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1 in 10 Americans are living paycheck to paycheck Financial health means more than just having a great credit score or money in a savings account. It includes being able to manage daily finances, save for the future and weather a financial shock. Here are some facts about Americans’ financial health: 46% of Americans are struggling financially. Roughly 31% of nonretired adults have no retirement savings or pension. Approximately 50% are unprepared for a financial emergency, and about 1 in 5 have no savings set aside to cover unexpected emergencies. It’s never too late for people to achieve financial health. By providing education on money management, you can drive new opportunities for increased engagement, loyalty and long-term revenue streams. Why financial health matters >
School’s out, and graduation brings excitement, anticipation and bills. Oh, boy, here come the student loans. Are graduates ready for the bills? Even before they have a job lined up? With lots of attention from the media, I was interested in analyzing student loan debt to see if this is a true issue or just a headline grab. There’s no shortage of headlines alluding to a student loan crisis: “How student loans are crushing millennial entrepreneurialism” “Student loan debt in 2017: A $1.3 trillion crisis” “Why the student loan crisis is even worse than people think” Certainly sounds like a crisis. However, I’m a data guy, so let’s look at the data. Pulling from our data, I analyzed student loan trades for the last four years starting with outstanding debt — which grew 21 percent since 2013 to reach a high of $1.49 trillion in the fourth quarter of 2016. I then drilled down and looked at just student loan trades. Created with Highstock 5.0.7Total Number of Student Loans TradesStudent Loan Total TradesNumber of trades in millions174,961,380174,961,380182,125,450182,125,450184,229,650184,229,650181,228,130181,228,130Q4 2013Q4 2014Q4 2015Q4 2016025M50M75M100M125M150M175M200MSource: Experian (function(){ function include(script, next) {var sc=document.createElement("script");sc.src = script;sc.type="text/javascript";sc.onload=function() {if (++next < incl.length) include(incl[next], next);};document.head.appendChild(sc);}function each(a, fn){if (typeof a.forEach !== "undefined"){a.forEach(fn);}else{for (var i = 0; i < a.length; i++){if (fn) {fn(a[i]);}}}}var inc = {},incl=[]; each(document.querySelectorAll("script"), function(t) {inc[t.src.substr(0, t.src.indexOf("?"))] = 1;});each(Object.keys({"https://code.highcharts.com/stock/highstock.js":1,"https://code.highcharts.com/adapters/standalone-framework.js":1,"https://code.highcharts.com/highcharts-more.js":1,"https://code.highcharts.com/highcharts-3d.js":1,"https://code.highcharts.com/modules/data.js":1,"https://code.highcharts.com/modules/exporting.js":1,"http://code.highcharts.com/modules/funnel.js":1,"http://code.highcharts.com/modules/solid-gauge.js":1}),function (k){if (!inc[k]) {incl.push(k)}});if (incl.length > 0) { include(incl[0], 0); } function cl() {if(typeof window["Highcharts"] !== "undefined"){new Highcharts.Chart("highcharts-79eb8e0a-4aa9-404c-bc5f-7da876c38b0f", {"chart":{"type":"column","inverted":true,"polar":false,"style":{"fontFamily":"Arial","color":"#333","fontSize":"12px","fontWeight":"normal","fontStyle":"normal"}},"plotOptions":{"series":{"dataLabels":{"enabled":true},"animation":true}},"title":{"text":"Student Loan Total Trades","style":{"fontFamily":"Arial","color":"#333333","fontSize":"18px","fontWeight":"bold","fontStyle":"normal","fill":"#333333","width":"792px"}},"subtitle":{"text":"","style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal","fill":"#666666","width":"792px"}},"exporting":{},"yAxis":[{"title":{"text":"Number of trades in millions","style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal"}},"labels":{"format":""},"type":"linear"}],"xAxis":[{"title":{"style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal"},"text":""},"reversed":true,"labels":{"format":"{value:}"},"type":"linear"}],"series":[{"data":[["Total Student Loans",174961380]],"name":"Q4 2013","turboThreshold":0,"_colorIndex":0,"_symbolIndex":0},{"data":[["Total Student Loans",182125450]],"name":"Q4 2014","turboThreshold":0,"_colorIndex":1,"_symbolIndex":1},{"data":[["Total Student Loans",184229650]],"name":"Q4 2015","turboThreshold":0,"_colorIndex":2,"_symbolIndex":2},{"data":[["Total Student Loans",181228130]],"name":"Q4 2016","turboThreshold":0,"_colorIndex":3,"_symbolIndex":3}],"colors":["#26478d","#406eb3","#632678","#982881"],"legend":{"itemStyle":{"fontFamily":"Arial","color":"#333333","fontSize":"12px","fontWeight":"normal","fontStyle":"normal","cursor":"pointer"},"itemHiddenStyle":{"fontFamily":"Arial","color":"#cccccc","fontSize":"18px","fontWeight":"normal","fontStyle":"normal"},"layout":"horizontal","floating":false,"verticalAlign":"bottom","x":0,"align":"center","y":0},"credits":{"text":"Source: Experian"}});}else window.setTimeout(cl, 20);}cl();})(); Over the past four years, student loan trades grew 4 percent, but saw a slight decline between 2015 and 2016. The number of trades isn’t growing as fast as the amount of money that people need. The average balance per trade grew 17 percent to $8,210. Either people are not saving enough for college or the price of school is outpacing the amount people are saving. I shifted the data and looked at the individual consumer rather than the trade level. Created with Highstock 5.0.7Student Loan Average Balance per Trade4.044.043.933.933.893.893.853.85Q4 2013Q4 2014Q4 2015Q4 201600.511.522.533.544.5Source: Experian (function(){ function include(script, next) {var sc=document.createElement("script");sc.src = script;sc.type="text/javascript";sc.onload=function() {if (++next < incl.length) include(incl[next], next);};document.head.appendChild(sc);}function each(a, fn){if (typeof a.forEach !== "undefined"){a.forEach(fn);}else{for (var i = 0; i < a.length; i++){if (fn) {fn(a[i]);}}}}var inc = {},incl=[]; each(document.querySelectorAll("script"), function(t) {inc[t.src.substr(0, t.src.indexOf("?"))] = 1;});each(Object.keys({"https://code.highcharts.com/stock/highstock.js":1,"https://code.highcharts.com/adapters/standalone-framework.js":1,"https://code.highcharts.com/highcharts-more.js":1,"https://code.highcharts.com/highcharts-3d.js":1,"https://code.highcharts.com/modules/data.js":1,"https://code.highcharts.com/modules/exporting.js":1,"http://code.highcharts.com/modules/funnel.js":1,"http://code.highcharts.com/modules/solid-gauge.js":1}),function (k){if (!inc[k]) {incl.push(k)}});if (incl.length > 0) { include(incl[0], 0); } function cl() {if(typeof window["Highcharts"] !== "undefined"){new Highcharts.Chart("highcharts-66c10c16-1925-40d2-918f-51214e2150cf", {"chart":{"type":"column","polar":false,"style":{"fontFamily":"Arial","color":"#333","fontSize":"12px","fontWeight":"normal","fontStyle":"normal"},"inverted":true},"plotOptions":{"series":{"dataLabels":{"enabled":true},"animation":true}},"title":{"text":"Student Loan Average Number of Trades per Consumer","style":{"fontFamily":"Arial","color":"#333333","fontSize":"18px","fontWeight":"bold","fontStyle":"normal","fill":"#333333","width":"356px"}},"subtitle":{"text":"","style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal","fill":"#666666","width":"356px"}},"exporting":{},"yAxis":[{"title":{"text":"","style":{"fontFamily":"Arial","color":"#666666","fontSize":"14px","fontWeight":"normal","fontStyle":"normal"}},"type":"linear","labels":{"format":"{value}"}}],"xAxis":[{"title":{"style":{"fontFamily":"Arial","color":"#666666","fontSize":"14px","fontWeight":"normal","fontStyle":"normal"}},"type":"linear","labels":{"format":"{}"}}],"colors":["#26478d","#406eb3","#632678","#982881","#ba2f7d"],"series":[{"data":[["Average Trades per Consumer",4.04]],"name":"Q4 2013","turboThreshold":0,"_colorIndex":0},{"data":[["Average Trade per Consumer",3.93]],"name":"Q4 2014","turboThreshold":0,"_colorIndex":1},{"data":[["Average Trade per Consumer",3.89]],"name":"Q4 2015","turboThreshold":0,"_colorIndex":2},{"data":[["Average Trades per Consumer",3.85]],"name":"Q4 2016","turboThreshold":0,"_colorIndex":3}],"legend":{"floating":false,"itemStyle":{"fontFamily":"Arial","color":"#333333","fontSize":"12px","fontWeight":"bold","fontStyle":"normal","cursor":"pointer"},"itemHiddenStyle":{"fontFamily":"Arial","color":"#cccccc","fontSize":"18px","fontWeight":"normal","fontStyle":"normal"},"layout":"horizontal"},"credits":{"text":"Source: Experian"}});}else window.setTimeout(cl, 20);}cl();})(); The number of overall student loan trades per consumer is down to 3.85, a decrease of 5 percent over the last four years. This is explained by an increase in loan consolidations as well as the better planning by students so that they don’t have to take more student loans in the same year. Lastly, I looked at the average balance per consumer. This is the amount that consumers, on average, owe for their student loan trades. Created with Highstock 5.0.7Balance in thousands ($)Quarterly $USD Debt per ConsumerQ4 Student Loan TrendsAverage Student Loan Debt Balance per Consumer27,93427,93429,22629,22630,52330,52332,06132,061Q4 2013Q4 2014Q4 2015Q4 201605,00010,00015,00020,00025,00030,00035,000Source: Experian (function(){ function include(script, next) {var sc=document.createElement("script");sc.src = script;sc.type="text/javascript";sc.onload=function() {if (++next < incl.length) include(incl[next], next);};document.head.appendChild(sc);}function each(a, fn){if (typeof a.forEach !== "undefined"){a.forEach(fn);}else{for (var i = 0; i < a.length; i++){if (fn) {fn(a[i]);}}}}var inc = {},incl=[]; each(document.querySelectorAll("script"), function(t) {inc[t.src.substr(0, t.src.indexOf("?"))] = 1;});each(Object.keys({"https://code.highcharts.com/stock/highstock.js":1,"https://code.highcharts.com/adapters/standalone-framework.js":1,"https://code.highcharts.com/highcharts-more.js":1,"https://code.highcharts.com/highcharts-3d.js":1,"https://code.highcharts.com/modules/data.js":1,"https://code.highcharts.com/modules/exporting.js":1,"http://code.highcharts.com/modules/funnel.js":1,"http://code.highcharts.com/modules/solid-gauge.js":1}),function (k){if (!inc[k]) {incl.push(k)}});if (incl.length > 0) { include(incl[0], 0); } function cl() {if(typeof window["Highcharts"] !== "undefined"){Highcharts.setOptions({lang:{"thousandsSep":","}});new Highcharts.Chart("highcharts-0b893a55-8019-4f1a-9ae1-70962e668355", {"chart":{"type":"column","inverted":true,"polar":false,"style":{"fontFamily":"Arial","color":"#333","fontSize":"12px","fontWeight":"normal","fontStyle":"normal"}},"plotOptions":{"series":{"dataLabels":{"enabled":true},"animation":true}},"title":{"text":"Average Student Loan Balance per Consumer","style":{"fontFamily":"Arial","color":"#333333","fontSize":"18px","fontWeight":"bold","fontStyle":"normal","fill":"#333333","width":"308px"}},"subtitle":{"text":"","style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal","fill":"#666666","width":"792px"}},"exporting":{},"yAxis":[{"title":{"text":"Balance numbers are in thousands ($)","style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal"}},"labels":{"format":"{value:,1f}"},"reversed":false}],"xAxis":[{"title":{"style":{"fontFamily":"Arial","color":"#666666","fontSize":"16px","fontWeight":"normal","fontStyle":"normal"},"text":"Balance in thousands ($)"},"labels":{"format":"{value:}"},"type":"linear","reversed":true,"opposite":false}],"series":[{"data":[["Average Balance per Consumer",27934]],"name":"Q4 2013","turboThreshold":0,"_colorIndex":0},{"data":[["Average Balance per Consumer",29226]],"name":"Q4 2014","turboThreshold":0,"_colorIndex":1},{"data":[["Average Balance per Consumer",30523]],"name":"Q4 2015","turboThreshold":0,"_colorIndex":2},{"data":[["Average Balance per Consumer",32061]],"name":"Q4 2016","turboThreshold":0,"_colorIndex":3}],"colors":["#26478d","#406eb3","#632678","#982881"],"legend":{"itemStyle":{"fontFamily":"Arial","color":"#333333","fontSize":"12px","fontWeight":"bold","fontStyle":"normal","cursor":"pointer"},"itemHiddenStyle":{"fontFamily":"Arial","color":"#cccccc","fontSize":"18px","fontWeight":"normal","fontStyle":"normal"}},"lang":{"thousandsSep":","},"credits":{"text":"Source: Experian"}});}else window.setTimeout(cl, 20);}cl();})(); Here we see a growth of 15 percent over the last four years. At the end of 2016, the average person with a student loan balance had just over $32,000 outstanding. While this is a large increase, we should compare it with other purchases: This balance is no more than a person purchasing a brand-new car without a down payment. While we’re seeing an increase in overall outstanding debt and individual loan balances, I’m not yet agreeing that this is the crisis the media portrays. If students are educated about the debt that they’re taking out and making sure that they’re able to repay it, the student loan market is performing as it should. It’s our job to help educate students and their families about making good financial decisions. These discussions need to be had before debt is taken out, so it’s not a shock to the student upon graduation.
There’s a new crew coming of age. Enter Generation Z. Gen Z — those born between the mid-1990s and the early 2000s — makes up one-quarter of the U.S. population. By 2020, they’ll account for 40% of all consumers. The oldest members of this next cohort — 18- to 20-year-olds — are coming of age. Here are some insights on how this initial segment of Gen Z is beginning to use credit. Credit scores averaged 631 in 2016. Debt levels — consisting largely of bankcards and auto and student loans — are low, with an average debt-to-income ratio of just 5.7%. Average income is $33,800. This generation is being raised in an era of instant, always-on access. They expect a quick, seamless and customized mobile experience. You have just 8 seconds to capture their attention. Webinar: A First Look at Gen Z and Credit
In March 2015, Experian, Equifax and Trans Union announced an agreement to enhance collecting accurate consumer information and providing consumers with a better experience interacting with the National Credit Reporting Agencies (CRA’s) about their credit reports, through the National Consumer Assistance Plan (NCAP). Since then, a series of mandatory updates to data reporting and collections procedures have been announced and implemented. Have you made the required changes and are you prepared for the next implementation? Understanding how these changes affect your business and reporting processes can be difficult to navigate. Some of these changes affect all data furnishers while others are relevant to collection agencies and debt buyers only. Here’s what you need to know: What’s coming up that ALL consumer data furnishers need to know? Effective Sept. 15, 2017, new requirements for reporting personally identifiable information will be in place. This new minimum standard will apply to accounts reported with a date opened after Sept. 15, 2017 and must be included for the CRAs to accept these records for processing. Following the Metro 2® Format, furnishers must report: Full name (First, middle or middle initial (if available), last and generation code/ suffix) Address Full Social Security Number (If full Social Security Number is not available, full Date of Birth (mmddyyyy) will be required) Date of birth (mmddyyyy) As of Feb. 1, 2018, consumer data will no longer be accepted by the CRAs in the older MetroTM format. Prior to the effective date you will need to take the necessary action to ensure that your organization will convert to the Metro 2® Format. You can access information about the Metro 2® Format on the Consumer Data Industry Association website. Should you have any questions about your Experian conversion, we’re here to help, contact us at Experian Experian_Metro2_Conv@experian.com Do you report Authorized User trades? Effective Sept. 15, 2017 you must report the full date of birth for newly added authorized users on all pre-existing and newly opened accounts. If you are a collection agency or debt buyer, the following changes are ALSO applicable to your business: As of Sept. 15, 2017, you will need to stop reporting medical debt collection accounts until they are at least 180 days past the date of first delinquency with the original creditor and delete any accounts that are being paid by insurance or paid in full through insurance. Effective Sept. 1, 2016, you must report a full file monthly. This means reporting all accounts monthly, including open collection accounts, collection accounts paid in full, and accounts requiring deletion or correction. In June 2016, the CRA’s agreed to adopt a certain industry standard with respect to the reporting of debts that did not arise from a contract or agreement to pay. Experian’s policy even prior to June 2016 is not to accept any data that falls outside of a contract or agreement to pay including, but not limited to, certain fines, tickets, and other assessments. For example, library fees or fines, parking tickets, speeding tickets, and court fees or fines. Also, the name of the Original Creditor and Creditor Classification Code became requirements to include in all reporting per the Metro 2® Format. These changes are important to the quality of our data and ultimately provide a positive impact to the consumer and your business. Are you prepared?
The State of Credit Unions 2017 In the financial services universe, there is no shortage of players battling for consumer attention and share of wallet. Here’s a look at how one player — credit unions — has fared over the past two years compared to banks and online lenders: Personal loans grew 2%, but online lenders and finance companies still own 51% of this market. Card originations at credit unions increased 18%, with total credit limits on newly originated cards approaching $100 billion in Q1 2017. Mortgage market share rose 7% for credit unions, while banks lost share to online lenders. Auto originations increased 25% for credit unions to 1.93 million accounts in Q1 2017. Whether your organization is a credit union, a financial institution or an online lender, a “service first” mentality is essential for success in this highly competitive market. The State of Credit Unions 2017
Financial health means more than just having a great credit score or money in a savings account. Although those things are good indicators of financial well-being, personal finance experts believe that financial health means more: being able to manage daily finances, save for the future and weather a financial shock, such as a job loss. As we approach #FinHealthMatters Day on June 27—a day created to bring attention to the 46 percent of Americans who are struggling financially—let’s take a look at financial health trends of Americans. Young adults not actively saving for retirement: Roughly 31% of non-retired adults have no retirement savings or a pension, according to a survey by the Federal Reserve. Nearly half of 18- to 29-year-olds surveyed had no retirement savings or pension, and about 75% of non-retired people 45 and older had some savings. Still, about 14% of adults 60 or older who are not retired and employed had no retirement savings, according to the report. Managing daily finances a challenge for many: Living paycheck to paycheck is a reality for about 1 in 10 Americans (11%), who say they spend more on monthly expenses than their household income allows, according to a Harris Poll. Of those surveyed, about one-third (32%) say they just make ends meet. Most lack an emergency fund: About 50% of people are unprepared for a financial emergency. Nearly 1 in 5 (19%) Americans have no savings set aside to cover unexpected emergencies, while about 1 in 3 (31%) Americans don’t have $500 reserved for an unexpected emergency expense, according to a survey released by HomeServe USA, a home repair service. Renewed focus on personal savings: On a positive note, Americans are sharpening their focus on personal savings, with slight increases among those who say they are saving more than last year (26% in 2017 vs. 24% in 2016). And the portion of those contributing income toward non-retirement savings has returned to its 2013 level of 69%. The good news is it’s never too late for people to achieve financial health. To do so, they need guidance to develop financial routines that build long-term resilience and opportunity. Promoting financial health is good for the financial services industry, as financially healthy consumers drive new opportunities for increased engagement, loyalty, and long-term revenue streams. We invite you to join the conversation and contribute your support and ideas for a healthier future.
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® credit score 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. 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.
Subprime vehicle loans When discussing automotive lending, it seems like one term is on everyone’s lips: “subprime auto loan bubble.” But what is the data telling us? Subprime auto lending reached a 10-year record low for Q1. The 30-day delinquency rate dropped 0.5% from Q1 2016 to Q1 2017. Super-prime share of new vehicle loans increased from 27.4% in Q1 2016 to 29.12% in Q1 2017. The truth is, lenders are making rational decisions based on shifts in the market. When delinquencies started going up, the lending industry shifted to more creditworthy customers — average credit scores for both new and used vehicle loans are on the rise. Read more>
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, the VantageScore® credit score allows for the scoring of 30 million to 35 million more people consumers who are typically unscoreable by other traditional generic credit models. The VantageScore® credit score does this by using a broader, deeper set of credit file data and more advanced modeling techniques. This allows the VantageScore® credit score 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 the VantageScore® credit score. 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?