Since the advent of the internet, our lives have changed drastically for the better. We can perform many of life’s daily activities from the comfort of our own home. According to Aite, in 2016 alone 36 million Americans made some form of mobile payment — paying a bill, purchasing something online, paying for fast food or making a mobile wallet purchase at a retailer. Simply put, the internet has made our lives easier. But with the good also comes the bad. While most consumers have moved to the digital world, so have fraudsters. With minimal risk and high reward at stake, e-commerce fraud attacks have increased dramatically over the last few years, with no signs of slowing down. We recently analyzed millions of transactions from the first half of 2017 to identify fraud attack rates based on billing and shipping addresses and broke down the findings into various geographic trends. Fraud attack rates represent the attempted fraudulent e-commerce transactions against the population of overall e-commerce orders. Consumers living out West and in the South have experienced more than their fair share of fraud. During the first half of 2017, the West and the South were the top two regions for both billing and shipping attacks. While both regions were at the top during the same time last year, the attacks themselves have increased substantially. Given the proximity to seaports and major international airports, this is somewhat unsurprising — particularly for shipping fraud — as many fraudsters will leverage reshippers to transport goods soon after delivery. .dataTb{margin:20px auto;width:100%}.dataTb:after{clear:both}.dataTb table{}.dataTb td,.dataTb th{border:1px solid #ddd;padding:.8em}.dataTb th{background:#F4F4F4}.tbL{float:left;width:49%}.tbR{float:right;width:49%;margin:0 0 0 2%} Shipping: Riskiest Regions Region Attack rate West 38.1 South 32.1 Northeast 27.0 North Central 20.7 Billing: Riskiest Regions Region Attack rate West 37.2 South 32.9 Northeast 27.3 North Central 24.0 At the state level, the top three shipping fraud states remained the same as 2016 — Delaware, Oregon and Florida — but the order changed. Oregon was the most targeted, with a fraud rate of 135.2 basis points, more than triple its rate at in the end of 2016. Though no longer in the top spot, Delaware saw alarming spikes as well, with shipping attack rates nearly triple last year’s rate at 128.6 basis points and billing attacks at 79.6 basis points. .dataTb{margin:20px auto;width:100%}.dataTb:after{clear:both}.dataTb table{}.dataTb td,.dataTb th{border:1px solid #ddd;padding:.8em}.dataTb th{background:#F4F4F4}.tbL{float:left;width:49%}.tbR{float:right;width:49%;margin:0 0 0 2%} Shipping: Riskiest States State Attack rate Oregon 135.2 Delaware 128.2 Florida 57.4 New York 45.0 Nevada 36.9 California 36.9 Georgia 33.5 Washington, D.C 30.8 Texas 29.6 Illinois 29.4 Billing: Riskiest States Region Attack rate Oregon 87.5 Delaware 79.6 Washington, D.C. 63.0 Florida 47.4 Nevada 38.8 California 36.9 Arkansas 36.6 New York 35.5 Vermont 34.2 Georgia 33.4 Diving a bit deeper, ZIPTM codes in Miami, Fla., make up a significant portion of the top 10 ZIP CodeTM lists for shipping and billing attacks — in fact, many of the same ZIP codes appear on both lists. The other ZIP Code that appears on both lists is South El Monte, Calif., which has a high percentage of industrial properties — common targets for fraudsters to ship packages, then reship overseas. You can download the top 100 riskiest Zip Codes in the U.S. for H1 2017. .dataTb{margin:20px auto;width:100%}.dataTb:after{clear:both}.dataTb table{}.dataTb td,.dataTb th{border:1px solid #ddd;padding:.8em}.dataTb th{background:#F4F4F4}.tbL{float:left;width:49%}.tbR{float:right;width:49%;margin:0 0 0 2%} Shipping: Top 10 riskiest ZIP™ Codes ZIP Code Attack rate 33122 [Miami, Fla.] 2409.4 91733 [South El Monte, Calif.] 1655.5 33198 [Miami, Fla.] 1295.2 33166 [Miami, Fla.] 1266.0 33195 [Miami, Fla.] 1037.3 33192 [Miami, Fla.] 893.9 97251 [Portland, Ore.] 890.6 07064 [Port Reading, NJ] 808.9 89423 [Minden, Nev.] 685.5 77072 [Houston, Tex.] 629.3 Billing: Top 10 riskiest ZIP™ Codes ZIP Code Attack rate 77060 [Houston, Tex.] 1337.6 33198 [Miami, Fla.] 1215.6 33122 [Miami, Fla.] 1106.2 33166 [Miami, Fla.] 1037.4 91733 [South El Monte, Calif.] 780.1 33195 [Miami, Fla.] 713.7 97252 [Portland, Ore.] 670.8 33191 [Miami, Fla.] 598.8 33708 [St. Petersburg, Fla.] 563.6 33792 [Miami, Fla.] 493.0 As e-commerce fraud continues to grow, businesses need to be proactive to keep themselves and their customers safe. That means incorporating multiple, layered fraud prevention strategies that work together seamlessly — for example, understanding details about users and their devices, knowing how users interact with the business and evaluating previous transaction history. This level of insight can help businesses distinguish real customers from nefarious ones without impacting the customer experience. While businesses are ultimately responsible for the safety of customers and their data, the onus doesn’t rest solely with them. Consumers should also be vigilant when it comes to protecting their digital identities and payment information. That means creating strong, unique passwords; actively monitoring online accounts; and using two-factor authentication to secure account access. At the end of the day, e-commerce fraud is a challenge that businesses and consumers will experience for the foreseeable future. But rising attack rates don’t have to spell doom and gloom for the industry. E-commerce growth is still extremely strong, as consumers interact through multiple channels (in-store, mobile and web) and expect a personalized experience. Establishing trust and verifying digital identities are key to meeting these latest expectations, which provide new opportunities for businesses and consumers to interact seamlessly and transact securely. With multiple safeguards in place, businesses have a variety of options to protect their customers and their brand reputation. Experian is a nonexclusive full-service provider licensee of the United States Postal Service®. The following trademarks are owned by the United States Postal Service®: ZIP and ZIP Code. The price for Experian’s services is not established, controlled or approved by the United States Postal Service.
School is nearly back in session. You know what that means? The next wave of college students is taking out their first student loans. It’s a milestone moment – and likely the first trade on the credit file for many of these individuals. According to the College Board, the average cost of tuition and fees for the 2016–2017 school year was $33,480 at private colleges, $9,650 for state residents at public colleges, and $24,930 for out-of-state residents attending public universities. So really, regardless of where students go, the cost of a college education is big. In fact, from January 2006 to July 2016, the Consumer Price Index for college tuition and fees increased 63 percent. So, unless mom and dad did a brilliant job saving, chances are many of today’s students will take on at least some debt to foot the college bill. But it’s not just the young who are consumed by student loan debt. In Experian’s latest State of Student Lending report, we dive into how the $1.4 trillion in student loan debt for Americans is impacting all generations in regards to credit scores, debt load and delinquencies. The document additionally looks at geographical trends, noting which states have the most consumers with student loan debt and which ones have the least. Overall, we discovered 13.4% of U.S. consumers have one or more student loan balances on their credit file with an average total balance of $34k. Additionally, these consumers have an average of 3.7 student loans with 1.2 student loans in deferment. The average VantageScore® for student loan carriers is 650. As we looked across the generations, every group – from the Silents (age 70+) to Gen Z (oldest are between 18 to 20) had some student loan debt. While we can make assumptions that the Silents and Boomers are likely taking out these loans to support the educational pursuits of their children and grandchildren, it can be mixed for Gen X, who might still be paying off their own loans and/or supporting their own kids. Gen X members also reported the largest average student loan total balance at $39,802. Gen Z, the newest members to the credit file, have just started to attend college, thus their generation has the largest percent of student loan balances in deferment at 77%. Their average student loan total balance is also the lowest of all generations at $11,830, but that is to be expected given their young ages. In regards to geographical trends, the Northern states tended to sport the highest average student loan total balances, with consumers in Washington D.C. winning that race with $52.5k. Southern states, on the other hand, reported higher percentages of consumers with student loan balances 90+ days past due. South Carolina, Louisiana, Mississippi, Arkansas and Texas held the top spots in the delinquency category. Access the complete State of Student Lending report. Data from this report is representative of student loan data on file as of June 2017.
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
This summer, Experian is releasing the market’s first-ever credit solution that enables consumers to apply for credit with a simple text message. Utilizing patent-pending mobile identification through our Smart Lookup process, most consumers will be recognized by their device credentials and in turn bypass the need to fill out a lengthy credit application. To learn more about this new technology and how the innovation came to market, we interviewed Steve Yin, Experian’s Vice President of New Ventures for the DataLabs. Yin has worked with a few dozen clients to gain feedback and gauge interest on this idea, which was birthed in early spring 2016. 1. Tell me about Experian’s DataLabs. How long has it existed and what type of work is produced here? The DataLabs was started a bit over six years ago by Eric Haller – current Global EVP of the DataLabs – with the vision of providing an advanced data R&D, analytics, and incubation capability for Experian and our largest clients. The group started with one lab in San Diego supporting a handful of business units in North America. Today we have DataLabs on three continents supporting all the business units and geographies where Experian does business around the world. The work we focus on is usually new market and new data/technology. We endeavor to follow an experimental design approach in most of our projects and products: identify a problem, develop a hypothesis, and execute on research to germinate a solution. Given our relatively small size compared to Experian overall, we must be very disciplined about the types of projects and products we take on. We filter projects by potential impact (big marketing, complex problems, leveraged opportunities) and probability of success. Our projects are initiated by one of three primary avenues: client discussion/request, internal discussion/request, and organic discovery within the DataLabs. 2. Text for CreditTM is one of Experian’s newest products, and I understand the DataLabs was instrumental in the development from concept to end-product. How did this idea emerge and shape in the Lab? The concept evolved after talking with our clients who were trying to figure out how they could provide access to credit to potential customers through their mobile devices. With the explosion of smartphones and the attachment people have to them, it felt odd that the credit application process had not evolved to synch up with the mobile experience individuals enjoy in every other aspect of their lives. Customers don’t want to fill out lengthy forms to apply for credit, nor do they want to discover they may not qualify for a credit offer at the cash register after being invited to open a store card. We wanted to find a way to provide the customer with the ability to apply for credit seamlessly on their devices, limiting the fields of data they needed to supply, and also enhance the overall credit experience for retailers and consumers. Our DataLabs allow us the runway to preform breakthrough experimentations with data. After trial and error with many different approaches in the lab, we knew we had a winner with text for credit. It’s easy to use, the customer experience is frictionless, it requires minimal infrastructure from our clients and it can be implemented to solve for a large variety of instant credit situations. 3. What excites you most about this product? This has been a fun project. There are several things that make it cool, and most revolve around the reception we’ve had with clients, knowing that we’re delivering something innovative and useful to our clients, their partners and ultimately to consumers. We’re enabling a segment of the consumer landscape to interact with financial institutions on their own terms, from their mobile devices. 4. Have clients been closely involved in providing feedback on the concept and overall product design? Definitely. We’ve been working with clients on refining the concept, design and delivery of Text for Credit since almost day one. We learned very early on that private branding and a flexible user experience would be important since clients have different needs and desires relative to user flow. We also learned that simple integration with existing systems would enable adoption. And critically, we confirmed that providing a total solution rather than small components would enable us to position Text for Credit very differently and facilitate a great end-user experience for our clients. 5. Mobile is now clearly a dominant channel for consumers, and it only makes sense for lenders to embrace it. Is there more on the horizon for the world of mobile and credit optimization? Text for Credit is a beachhead of our broader mobile discovery and development. As clients begin to embrace delivery of credit in a mobile-first environment, we can foresee evolutions like the location-based triggers and integration with cross-channel marketing initiatives spanning social media, paid electric media, market places, and perhaps even integration with voice. In addition to this broadening of offerings, we may also look to creating greater functional value for clients in specific verticals where mobile devices are inherently suited for data and decision delivery. This could include streamlined credit in retail, auto, and mortgage markets where being able to offer credit at the right time, at the right place, to the right people would deliver great value for our clients. I think we’re in a unique position as a company to deliver on this promise within the credit space. Learn more
Millennials have long been the hot topic over the course of the past few years with researchers, brands and businesses all seeking to understand this large group of people. As they buy homes, start families and try to conquest their hefty student loan burdens, all will be watching. Still, there is a new crew coming of age. Enter Gen Z. It is estimated that they make up ¼ of the U.S. population, and by 2020 they will account for 40% of all consumers. Understanding them will be critical to companies wanting to succeed in the next decade and beyond. The oldest members of this next cohort are between the ages of 18 and 20, and the youngest are still in elementary school. But ultimately, they will be larger than the mystical Millennials, and that means more bodies, more buying power, more to learn. Experian recently took a first look at the oldest members of this generation, seeking to gain insights into how they are beginning to use credit. In regards to credit scores, the eldest Gen Z members sported a VantageScore® of 631 in 2016. By comparison, younger Millennials were at 626 and older Millennials were at 638. Given their young age, Gen Z debt levels are low with an average debt-to-income at just 5.7%. Their tradelines largely consist of bankcards, auto and student loans. Their average income is at $33.8k. For their total annual plastic spend, data reveals this oldest group of Gen Z spent about $9.5k, slightly more than the younger Millennials who came in at roughly $9k. Surprisingly, there was a very small group of Gen Z already on file with a mortgage, but this figure was less than .5%. Auto loans were also small, but likely to grow. Of those Gen Z members who have a credit file, an estimated 12% have an auto trade. This is just the beginning, and as they age, their credit files will thicken, and more insights will be gained around how they are managing credit, debt and savings. While they are young today, some studies say they already receive about $17 a week in allowance, equating to about $44 billion annually in purchase power in the U.S. Factor in their influence on parental or household purchases and the number could be closer to $200 billion! For all brands, financial services companies included, it is obvious they will need to engage with this generation in not just a digital manner, but a mobile manner. They are being raised in an era of instant, always-on access. They expect a quick, seamless and customized mobile experience. Retailers have 8 seconds or less — err on the side of less — to capture their attention. In general, marketers and lenders should consider the following suggestions: Message with authenticity Maintain a long-term vision Connect them with something bigger Provide education for financial literacy and of course Keep up with technological advances. Learn more by accessing our recorded webinar, A First Look at Gen Z and Credit.
Mitigating synthetic identities Synthetic identity fraud is an epidemic that does more than negatively affect portfolio performance. It can hurt your reputation as a trusted organization. Here is our suggested 4-pronged approach that will help you mitigate this type of fraud: Identify how much you could lose or are losing today to synthetic fraud. Review and analyze your identity screening operational processes and procedures. Incorporate data, analytics and cutting-edge tools to enable fraud detection through consumer authentication. Analyze your portfolio data quality as reported to credit reporting agencies. Reduce synthetic identity fraud losses through a multi-layer methodology design that combats both the rise in synthetic identity creation and use in fraud schemes. Mitigating synthetic identity fraud>
The creation of synthetic identities (synthetic id) relies upon an ecosystem of institutions, data aggregators, credit reporting agencies and consumers. All of which are exploited by an online and mobile-driven market, along with an increase in data breaches and dark web sharing. It’s a real and growing problem that’s impacting all markets. With significant focus on new customer acquisition and particular attention being paid to underbanked, emerging, and new-to-country consumers, this poses a large threat to your onboarding and customer management policies, in addition to overall profitability. Synthetic identity fraud is an epidemic that does more than negatively affect portfolio performance. It can hurt your reputation as a trusted organization and expose institutions, like yours, as paths of lesser resistance for fraudsters to use in the creation and farming of synthetic identities. Here is a suggested four-pronged approach to mitigate this type of fraud: The first step is knowing your risk exposure to synthetic identity fraud. Identify how much you could lose or are losing today using a targeted segmentation analysis to examine portfolios or customer populations. Next, review your front- and back-end identity screening operational processes and procedures and analyze that information to ensure you have industry best practices, procedures and verification tools deployed. Then incorporate data, analytics and some of the industry’s cutting edge tools. This enables you to perform targeted consumer authentication and identify opportunities to better capture the majority of fraud and operational waste. Lastly, ensure your organization is part of the solution – not the problem. Analyze your portfolio data quality as reported to credit reporting agencies and then minimize your exposure to negative compliance audit results and reputational risk. Our fraud and identity management consultants can help you reduce synthetic identity fraud losses through a multilayer methodology design that combats the rise in synthetic identity creation and use in fraud schemes.
On June 7, the Consumer Financial Protection Bureau (CFPB) released a new study that found that the ways “credit invisible” consumers establish credit history can differ greatly based on their economic background. The CFPB estimated in its May 2015 study \"Data Point: Credit Invisibles\" that more than 45 million American consumers are credit invisible, meaning they either have a thin credit file that cannot be scored or no credit history at all. The new study reviewed de-identified credit records on more than one million consumers who became credit visible. It found that consumers in lower-income areas are 240 percent more likely to become credit visible due to negative information, such as a debt in collection. The CFPB noted consumers in higher-income areas become credit visible in a more positive way, with 30 percent more likely to become credit visible by using a credit card and 100 percent more likely to become credit visible by being added as a co-borrower or authorized user on someone else’s account. The study also found that the percentage of consumers transitioning to credit visibility due to student loans more than doubled in the last 10 years. CFPB’s research highlights the need for alternative credit data The new study demonstrates the importance of moving forward with inclusion of new sources of high-quality financial data — like on-time payment data from rent, utility and telecommunications providers — into a consumer’s credit file. Experian recently outlined our beliefs on the issue in comments responding to the CFPB’s Request for Information on Alternative Data. As a brand, we have a long history of using alternative credit data to help lenders make better lending decisions. Extensive research has shown that there is an immense opportunity to facilitate greater access to fair and affordable credit for underserved consumers through the inclusion of on-time telecommunications, utility and rental data in credit files. While these consumers may not have a traditional credit history, many make on-time payments for telephone, rent, cable, power or mobile services. However, this data is not typically being used to enhance traditional credit files held by the nationwide consumer reporting agencies, nor is it being used in most third-party or custom credit scoring models. Further, new advances in financial technology and data analytics through account aggregation platforms are also integral to the credit granting process and can be applied in a manner to broaden access to credit. Experian is currently using account aggregation software to obtain consumer financial account information for authentication and income verification to speed credit decisions, but we are looking to expand this technology to increase the collection and utilization of alternative data for improving credit decisions by lenders. Policymakers should act to help credit invisible consumers While Experian continues to work with telecommunications and utility companies to facilitate the furnishing of on-time credit data to the nationwide consumer reporting agencies, regulatory barriers continue to exist that deter utility and telecommunications companies from furnishing on-time payment data to credit bureaus. To help address this issue, Congress is currently considering bipartisan legislation (H.R. 435, The Credit Access and Inclusion Act of 2017) that would amend the FCRA to clarify that utility and telecommunication companies can report positive credit data, such as on-time payments, to the nation\' s credit reporting bureaus. The legislation has bipartisan support in Congress and Experian encourages lawmakers to move forward with this important initiative that could benefit tens of millions of American consumers. In addition, Experian believes policymakers should more clearly define the term alternative data. In public policy debates, the term \"alternative data\" is a broad term, often lumping data sources that can or have been proven to meet regulatory standards for accuracy and fairness required by both the Fair Credit Reporting Act and the Equal Credit Opportunity Act with data sources that cannot or have not been proven to meet these standards. In our comment letter, Experian encourages policymakers to clearly differentiate between different types of alternative data and focus the consumer and commercial credit industry on public policy recommendations that will increase the use of those sources of data that have or can be shown to meet legal and societal standards for accuracy, validity, predictability and fairness. More info on Alternative Credit Data More Info on Alternative Financial Services
There are about as many definitions for people-based marketing as there are companies using the term. Each company seems to skew the definition to fit their particular service offering. The distinctions are vast, and especially for financial services companies running regulated campaigns, they can be incredibly important. At Experian, we define people-based marketing in its purest form: targeting at the individual level across channels. This is a practice we’re very familiar with in offline marketing, having honed arguably one of the most accurate views of U.S. consumers over the past three decades. And now we’re taking those tried and true principals and applying them to digital channels. It’s not as easy as it sounds. The challenge with people-based marketing With direct mail, people-based marketing was easy. Jane Doe lives at 123 Main St. If I want to reach her, I can simply send her a direct mail piece at that address. To help, I can utilize any number of services, including the National Change of Address database, to know where to reach her if she ever moves. People-based marketing through digital channels is exponentially more difficult. While direct mail has one signal with which you use to identify a consumer (the address), digital channels offer countless signals. And not all of those signals can be used, either individually or in conjunction with other signals, to reliably tie a consumer to a persistent offline ID. A prime example of this is cookies. The problem with cookies A cookie, in and of itself, isn’t the problem. The problem is the linkage. How was a cookie associated with the person to whom the ad is being served? As marketers, we need to make sure that we are reaching the right people with the right ad … and more importantly not reaching those people who have opted out. This is especially true in the world of regulated data, where you need to know who you are targeting. And cookie-based linkage is controlled by a handful of companies, many of which are walled gardens who don’t share how they link offline people to online cookies and don’t collect this information directly. They rely on other third-party websites to gather PII, and connect it to their cookies. In some cases, the data is very accurate (especially with transaction data). In some cases, it is not (think websites that collect PII when giving surveys, offering coupons, etc.). In short, in order for you to use cookie-based targeting accurately, you need to have insight into the source of the base linkage data that was used to connect the offline consumer record to the online cookie. This same concept applies to all forms of digital linkage that drive people-based marketing. Why does people-based marketing matter in digital credit marketing? With campaigns that utilize non-regulated data, such as “Invitation to Apply” campaigns that are driven from demographic and psychographic data, the consequences of not reaching the consumer you meant to target are negligible. But with campaigns that utilize regulated data, you must ensure you’re targeting the exact consumer you meant to reach. More importantly, you must make sure you’re not targeting an ad to a consumer who had previously opted out of receiving offers driven with regulated data (prescreen offers, for example). Even if you’ve already delivered a direct mail piece with the same offer, this doesn’t negate your responsibility to reach only approved consumers who have not opted out. --- Bottom line, the world of 1:1 marketing is growing more sophisticated, and that’s a good thing. Marketers just need to understand that while regulated data can be powerful, they must also take great responsibility when handling it. The data exists to deliver firm offers of credit to your very specific target in all-new mediums. People-based marketing has its place, and it can now be done in a compliant, digitally-savvy way – in the financial services space, nonetheless. Register for our webinar on Credit Marketing Strategies to Drive Today\'s Digital Consumer.
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 …
In a May 4 speech before the ACA International Conference in Washington, FCC Commissioner Michael O’Rielly criticized the FCC’s past decisions on Telephone Consumer Protection Act (TCPA) and outlined his vision on the direction that the new Commission should head to provide more certainty to businesses. Commissioner O’Rielly noted that prior decisions by the FCC and courts have “expanded the boundaries of TCPA far beyond what I believe Congress intended.” He said that the new leadership at the Commission and a new Bureau head overseeing TCPA, provides the FCC with the opportunity to “undo the misguided and harmful TCPA decisions of the past that exposed legitimate companies to massive legal liability without actually protecting consumers.” O’Rielly laid out three principles that he thought would help to frame discussions and guide the development of replacement rules. First, he said that legitimate businesses need to be able to contact consumers to communicate information that they want, need or expect to receive. This includes relief for informational calls, as well as valid telemarketing calls or texts. Second, Commissioner O’Rielly said that FCC should change the definition of an autodialer so that valid callers can operate in an efficient manner. He went on to say that if FCC develops new rules to clarify revocation of consent, it should do so in a clear and convenient way for consumers, but also does not upend standard best practices of legitimate companies. Third, O’Rielly said that the FCC should focus on actual harms and bad actors, not legitimate companies. While Commissioner O’Rielly’ s comments signal his approach to TCPA reform, it is important to note that FCC action on the issue us unlikely to happen overnight. A rule must be considered by the Commission, which will have to allow for public notice and comment. Experian will continue to monitor regulatory and legislative developments on TCPA.
