Data & Analytics
There’s no shortage of headlines alluding to a student loan crisis. But is there a crisis brewing or is this just a headline grab? Let’s look at the data over the past 4 years to find out. Outstanding student loan (should be loan) debt grew 21%, reaching a high of $1.49 trillion in Q4 2016. Over the past 4 years, student loan trades grew 4%, with a slight decline from 2015 to 2016. Average balance per trade grew 17% to reach $8,210. Number of overall student loan trades per consumer is down 5% to just 3.85. The average person with a student loan balance had just over $32,000 outstanding at the end of 2016 — a rise of 15%. While we’re seeing some increases, the data tells us this is a media headline grab. If students are educated about the debt they’re acquiring and are confident they can repay it, student loan debt shouldn’t be a crippling burden. More student loan insights
We live in a digital world where online identities are ubiquitous. But with the internet’s inherent anonymity, how do you know you’re interacting with a legitimate individual rather than an imposter? Too often we hear stories about consumers who see unauthorized purchases on their credit cards, enable access to their devices based on an imposter claiming to be a security vendor or send money to someone they met online only to learn they’ve been “catfished” by a fraudster. These are growing problems, as more consumers transition to digital services and look to businesses to protect them, enable seamless trusted interactions and maintain their privacy. I recently chatted with MarketWatch about how consumers can protect themselves and their privacy when using online dating apps, as well as what businesses are doing to safeguard digital data. As part of the discussion, I mentioned that a simple, standard verification process companies of all sizes can leverage is vital to our rapidly evolving digital economy. Today, companies have their own policies, processes and definitions of identity verification, depending on the services they offer. This ranges from secure access requiring strong identity proofing, document verification, multifactor authentication and biometric enrollment to new social profiles that do little more than validate receipt of an email to establish an online account. To satisfy those diverse risk-based needs, more organizations are turning to federated identity verification options. A federated system allows businesses to leverage trusted, reputable, third-party sources to validate identity by cross-referencing the information they’ve received from a consumer against these sources to determine whether to establish an account or allow a transaction. While some organizations have attempted to develop similar identity verification capabilities, many lack a trusted identity source. For example, there are solutions that leverage data from social media accounts or provide multifactor fraud and authentication options, but they often become easily compromised because of the absence of verifiable data. A trusted solution aggregates data across multiple providers that have undergone thorough security and data quality vetting to ensure the identity data is accurately submitted in accordance with business and compliance requirements. In fact, there are only a handful of trusted identity sources with this level of due diligence and oversight. At Experian, we assess verification requests against an aggregate of hundreds of millions of records that include identity relationships, profile risk attributes, historical usage records and demographic data assets. With decades of knowledge about identity management and fraud prevention, we help companies of all sizes balance risk mitigation and maintain compliance requirements — all while ensuring consumer data privacy. Trust takes years to build and mere seconds to lose, and the industry has made undeniable progress in security. But there is much left to do. Consumers are increasingly involved in the protection and use of their data. However, they often don’t realize downloading a hot new app and entering personal details or linking to their friends exposes them to unnecessary risk. It’s important for businesses to be clear about their identity verification processes so consumers can make educated decisions before electing to provide invaluable identity data. The most effective fraud prevention and identity strategy is one that quickly establishes trust without inconveniencing the consumer. By staying up to date on verification methods, businesses can ensure customers have a smooth, personalized and engaging online experience.
Many institutions take a “leap of faith” when it comes to developing prospecting strategies as it pertains to credit marketing. But effective strategies are developed from deep, analytical analysis with clearly identified objectives. They are constantly evolving – no setting and forgetting. So what are the basics to optimizing your prospecting efforts? Establish goals Unfortunately, far too many discussions begin with establishing targeting criteria before program goals are set. But this leads to confusion. Developing targeting criteria is kind of like squeezing a balloon; when you restrict one end, the other tends to expand. Imagine the effect of maximizing response rates when soliciting new loans. If no other criteria are considered, you could end up targeting high-risk individuals who cannot get approved elsewhere. Obviously, we’re not interested in increasing originations at all cost; risk must be understood as well. But this is where things get complicated. Lower-risk consumers tend to be the most coveted, get the best offers, and therefore have lower response rates and margins. Simplicity is best The US Navy developed the KISS acronym (keep it simple, stupid) in the 1960s on the philosophy that complexity increases the probability of error. This is largely true in targeting methodologies, but don’t mistake limiting complexity for simplicity. Perhaps the most simplistic approach to prescreen credit marketing is using only risk criteria to set an eligible population. Breaking a problem down to this single dimension generally results in low response rates and wasted budget. Propensity models and estimated interest rates are great tools for identifying consumers that are more likely to respond. Adding them as an additional filter to a credit-qualified population can help increase response rates. But what about ability to pay? So far we’ve considered propensity to open and risk (the latter being based on current financial obligations). Imagine a consumer with on-time payment behavior and a solid credit score who takes a loan only to be unable to meet their obligations. You certainly don’t want to extend debt that will cause a consumer to be overextended. Instead of going through costly income verification, income estimation models can assist with identifying the ability to repay the loan you are marketing. Simplicity is great, but not to the point of being one-dimensional. Take off the blindfold Even in the days of smartphones and GPS navigation, most people develop a plan before setting off on a road trip. In the case of credit marketing, this means running an account review or archive analysis. Remember that last prescreen campaign you ran? What could have happened with a more sophisticated targeting strategy? Having archive data appended to a past marketing campaign allows for “what if” retrospective analysis. What could response rates have been with a propensity tool? Could declines due to insufficient income have been reduced with estimated income? Archive data gives 20/20 hindsight to what could have been. Just like consulting a map to determine the shortest distance to a destination or the most scenic route, retrospective analysis on past campaigns allows for proactive planning for future efforts. Practice makes perfect Even with a plan, you probably still want to have the GPS running. Traffic could block your planned route or an unforeseen detour could divert you to a new path. Targeting strategies must continually be refined and monitored for changes in customer behavior. Test and control groups are essential to continued improvement of your targeting strategies. Every campaign should be analyzed against the goals and KPIs established at the start of the process. New hypotheses can be evaluated through test populations or small groups designed to identify new opportunities. Let’s say you typically target consumers in a risk range of 650-720, but an analyst spots an opportunity where consumers with a range of 625-649 with no delinquencies in the past 12 months performs nearly at the rate of the current population. A small test group could be included in the next campaign and studied to see if it should be expanded in future campaigns. Never “place bets” Assumptions are only valid when they are put to the test. Never dive into a strategy without testing your hypothesis. The final step in implementing a targeting strategy should be the easiest. If goals are clearly understood and prioritized, past campaigns are analyzed, and hypotheses are laid out with test and control groups, the targeting criteria should be obvious to everyone. Unfortunately, the conversation usually starts at this phase, which is akin to placing bets at the track. Ever notice that score breaks are discussed in round numbers? Consider the example of the 650-720 range. Why 650 and not 649 or 651? Without a test and learn methodology, targeting criteria ends up based on conventional wisdom – or worse, a guess. As you approach strategic planning season, make sure you run down these steps (in this order) to ensure success next year. Establish program goals and KPIs Balance simplicity with effectiveness Have a plan before you start Begin with an archive Learn and optimize In God we trust, all others bring data
CFPB and credit invisibles A recent study by the Consumer Financial Protection Bureau (CFPB) found that American consumers establish credit differently depending on their economic background. The study revealed that: Consumers in lower-income areas are 240% more likely to become credit visible due to negative information, such as a debt in collection. Those in higher-income areas become credit visible in a more positive way. For example, these consumers are 30% more likely to become credit visible through the use of a credit card. The percentage of consumers transitioning to credit visibility due to student loans more than doubled in the last 10 years. Policymakers can make it easier for consumers to become more credit visible by clearly defining the term alternative data and supporting the use of alternative data sources that meet legal and societal standards for accuracy, validity, predictability and fairness. Learn more >
Historical data that illustrates lower credit card use and increases in payments is key to finding consumers whose credit trajectory is improving. But positive changes in consumer behavior—especially if it happens slowly over time—don’t necessarily impact a consumer’s credit score. And many lenders are missing out on capturing new business by failing to take a closer look. It’s easy to categorize consumers by their credit score alone, but you owe it to your bottom line to investigate further: Are the consumer’s overall payments increasing? Is his credit card utilization decreasing? Are the overall card balances remaining consistent or declining? Could the consumer be within your credit score guidelines within a month or two? And most importantly, could a competitor acquire the consumer a month or two after you declined him? Identifying new customers who previously used credit responsibly is relatively easy since they typically have rich credit profiles that may include a mortgage and numerous types of credit accounts. But how do you evaluate consumers who may look identical? Trended data and attributes provide insight into whether a consumer is headed in the right direction: With more than 613 trended attributes available for real-time decisioning and for batch campaigns, Experian trends key factors that provide the insight needed for lenders to lend deeper without sacrificing credit quality. Looking at trended data and attributes is critical for portfolio growth, and credit line increases based on good credit behavior is a must for lenders for two reasons. First, you’ve already spent the money acquiring the consumer and you should not waste the opportunity to maximize returns. Second, competition is fierce; another lender could reward the consumer for great credit behavior they’ve displayed with your company. Be there first, be consistent, or be left behind. Use Experian’s Payment Stress Attributes and Short-term Utilization Attributes in custom scores or swap-set strategies in order to find quality customers who may be worthy of line increases or other attribute credit terms. Look to trended data to swap in consumers who may fall within a few points under your credit score guidelines, and reward your existing customers before another lender does. Near-prime consumers of today are the prime consumers of tomorrow.
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 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 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 $USD Debt per ConsumerQ4 Student Loan TrendsAverage Student Loan Debt Balance per Consumer27,93427,93429,22629,22630,52330,52332,06132,061Q4 2013Q4 2014Q4 2015Q4 201605,00010,00015,00020,00025,00030,00035,000Source: Experian (function(){ function include(script, next) {var sc=document.createElement(\"script\");sc.src = script;sc.type=\"text/javascript\";sc.onload=function() {if (++next < incl.length) include(incl[next], next);};document.head.appendChild(sc);}function each(a, fn){if (typeof a.forEach !== "undefined"){a.forEach(fn);}else{for (var i = 0; i 0) { include(incl[0], 0); } function cl() {if(typeof window[\"Highcharts\"] !== \"undefined\"){Highcharts.setOptions({lang:{\"thousandsSep\":\",\"}});new Highcharts.Chart(\"highcharts-0b893a55-8019-4f1a-9ae1-70962e668355\", {\"chart\":{\"type\":\"column\",\"inverted\":true,\"polar\":false,\"style\":{\"fontFamily\":\"Arial\",\"color\":\"#333\",\"fontSize\":\"12px\",\"fontWeight\":\"normal\",\"fontStyle\":\"normal\"}},\"plotOptions\":{\"series\":{\"dataLabels\":{\"enabled\":true},\"animation\":true}},\"title\":{\"text\":\"Average Student Loan Balance per Consumer\",\"style\":{\"fontFamily\":\"Arial\",\"color\":\"#333333\",\"fontSize\":\"18px\",\"fontWeight\":\"bold\",\"fontStyle\":\"normal\",\"fill\":\"#333333\",\"width\":\"308px\"}},\"subtitle\":{\"text\":\"\",\"style\":{\"fontFamily\":\"Arial\",\"color\":\"#666666\",\"fontSize\":\"16px\",\"fontWeight\":\"normal\",\"fontStyle\":\"normal\",\"fill\":\"#666666\",\"width\":\"792px\"}},\"exporting\":{},\"yAxis\":[{\"title\":{\"text\":\"Balance numbers are in thousands ($)\",\"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.
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 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 …
Experian recently acquired a minority stake in Finicity, a leading financial data aggregator enabling innovation in the FinTech industry through its modern RESTful API and Finicity Aggregation Platform. Steve Smith—chairman, CEO and co-founder of Finicity—has a passion and experience in developing innovative and disruptive technology, products and services that leads to efficiency for markets and, ultimately, improvements for consumers. Here he shares his thoughts about disruptive technology in the lending space and its benefits to lenders and consumers. Q: Finicity has said its objective is to take a loan application approval from weeks to minutes using its technology. That sounds pretty great, but how is that possible? How does this play out behind the scenes? A: Well, we’re living in a world where we, as consumers, expect very user-friendly experiences and we expect things to happen at digital speeds. The loan process is no exception. To deliver the experience consumers are expecting requires us to leverage the technology trends of digitization, mobility and big data. Finicity plays a foundational role by leveraging thousands of digital connections across financial institutions to aggregate consumer-permissioned account data. Once we have this data, we’re able to deliver real-time insights into an individual\'s financial health. This financial health assessment includes income and assets, two critical components to the loan approval process. All that’s required is the borrower to permission use of the data. Once that’s done, we’re able to gather all appropriate data across multiple accounts, rapidly analyze it and send a verification report to the lender. No papers. No multiple requests. No questions on the validity of the data. All done in minutes, not weeks. Q: This is very disruptive technology. What are the benefits for lenders? Consumers? A: Well, as we discussed, one of the major benefits is the speed to a loan. Furthermore, this reduces cost for the lender by maximizing loan officer’s time, while also freeing up loan capital as they can move through loans more quickly with a higher quality assessment. Another benefit for lenders is reduced fraud. Our information on income and assets is coming from real-time bank validated information. This eliminates the possibility of altered data. For consumers, it’s a dramatically simplified process. No need to chase down multiple documents. There are virtually no second requests for information, which we often see in the process. And they’re always in control of their information. All in all, it’s a dramatically better experience for both the lender and the borrower. Q: What sets this solution apart from others in the market? A: A few things set Finicity apart in delivering the quality of insights required. First, we are an industry leader in the number of financial institutions we connect with, ensuring broader access for more customers. Second, 95 percent of our integrations provide access to formatted data, something that’s critical to credit decisioning solutions. In these cases, we’re not screen scraping. This enhances our ability to collect bank validated transactions; we provide the financial institution transaction ID. This provides assurance of data quality. Finally, is our ability to categorize and analyze the transactions. This allows us to identify income streams and assets. Through this process, we’re also able to flag unusual transactions, like large deposits, that may skew actual assets. Q: The future of financial technology is still evolving. What lies ahead? A: We’re very excited about the future of financial technology and the impact that aggregation will have. Whether it’s financial management, digital payments or credit decisioning, real-time data will improve the experiences and the outcomes. As we’re talking about lending, this is one of the spaces that could see significant disruption. Our ability to generate a richer view of an individual’s or organization’s financial health will more accurately determine their ability to repay a loan. This will be a great benefit for those that have thin file or no credit history. We see a world where suitability for a loan will be driven by their actual financial life independent of their use of credit. One of the largest markets in the US is millennials. However, for consumers under 30, two-thirds have subprime or non-prime credit scores and one-third of millennials don\'t have any credit history. This is just one group underserved because legacy models don’t leverage the full extent of data available. Q: Is there anything else you can tell us about Finicity and its role changing customer experiences across financial service? A: For us, it all comes down to one thing: enabling individuals and organizations to have the information and insights they need to make smarter financial decisions. The data is there. We’re helping to unlock the potential of that data by working with innovative partners like Experian. To learn more about Experian and Finicity\'s account aggregation solutions, visit www.experian.com/finicity
Reactivation campaigns make economic sense. They build on a brand’s previous investments, targeting customers who already are aware of and previously have engaged with your brand. Use these 4 steps to build a successful reactivation framework: 1. Analyze subscriber data to identify reactivation segments to target. 2. Identify subscriber activity to divide customers into at least 3 unique segments. 3. Develop messaging strategies for each segment. 4. Integrate or suppress inactive subscribers based on whether they re-engage. Reactivation campaigns can deliver significant incremental revenue and position inactive subscribers for further engagement in future campaigns. Download report>
There has been a lot of discussion around the auto loan market regarding delinquency rates in the past year. It is a topic Experian is asked about frequently from clients in regard to what particular economic market behaviors mean for the overall consumer lending. To understand this issue more clearly, I ran a deeper dive on the data from our Q3 Experian-Oliver Wyman Market Intelligence report. There are some interesting, and perhaps concerning, trends in the data for automotive loans and leases. Want Insights on the latest consumer credit trends? Register for our 2016 year-end review webinar. Register now Auto loan delinquency rates are at their highest mark since 2008 The findings indicate that the performance of the most recent loans opened from Q4 2015 are now performing as poorly as the loans from the credit crisis back in 2008. In fact, you have to go back to 2008, and in some cases, 2007, to see loan default rates as poorly as the Q4 2015 auto loans originated in the last year. Below we have the auto loan vintage performance for loans originated in Q4 of the last 8 years — going back to 2008. The lines on the chart each represent 60 days late or more (60+) delinquency rates over specific time period grades. For these charts, I analyzed the first three, six, and nine months from the loan origination date. As you can see, the rates of delinquency have steadily increased in recent years, with the increase in the Q4 2015 loans opened equaling or even surpassing 2008 levels. The above chart reflects all credit grades, so one might think that this change is a result of the change in the credit origination mix. By digging a little deeper into the data, we can control for the VantageScore at the loan opening, or origination date, and review performance by looking at two different score segments separately. Is there concern for Superprime and Prime consumers auto loans? In the chart immediately below, the same analysis as above has been conducted, but only for trades originated by Superprime and Prime consumers at the time of origination. You can see that although the trend is not as pronounced as when all grades are considered, even these tiers of consumers are showing significant increases in their 60+ days past due (DPD) rates in recent vintages. Separately, looking at the Subprime and Deep Subprime segments, you can really see the dramatic changes that have occurred in the performance of recent auto vintages. Holding score segments constant, the data indicates a rate of credit deterioration in the Subprime and Deep Subprime segments that we have not observed since at least 2008 — back to when we started tracking this data. What’s concerning here is not only the absolute values of the vintage delinquencies but also the trend, which is moving upward for all three time periods. Where does the risk fall? Now that we see the evidence of the deterioration of credit performance across the credit spectrum, one might ask – who is bearing the risk in these recent vintages? Taking a closer look at the chart below, you can see the significant increase in the volumes of loans across lender type, but particularly interesting to me is the increase in 2016 for the Captive Auto lenders and Credit Unions, who are hitting highs in their lending volumes in recent quarters. If the above trend holds and the trajectory continues, this suggests exposure issues for those lenders with higher volumes in recent months. What does this mean for your business? Speak to Experian\'s global consulting practice to learn more. Learn more Just to be thorough, let\'s continue and look at the relative amounts of loans going to the different score segments by each of the lender types. Comparing the lender type and the score segments (below) reveals that finance lenders have a greater than average exposure to the Subprime and Deep Subprime segments. To summarize, although auto lending has recently been viewed as a segment where loan performance is good, relative to historical levels, I believe, the above data signals a striking change in that perspective. Recent loan performance has weakened to a point where comparing the 2008 vintage with 2015 vintage, one might not be able to distinguish between the two. // <![CDATA[ var elems={'winWidth':window.innerWidth,'winTol':600,'rotTol':800,'hgtTol':1500}, updRes=function(){var xAxislabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'14px'}},xAxislabelRotation=function(){if(elems.winWidth<elems.rotTol){return-90}else{return 0}},seriesLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},legenLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},chartHeight=function(){if(elems.winWidth<elems.rotTol){return 600}else{return 400}},labelInside=function(){if(elems.winWidth<elems.rotTol){return false}else{return true}},chartStack=function(){if(elems.winWidth<elems.rotTol){return null}else{return'normal'}};this.sourceRef=function(){return['Source: Experian.com']};this.seriesColor=function(){return['#982881','#0d6eb6','#26478D','#d72b80','#575756','#b02383']};this.chartFontFamily=function(){return'"Roboto",Helvetica,Arial,sans-serif'};this.xAxislabelSize=function(){return xAxislabelSize()};this.xAxislabelOverflow=function(){return'none'};this.xAxislabelRotation=function(){return xAxislabelRotation()};this.seriesLabelSize=function(){return seriesLabelSize()};this.legenLabelSize=function(){return legenLabelSize()};this.chartHeight=function(){return chartHeight()};this.labelInside=function(){return labelInside()};this.chartStack=function(){return chartStack()}}(), updY=function(chart){var points=chart.series[0].points;for(var i=0;i elems.rotTol){if(thisWidth<20){var y=points[i].dataLabel.y;y-=10;points[i].dataLabel.css({color:'#575756'}).attr({y:y-thisWidth})}}}},updX=function(chart){var points=chart.series[0].points;for(var i=0;i elems.rotTol){if(thisWidth
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