Industries

Retailers are already starting to display their Christmas decorations in stores and it’s only early November. Some might think they are putting the cart ahead of the horse, but as I see this happening, I’m reminded of the quote by the New York Yankee’s Yogi Berra who famously said, “It gets late early out there.” It may never be too early to get ready for the next big thing, especially when what’s coming might set the course for years to come. As 2019 comes to an end and we prepare for the excitement and challenges of a new decade, the same can be true for all of us working in the lending and credit space, especially when it comes to how we will approach the use of alternative data in the next decade. Over the last year, alternative data has been a hot topic of discussion. If you typed “alternative data and credit” into a Google search today, you would get more than 200 million results. That’s a lot of conversations, but while nearly everyone seems to be talking about alternative data, we may not have a clear view of how alternative data will be used in the credit economy. How we approach the use of alternative data in the coming decade is going to be one of the most important decisions the lending industry makes. Inaction is not an option, and the time for testing new approaches is starting to run out – as Yogi said, it’s getting late early. And here’s why: millennials. We already know that millennials tend to make up a significant percentage of consumers with so-called “thin-file” credit reports. They “grew up” during the Great Recession and that has had a profound impact on their financial behavior. Unlike their parents, they tend to have only one or two credit cards, they keep a majority of their savings in cash and, in general, they distrust financial institutions. However, they currently account for more than 21 percent of discretionary spend in the U.S. economy, and that percentage is going to expand exponentially in the coming decade. The recession fundamentally changed how lending happens, resulting in more regulation and a snowball effect of other economic challenges. As a result, millennials must work harder to catch up financially and are putting off major life milestones that past generations have historically done earlier in life, such as homeownership. They more often choose to rent and, while they pay their bills, rent and other factors such as utility and phone bill payments are traditionally not calculated in credit scores, ultimately leaving this generation thin-filed or worse, credit invisible. This is not a sustainable scenario as we enter the next decade. One of the biggest market dynamics we can expect to see over the next decade is consumer control. Consumers, especially millennials, want to be in the driver’s seat of their “credit journey” and play an active role in improving their financial situations. We are seeing a greater openness to providing data, which in turn enables lenders to make more informed decisions. This change is disrupting the status quo and bringing new, innovative solutions to the table. At Experian, we have been testing how advanced analytics and machine learning can help accelerate the use of alternative data in credit and lending decisions. And we continue to work to make the process of analyzing this data as simple as possible, making it available to all lenders in all verticals. To help credit invisible and thin-file consumers gain access to fair and affordable credit, we’ve recently announced Experian Lift, a new suite of credit score products that combines exclusive traditional credit, alternative credit and trended data assets to create a more holistic picture of consumer creditworthiness that will be available to lenders in early 2020. This new Experian credit score may improve access to credit for more than 40 million credit invisibles. There are more than 100 million consumers who are restricted by the traditional scoring methods used today. Experian Lift is another step in our commitment to helping improve financial health of consumers everywhere and empowers lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. This isn’t just a trend in the United States. Brazil is using positive data to help drive financial inclusion, as are others around the world. As I said, it’s getting late early. Things are moving fast. Already we are seeing technology companies playing a bigger role in the push for alternative data – often powered by fintech startups. At the same time, there also has been a strong uptick in tech companies entering the banking space. Have you signed up for your Apple credit card yet? It will take all of 15 seconds to apply, and that’s expected to continue over the next decade. All of this is changing how the lending and credit industry must approach decision making, while also creating real-time frictionless experiences that empower the consumer. We saw this with the launch of Experian Boost earlier this year. The results speak for themselves: hundreds of thousands of previously thin-file consumers have seen their credit scores instantly increase. We have also empowered millions of consumers to get more control of their credit by using Experian Boost to contribute new, positive phone, cable and utility payment histories. Through Experian Boost, we’re empowering consumers to play an active role in building their credit histories. And, with Experian Lift, we’re empowering lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. That’s game-changing. Disruptions like Experian Boost and newly announced Experian Lift are going to define the coming decade in credit and lending. Our industry needs to be ready because while it may seem early, it’s getting late.

It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?

It’s Halloween time – time for trick or treating, costume parties and monsters lurking in the background. But this year, the monsters aren’t just in the background. They’re in your portfolio.  This year, “Frankenstein” has another meaning. Much more ominous than the neighbor kid in the costume.   “Frankenstein IDs” refer to synthetic identities — a type of fraud carried out by criminals that have created fictitious identities. Just as Dr. Frankenstein’s monster was stitched together from parts, synthetic IDs are stitched together pieces of mismatched identities — some fake, some real, some even deceased.   It typically takes fraudsters 12 to 18 months to create and nurture a synthetic identity before it’s ready to "bust out" – the act of building a credit history with the intent of maxing out all available credit and eventually disappearing. That means fraudsters are investing money and time to build numerous tradelines, ensure these "fake" identities are in good credit standing, and ultimately steal the largest amount of money possible.   “Wait Master, it might be dangerous . . . you go, first.” — Igor   Synthetic identities are a notable challenge for many financial institutions and retail organizations. According to the recently released Federal Reserve Board White Paper, synthetic identity fraud accounts for roughly 20% of all credit losses, and cost U.S. businesses roughly $6 billion in 2016 with an estimated 41% growth over 2 years. 85-95% of applicants identified as potential synthetic are not even flagged by traditional fraud models.   The Social Security Administration recently announced plans for the electronic Consent Based Social Security Number Verification service – pilot program scheduled for June 2020. This service is designed to bring efficiency to the process for verifying Social Security numbers directly with the government agency. Once available, this verification could be an important tool in the fight against the elusive “Frankenstein” identity monster.   But with the Social Security Administration's pilot program not scheduled for launch until the middle of next year, how can financial institutions and other organizations bridge the gap and adequately prepare for a potential uptick in synthetic identity fraud attacks? It comes down to a multilayered approach that relies on advanced data, analytics, and technology — and focuses on identity.   Any significant progress in making synthetic identities easier to detect could cost fraudsters significant time and money.   Far too many financial institutions and other organizations depend solely on basic demographic information and snapshots in time to confirm the legitimacy of an identity. These organizations need to think beyond those capabilities. The real value of data in many cases lies between the data points. We have seen this with synthetic identity — where a seemingly legitimate identity only shows risk when we can analyze its connections and relationships to other individuals and characteristics.   In addition to our High Risk Fraud Score, we now have a Synthetic Fraud Risk Level Indicator available on credit profiles. These advanced detection capabilities are delivered via the simplicity of a straightforward indicator returned on the credit profile which lenders can use to trigger additional identity verification processes.   While there are programs and initiatives in the works to help financial institutions and other organizations combat synthetic identity fraud, it's important to keep in mind there's no silver bullet, or stake to the heart, to completely keep these Frankenstein IDs out.   Oh, and don’t forget… “It’s pronounced ‘Fronkensteen.’ ” — Dr. Frankenstein

As credit unions look to grow their loan portfolios and acquire new members, improving the member experience is critical to the process and remains a primary focus. In order to compete in the lending universe, financial tools that empower and enable a positive experience are critical to meeting these requirements. That being said, an Experian study reveals that 90% of executives agree that embracing a digital transformation is critical to providing excellent experiences. In this connected, data-driven world, digital transformations are opening the door for better and greater opportunities. With data and analytics, credit unions will be able to gain data-driven insights, to identify key channels of member engagement, create complete member views and further maximize growth and lending strategies. Data-driven organizations that can anticipate their members’ needs and preferences will be able to deepen relationships and maintain relevance – gaining an edge in a highly-competitive environment. The digital revolution is happening now – and it’s time for future-focused credit unions to adapt to changing expectations. However, according to an Experian report, 39% of organizations lack the customer insight and data required to provide these member experiences. That’s where Experian comes in. Join Mike Thibodeaux, Experian’s Senior Director, Fraud and Identity Sales Engineers, for a breakout session at CUNA Lending 2019 on Monday, Nov. 4 at 1:45 p.m. or 3:15 p.m. He will take a closer look at best practices and digital tools that credit unions can use to maximize credit union membership growth, while managing and mitigating fraud. The discussion will revolve around multiple topics, critical to the member experience conversation, including: Increasing profitable loan growth Lending deeper to the underserved Levering digital services and tools for your credit union Minimizing fraud activity (specifically synthetic identity fraud) and credit losses Enhancing and maintaining positive member experiences Experian is excited to once again take part in the 2019 CUNA Lending Council Conference, an event that brings together the credit union movement’s best and brightest in lending. If you’re attending, make sure to engage and connect with our thought leaders at our booth and learn how we’re dedicated to helping credit unions of all sizes advance their decisioning and services. Our team is committed to being a trusted partner – providing solutions that enable you to further grow, protect and serve within your field of membership. Learn More

To provide consumers with clear-cut protections against disturbance by debt collectors, the Consumer Financial Protection Bureau (CFPB) issued a Notice of Proposed Rulemaking (NPRM) to implement the Fair Debt Collection Practices Act (FDCPA) earlier this year. Among many other things, the proposal would set strict limits on the number of calls debt collectors may place to reach consumers weekly and clarify requirements for consumer-facing debt collection disclosures. A bigger discussion Deliberation of the debt collection proposal was originally scheduled to begin on August 18, 2019. However, to allow commenters to further consider the issues raised in the NPRM and gather data, the comment period was extended by 20 days to September 18, 2019. It is currently still being debated, as many argue that the proposed rule does not account for modern consumer preferences and hinders the free flow of information used to help consumers access credit and services. The Association of Credit and Collection Professionals (ACA International) and US House lawmakers continue to challenge the proposal, stating that it doesn’t ensure that debt collectors’ calls to consumers are warranted, nor does it do enough to protect consumers’ privacy. Many consumer advocates have expressed doubts about how effective the proposed measures will be in protecting debtors from debt collector harassment and see the seven-calls-a-week limit on phone contact as being too high. In fact, it’s difficult to find a group of people in full support of the proposal, despite the CFPB stating that it will help clarify the FDCPA, protect lenders from litigation and bring consumer protection regulation into the 21st century. What does this mean? Although we don’t know when, or if, the proposed rule will go into effect, it’s important to prepare. According to the Federal Register, there are key ways that the new regulation would affect debt collection through the use of newer technologies, required disclosures and limited consumer contact. Not only will the proposed rules apply to debt collectors, but its provisions will also impact creditors and servicers, making it imperative for everyone in the financial services space to keep watch on the regulation’s status and carefully analyze its proposed rules. At Experian, our debt collection solutions automate and moderate dialogues and negotiations between consumers and collectors, making it easier for collection agencies to connect with consumers while staying compliant. Our best-in-class data and analytics will play a key role in helping you reach the right consumer, in the right place, at the right time. Learn more

The Experian Automotive Intelligence Engine™ enables dealers to find and reach potential customers

Experian is excited to have been chosen as one of the first data and analytics companies that will enable access to Social Security Administration (SSA) data for the purposes of verifying identity against the Federal Agency’s records. The agency’s involvement in the wake of Congressional interest and successful legislation will create a seismic shift in the landscape of identity verification. Ultimately, the ability to leverage SSA data will reduce the impact of identity fraud and synthetic identity and put real dollars back into the pockets of people and businesses that absorb the costs of fraud today. As this era of government and private sector collaboration begins, many of our clients and partners are breathing a sigh of relief. We see this in a common question our customers ask every day, “Do I still need an analytical solution for synthetic ID now that eCBSV is on the horizon?” The common assumption is that help is on the way and this long tempest of rising losses and identity uncertainty is about to leave us. Or is it? We don’t believe it’s the end of the synthetic ID storm. This is the eye. Rather than basking in the calm light of this moment, we should be thinking ahead and assessing our vulnerabilities because the second half of this storm will be worse than the first. Consider this: The people who develop and exploit synthetic IDs are playing a long game. It takes time, research, planning and careful execution to create an identity that facilitates fraud. The bigger the investment, the bigger the spoils will be. Synthetic ID are being used to purchase luxury automobiles. They’re passing lender marketing criteria and being offered credit. The criminals have made their investment, and it’s unlikely they will walk away from it. So, what does SSA’s pending involvement mean to them? How will they prepare? These aren’t hard questions. They’ll do what you would do in the eye of a storm — maximize the value of the preparations that are in place. Gather what you can quickly and brace yourself for the uncertainty that’s coming. In short, there’s a rush to monetize synthetic IDs on the horizon, and this is no time to declare ourselves safe. It’s doubtful that the eCBSV process will be the silver bullet that ends synthetic ID fraud — and certainly not on day one. It’s more likely that the physical demands of the data exchange, volume constraints, response times and the actionability of the results will take time to optimize. In the meantime, the criminals aren’t going to sit by and watch as their schemes unravel and lose value. We should take some comfort that we’ve made it through the first half of the storm, but recognize and prepare for what still needs to be faced.

A few months ago, I got a letter from the DMV reminding me that it was finally time to replace my driver’s license. I’ve had it since I was 21 and I’ve been dreading having to get a new one. I was especially apprehensive because this time around I’m not just getting a regular old driver’s license, I’m getting a REAL ID. For those of you who haven’t had this wonderful experience yet, a REAL ID is the new form of driver’s license (or state ID) that you’ll need to board a domestic flight starting October 1, 2020. Some states already offered compliant IDs, but others—like California, where I’m from—didn’t. This means that if I want to fly within the U.S. using my driver’s license next year, I can’t renew by mail. It’s Easier Than It Looks Imagine my surprise when I started the process to schedule my appointment, and the California DMV website made things really easy! There’s an online application you can fill out before you get to the DMV and they walk you through the documents to bring to the appointment (which I was able to schedule online). Despite common thought that the DMV and agencies like it are slow to adopt technology, the ease of this process may indicate a shift toward a digital-first mindset. As financial institutions embrace a similar shift, they’ll be better positioned to meet the needs of customers. Case in point, the electronic checklist the DMV provided to prepare me for my appointment. I sailed through the first two parts of the checklist, confirming that I’ll bring my proof of identity and social security number, but I paused when I got to the “Two Proofs of Residency” screen. Like many people my age—read: 85% of the millennial population, according to a recent Experian study—I don’t have a mortgage or any other documents relating to property ownership. I also don’t have my name on my utilities (thanks, roomie) or my cell phone bill (thanks Mom). I do however have a signed lease with my name on it, plus my renter’s insurance, both of which are acceptable as proof of residency. And just like that, I’m all set to get my REAL ID, even though I don’t have some of the basic adulting documents you might expect, because the DMV took into account the fact that not all REAL ID applicants are alike. Imagine if lenders could adopt that same flexibility and create opportunities for the more than 45 million U.S. consumers1 who lack a credit report or have too little information to generate a credit score. The Bigger Picture By removing some of the usual barriers to entry, the DMV made the process of getting my REAL ID much easier than it might have been and corrected my assumptions about how difficult the process would be. Experian has the same line of thought when it comes to helping you determine whether a borrower is credit-worthy. Just because someone doesn’t have a credit card, auto loan or other traditional credit score contributor doesn’t mean they should be written off. That’s why we created Experian BoostTM, a product that lets consumers give read-only access to their bank accounts and add in positive utility and telecommunications bill payments to their credit file to change their scores in real time and demonstrate their stability, ability and willingness to repay. It’s a win-win for lenders and consumers. 2 out of 3 users of Experian Boost see an increase in their FICO Score and of those who saw an increase, 13% moved up a credit tier. This gives lenders a wider pool to market to, and thanks to their improved credit scores, those borrowers are eligible for more attractive rates. Increasing your flexibility and removing barriers to entry can greatly expand your potential pool of borrowers without increasing your exposure to risk. Learn more about how Experian can help you leverage alternative credit data and expand your customer base in our 2019 State of Alternative Data Whitepaper. Read Full Report 1Kenneth P. Brevoort, Philipp Grimm, Michelle Kambara. “Data Point: Credit Invisibles.” The Consumer Financial Protection Bureau Office of Research. May 2015.

Retail banking leaders in a variety of industries (including risk management, credit, information technology and other departments) want to incorporate more data into their business strategies. By doing so, consumer banks and other financial companies benefit by expanding their markets, controlling risk, improving compliance and the customer experience. However, many companies don’t know how or where to start. The challenges? There’s just too much data – and it’s overwhelming. Technical integration issues Maintaining regulatory data and attribute governance and compliance The slow speed of adoption Join Jim Bander, PhD, analytics and optimization leader at Experian, in an upcoming webinar with the Consumer Bankers Association on Tuesday, Oct. 1, 2019 at 9:00-10:00 a.m. PT. The webinar will discuss how some of the country’s best banks – big and small – are making better, faster and more profitable decisions by using the right set of data sources, while avoiding data overload. Key topics will include: Technology Trends: Discover how the latest technology, including the cloud and machine learning, makes it easier than ever to access data, define and manage attributes throughout the enterprise and perform complex calculations in real time. Time to Market: Discover how consumer banks and other financial companies that have mastered data and attribute management are able to integrate data and attributes quickly and seamlessly. Business Benefits: Understand how advanced analytics helps financial institutions of all sizes make better business decisions. This includes growing their portfolios, mitigating fraud and credit risk, controlling operating expenses, improving compliance and enhancing the customer experience. Critical Success Factors: Learn how to stay ahead of ever-evolving business and data requirements and continuously improve your lending operations. Join us as we unveil the secrets to avoiding data overload in consumer banking. Special Offer For non-current CBA members, this webinar costs $95 to attend. However, with special discount code: EX1001, non-CBA members can attend for FREE. Register Now

The average new vehicle loan hit $32,119 in Q2 2019. Average used vehicle loan amounts reached $20,156 in Q2 2019.

The future is, factually speaking, uncertain. We don't know if we'll find a cure for cancer, the economic outlook, if we'll be living in an algorithmic world or if our work cubical mate will soon be replaced by a robot. While futurists can dish out some exciting and downright scary visions for the future of technology and science, there are no future facts. However, the uncertainty presents opportunity. Technology in today's world From the moment you wake up, to the moment you go back to sleep, technology is everywhere. The highly digital life we live and the development of our technological world have become the new normal. According to The International Telecommunication Union (ITU), almost 50% of the world's population uses the internet, leading to over 3.5 billion daily searches on Google and more than 570 new websites being launched each minute. And even more mind-boggling? Over 90% of the world's data has been created in just the last couple of years. With data growing faster than ever before, the future of technology is even more interesting than what is happening now. We're just at the beginning of a revolution that will touch every business and every life on this planet. By 2020, at least a third of all data will pass through the cloud, and within five years, there will be over 50 billion smart connected devices in the world. Keeping pace with digital transformation At the rate at which data and our ability to analyze it are growing, businesses of all sizes will be forced to modify how they operate. Businesses that digitally transform, will be able to offer customers a seamless and frictionless experience, and as a result, claim a greater share of profit in their sectors. Take, for example, the financial services industry - specifically banking. Whereas most banking used to be done at a local branch, recent reports show that 40% of Americans have not stepped through the door of a bank or credit union within the last six months, largely due to the rise of online and mobile banking. According to Citi's 2018 Mobile Banking Study, mobile banking is one of the top three most-used apps by Americans. Similarly, the Federal Reserve reported that more than half of U.S. adults with bank accounts have used a mobile app to access their accounts in the last year, presenting forward-looking banks with an incredible opportunity to increase the number of relationship touchpoints they have with their customers by introducing a wider array of banking products via mobile. Be part of the movement Rather than viewing digital disruption as worrisome and challenging, embrace the uncertainty and potential that advances in new technologies, data analytics and artificial intelligence will bring. The pressure to innovate amid technological progress poses an opportunity for us all to rethink the work we do and the way we do it. Are you ready? Learn more about powering your digital transformation in our latest eBook. Download eBook Are you an innovation junkie? Join us at Vision 2020 for future-facing sessions like:  -  Cloud and beyond - transforming technologies - ML and AI - real-world expandability and compliance

In today’s age of digital transformation, consumers have easy access to a variety of innovative financial products and services. From lending to payments to wealth management and more, there is no shortage in the breadth of financial products gaining popularity with consumers. But one market segment in particular – unsecured personal loans – has grown exceptionally fast. According to a recent Experian study, personal loan originations have increased 97% over the past four years, with fintech share rapidly increasing from 22.4% of total loans originated to 49.4%. Arguably, the rapid acceleration in personal loans is heavily driven by the rise in digital-first lending options, which have grown in popularity due to fintech challengers. Fintechs have earned their position in the market by leveraging data, advanced analytics and technology to disrupt existing financial models. Meanwhile, traditional financial institutions (FIs) have taken notice and are beginning to adopt some of the same methods and alternative credit approaches. With this evolution of technology fused with financial services, how are fintechs faring against traditional FIs? The below infographic uncovers industry trends and key metrics in unsecured personal installment loans: Still curious? Click here to download our latest eBook, which further uncovers emerging trends in personal loans through side-by-side comparisons of fintech and traditional FI market share, portfolio composition, customer profiles and more. Download now  

Introducing the newly designed AutoCheck Score™ Quickly compare and select used vehicles As an auto industry professional, you use vehicle history reports every day. But they’re long, complex — easily misinterpreted. You always aim to conduct a thorough inspection. But what if you’re at a busy auction house or browsing online, where there’s simply not enough time or context? The tool you use every day to make critical decisions about used vehicles should be accurate and easy to understand — built for streamlined evaluation. So we made one. New look, same impact We’ve revamped the AutoCheck score with a modern look and feel that’s easier than ever to read. And it’s still invaluable for quickly comparing and selecting used vehicles. What, exactly, is it? Experian® analyzes the detailed records in an AutoCheck® vehicle history report to generate the AutoCheck Score. Like a credit score or gas mileage rating for new vehicles, it’s a single number on a standardized scale. The new gauge shows the score range (from 1 - 100) for vehicles of similar age in the same class. If a car is above average in its range, you can feel confident that it’s a solid investment. The score makes it much simpler to assess how a used vehicle measures up, estimating its: Overall roadworthiness Reliability compared to other vehicles in its class Likelihood of being on the road in five years It is invaluable for making informed decisions, managing inventory, mitigating risk and instilling confidence in customers. Bigger, better You can depend on the AutoCheck Score to deliver a high-quality, more accurate assessment. That’s because it’s derived from Experian’s world-class, continually updated database, which leverages reliable information from extensive sources, including: Tens of thousands of distinct accident sources, many exclusive to Experian 95% of U.S. auction houses — most providing structural damage, salvage-and-junk and export-data announcements exclusively to Experian Important OEM safety and open recall data State departments of motor vehicles and departments of public safety, insurance companies and other independent sources Police department/state agency accident information from all 50 states and Washington D.C. Federal sources, like import records That’s a lot of data. And some complex statistical modeling. Don’t worry; we’ll take care of the heavy lifting. All you have to do is keep score. Why you need it Whether you’re a dealer, lender, manufacturer certified pre-owned program or consumer portal, the score will transform the way you do business to boost your bottom line. Dealers: Use the score to mitigate risk, manage and market your inventory, close sales faster and build customer loyalty. Lenders/Credit Unions: Use the score to more accurately estimate a vehicle’s value at every stage of the loan life cycle, from origination to portfolio review, account management and asset collection. Manufacturer Certified Pre-Owned Programs: Use the AutoCheck report for vehicle certification. Consumer Portals: Increase customer satisfaction — and traffic — by allowing OEMs and dealers to post the score with their listings and make online car shopping a breeze. Count on the AutoCheck Score To learn more about the score — or how to wield its power to maximal effect — find its secrets in this treasure trove of a white paper or call 1 888 675 5596.  What are you waiting for? Redesign your business with the redesigned score.

Experian has been named one of the 10 participants, and only credit bureau, in the initial rollout of the SSA's new eCBSV service.

Experian Boost provides a unique opportunity to help dealers build loyalty while helping consumers.