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

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.

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 >

A combination of mass identity data compromise and the increasing abilities of organized fraud rings has created a synthetic identity epidemic that is impacting all markets. Here are the three ways that synthetic identities are generally created: Credit applications and inquiries that result in synthetic credit profile creation or build. Exploitation of the authorized user process designed to take over or piggyback on legitimate credit profiles. Data furnishing schemes that falsify regular credit reporting agency updates. When it comes to fighting synthetic fraud, we all need to be a part of the solution – or we are just a part of the problem. Mitigate synthetic identity fraud >

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

Most companies aren’t prepared to respond to a global data breach, and aren’t yet ready to comply with the European Union’s General Data Protection Regulation (GDPR), even though it takes effect in less than a year, according to the latest Ponemon Institute report sponsored by Experian® Data Breach Resolution. Nearly a third of the 588 information security and compliance professionals interviewed for the survey said their organizations had no global incident response plan in place, and 38 percent have a single plan that’s applied around the world. Just 27 percent reported having separate plans at the country or regional level, but even those who had a plan weren’t confident about its efficacy. The global scope of data breaches The number of data breaches reached a record high in 2016 — 4,149 incidents in 102 countries around the world exposed more than 4.2 billion records, according to cybersecurity company Risk Based Security. Ponemon’s survey underscores the scope of global data breaches; 51 percent of respondents reported their companies experienced a global data breach in the past five years, and 56 percent of breached companies had more than one incident. When the GDPR goes into effect in May 2018, any company that processes and/or holds the personal data of European Union consumers will be required to comply with the regulation, regardless of where the company is located. Failure to comply can lead to fines ranging from 2 percent to 4 percent of a company’s annual global turnover. Despite the escalating risks of falling victim to a global data breach and the possible repercussions of not complying with the GDPR, Ponemon’s survey shows a widespread lack of preparedness among companies. Levels of unpreparedness When it comes to preventing and responding to a global data breach, and ensuring they comply with the GDPR’s strict notification rules, many survey respondents expressed significant shortfalls in preparedness: Outdated and inadequate security solutions would hinder the ability of 49 percent to cope with a global data breach. Just 40 percent of respondents felt confident their organizations’ security technologies would adequately protect information assets and IT infrastructures overseas, and only 39 percent said they had the right policies and procedures to do so. Slightly more than a third thought their companies could successfully manage cultural differences and privacy and data security expectations in different areas of the world. A majority of respondents (89 percent) predicted the GDPR will significantly affect their data protection practices, and 69 percent felt non-compliance would hinder their companies’ ability to do business globally. Yet only a quarter said their companies were ready to comply with the new regulation. While most understand GDPR is something they need to worry about, many aren’t sure what to do. The survey reveals some companies may be feeling desperate enough about the looming regulation to take drastic measures; 34 percent said their preparations include closing operations in countries with high non-compliance rates. Timely notification of regulators and EU citizens affected by a data breach is a key component of the GDPR, yet the majority of our survey respondents (69 percent) said they would have trouble meeting the time limitations. The GDPR requires breached companies to notify regulators within 72 hours of discovering a breach, and affected consumers “without undue delay.” Half of our survey respondents said they experienced a global breach that required notification of victims. Only 10 percent were able to do so within the GDPR’s 72-hour window; 38 percent reported notification took two to five months to complete. Obstacles to preparedness The years-long evolution of the GDPR, which will replace older regulations, is evidence that world governments are taking data breach risks seriously. Unfortunately, our study indicates not all C-suite decision-makers are as concerned about global data breach risks as they should be and their antipathy is impairing their organizations’ ability to prepare for a global data breach. While the security professionals surveyed cited high-volume breaches (65 percent) and breaches involving high-value information (50 percent) as the data risks that concern them the most, only 30 percent said their organization’s C-suite was fully aware of the company’s compliance status. Further, just 38 percent said their executives viewed global data regulations as a top priority. Technology limitations and lack of executive support are significant obstacles to preparedness and compliance, but they’re not the only ones. Additionally, survey respondents cited: Reluctance to make needed comprehensive changes in business practices (60 percent) Not enough budget to hire staff (37 percent) Unrealistic demands from regulators/regulations (35 percent) Not enough money for appropriate security technology (34 percent) Lack of knowledge about global data breach response (29 percent) What companies must do Some survey respondents indicated their organizations are taking the right steps toward preparedness and compliance. They are putting in place security technologies to quickly detect a data breach (48 percent), have tested and proven response plans (44 percent), can quickly identify whether a breach will require notification (15 percent) and are prepared to notify regulators within 72 hours of breach discovery (13 percent). However, many organizations could be doing more to prepare for a global data breach and to comply with the GDPR. Global data breach risks continue to increase in number, scope and impact, and the potential loss of business and financial impact of a breach could prove catastrophic for affected companies. With less than a year to go until the GDPR takes effect, any company that conducts business internationally needs to act now to ensure it will be ready to deal with a global data breach when it occurs. Learn more about our Data Breach solutions

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&gt; &nbsp;

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&gt;