faring on the credit score slider, but many don’t know that it has also proven popular in identifying people who may get a tax refund, and who is likely to use a refund to pay down delinquent balances. The past two tax seasons are evaluated to provide a complete picture of a customer’s behavior during tax refund season. Balance, credit limit and other historical fields are incorporated with tradeline-level data to determine who paid down their delinquent balances during this time. According to the IRS, in fiscal year 2016, the average individual income tax refund was about $3,050, so it’s a prime time for consumers to come into some unexpected cash to either pay down debt or spend. It’s estimated that 35% of consumers who get a refund will pay down debt. “Using Experian’s trended data attributes, we’ve identified past-due customers who paid down a delinquent tradeline balance by at least 10% and made a large payment during tax season,” said Rose. “With these specific attributes, we can help clients target a very desirable population during the critical collections months, helping them to refine their campaigns and create offers geared toward this population.” Anticipating who is likely to receive a refund and use it to pay down debt can influence how collections departments develop their messaging, call outreach and mailings. And for consumers who owe multiple debts, these well-timed touchpoints and messages could influence who they pay back first. The collectors with the best data, once again win, with trended data providing the secret sauce for predictions. ‘Tis the season for taxes.

You just finished redeveloping an existing scorecard, and now it’s time to replace the old with the new. If not properly planned, switching from one scorecard to another within a decisioning or scoring system can be disruptive. Once a scorecard has been redeveloped, it’s important to measure the impact of changes within the strategy as a result of replacing the old model with the new one. Evaluating such changes and modifying the strategy where needed will not only optimize strategy performance, but also maximize the full value of the newly redeveloped model. Such an impact assessment can be completed with a swap set analysis. The phrase swap set refers to “swapping out” a set of customer accounts — generally bad accounts — and replacing them with, or “swapping in,” a set of good customer accounts. Swap-ins are the customer population segment you didn’t previously approve under the old model but would approve with the new model. Swap-outs are the customer population segment you previously approved with the old model but wouldn’t approve with the new model. A worthy objective is to replace bad accounts with good accounts, thereby reducing the overall bad rate. However, different approaches can be used when evaluating swap sets to optimize your strategy and keep: The same overall bad rate while increasing the approval rate. The same approval rate while lowering the bad rate. The same approval and bad rate but increase the customer activation or customer response rates. It’s also important to assess the population that doesn’t change — the population that would be approved or declined using either the old or new model. The following chart highlights the three customer segments within a swap set analysis. With the incumbent model, the bad rate is 8.3%. With the new model, however, the bad rate is 4.9%. This is a reduction in the bad rate of 3.4 percentage points or a 41% improvement in the bad rate. This type of planning also is beneficial when replacing a generic model with another or a custom-developed model. Click here to learn more about how the Experian Decision Analytics team can help you manage the impacts of migrating from a legacy model to a newly developed model.

When gearing up to buy a car, having a checklist of things to look for is important. Happily, it looks like consumers have added something new to the top of that list: managing their credit better. According to Experian’s latest State of Automotive Finance Market report, the average credit score for purchasing a vehicle has increased four points across the board, reaching 722 for new vehicles and 682 for used vehicles. That four-point increase may seem insignificant, but it reveals that consumers are actively managing their credit. With the recent trepidation over the so-called subprime auto finance bubble, the positive change is great news for people in the market to get a new ride. Per the Q3 report, subprime originations reached the lowest level of market share since 2012 (16.6 percent), while prime and super-prime originations showed the largest increases in market share. So what does all this mean? In addition to better managed credit, the increased market share of prime and super-prime consumers shows that industry professionals are leveraging data and analytics when making lending decisions. So if you’re looking to buy a car soon, how can you use that knowledge to your advantage? About six months before zooming to the dealership, take the time to check your credit report to make sure there are no surprises when a potential lender looks at it. Once you know where you stand, you can take steps to improve your credit (if needed) by paying bills on time, keeping balances low and not applying for any new credit before you’re ready to buy that car. Your credit is in good shape, so now what? When car shopping, don’t just look into vehicle make and model. Consider your financing options as well. Different lenders offer different terms and conditions, so comparing options can make sure that you’re getting the best deal possible. For example, our Q3 report shows that credit unions and captive financers (like Ford Motor Credit or Toyota Financial Services) are earning more customers and taking a greater market share of auto lending, at 21 percent and 29.8 percent, respectively. Banks still hold the largest percentage of auto loans at 32.9 percent, but they don’t dominate auto financing like they once did. Leasing is another financing option. Consumers often choose to lease because of lower monthly payments ($412 for a new lease as opposed to $502 for a new loan). The report shows that leasing continues to be a large part of new auto financing, coming in at 29 percent of all financed new vehicles. Lastly, the report shows that loan terms continue to be extended, with the average length of a loan hitting 69 months for new cars and nearly 64 months for used. Extending loan terms can lower your monthly payment, but you should use it mindfully, so the total cost of the vehicle loan doesn’t exceed your budget. For more information about the current State of the Automotive Finance Market report or to view a recording of the webinar, visit our website.

Student loan debt is weighing down Americans of all generations, but a college education is still prized as the ticket to opportunity. So will the debt continue to climb? Where will students turn for funding? We interviewed Vince Passione, founder and CEO of LendKey, a lending-as-a-service platform specializing in student lending, to gain his perspective on the state of student lending and how the space is evolving for both consumers and lenders. We’ve all seen the headlines about U.S. student loan debt now accounting for $1.4 trillion. The majority of these loans are government-funded, but do you see this shifting? There are many factors at play here. Tuition is rising rapidly and will soon outpace the current level of governmental support available to students searching for loans. Meanwhile, today’s geopolitical climate signals that the current levels of federal funding will also decrease. With these two confounding trends, the need for competitively priced private financing and refinancing options will increase. The student loan industry will shift toward private lenders such as credit unions and banks in order for students to continue to obtain the funds they need for tuition and other college expenses. The key to helping this transition happen is for banks and credit unions to adopt the user-friendly technology platforms that appeal to these prospective student borrowers. Your end-to-end cloud-based technology platform enables lenders to get into the student loan space. How does this work and what must lenders consider as they underwrite and manage a student loan portfolio? Our turnkey platform is unique, in that it lets lenders control underwriting and pricing, unlike the “disruptive” model utilized by many other technology companies in the industry. Most community banks and credit unions lack the in-house resources to develop, implement and maintain an online lending platform. At the same time, millennials and young borrowers continue to prefer the online interface rather than engaging with a brick-and-mortar establishment. We’re committed to partnering with banks and credit unions to allow them to offer private consumer loans, such as student loans, and support them with our technology (loan application and decisioning) and people (customer service agents and loan processors). A strong grasp on the technology and support aspects of online lending platforms alone is merely the foundation for a successful program. As the student lending asset classes are highly regulated, and the regulations are constantly changing, lenders must look to partner with a firm that has a concrete understanding of the regulation, risk and customer service to translate the information to prospective borrowers. I’ve heard you use the phrase “HENRY.” Can you explain what this is and why these individuals are so lucrative for lenders? HENRY stands for “High Earners, Not Rich Yet” and is a term that can be applied to many millennials and young people in today’s economy. This demographic is typically college graduates with well-paying jobs, but have not yet established themselves financially or accrued enough wealth to subsidize larger purchases like cars, homes, renovations and advanced degrees. This is also why they are so lucrative for lenders. HENRYs have just entered their prime borrowing years and are consumers who will easily be able to pay back loans for cars, homes and renovations. But for most of this demographic, their first experience with a financial service product will be a student loan. It is important to get in on the ground floor with these borrowers through student lending to establish a trusted relationship that will result in repeat loans and referrals. You’ve done a great deal of research on millennials and how they are managing student loans. Can you share some of your key learnings? Do you believe Generation Z will behave and manage student debt similarly? It’s no secret that millennials are more apprehensive of student loans than previous generations. As Gen Z begins to enter college, many are plagued with stigmas set forward by the poor experiences millennials experienced with student loans, making them wary of debt. According to a study, 63 percent of the students said they would “possibly” take on student debt, down from 71 percent in 2016. Gen Z is better prepared by seeing the preceding generation grapple with loan issues. Many are making smarter decisions on schools and programs, and are attentive when it comes to monitoring for updates in regulation. As this generation continues to go through the typical collegiate years, the geopolitical climate, as well as rising tuition costs, will increase the need for competitively priced private financing options for Gen Zers. Finally, what trends or predictions do you see occurring in the student lending space over the next five years? The need for student loans continues to exist and shows no sign of slowing down anytime soon, but lenders are only recently opening their eyes to the opportunity that this massive market presents. With the impending drop in federal funding, more FinTech companies will continue to pop up to address this need. This spike in disruption also poses a threat to banks and credit unions, however. With more FinTechs available to help shoulder the burden of student lending, banking and credit union executives must be more judicious when vetting technology partners to ensure they’re working with a partner that meets their regulatory standards, supports their current and prospective clients, and lets them retain the control they wish to keep in-house.

Auto originations continue to increase — particularly within prime categories. According to Experian’s latest State of the Automotive Finance Market report: Prime consumers grabbed the lion’s share of the total finance market, at 40.9%. Super-prime buyers showed the largest increase, reaching 20.2%. Consumers outside the prime category (credit score of 600 or lower) decreased to the lowest share on record since 2012. Credit unions and captive lenders increased market share of total vehicle financing, growing to 21% and 29.8% — an increase of 6.9% and 35.1%, respectively. As auto loan originations continue their upward trend, lenders can stay ahead of the competition by using advanced analytics to target the right customers and increase profitability.  

  The journey to a mortgage is complex and expensive, so of course the transaction will require more than a few swipes on a smartphone. The U.S. existing home median sales price in October was $274,000 – not cheap. Still, with advancements in digital verification, lenders can dramatically accelerate the process, providing benefits to both their own operations and the consumer mortgage experience. Underwriting a sizeable loan can take weeks with the task of collecting income and asset documents to analyze and verify. In fact, one source from the Mortgage Bankers Association says the average mortgage application has ballooned to 500 pages. The consumer is typically asked to find, print and scan papers revealing insights around employment status and wages, bank and retirement accounts, debts and beyond. The good news is that this process can be handled digitally, and I’m not talking about simply scanning and emailing. Verification solutions exist to enable consumers to grant limited and secure access to digitally verify assets and income. As lenders evaluate verification solutions, one of the key differentiators to seek is Fannie Mae Day 1 Certainty, which claims to slash the average cycle time for income validation by 8.1 days, employment validation by 11.9 days, and asset validation by 6.1 days. * Fannie Mae features a list of approved vendors who provide Fannie Mae-approved verification reports. This group of authorized suppliers receive freedom from representations and warranties for more efficient risk management, and additionally receive the benefit of a more streamlined process through Fannie Mae’s Desktop Underwriter® (DU®). DU’s latest enhancement leverages a verification of asset report derived from aggregated bank account data, something Finicity (an Experian partner) is approved to utilize. Building on Day 1 Certainty, Finicity is participating in a new single source pilot with Fannie Mae to validate income, assets and employment. While it will take time for lenders to embrace this new technology – and consumers will need to feel comfortable granting the digital access and understanding how the process works – the thought is the mortgage journey will become faster and offer an optimized borrower experience. Like so many other aspects in our lives, mortgage is bound to go digital. *Average days saved reflects data captured between January 2017 and June 2017.

Alternative Data Shedding New Light on Consumers Why Investors Want Alternative Data Banks and Tech Firms Battle Over Something Akin to Gold: Your Data Alternative data for credit has created national headlines in the past year and a lasting buzz in the financial services world. But what exactly qualifies as alternative data in credit? How can it benefit lenders? Consumers? Ask two people these questions and you may get very different answers. Experian defines alternative data as FCRA-compliant data points that are not typically considered when evaluating a potential customer’s creditworthiness. These data points may include rent payments; utility payments, including gas, electric; telecommunications payments, such as mobile telephones; insurance payments; and any other recurring financial obligations. Taking these alternative data points into account can benefit consumers and lenders in multiple ways. Consider that roughly 45 million Americans have either no credit history, or a credit history that is too scarce or outdated to manufacture a credit score. This group of consumers includes not only minority consumers or those from low income neighborhoods, but also the shared economy workforce and millennials without traditional credit histories. Some estimate 121 million U.S. adults are credit-challenged with thin-to-no credit file and subprime credit scores below 600. “People with little or no credit history, or who lack a credit score, have fewer opportunities to borrow money to build a future, and any credit that is available usually costs more,” said Richard Cordray, while he was director of the Consumer Financial Protection Bureau. Indeed, these consumers are in a catch-22; many lenders will not lend to consumers with credit scores of under 620. In turn, these consumers have trouble building credit, and they are blocked from achieving goals like buying a car, owning a home or starting a business. By combining credit reports with alternative data, a more complete picture of subprime, near-prime and thin-file consumers can develop. And analysis of this data can help lenders evaluate a consumer’s ability to pay. When alternative data like rent payments and an individual’s short term lending history are trended appropriately, it can be an accurate predictor of an individual’s financial behavior, and can be an important step toward promoting greater financial inclusion for more consumers. In addition to using alternative data in underwriting, lenders can leverage the data to help with: Expanding the prospecting universe. Data can be used to enrich batch prospecting decisioning criteria to identify better qualified prospects, suppress high-risk consumers, and offer a more complete borrowing history Account review. Alternative data can help signal a consumer’s financial distress earlier, better manage credit lines and grow relationships with existing consumers. Collections. Identify consumers who are rebuilding credit with specialty finance trades, or who are exhibiting high-risk behaviors in the alternative financial services space. More info on Alternative Credit Data More Info on Alternative Financial Services

It’s no secret. Consumers engage and interact with brands through a variety of channels, including email, direct mail, websites and mobile. And since most organizations work to keep the consumer experience at the core, they tend to invest in an omnichannel approach that caters to the consumer’s preferences. The lone exception may be during the collections process. Often, once an account falls behind on payment, the consumer experience falls behind with it. But should it? While many banks and financial institutions view the collections process merely as an opportunity to collect outstanding debt, the potential is much more. If treated effectively, the collections process can present an opportunity to develop a positive customer relationship that builds loyalty over time. If handled poorly, the collections process could cost an organization a number of lifetime customers. To correct this, banks and financial institutions need to implement the same omnichannel approach in the collections process as they do with every other consumer interaction. Collections can no longer be treated as a linear process that leads from one channel to the next. There needs to be a more personalized touch — communicating with consumers through preferred channels, contacting them at the most opportune times. Sound complex? Sure. But consider a recent Experian analysis that invited consumers to establish a nonthreatening dialogue with an online debt recovery system. The analysis revealed 21 percent of visits to an organization’s website were outside the traditional working hours of 8 a.m. to 8 p.m. Furthermore, of the consumers who committed to a repayment plan, only 56 percent did so in a single visit. Each consumer is different. So is each situation. And banks and financial institutions need to acknowledge those differences. Luckily, technology can address the complexities of an omnichannel and personalized approach. Platforms such as Experian’s PowerCurve® Collections enable banks and financial institutions to simplify the collections process for both the consumer and the organization. By treating the collections process the same as any other stage in the consumer journey, organizations have an opportunity to build a relationship. And to do so, banks and financial institutions need to leverage the data and technology at their disposal. If they do so appropriately, they’ll minimize their charge-offs and also create a lifetime customer. To learn more about leveraging the collections process to build customer loyalty, download our white paper Getting in front of the shift to omnichannel collections.

Experian on the State of Identity podcast In today’s environment, any conversation on the identity management industry needs to include some mention of synthetic identity risk. The fact is, it’s top of mind for almost everyone. Institutions are trying to scope their risk level and identify losses, while service providers are innovating ways to solve the problem. Even consumers are starting to understand the term, albeit via a local newscast designed to scare the heck out of them. With all this in mind, I was very happy to be invited to speak with Cameron D’Ambrosi at One World Identity (OWI) on the State of Identity podcast, focusing on synthetic identity fraud. Our discussion focused on some of the unique findings and recommended best practices highlighted in our recently published white paper on the subject, Synthetic identities: getting real with customers. Additionally, we discussed how a lack of agreement on the definition and size of the synthetic identity problem further complicates the issue. This all stems from inconsistent loss reporting, a lack of confirmable victims and an absence of an exact definition of a synthetic identity to begin with. Discussions must continue to better align us all. I certainly appreciate that OWI dedicated the podcast to this subject. And I hope listeners take away a few helpful points that can assist them in their organization’s efforts to better identify synthetic identities, reduce financial losses and minimize reputation risks.