Financial Services

Traditional credit attributes provide immense value for lenders when making decisions, but when used alone, they are limited to capturing credit behavior during a single moment of time. To add a deeper layer of insight, Experian® today unveiled new trended attributes, aimed at giving lenders a wider view into consumer credit behavior and patterns over time. Ultimately, this helps them expand into new risk segments and better tailor credit offers to meet consumer needs. An Experian analysis shows that custom models developed using Trended 3DTM attributes provide up to a 7 percent lift in predictive performance when compared with models developed using traditional attributes only. “While trended data has been shown to provide additional insight into a consumer’s credit behavior, lack of standardization across different providers has made it a challenge to gain those insights,” said Steve Platt, Experian’s Group President of Decision Analytics and Data Quality. “Trended 3D makes it easy for our clients to get value from trended data in a consistent manner, so they can make more informed decisions across the credit life cycle and, more importantly, give consumers better access to lending options.” Experian’s Trended 3D attributes help lenders unlock valuable insights hidden within credit reports. For example, two people may have similar balances, utilization and risk scores, but their paths to that point may be substantially different. The solution synthesizes a 24-month history of five key credit report fields — balance, credit limit or original loan amount, scheduled payment amount, actual payment amount and last payment date. Lenders can gain insight into: Changes in balances over time Migration patterns from one tradeline or multiple tradelines to another Variations in utilization and credit limits Changes in payment activity and collections Balance transfer and debt consolidation behavior Behavior patterns of revolving trades versus transactional trades Additionally, Trended 3D leverages machine learning techniques to evaluate behavioral data and recognize patterns that previously may have gone undetected. To learn more information about Experian’s Trended 3D attributes, click here.

Many data furnishers are experiencing increases in dispute rates. It’s a tough spot to be in. Data furnishers are not only obligated under the FCRA to investigate and respond to all consumer disputes – reviewing every Automated Consumer Dispute Verification – but they must also do so within less than 30 days. As the number of disputes rise, resources become taxed and the risk of not meeting Fair Credit Reporting Act (FCRA) obligations increases. Let’s face it, consumer disputes aren’t going away, but understanding the reported data and metrics behind disputes can help data furnishers minimize them and defend reporting strategies and processes. 5 Way to Uncover Data Inaccuracy 1. Gain perspective against the industry and peers. Depending on the industry you service, the general benchmarks for dispute rates can vary. It’s important to understand where you fall in regards to dispute rates. Are you trending high or low? As an annualized average, we’ve recently experienced the following industry dispute rates through the end of the year: However, industry averages are just the tip of the iceberg. Measurement against peers can provide a clearer picture of where you fall. Are you an outlier or on par? How do you respond in comparison to peers? Are you deleting the trade as the result of the dispute at a higher rate? This could be an indicator of a systemic problem that needs addressing. 2. Implement pre-submission quality checks. Once you know where you stand, make sure your data is accurate before it heads out the door and hits the consumer’s credit report. Implement manual checks against Metro 2 rules. Build SQL queries to perform your checks. Better yet, use data validation software to automatically identify, track and remediate errors before sending the file to the bureaus. These steps can catch disputes before they happen. 3. Review any data being rejected after submission. Even if your new reporting motto is ‘know before it goes’; once the data has been transmitted, you’ll still want to monitor data being rejected due to Metro 2® errors. When data is rejected that means the update you provided did not make it to file. This leaves room for disputes. Incorporating a robust review of all rejected data in a timely and detailed manner, with updates made before the next reporting period, can improve the accuracy of your data. 4. Audit to identify and correct any stale data on file. An audit for any stale data  - which includes open accounts with a balance greater than zero that have not been updated recently - should be performed at least annually. Review, research and remediate any outdated data that could affect your customer, making it susceptible to a dispute. 5. Educate your customers. Why are your customers disputing? Are there common themes within your customer base? Often, a dispute can be eliminated before it happens, with some explanation on the way an account is reported. By providing proactive access to materials and resources that help demystify the credit reporting process, a potentially negative interaction can be turned into a positive learning opportunity, helping the overall customer experience. Learn more about data accuracy solutions.

Expert offers insights into turnkey big data access   The data is out there – and there is a lot of it. In the world of credit, there are more than 220 million credit-active consumers. Bolt on insights from the alternative financial services space and that number climbs even higher. So, what can analysts do with this information? With technology and the rise of data scientists, there are certainly opportunities to dig in and explore. To learn more, we chatted with Chris Fricks, data and product expert, responsible for Experian’s Analytical Sandbox™. 1. With the launch of Experian’s all-new Ascend platform, one of the key benefits is full-file access to our Sandbox environment. What exactly can clients access and are there specific tools they need to dig into the data? Clients will have access to monthly snapshots of 12-plus years of the full suite of Experian scores, attributes, and raw credit data covering the full national consumer base. Along with the data access, clients can interact and manipulate the data with the analytic tools they prefer. For example, a client can log into the environment through a standard Citrix portal and land on a Windows desktop. From there, they can access applications like SAS, R, Python, or Tableau to interrogate the data assets and derive the necessary value. 2. How are clients benefiting from this access? What are the top use cases you are seeing? Clients are now able to speed analytic findings to market and iterate through the analytics lifecycle much faster. We are seeing clients are engaging in new model development, reject inferencing, and industry/peer benchmarking. One of the more advanced use cases is related to machine learning – think of artificial intelligence for data analytics. In this instance, we have tools like H2O, a robust source of data for users to draw on, and a platform that is optimized to bring it all together in a cohesive, easy-to-use manner. 3. Our Experian database has details on 220 million credit-active consumers. Is this data anonymized, and how are we ensuring sensitive details are secure? We use the data from our credit database, but we’ve assigned unique consumer-level and trade-level encrypted pins to ensure security.  Once the encrypted PINs are assigned, they remain the same over time. Then all PII is scrubbed and everything is rendered de-identifiable from an individual consumer and lender perspective. Our pinning technique allows users to accurately track individual trades and consumers through time, but also prevents any match back to individual consumers and lenders. 4. I imagine having access to so much data could be overwhelming for clients. Is more necessarily better? You’re right. Access to our full credit file can be a lot to handle. While general users will not “actively” use the full file daily, statisticians and data scientists will see an advantage to having access to the larger universe. For example, if a statistician only has access to 10% of the Sandbox and wants to look at a specific region of the country, they may find their self in a situation with limited data that it is no longer statistically significant. By accessing the full file, they can sample down based on the full population from the region they are concerned with analyzing. 5. Who are the best-suited individuals to dig into the Sandbox environment and assess trends and findings? The environment is designed to serve the front-line analysts responsible for coding and analytics that gets reported out to various levels of leadership. It also enables the socialization of those findings with leadership, helping them to interact and give feedback on what they are seeing. Learn more about Experian’s Analytical Sandbox and request a demo.

Today’s consumer lending environment is more dynamic and competitive than ever, with renewed focus on personal loans, marketplace lending and the ever-challenging credit card market. One of the significant learnings from the economic crisis is how digging deeper into consumer credit data can help provide insights into trending behavior and not just point-in-time credit evaluation. For example, I’ve found consumer trending behavior to be very powerful when evaluating risks of credit card revolvers versus transactors. However, trended data can come with its own challenges when the data isn’t interpreted uniformly across multiple data sources. To address these challenges, Experian® has developed trended attributes, which can provide significant lift in the development of segmentation strategies and custom models. These Trended 3DTM attributes are used effectively across the life cycle to drive balance transfers, mitigate high-risk exposure and fine-tune strategies for customers near score cutoffs. One of the things I look for when exploring new trended data is the ability to further understand payment velocity. These characteristics go far beyond revolver and transactor flags, and into the details of consumer usage and trajectory. As illustrated in the chart, a consumer isn’t easily classified into one borrowing persona (revolver, transactor, etc.) or another — it’s a spectrum of use trends. Experian’s Trended 3D provides details needed to understand payment rates, slope of balance growth and even trends in delinquency. These trends provide strong lift across all decisioning strategies to improve your business performance. In recent engagements with lenders, new segmentation tools and data for the development of custom models is at the forefront of the conversation. Risk managers are looking for help leveraging new modeling techniques such as machine learning, but often have challenges moving from prior practices. In addition, attribute governance has been a key area of focus that is addressed with Trended 3D, as it was developed using machine learning techniques and is delivered with the necessary documentation for regulatory conformance. This provides an impressive foundation, allowing you to integrate the most advanced analytics into your credit decisioning. Alternative data isn’t the only source for new consumer insights. Looking at the traditional credit report can still provide so much insight; we simply need to take advantage of new techniques in analytics development. Trended attributes provide a high-definition lens that opens a world of opportunity.

Experian® is honored to be an MRC Technology Award nominee. But we can’t win the MRC People’s Choice Award without your help! The annual MRC Technology Awards recognize the most elite solution providers making significant contributions in the fraud, payments and risk industries. CrossCore® is the first smart, open, plug-and-play platform for fraud and identity services. We know, and our clients agree, that it delivers a better way to modify strategies quickly, catch fraud faster, improve compliance and enhance the customer experience. Need further convincing? Here are the top 3 reasons you should vote for CrossCore. Reason 1: common access Manage your entire fraud and identity portfolio. Start immediately by turning on Experian services through a single integration. Connect to services quickly with a common, flexible API. Reason 2: open approach Control the data being used in decisions. CrossCore supports a best-in-class approach to managing a portfolio of services that work together in any combination — including Experian solutions, third-party services and client systems — delivering the level of confidence needed for each transaction. Reason 3: workflow decisioning Act quickly and adapt to new risks with built-in strategy design and workflow capabilities. You can precisely tailor strategies based on transaction type or risk threshold. Make changes dynamically, with no downtime. We hope you’ll vote for CrossCore as a better way to manage fraud prevention and identity services.

Are you ready to launch a new product to capture the revenue growth opportunities in today’s market? The competition is heating up for new growth, as banks increased personal loan balances by 10 percent year-over-year in 2015 and another 6 percent in 2016.* Many lenders are now looking for robust data to understand the market opportunity based on their risk appetite. This challenge usually takes a significant investment in consumer credit data to gain the necessary insights. In helping lenders launch new products, I’ve found there are common areas of focus and specific steps you must take to move from the initial business case to more tactical planning. The following details come to mind: refining risk thresholds, pricing, loss forecasting and use of models within the initial go-to-market strategy. These project tasks can’t be successfully completed without having the right breadth and depth of data available. Knowing the past can help you create a better future for your business. When I start working with a client on a new product launch, I want to ensure they have sufficient data that can provide a comprehensive historical consumer view. In my experience, the best data to use will show an exhaustive view of consumer behaviors through the economic cycle. Having this large volume of data enables me to evaluate the business strategy and risks through the financial crisis while also giving my clients the foundation for compliance with loss forecasting regulations. Obtaining this breadth of data often can be a significant, but necessary, investment. Data is a great starting point, but it isn’t enough. Understanding the data sufficiently to design an effective go-to-market strategy is critical for success. I’ve found that identifying specific attributes helps give my clients a deep dive into the structure of a consumer’s credit history at the trade level. This level of information provides insight into the structure of the consumer’s wallet and preferences. Additionally, this depth of data allows my clients to develop powerful custom models for use in their business strategy. Being prepared is half the victory. Having comprehensive data that will help you understand consumer spending behavior and the risk they carry through the economic cycle will assist in creating a successful go-to-market strategy. Our Market Entry ServicesTM data sets are analytics-ready, including attributes and performance flags, to give you a holistic view of your target market. Having this breadth and depth of data, along with strong tactical planning and execution, will ensure your success in launching new products and entering new markets.   *Experian–Oliver Wyman Market Intelligence Report

Consumers are hungry for more personalized marketing, and I’m an actual example. As a new stepmom to two young kids, who has a full-time job, I rarely have any down time. No revelation there. I no longer have time to surf the web to buy clothes. And shepherding everyone to an actual store to shop? #forgetaboutit I’m not alone. Of the 57 percent of women in the U.S. workforce, 70 percent have a child under the age of 18. We don’t always have the time to shop for clothes, financial products, and nearly anything else, but it doesn’t mean we don’t need or want to. I would give the right bank or retailer my data in exchange for personalized marketing offers in my inbox, social feeds and mailbox. And many others would, too. Sixty-three percent of Millennial consumers and 58 percent of Gen Xers are willing to share data with companies in exchange for personalized offers, discounts and rewards. This indicates consumers are craving more customized marketing. Providing their personal data to get that is acceptable to them. In the financial services space, Mintel research shows that just 61 percent of male consumers, 49 percent of consumers aged 18-44, and 44 percent of Hispanic Millennials have a general-purpose credit card, either with or without rewards (Mintel’s Marketing Financial Services Report for June 2017). This indicates a significant market opportunity for cards that offer segmented or boosted rewards based on specific sectors and categories. Here are some other interesting trends specific to financial services: Relying on Experts Although chatbots and robo-advisors allow easy access to many financial services, 81 percent of consumers prefer in-person meetings when it comes to personalized financial advice. According to Mintel, men aged 18-44 are most interested in a free consultation with a financial advisor, and 19 percent of consumers are open to a free consultation. This interest surpasses attending free classes about finance and receiving email and mobile alerts from a financial institution. Quick, Efficient Delivery While consumers are calling for increased personalization, they also want it delivered quickly and efficiently. These expectations create unique challenges for financial institutions of all sizes. Some banks have embraced “card finder” apps, which allow consumers the convenience of inputting personal information to generate customized offers. There is a huge opportunity for financial institutions to leverage available consumer data to understand their target audience, and then deliver relevant products via multiple channels where they are consuming media now. Those who do will be positioned to provide personalized financial recommendations that were impossible just a few years ago.

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.