Financial Services
Millennials have long been the hot topic over the course of the past few years with researchers, brands and businesses all seeking to understand this large group of people. As they buy homes, start families and try to conquest their hefty student loan burdens, all will be watching. Still, there is a new crew coming of age. Enter Gen Z. It is estimated that they make up ¼ of the U.S. population, and by 2020 they will account for 40% of all consumers. Understanding them will be critical to companies wanting to succeed in the next decade and beyond. The oldest members of this next cohort are between the ages of 18 and 20, and the youngest are still in elementary school. But ultimately, they will be larger than the mystical Millennials, and that means more bodies, more buying power, more to learn. Experian recently took a first look at the oldest members of this generation, seeking to gain insights into how they are beginning to use credit. In regards to credit scores, the eldest Gen Z members sported a VantageScore® of 631 in 2016. By comparison, younger Millennials were at 626 and older Millennials were at 638. Given their young age, Gen Z debt levels are low with an average debt-to-income at just 5.7%. Their tradelines largely consist of bankcards, auto and student loans. Their average income is at $33.8k. For their total annual plastic spend, data reveals this oldest group of Gen Z spent about $9.5k, slightly more than the younger Millennials who came in at roughly $9k. Surprisingly, there was a very small group of Gen Z already on file with a mortgage, but this figure was less than .5%. Auto loans were also small, but likely to grow. Of those Gen Z members who have a credit file, an estimated 12% have an auto trade. This is just the beginning, and as they age, their credit files will thicken, and more insights will be gained around how they are managing credit, debt and savings. While they are young today, some studies say they already receive about $17 a week in allowance, equating to about $44 billion annually in purchase power in the U.S. Factor in their influence on parental or household purchases and the number could be closer to $200 billion! For all brands, financial services companies included, it is obvious they will need to engage with this generation in not just a digital manner, but a mobile manner. They are being raised in an era of instant, always-on access. They expect a quick, seamless and customized mobile experience. Retailers have 8 seconds or less — err on the side of less — to capture their attention. In general, marketers and lenders should consider the following suggestions: Message with authenticity Maintain a long-term vision Connect them with something bigger Provide education for financial literacy and of course Keep up with technological advances. Learn more by accessing our recorded webinar, A First Look at Gen Z and Credit.
Mitigating synthetic identities Synthetic identity fraud is an epidemic that does more than negatively affect portfolio performance. It can hurt your reputation as a trusted organization. Here is our suggested 4-pronged approach that will help you mitigate this type of fraud: Identify how much you could lose or are losing today to synthetic fraud. Review and analyze your identity screening operational processes and procedures. Incorporate data, analytics and cutting-edge tools to enable fraud detection through consumer authentication. Analyze your portfolio data quality as reported to credit reporting agencies. Reduce synthetic identity fraud losses through a multi-layer methodology design that combats both the rise in synthetic identity creation and use in fraud schemes. Mitigating synthetic identity fraud>
Call it big data, smart data or evidence-based decision-making. It’s not just the latest fad, it’s the future of how business will be guided and grow. Here are a few telling stats that show data is exploding and a new age is upon us: Data is growing faster than ever before, and we’re on track to create about 1.7 megabytes of new information per person every second by 2020. The social universe—which includes every digitally connected person—doubles in size every two years. By 2020, it will reach 44 zettabytes or 44 trillion gigabytes, according to CIO. In 2015, more than 1 billion people used Facebook and sent an average of 31.25 million messages and viewed 2.77 million videos each minute. More than 100 terabytes of data is uploaded daily to the social channel. By 2020, more than 6.1 billion smartphone users will exist globally. And there will be more than 50 billion smart connected devices in the world, all capable of collecting, analyzing and sharing a wealth of data. More than one-third of all data will pass through or exist in the cloud by 2020. The IDC estimates that by 2020, business transactions on the internet—business-to-business and business-to-consumer—will reach 450 billion per day. All of this new data means we’ll be looking at a whole new set of possibilities and a new level of complexity in the years ahead. The data itself is of great value, however, lenders need the right automated decisioning platform to store, collect, quickly process and analyze the volumes of consumer data to gain accurate consumer stories. While lenders don’t necessarily need to factor in decisioning on social media uploads and video views, there is an expectation for immediacy to know if a consumer is approved, denied or conditioned. Online lenders have figured out how to quickly capture and understand big data, and are expected to account for $122 billion in lending by 2020. This places more pressure on banks and credit unions to enhance their technology to cut down on loan approval times and move away from various manual touch points. Critics of automated decisioning solutions used in lending cite compliance issues, complacency by lenders and lack of human involvement. But a robust platform enables lenders to improve and supplement their current decisioning processes because it is: Agile: Experian hosts our client’s solutions and decisioning strategies, so we are able to make and deploy changes quickly as the needs of the market and business change, and deliver real-time instant decisions while a consumer is at the point of sale. A hosted environment also means reduced implementation timelines, as no software or hardware installation is required, allowing lenders to recognize value faster. A data work horse: Internal and external data can be pulled from multiple sources into a lender’s decisioning model. Lenders may also access an unlimited number of scores and attributes—including real-time access to credit bureau data—and integrate third-party data sources into the decisioning engine. Powerful: A robust decision engine is capable of calculating numerous predictive attributes and custom scoring models, and can also test new strategies against current decision models or perform “what if” simulations on historical data. Data collection, storage and analysis are here to stay. As will be the businesses which are savvy enough to use a solution that can find opportunities and learnings in all of that complex data, quickly curate the best possible actions to take for positive outcomes, and allow lenders and marketers to execute on those recommendations with the click of a button. To learn more about Experian’s decisioning solutions, you can additionally explore our PowerCurve and Attribute Toolbox solutions.
The creation of synthetic identities (synthetic id) relies upon an ecosystem of institutions, data aggregators, credit reporting agencies and consumers. All of which are exploited by an online and mobile-driven market, along with an increase in data breaches and dark web sharing. It’s a real and growing problem that’s impacting all markets. With significant focus on new customer acquisition and particular attention being paid to underbanked, emerging, and new-to-country consumers, this poses a large threat to your onboarding and customer management policies, in addition to overall profitability. Synthetic identity fraud is an epidemic that does more than negatively affect portfolio performance. It can hurt your reputation as a trusted organization and expose institutions, like yours, as paths of lesser resistance for fraudsters to use in the creation and farming of synthetic identities. Here is a suggested four-pronged approach to mitigate this type of fraud: The first step is knowing your risk exposure to synthetic identity fraud. Identify how much you could lose or are losing today using a targeted segmentation analysis to examine portfolios or customer populations. Next, review your front- and back-end identity screening operational processes and procedures and analyze that information to ensure you have industry best practices, procedures and verification tools deployed. Then incorporate data, analytics and some of the industry’s cutting edge tools. This enables you to perform targeted consumer authentication and identify opportunities to better capture the majority of fraud and operational waste. Lastly, ensure your organization is part of the solution – not the problem. Analyze your portfolio data quality as reported to credit reporting agencies and then minimize your exposure to negative compliance audit results and reputational risk. Our fraud and identity management consultants can help you reduce synthetic identity fraud losses through a multilayer methodology design that combats the rise in synthetic identity creation and use in fraud schemes.
On June 7, the Consumer Financial Protection Bureau (CFPB) released a new study that found that the ways “credit invisible” consumers establish credit history can differ greatly based on their economic background. The CFPB estimated in its May 2015 study \"Data Point: Credit Invisibles\" that more than 45 million American consumers are credit invisible, meaning they either have a thin credit file that cannot be scored or no credit history at all. The new study reviewed de-identified credit records on more than one million consumers who became credit visible. It found that consumers in lower-income areas are 240 percent more likely to become credit visible due to negative information, such as a debt in collection. The CFPB noted consumers in higher-income areas become credit visible in a more positive way, with 30 percent more likely to become credit visible by using a credit card and 100 percent more likely to become credit visible by being added as a co-borrower or authorized user on someone else’s account. The study also found that the percentage of consumers transitioning to credit visibility due to student loans more than doubled in the last 10 years. CFPB’s research highlights the need for alternative credit data The new study demonstrates the importance of moving forward with inclusion of new sources of high-quality financial data — like on-time payment data from rent, utility and telecommunications providers — into a consumer’s credit file. Experian recently outlined our beliefs on the issue in comments responding to the CFPB’s Request for Information on Alternative Data. As a brand, we have a long history of using alternative credit data to help lenders make better lending decisions. Extensive research has shown that there is an immense opportunity to facilitate greater access to fair and affordable credit for underserved consumers through the inclusion of on-time telecommunications, utility and rental data in credit files. While these consumers may not have a traditional credit history, many make on-time payments for telephone, rent, cable, power or mobile services. However, this data is not typically being used to enhance traditional credit files held by the nationwide consumer reporting agencies, nor is it being used in most third-party or custom credit scoring models. Further, new advances in financial technology and data analytics through account aggregation platforms are also integral to the credit granting process and can be applied in a manner to broaden access to credit. Experian is currently using account aggregation software to obtain consumer financial account information for authentication and income verification to speed credit decisions, but we are looking to expand this technology to increase the collection and utilization of alternative data for improving credit decisions by lenders. Policymakers should act to help credit invisible consumers While Experian continues to work with telecommunications and utility companies to facilitate the furnishing of on-time credit data to the nationwide consumer reporting agencies, regulatory barriers continue to exist that deter utility and telecommunications companies from furnishing on-time payment data to credit bureaus. To help address this issue, Congress is currently considering bipartisan legislation (H.R. 435, The Credit Access and Inclusion Act of 2017) that would amend the FCRA to clarify that utility and telecommunication companies can report positive credit data, such as on-time payments, to the nation\' s credit reporting bureaus. The legislation has bipartisan support in Congress and Experian encourages lawmakers to move forward with this important initiative that could benefit tens of millions of American consumers. In addition, Experian believes policymakers should more clearly define the term alternative data. In public policy debates, the term \"alternative data\" is a broad term, often lumping data sources that can or have been proven to meet regulatory standards for accuracy and fairness required by both the Fair Credit Reporting Act and the Equal Credit Opportunity Act with data sources that cannot or have not been proven to meet these standards. In our comment letter, Experian encourages policymakers to clearly differentiate between different types of alternative data and focus the consumer and commercial credit industry on public policy recommendations that will increase the use of those sources of data that have or can be shown to meet legal and societal standards for accuracy, validity, predictability and fairness. More info on Alternative Credit Data More Info on Alternative Financial Services
How do credit unions stack up in a pack filled with heavy-hitting banks and aggressive online lenders? Do credit scores, debt levels and utilization rates look different between credit union members and non-credit union members? Where is the greatest concentration of credit unions in the country? Experian took a deep dive into the data and performance surrounding the credit union universe in their first-ever “State of Credit Unions” report, featuring insights utilizing data from both 2015 and 2017. What did the analysis reveal? “In general, we saw credit unions continuing to increase their auto lending market share, but we also saw them growing their member relationships and increasing market share in mortgage, personal loan and bankcard,” said Michelle Cocchiarella, the Experian analyst who pulled the data. A few of the key data points include: Credit union auto originations increased from 1.54M new accounts in Q1 2015 to 1.93M in Q1 2017 – a 25% increase. And not only did originations rise dramatically in this space, but credit unions topped banks, captives and other finance sources. Credit union auto market share rose 5% between Q1 2015 and Q1 2017, while bank market share declined by 4%. Credit unions also saw growth in the personal loan arena, with market share rising 2% between Q1 2015 and Q1 2017. Still, with the rise of online lenders, that sector saw a 7% increase during the same period. Banks declined by 5%. While most bankcards are opened with banks, credit unions did experience an 18% increase in bankcard originations from Q1 2015 to Q1 2017. Market share rose 1% between Q1 2015 and Q1 2017 for credit unions in the bankcard space. Banks reign with market share at 96%. Credit union mortgage market share rose 7% between Q1 2015 and Q1 2017. Banks declined by 4%. “Collectively, the credit union space is enjoying remarkable membership and loan growth,” said Scott Butterfield, principal of Your Credit Union Partner, a consulting agency to credit union leaders. “However, this bountiful experience is not enjoyed at all credit unions. The financial services environment has never been more competitive. The best credit unions are relentless at investigating a better way to find and serve more members, and as such, are seeing great growth.” For the complete results, including insights on how credit union members with at least one trade compare to non-credit union members, access the report on our credit union insights page.
When discussing automotive lending, it seems like one term is on everyone’s lips: “subprime auto loan bubble.” There’s always someone who claims that the bubble is bursting. But a level-headed look at the data shows otherwise. According to our Q1 2017 State of the Automotive Finance Market report, 30-day delinquencies dropped and subprime auto lending reached a 10-year record low for Q1. The 30-day delinquency rate dropped from 2.1 percent in Q1 2016 to 1.96 percent in Q1 2017, while the total share of subprime and deep-subprime loans dropped from 26.48 percent in Q1 2016 to 24.1 percent in Q1 2017. The truth is, lenders are making rational decisions based on shifts in the market. When delinquencies started to go up, the lending industry shifted to more creditworthy customers. This is borne out in the rise in customers’ average credit scores for both new and used vehicle loans: The average customer credit score for a new vehicle loan rose from 712 in Q1 2016 to 717 in Q1 2017. The average customer credit score for a used vehicle loan rose from 645 in Q1 2016 to 652 in Q1 2017. In a clear indication that lenders have shifted focus to more creditworthy customers, super prime was the only risk tier to grow for new vehicle loans from Q1 2016 to Q1 2017. Super-prime share moved from 27.4 percent in Q1 2016 to 29.12 percent in Q1 2017. All other risk tiers lost share in the new vehicle loan category: Prime — 43.36 percent, Q1 2016 to 43.04 percent, Q1 2017. Nonprime — 17.83 percent, Q1 2016 to 16.96 percent in Q1 2017. Subprime — 10.64 percent, Q1 2016 to 10.1 percent in Q1 2017. For used vehicle loans, there was a similar upward shift in creditworthiness. Prime and super-prime risk tiers combined for 47.4 percent market share in Q1 2017, up from 43.99 percent in Q1 2017. At the low end of the credit spectrum, subprime and deep-subprime share fell from 34.31 percent in Q1 2016 to 31.27 percent in Q1 2017. The upward shift in used vehicle loan creditworthiness is likely caused by an ample supply of late model used vehicles. Leasing has been on the rise for the past several years (and is at 31.06 percent of all new vehicle financing today). Many of these leased vehicles have come back to the market as low-mileage used vehicles, perfect for CPO programs. Another key indicator of the lease-to-CPO impact is the rise in used vehicle loan share for captives. In Q1 2017, captives had 8.3 percent used vehicle loan share, compared with 7.2 percent in Q1 2016. In other findings: Captives continued to dominate new vehicle loan share, moving from 49.4 percent in Q1 2016 to 53.9 percent in Q1 2017. 60-day delinquencies showed a slight rise, going from 0.61 percent in Q1 2016 to 0.67 percent in Q1 2017. The average new vehicle loan reached a record high: $30,534. The average monthly payment for a new vehicle loan reached a record high: $509. For more information regarding Experian’s insights into the automotive marketplace, visit https://www.experian.com/automotive.
The 1990s brought us a wealth of innovative technology, including the Blackberry, Windows 98, and Nintendo. As much as we loved those inventions, we moved on to enjoy better technology when it became available, and now have smartphones, Windows 10 and Xbox. Similarly, technological and modeling advances have been made in the credit scoring arena, with new software that brings significant benefits to lenders who use them. Later this year, FICO will retire its Score V1, making it mandatory for those lenders still using the old software to find another solution. Now is the time for lenders to take a look at their software and myriad reasons to move to a modern credit score solution. Portfolio Growth As many as 70 million Americans either have no credit score or a thin credit file. One-third of Millennials have never bothered to apply for a credit card, and the percentage of Americans under 35 with credit card debt is at its lowest level in more than 25 years, according to the Federal Reserve. A recent study found that Millennials use cash and debit cards much more than older Americans. Over time, Millennials without credit histories could struggle to get credit. Are there other data sets that provide a window into whether a thin file consumer is creditworthy or not? Modern credit scoring models are now being used in the marketplace without negatively impacting credit quality. For example, VantageScore 3.0 allows for the scoring of 30 million to 35 million more people consumers who are typically unscoreable by other traditional generic credit models. VantageScore 3.0 does this by using a broader, deeper set of credit file data and more advanced modeling techniques. This allows the VantageScore model to more accurately predict unique consumer behaviors—is the consumer paying his utility bill on time?—and better evaluate thin file consumers. Mitigate Risk In today’s ever-changing regulatory landscape, lenders can stay ahead of the curve by relying on innovative credit score models like VantageScore. These models incorporate the best of both worlds by leaning on innovative scoring analytics that are more inclusive, while providing marketplace lenders with assurances the decisioning is both statistically sound and compliant with fair lending laws. Newer solutions also offer enhanced documentation to ease the burden associated with model risk management and regulatory compliance responsibilities. Updated scores Consumer credit scores can vary depending on the type of scoring model a lender uses. If it\'s an old, outdated version, a consumer might be scored lower. If it\'s a newer, more advanced model, the consumer has a better shot at being scored more fairly. Moving to a more advanced scoring model can help broaden the base of potential borrowers. By sticking to old models—and older scores—a sizable number of consumers are left at a disadvantage in the form of a higher interest rate, lower loan amount or even a declined application. Introducing advanced scoring models can provide a more accurate picture of a consumer. As an example, for many of the newest consumer risk models, like FICO Score 9, a consumer’s unpaid medical collection agency accounts will be assessed differently from unpaid non-medical collection agency accounts. This isn\'t true for most pre-2012 consumer risk score versions. Each version contains different nuances for increasing your score, and it’s important to understand what they are. Upgrading your credit score to the latest VantageScore credit score or FICO solution is easier than you think, with a switch to a modern solution taking no longer than eight weeks and your current business processes still in place. Are you ready to reap the rewards of modern credit scoring?
Weekend getaways, beach vacations and summer camp are all part of the beauty of summer. But they can come with a hefty price tag, and many consumers delay payment by placing summer fun costs on a credit card. In a recent study by Experian and Edelman Berland, travelers rely heavily on credit for vacation purchases and unexpected costs, and many charge more than half of their vacation this summer. A whopping 86 percent spent money on a summer vacation in 2016—an average of $2,275 per person with $1,308 of that amount on credit card spending. And 35 percent of those surveyed had not saved in advance. Even consumers who budgeted for vacation typically accrue unexpected costs. Sixty-one percent of those who set a budget ended up spending more than they planned. Accumulated debt doesn’t bode well for consumers. In the first quarter of 2016, consumers had an average of $3,910 in credit card debt, according to Experian data. That\'s $44 less than in the fourth quarter of 2015, but up $142 year over year. Overspending on vacation puts consumers in a more hazardous position to rack up debt during the holiday season and carry even higher balances into 2017 and beyond. Many consumers who are overspending consolidate summer debt, and proactive lenders can take advantage of that activity by making timely offers to consumers in need. At the same time, reactive lenders may feel the pain as balances transfer out of their portfolio. By identifying consumers who are likely to engage in card-to-card balance transfers, lenders can prepare for these consumer bankcard trends. Insights can then be used to acquire new customers and balances through prescreen campaigns, while protecting existing balances before they can transfer out of an existing loan portfolio. Lenders can also use tools to estimate a consumer’s spend on all general purpose credit and charge cards over the past year, and then target high-spending consumers with customized offers. With Memorial Day and the end-of-the-school-year fast approaching, card balances are likely already on the rise. Now is the time for lenders to prepare.
There are about as many definitions for people-based marketing as there are companies using the term. Each company seems to skew the definition to fit their particular service offering. The distinctions are vast, and especially for financial services companies running regulated campaigns, they can be incredibly important. At Experian, we define people-based marketing in its purest form: targeting at the individual level across channels. This is a practice we’re very familiar with in offline marketing, having honed arguably one of the most accurate views of U.S. consumers over the past three decades. And now we’re taking those tried and true principals and applying them to digital channels. It’s not as easy as it sounds. The challenge with people-based marketing With direct mail, people-based marketing was easy. Jane Doe lives at 123 Main St. If I want to reach her, I can simply send her a direct mail piece at that address. To help, I can utilize any number of services, including the National Change of Address database, to know where to reach her if she ever moves. People-based marketing through digital channels is exponentially more difficult. While direct mail has one signal with which you use to identify a consumer (the address), digital channels offer countless signals. And not all of those signals can be used, either individually or in conjunction with other signals, to reliably tie a consumer to a persistent offline ID. A prime example of this is cookies. The problem with cookies A cookie, in and of itself, isn’t the problem. The problem is the linkage. How was a cookie associated with the person to whom the ad is being served? As marketers, we need to make sure that we are reaching the right people with the right ad … and more importantly not reaching those people who have opted out. This is especially true in the world of regulated data, where you need to know who you are targeting. And cookie-based linkage is controlled by a handful of companies, many of which are walled gardens who don’t share how they link offline people to online cookies and don’t collect this information directly. They rely on other third-party websites to gather PII, and connect it to their cookies. In some cases, the data is very accurate (especially with transaction data). In some cases, it is not (think websites that collect PII when giving surveys, offering coupons, etc.). In short, in order for you to use cookie-based targeting accurately, you need to have insight into the source of the base linkage data that was used to connect the offline consumer record to the online cookie. This same concept applies to all forms of digital linkage that drive people-based marketing. Why does people-based marketing matter in digital credit marketing? With campaigns that utilize non-regulated data, such as “Invitation to Apply” campaigns that are driven from demographic and psychographic data, the consequences of not reaching the consumer you meant to target are negligible. But with campaigns that utilize regulated data, you must ensure you’re targeting the exact consumer you meant to reach. More importantly, you must make sure you’re not targeting an ad to a consumer who had previously opted out of receiving offers driven with regulated data (prescreen offers, for example). Even if you’ve already delivered a direct mail piece with the same offer, this doesn’t negate your responsibility to reach only approved consumers who have not opted out. --- Bottom line, the world of 1:1 marketing is growing more sophisticated, and that’s a good thing. Marketers just need to understand that while regulated data can be powerful, they must also take great responsibility when handling it. The data exists to deliver firm offers of credit to your very specific target in all-new mediums. People-based marketing has its place, and it can now be done in a compliant, digitally-savvy way – in the financial services space, nonetheless. Register for our webinar on Credit Marketing Strategies to Drive Today\'s Digital Consumer.
For an industry that has grown accustomed to sustained year-over-year growth, recent trends are concerning. The automotive industry continued to make progress in the fourth quarter of 2016 as total automotive loan balances grew 8.6% over the previous year and exceeded $1 trillion. However, the positive trend is slowing and 2017 may be the first year since 2009 to see a market contraction. With interest rates on the rise and demand peaking, automotive lending will continue to become more competitive. Lenders can be successful in this environment, but must implement data-driven targeting strategies. Credit Unions Triumph Credit unions experienced the largest year-over-year growth in the fourth quarter of 2016, increasing 15% over the previous period. As lending faces increasing headwinds amid rising rates, credit unions can continue to play a greater role by offering members more competitive rates. For many consumers, a casual weekend trip to the auto mall turns into a big new purchase. Unfortunately, many get caught up in researching the vehicle and don’t think to shop for financing options until they’re in the F&I office. With approximately 25% share of total auto loan balances, credit unions have significant potential to recapture loans of existing members. Successful targeting starts with a review of your portfolio for opportunities with current members who have off-book loans that could be refinanced at a lower rate. After developing a strategy, many credit unions find success targeting these members with refinance offers. Helping members reduce monthly payments and interest expense provides an unexpected service that can deepen loyalty and engagement. But what criteria should you use to identify prospects? Target Receptive Consumers As originations continue to slow, marketing response rates will as well, leading to reduced marketing ROI. Maintaining performance is possible, but requires a proactive approach. Propensity models can help identify consumers who are more likely to respond, while estimated interest rates can provide insight on who is likely to benefit from refinance offers. Propensity models identify who is most likely to open a new trade. By focusing on these populations, you can cut a mail list in half or more while still focusing on the most viable prospects. It may be okay in a booming economy to send as many offers as possible, but as things slow down, getting more targeted can maintain campaign performance while saving resources for other projects. When it comes to recapture, consumers refinance to reduce their payment, interest rate, or both. Payments can often be reduced simply by ‘resetting’ the clock on a loan, or taking the remaining balance and resetting the term. Many consumers, however, will be aware of their current interest rate and only consider offers that reduce the rate as well. Estimated interest rates can provide valuable insight into a consumer’s current terms. By targeting those with high rates, you are more likely to make an offer that will be accepted. Successful targeting means getting the right message to the right consumers. Propensity models help identify “who” to target while estimated interest rates determine “what” to offer. Combining these two strategies will maximize results in even the most challenging markets. Lend Deeper with Trended Data Much of the growth in the auto market has been driven by relatively low-risk consumers, with more than 60% of outstanding balances rated prime and above. This means hypercompetition and great rates for the best consumers, while those in lower risk tiers are underserved. Many lenders are reluctant to compete for these consumers and avoid taking on additional risk for the portfolio. But trended data holds the key to finding consumers who are currently in a lower risk tier but carry significantly less risk than their current score suggests. In fact, historical data can provide much deeper insight on a consumer’s past use of credit. As an example, consider two consumers with the same risk score at a point in time. While they may be judged as carrying similar risk, trended data shows one has taken out two new trades in the past 6 months and has increasing utilization, while the other is consolidating and paying down balances. They may have the same risk score today, but what will the impact be on your future profitability? Most risk scores take a snapshot approach to gauging risk. While effective in general, it misses out on the nuance of consumers who are trending up or down based on recent behavior. Trended data attributes tell a deeper story and allow lenders to find underserved consumers who carry less risk than their current score suggests. Making timely offers to underserved consumers is a great way to grow your portfolio while managing risk. Uncertain Future The automotive industry has been a bright spot for the US economy for several years. It’s difficult to say what will happen in 2017, but there will likely be a continued slowing in originations. When markets get more competitive, data-driven targeting becomes even more important. Propensity models, estimated interest rates, and trended data should be part of every prescreen campaign. Those that integrate them now will likely shrug off any downturn and continue growing their portfolio by providing valuable and timely offers to their members.
The final day of Vision 2017 brought a seasoned group of speakers to discuss a wide range of topics. In just a few short hours, attendees dove into a first look at Gen Z and their use of credit, ecommerce fraud, the latest in retail, the state of small business and leadership. Move over Millennials – Gen Z is coming of credit age Experian Analytics leaders Kelley Motley and Natasha Madan gave audience members an exclusive look at how the first wave of Gen Z is handling and managing credit. Granted most of this generation is still under the age of 18, so the analysis focused on those between the ages of 18 to 20. Yes, Millennials are still the dominant generation in the credit world today, standing strong at 61 million individuals. But it’s important to note Gen Z is sized at 86 million, so as they age, they’ll be the largest generation yet. A few stats to note about those Gen Z individuals managing credit today: Their average debt is $12,679, compared to younger Millennials (21 to 27) who have $65,473 in debt and older Millennials (28 to 34) who sport $121,460. Given their young age, most of Gen Z is considered thin-file (less than 5 tradelines) Average Gen Z income is $33,000, and average debt-to-income is low at 5.7%. New bankcard balances are averaging around $1,574. As they age, acquire mortgages and vehicles, their debt and tradelines will grow. In the meantime, the speakers provided audience members a few tips. Message with authenticity. Think long-term with this group. Maintain their technological expectations. Build trust and provide financial education. State of business credit and more on the economy Moody’s Cris deRitis reiterated the U.S. economy is looking good. He quoted unemployment at 4.5%, stating “full employment is here.” Since the recession, he said we’ve added 15 million jobs, noting we lost 8 million during the recession. The great news is that the U.S. continues to add about 200,000 jobs a month, and that job growth is broad-based. Small business loans are up 10% year-to-date vs. last year. While there has been a tremendous amount of buzz around small business, he adds that most job creation has come from mid0size business (50 to 499 employees). The case for layered fraud systems Experian speaker John Sarreal shared a case study that revealed by layering on fraud products and orchestrating collaboration, a business can go from a string 75% fraud detection rate to almost 90%. Additionally, he commented that Experian is working to leverage dark web data to mine for breached identity data. More connections for financial services companies to make with mobile and social Facebook speaker Olivia Basu reinforced the need for all companies to be thinking about mobile. “Mobile is not about to happen,” she said. “Mobile is now. Mobile is everything. You look at the first half of 2017 and we’re seeing 40% of all purchases are happening on mobile devices.” Her challenge to financial services companies is to make marketing personal again, and of course leverage the right channels. Experian Sr. Director of Credit Marketing Scott Gordon commented on Experian’s ability to reach consumers accurately – whether that be through direct or digital delivery channels. A great deal of focus has been around person-based marketing vs. leveraging the cookie. -- The Vision conference was capped off with a keynote speech from legendary quarterback and Super Bowl MVP Tom Brady. He chatted about the details of this past season, and specifically the comeback Super Bowl win in February 2017. He additionally talked about leadership and what that means to creating a winning team and organization. -- Multiple keynote speeches, 65 breakout sessions, and hours of networking designed to help all attendees ready themselves for growing profits and customers, step up to digital, regulatory and fraud challenges, and capture the latest data insights. Learn more about Experian’s annual Vision conference.
Risk analysts are insatiable consumers of big data who require better intelligence to develop market insights, evaluate risk and confirm business strategies. While every credit decision, risk assessment model or marketing forecast improves when it is based on better, faster and more current data, leveraging large data sets can be challenging and unproductive. That’s why Experian added a new functionality to its Analytical Sandbox, giving clients the flexibility they need to analyze big data efficiently. Experian’s Analytical Sandbox now utilizes H2O –an open source machine learning and deep learning platform that can model and predict with high accuracy billions of rows of high-dimensional data from multiple sources in various formats. Through machine learning and advanced predictive modeling, the platform enables Experian to better provide on-demand data insights that empowers analysts with high-quality intelligence to inform regional trends, provide consumer transactional insight or expose marketing opportunities. As a hosted service, Sandbox is offered as a plug-and-play, meaning no internal development is required. Clients can instantly access the data through a secure Web interface on their desktop, giving users access to powerful artificial and business intelligence tools from their own familiar applications. No special training is required. “AI monetizes data,” said SriSatish Ambati, CEO of H2O.ai. “Our partnership with Experian democratizes and delivers AI to the wider community of financial and risk analysts. Experian\'s analytics sandbox can now model and predict with high accuracy billions of rows of high-dimensional data in mere seconds.” Through H2O and the Experian Sandbox, machine learning and predictive analytics are giving risk managers from financial institutions of all sizes the ability to incorporate machine learning models into their own big data processing systems.
In just a few short hours, Vision attendees immersed themselves into the depths of the economy, risk models, specialty finance data, credit invisibles, student loan data, online marketplace lending and more. The morning kicked off with one of the most respected and trusted macroeconomists in the U.S., Diane Swonk. With a rap sheet filled with advising central banks and multinational companies, Swonk treated a packed house to a look back on what has transpired in the U.S. economy since the Great Recession, as well as launching into current state and speculating on the months ahead. She described the past decade not as “lost, but rather lagging.” She went onto to say this past year was transitional, and while markets slowed slightly during the months leading up the U.S. presidential election, good things are happening: We’ve finally broken out of the 2% wage rut Recruiting on college campuses has picked up The labor force is growing Debt-to-income levels have returned to where they were prerecession and Investment is coming back. “I believe we’ll see growth over 2% this year,” said Swonk. Still, change is underway. She commented on how the way U.S. consumer spending is changing, and of course we’re seeing a restructuring in the retail space. While JC Penney announces store closings, you simultaneously see Amazon moving from “click to brick,” dabbling in the opening of some actual storefronts. Globally, she said the economy is the strongest it has been in eight years. She closed by noting there is a great deal of political change and unrest in the world today, but says, “Never underestimate our abilities when we tap our human capital.” -- More than 100 attendees filled a room to hear about the current trends and the future of online lending with featured guests from Oliver Wyman, Marlette Funding and Lending USA. While speakers commented on the “hiccup” in the space last year with some layoffs and mergers, volume has continued to double every year for the past several years with roughly $40 billion in cumulative originations today. Panelists discussed the use of alternative data to decision, channel bias, the importance of partnerships and how the market will see fewer and fewer players offering just one product specialty. “It is expensive to acquire customers, so you don’t just want to have one product to sell, but rather a range,” said Sharat Shankar of Lending USA. -- The numbers in the student lending universe are astounding. In a session focused on the U.S. student loan market, new Experian data reveals there is $1.49 billion in total student loan outstandings. In fact, total outstandings have grown 21% over the past four years, while the number of trades have only grown 4%. Costs are skyrocketing. The average balance per trade has grown 17% over the past four years. “We don’t ration education in this country,” said Joe DePaulo of College Ave. Student Loans. “We give everyone access to liquidity when it comes to federal student loans – and it’s not like that in other countries.” While DePaulo notes the access is great, offering many students the opportunity to obtain higher education, he says the problem is with disclosures. Guardians are often the individuals filling out the FAFSA, but the students inherit the loans. Students, he says, rarely understand how much their monthly payment will ultimately be after graduation. For every $10,000 in student loans, he says that will generally equate to a $100 monthly payment. -- Tomorrow, Vision attendees will be treated to more breakout sessions and a concluding keynote with legendary quarterback Tom Brady.
So many insights and learnings to report after the first full day of 2017 Vision sessions. From the musings shared by tech engineer and pioneer Steve Wozniak, to a panel of technology thought leaders, to countless breakout sessions on a wide array of business topics … here’s a look at our top 10 from the day. A mortgage process for the digital age. At last. In his opening remarks, Experian President of Credit Services Alex Lintner asked the audience to imagine a world when applying for a mortgage simply required a few clicks or swipes. Instead of being sent home to collect a hundred pieces of paper to verify employment, income and assets, a consumer could click on a link and provide a few credentials to verify everything digitally. Finally, lenders can make this a reality, and soon it will be the only way consumers expect to go through the mortgage process. The global and U.S. economies are stable. In fact, they are strong. As Experian Vice President of Analytics Michele Raneri notes, “the fundamentals and technicals look really solid across the countries.” While many were worried a year ago that Brexit would turn the economy upside down, it appears everything is good. Consumer confidence is high. The Dow Jones Index is high. The U.S. unemployment rate is at 4.7%. Home prices are up year-over-year. While there has been a great deal of change in the world – politically and beyond – the economy is holding strong. The rise of the micropreneur. This term is not officially in the dictionary … but it will be. What is it? A micropreneur is a business with 0 to 4 employees bringing in no more than $200k in annual revenue. But the real story is that numbers show microbusiness are improving on many fronts when it comes to contribution to the economy and overall performance compared to other small businesses. Keep an eye on these budding business people. Fraud is running fierce. Synthetic identity losses are estimated in the hundreds of millions annually, with 50% year-over year growth. Criminals are now trying to use credit cleaners to get tradelines removed from used Synthetic IDs. Oh, and it is essential for businesses to ready themselves for “Dark Web” threats. Experts advise to harden your defenses (and play offense) to keep pace with the criminal underground. As soon as you think you’ve protected everything, the criminals will find a gap. The cloud is cool and so are APIs. A panel of thought leaders took to the main stage to discuss the latest trends in tech. Experian Global CIO Barry Libenson said, “The cloud has changed the way we deliver services to our customers and clients, making it seamless and elastic.” Combine that with API, and the goal is to ultimately make all Experian data available to its customers. Experian President of Decision Analytics Steve Platt added, “We are enabling you to tap into what you need, when you need it.” No need to “rip and replace” all your tech. Expect more regulation – and less. A panel of regulatory experts addressed the fast-changing regulatory environment. With the new Trump administration settling in, and calls for change to Dodd-Frank and the Consumer Financial Protection Bureau (CFPB), it’s too soon to tell what will unfold in 2017. CFPB Director Richard Cordray may be making a run for governor of Ohio, so he could be transitioning out sooner than the scheduled close of his July 2018 term. The auto market continues to cruise. Experian’s auto expert, Malinda Zabritski, revealed the latest and greatest stats pertaining to the auto market. A few numbers to blow your mind … U.S. passenger cars and light trucks surpassed 17 million units for the second consecutive year Most new vehicle buyers in the U.S. are 45 years of age or older Crossover and sport utility vehicles remain popular, accounting for 40% of the market in 2016 – this is also driving up finance payments since these vehicles are more expensive. There are signs the auto market is beginning to soften, but interest rates are still low, and leasing is hot. Defining alternative data. As more in the industry discuss the need for alternative data to decision, it often gets labeled as something radical. But in reality, alternative data should be simple. Experian Sr. Director of Government Affairs Liz Oesterle defined it as “getting more financial data in the system that is predicted, validated and can be disputed.” #DeathtoPasswords – could it be a reality? It’s no secret we live in a digital world where we are increasingly relying on apps and websites to manage our lives, but let’s throw out some numbers to quantify the shift. In 2013, the average U.S. consumer had 26 online accounts. By 2015, that number increased to 118 online accounts. By 2020, the average person will have 207 online accounts. When you think about this number, and the passwords associated with these accounts, it is clear a change needs to be made to managing our lives online. Experian Vice President David Britton addressed his session, introducing the concept of creating an “ultimate consumer identity profile,” where multi-source data will be brought together to identify someone. It’s coming, and all of us managing dozens of passwords can’t wait. “The Woz.” I guess you needed to be there, but let’s just say he was honest, opinionated and notes that while he loves tech, he loves it even more when it enables us to live in the “human world.” Too much wonderful content to share, but more to come tomorrow …
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