Credit Lending
Traditional credit attributes provide immense value for lenders when making decisions, but when used alone, they are limited to capturing credit behavior during a single moment of time. To add a deeper layer of insight, Experian® today unveiled new trended attributes, aimed at giving lenders a wider view into consumer credit behavior and patterns over time. Ultimately, this helps them expand into new risk segments and better tailor credit offers to meet consumer needs. An Experian analysis shows that custom models developed using Trended 3DTM attributes provide up to a 7 percent lift in predictive performance when compared with models developed using traditional attributes only. “While trended data has been shown to provide additional insight into a consumer’s credit behavior, lack of standardization across different providers has made it a challenge to gain those insights,” said Steve Platt, Experian’s Group President of Decision Analytics and Data Quality. “Trended 3D makes it easy for our clients to get value from trended data in a consistent manner, so they can make more informed decisions across the credit life cycle and, more importantly, give consumers better access to lending options.” Experian’s Trended 3D attributes help lenders unlock valuable insights hidden within credit reports. For example, two people may have similar balances, utilization and risk scores, but their paths to that point may be substantially different. The solution synthesizes a 24-month history of five key credit report fields — balance, credit limit or original loan amount, scheduled payment amount, actual payment amount and last payment date. Lenders can gain insight into: Changes in balances over time Migration patterns from one tradeline or multiple tradelines to another Variations in utilization and credit limits Changes in payment activity and collections Balance transfer and debt consolidation behavior Behavior patterns of revolving trades versus transactional trades Additionally, Trended 3D leverages machine learning techniques to evaluate behavioral data and recognize patterns that previously may have gone undetected. To learn more information about Experian’s Trended 3D attributes, click here.
Today’s consumer lending environment is more dynamic and competitive than ever, with renewed focus on personal loans, marketplace lending and the ever-challenging credit card market. One of the significant learnings from the economic crisis is how digging deeper into consumer credit data can help provide insights into trending behavior and not just point-in-time credit evaluation. For example, I’ve found consumer trending behavior to be very powerful when evaluating risks of credit card revolvers versus transactors. However, trended data can come with its own challenges when the data isn’t interpreted uniformly across multiple data sources. To address these challenges, Experian® has developed trended attributes, which can provide significant lift in the development of segmentation strategies and custom models. These Trended 3DTM attributes are used effectively across the life cycle to drive balance transfers, mitigate high-risk exposure and fine-tune strategies for customers near score cutoffs. One of the things I look for when exploring new trended data is the ability to further understand payment velocity. These characteristics go far beyond revolver and transactor flags, and into the details of consumer usage and trajectory. As illustrated in the chart, a consumer isn’t easily classified into one borrowing persona (revolver, transactor, etc.) or another — it’s a spectrum of use trends. Experian’s Trended 3D provides details needed to understand payment rates, slope of balance growth and even trends in delinquency. These trends provide strong lift across all decisioning strategies to improve your business performance. In recent engagements with lenders, new segmentation tools and data for the development of custom models is at the forefront of the conversation. Risk managers are looking for help leveraging new modeling techniques such as machine learning, but often have challenges moving from prior practices. In addition, attribute governance has been a key area of focus that is addressed with Trended 3D, as it was developed using machine learning techniques and is delivered with the necessary documentation for regulatory conformance. This provides an impressive foundation, allowing you to integrate the most advanced analytics into your credit decisioning. Alternative data isn’t the only source for new consumer insights. Looking at the traditional credit report can still provide so much insight; we simply need to take advantage of new techniques in analytics development. Trended attributes provide a high-definition lens that opens a world of opportunity.
Are you ready to launch a new product to capture the revenue growth opportunities in today’s market? The competition is heating up for new growth, as banks increased personal loan balances by 10 percent year-over-year in 2015 and another 6 percent in 2016.* Many lenders are now looking for robust data to understand the market opportunity based on their risk appetite. This challenge usually takes a significant investment in consumer credit data to gain the necessary insights. In helping lenders launch new products, I’ve found there are common areas of focus and specific steps you must take to move from the initial business case to more tactical planning. The following details come to mind: refining risk thresholds, pricing, loss forecasting and use of models within the initial go-to-market strategy. These project tasks can’t be successfully completed without having the right breadth and depth of data available. Knowing the past can help you create a better future for your business. When I start working with a client on a new product launch, I want to ensure they have sufficient data that can provide a comprehensive historical consumer view. In my experience, the best data to use will show an exhaustive view of consumer behaviors through the economic cycle. Having this large volume of data enables me to evaluate the business strategy and risks through the financial crisis while also giving my clients the foundation for compliance with loss forecasting regulations. Obtaining this breadth of data often can be a significant, but necessary, investment. Data is a great starting point, but it isn’t enough. Understanding the data sufficiently to design an effective go-to-market strategy is critical for success. I’ve found that identifying specific attributes helps give my clients a deep dive into the structure of a consumer’s credit history at the trade level. This level of information provides insight into the structure of the consumer’s wallet and preferences. Additionally, this depth of data allows my clients to develop powerful custom models for use in their business strategy. Being prepared is half the victory. Having comprehensive data that will help you understand consumer spending behavior and the risk they carry through the economic cycle will assist in creating a successful go-to-market strategy. Our Market Entry ServicesTM data sets are analytics-ready, including attributes and performance flags, to give you a holistic view of your target market. Having this breadth and depth of data, along with strong tactical planning and execution, will ensure your success in launching new products and entering new markets. *Experian–Oliver Wyman Market Intelligence Report
Consumers are hungry for more personalized marketing, and I’m an actual example. As a new stepmom to two young kids, who has a full-time job, I rarely have any down time. No revelation there. I no longer have time to surf the web to buy clothes. And shepherding everyone to an actual store to shop? #forgetaboutit I’m not alone. Of the 57 percent of women in the U.S. workforce, 70 percent have a child under the age of 18. We don’t always have the time to shop for clothes, financial products, and nearly anything else, but it doesn’t mean we don’t need or want to. I would give the right bank or retailer my data in exchange for personalized marketing offers in my inbox, social feeds and mailbox. And many others would, too. Sixty-three percent of Millennial consumers and 58 percent of Gen Xers are willing to share data with companies in exchange for personalized offers, discounts and rewards. This indicates consumers are craving more customized marketing. Providing their personal data to get that is acceptable to them. In the financial services space, Mintel research shows that just 61 percent of male consumers, 49 percent of consumers aged 18-44, and 44 percent of Hispanic Millennials have a general-purpose credit card, either with or without rewards (Mintel’s Marketing Financial Services Report for June 2017). This indicates a significant market opportunity for cards that offer segmented or boosted rewards based on specific sectors and categories. Here are some other interesting trends specific to financial services: Relying on Experts Although chatbots and robo-advisors allow easy access to many financial services, 81 percent of consumers prefer in-person meetings when it comes to personalized financial advice. According to Mintel, men aged 18-44 are most interested in a free consultation with a financial advisor, and 19 percent of consumers are open to a free consultation. This interest surpasses attending free classes about finance and receiving email and mobile alerts from a financial institution. Quick, Efficient Delivery While consumers are calling for increased personalization, they also want it delivered quickly and efficiently. These expectations create unique challenges for financial institutions of all sizes. Some banks have embraced “card finder” apps, which allow consumers the convenience of inputting personal information to generate customized offers. There is a huge opportunity for financial institutions to leverage available consumer data to understand their target audience, and then deliver relevant products via multiple channels where they are consuming media now. Those who do will be positioned to provide personalized financial recommendations that were impossible just a few years ago.
You’ve been tasked with developing a new model or enhancing an existing one, but the available data doesn’t include performance across the entire population of prospective customers. Sound familiar? A standard practice is to infer customer performance by using reject inference, but how can you improve your reject inference design? Reject inference is a technique used to classify the performance outcome of prospective customers within the declined or nonbooked population so this population’s performance reflects its performance had it been booked. A common method is to develop a parceling model using credit bureau attributes pulled at the time of application. This type of data, known as pre-diction data, can be used to predict the outcome of the customer prospect based on a data sample containing observations with known performance. Since the objective of a reject inference model is to classify, not necessarily predict, the outcome of the nonbooked population, data pulled at the end of the performance window can be used to develop the model, provided the accounts being classified are excluded from the attributes used to build the model. This type of data is known as post-diction data. Reject inference parceling models built using post-diction data generally have much higher model performance metrics, such as the KS statistic, also known as the Kolmogorov-Smirnov test, or the Gini coefficient, compared with reject inference parceling models built using pre-diction data. Use of post-diction data within a reject inference model design can boost the reliability of the nonbooked population performance classification. The additional lift in performance of the reject inference model can translate into improvements within the final model design. Post-diction credit bureau data can be easily obtained from Experian along with pre-diction data typically used for predictive model development. The Experian Decision Analytics team can help get you started.
Early reports suggest the 2017 holiday season was a good one for retailers. Consumers were in the mood to spend, and as such, Americans’ total credit card debt continued to climb. Americans planned to spend $862 on gifts for the season, a huge jump from the $752 they planned on spending in 2016. And the numbers were significantly higher than their estimate in any November since 2007 -- just before the 2007-2009 recession. 29% of Americans said they planned to spend more than $1,000. What does this mean for card portfolios? Well, business is booming, but they should also prepare for the time of year when consumers are most apt to seek out debt consolidation and transfer options. A recent NerdWallet analysis revealed the average household that’s carrying credit card debt has a balance of roughly $15,654. Dig deeper into retail card specifically and reports indicate Americans are carrying $1,841 in retail debt. “There is seasonality to consumer credit card behavior,” said Denise McKendall, a credit card and trended data specialist for Experian. “As we roll into the late winter months and early spring, consumers often seek ways to transfer card debt to lower interest rate options, consolidate debt from multiple cards and perhaps even pull out personal loans. This makes it an ideal time for card portfolio managers to leverage data to anticipate consumer behaviors and be able to offer the best rates and options to retain cardholders and grow.” Card portfolio managers should consider these questions: What is my portfolio risk? Did some of my consumers overextend themselves? Do I have collections triggers on my accounts to mitigate risk and manage delinquencies? Which consumers in my portfolio will be looking to consolidate debt? Should I reassess credit line limits? Which of my consumers show a high propensity to make a balance transfer? Do I have opportunities to grow my portfolio by offering attractive rates to new customers? Which customers will leave after low introductory rates expire? Can I use this time of year to become the first credit card consumers’ consistently use, rather than the second or third card they pull from wallet? At first glance, it might appear challenging to answer many of these questions, but with the right data and analytics, a card manager can easily establish a game plan to conquest new business, mitigate risk and retain existing, high-value consumers. The robust holiday season was a boom for the economy. Now card companies need to ready themselves for the aftermath.
Student loan debt is weighing down Americans of all generations, but a college education is still prized as the ticket to opportunity. So will the debt continue to climb? Where will students turn for funding? We interviewed Vince Passione, founder and CEO of LendKey, a lending-as-a-service platform specializing in student lending, to gain his perspective on the state of student lending and how the space is evolving for both consumers and lenders. We’ve all seen the headlines about U.S. student loan debt now accounting for $1.4 trillion. The majority of these loans are government-funded, but do you see this shifting? There are many factors at play here. Tuition is rising rapidly and will soon outpace the current level of governmental support available to students searching for loans. Meanwhile, today’s geopolitical climate signals that the current levels of federal funding will also decrease. With these two confounding trends, the need for competitively priced private financing and refinancing options will increase. The student loan industry will shift toward private lenders such as credit unions and banks in order for students to continue to obtain the funds they need for tuition and other college expenses. The key to helping this transition happen is for banks and credit unions to adopt the user-friendly technology platforms that appeal to these prospective student borrowers. Your end-to-end cloud-based technology platform enables lenders to get into the student loan space. How does this work and what must lenders consider as they underwrite and manage a student loan portfolio? Our turnkey platform is unique, in that it lets lenders control underwriting and pricing, unlike the “disruptive” model utilized by many other technology companies in the industry. Most community banks and credit unions lack the in-house resources to develop, implement and maintain an online lending platform. At the same time, millennials and young borrowers continue to prefer the online interface rather than engaging with a brick-and-mortar establishment. We’re committed to partnering with banks and credit unions to allow them to offer private consumer loans, such as student loans, and support them with our technology (loan application and decisioning) and people (customer service agents and loan processors). A strong grasp on the technology and support aspects of online lending platforms alone is merely the foundation for a successful program. As the student lending asset classes are highly regulated, and the regulations are constantly changing, lenders must look to partner with a firm that has a concrete understanding of the regulation, risk and customer service to translate the information to prospective borrowers. I’ve heard you use the phrase “HENRY.” Can you explain what this is and why these individuals are so lucrative for lenders? HENRY stands for “High Earners, Not Rich Yet” and is a term that can be applied to many millennials and young people in today’s economy. This demographic is typically college graduates with well-paying jobs, but have not yet established themselves financially or accrued enough wealth to subsidize larger purchases like cars, homes, renovations and advanced degrees. This is also why they are so lucrative for lenders. HENRYs have just entered their prime borrowing years and are consumers who will easily be able to pay back loans for cars, homes and renovations. But for most of this demographic, their first experience with a financial service product will be a student loan. It is important to get in on the ground floor with these borrowers through student lending to establish a trusted relationship that will result in repeat loans and referrals. You’ve done a great deal of research on millennials and how they are managing student loans. Can you share some of your key learnings? Do you believe Generation Z will behave and manage student debt similarly? It’s no secret that millennials are more apprehensive of student loans than previous generations. As Gen Z begins to enter college, many are plagued with stigmas set forward by the poor experiences millennials experienced with student loans, making them wary of debt. According to a study, 63 percent of the students said they would “possibly” take on student debt, down from 71 percent in 2016. Gen Z is better prepared by seeing the preceding generation grapple with loan issues. Many are making smarter decisions on schools and programs, and are attentive when it comes to monitoring for updates in regulation. As this generation continues to go through the typical collegiate years, the geopolitical climate, as well as rising tuition costs, will increase the need for competitively priced private financing options for Gen Zers. Finally, what trends or predictions do you see occurring in the student lending space over the next five years? The need for student loans continues to exist and shows no sign of slowing down anytime soon, but lenders are only recently opening their eyes to the opportunity that this massive market presents. With the impending drop in federal funding, more FinTech companies will continue to pop up to address this need. This spike in disruption also poses a threat to banks and credit unions, however. With more FinTechs available to help shoulder the burden of student lending, banking and credit union executives must be more judicious when vetting technology partners to ensure they’re working with a partner that meets their regulatory standards, supports their current and prospective clients, and lets them retain the control they wish to keep in-house.
Alternative Data Shedding New Light on Consumers Why Investors Want Alternative Data Banks and Tech Firms Battle Over Something Akin to Gold: Your Data Alternative data for credit has created national headlines in the past year and a lasting buzz in the financial services world. But what exactly qualifies as alternative data in credit? How can it benefit lenders? Consumers? Ask two people these questions and you may get very different answers. Experian defines alternative data as FCRA-compliant data points that are not typically considered when evaluating a potential customer’s creditworthiness. These data points may include rent payments; utility payments, including gas, electric; telecommunications payments, such as mobile telephones; insurance payments; and any other recurring financial obligations. Taking these alternative data points into account can benefit consumers and lenders in multiple ways. Consider that roughly 45 million Americans have either no credit history, or a credit history that is too scarce or outdated to manufacture a credit score. This group of consumers includes not only minority consumers or those from low income neighborhoods, but also the shared economy workforce and millennials without traditional credit histories. Some estimate 121 million U.S. adults are credit-challenged with thin-to-no credit file and subprime credit scores below 600. “People with little or no credit history, or who lack a credit score, have fewer opportunities to borrow money to build a future, and any credit that is available usually costs more,” said Richard Cordray, while he was director of the Consumer Financial Protection Bureau. Indeed, these consumers are in a catch-22; many lenders will not lend to consumers with credit scores of under 620. In turn, these consumers have trouble building credit, and they are blocked from achieving goals like buying a car, owning a home or starting a business. By combining credit reports with alternative data, a more complete picture of subprime, near-prime and thin-file consumers can develop. And analysis of this data can help lenders evaluate a consumer’s ability to pay. When alternative data like rent payments and an individual’s short term lending history are trended appropriately, it can be an accurate predictor of an individual’s financial behavior, and can be an important step toward promoting greater financial inclusion for more consumers. In addition to using alternative data in underwriting, lenders can leverage the data to help with: Expanding the prospecting universe. Data can be used to enrich batch prospecting decisioning criteria to identify better qualified prospects, suppress high-risk consumers, and offer a more complete borrowing history Account review. Alternative data can help signal a consumer’s financial distress earlier, better manage credit lines and grow relationships with existing consumers. Collections. Identify consumers who are rebuilding credit with specialty finance trades, or who are exhibiting high-risk behaviors in the alternative financial services space. More info on Alternative Credit Data More Info on Alternative Financial Services
As we enter the holiday season, headlines abound around the shifts and trends in retail. How are consumers shopping? What are they buying online versus in-store? How can retailers maintain share and thrive? To gain some fresh perspective on the retail space, we interviewed John Squire, CEO and co-founder of DynamicAction, a business featuring advanced analytics solutions designed specifically for eCommerce, store and omnichannel retail teams. Squire has had a tenured career in the retail and technology sectors serving in key executive roles for IBM Smarter Commerce and Coremetrics. He has spent the past decade guiding nearly every retail brand to a better understanding of their customers and utilization of their data to make profitable decisions. Business headlines claim we are in the midst of a retail apocalypse. Is this statement a reality? The reality is that retail is in a renaissance – a revolution driven by the most empowered, connected consumer in history, a burgeoning technology infrastructure and retail tech innovators who have disrupted the status quo. The most agile of retailers and brands are leaning forward to serve their customer with remarkable experiences in the store, online and anywhere the customer decides to interact with the brand. And for those retailers, the days ahead are filled with newfound opportunity. However, the retailers and brands who don’t have a strong core purpose beyond being filler between anchor stores may no longer have a place in this new world of retail. The strongest retailers and brands will tap into their wealth of customer data to better understand, and therefore better serve their customers, creating long-term relationships. They should only continue gain in strength as consumers concentrate more and more of their time (and wallets) with businesses that passionately focus on their unique needs and buying patterns. It seems like shoppers are increasingly turning online to make their purchases. Is this the case, and do we see seasonal spikes with this trend? The key for successful retailers is to understand that customers aren’t just searching, browsing, buying and returning online OR in-store. They are shopping online AND in-store … and even online while in-store. Shoppers simply do not see channels, and the sooner that retailers reorganize their mindset, their organizations and their data understanding around this reality, the more successful they will become. Shoppers are indeed moving online with increasing frequency and larger amounts of their overall spending. Connecting data across the enterprise, across their partners and across social channels is critical in enabling their retailing teams to make decisions on how best to simultaneously serve their customers and their company’s shareholders. If retailers have a store credit card to offer to consumers, how can they encourage use and get them to maximize spend? Are there particular strategies they should employ? As with any loyalty program or service item, consumers are looking for tools and offers they value. Therein lies the opportunity and the challenge. Value can come in many forms, depending on the individual. Does the credit card offer travel or retail points, or dollars that they can accumulate? Does the credit card save them time? Provide them with additional purchasing power? Reduce their friction of making large purchases? Increase the security of the initial purchase and long-time use of the product or service? The competition for just a consumer’s current and future wallet is being upended by retailing offers that are serving up entertainment, services, convenience and broader product selections. Understanding the high-value activities correlated to their VIP consumers generating the highest amount of profit for the business is the essential to building strategies for encouraging card use. Beyond online shopping, are there other retail trends you see emerging in the coming year? What excites you about the space? Online shopping is not a trend; it is retail’s greatest disruption of the last 100 years. Digitization of shopping in both the online and store setting is what thrills me. One to watch is Wal-mart. The company is taking a highly energized track to build a business of next-gen brands and using their supply chain acumen to battle Amazon, while simultaneously gaining huge amounts of market share from other less sophisticated and strong retailers. In addition, seeing how next-gen brands like Warby Parker, Everlane, Untuckit, Bonobos, Indochino and Rent the Runway are rapidly building out a store experience, albeit radically different than the stores of the past, is exciting to watch. Seeing the growth in Drone deliveries outside the US for retail and commercial applications is surely the next big jump for ‘Next Hour’ in-home delivery. Made-to-order with a very short lead-time is also a big trend to keep an eye on. However, what excites me most in the industry is the universal mind shift that is becoming undeniable in retail: that data understanding and action will be the very basis for customer centricity and companies’ growth. Retailers have had access to these data pools for ages, but the ability to sync the data sets across channels, make sense of the findings and take action at the speed the consumer expects is truly the next leap forward for great retailers. To learn more about the state of retail credit cards, access our latest report.
With 81% of Americans having a social media profile, you may wonder if social media insights can be used to assess credit risk. When considering social media data as it pertains to financial decisions, there are 3 key concerns to consider. The ECOA requires that credit must be extended to all creditworthy applicants regardless of race, religion, gender, marital status, age and other personal characteristics. Social media can reveal these characteristics and inadvertently affect decisions. Social media data can be manipulated. Individuals can represent themselves as financially responsible when they’re not. On the flip side, consumers can’t manipulate their payment history. When it comes to credit decisions, always remember that the FCRA trumps everything. Data is essential for all aspects of the financial services industry, but it’s still too early to click the “like” button for social media. Make more insightful decisions with credit attributes>
If someone asked you for stats on your retail card portfolio, would you respond with the number of accounts? Average spend per month? Or maybe you know the average revolving balance and profitability. Notice something about that list? Too many lenders think of their portfolio and customers as numbers when in reality these are individuals expressing themselves through their transactions. In an age where consumers increasingly expect customized experiences, marketing to account #5496115149251 is likely to fall on deaf ears. Credit card transaction data including bankcard, retail, and debit cards holds a wealth of information about your consumers\' tastes and preferences. Think about all the purchases you made using a credit card this past month. Did you shop at high-end retail stores or discount stores? Expensive restaurants or fast food? Did you buy new clothes for your kids? Maybe you went to the movies, or met friends at a bar. How you use your card paints a picture of who you are. The trick is turning all those numbers into insights. You may have been swept up in all the excitement around Apple’s announcement of the iPhone X in August. However, you may have overlooked the incorporation of Neural Embedding, or machine learning, as one of the most powerful features of the new phone. Experian DataLabs has developed an innovative approach to analyzing transaction data using similar techniques. Unstructured machine learning is applied and patterns begin to emerge around customer spending. The patterns are highly intuitive and give personality to what was previously an indecipherable stream of data. For example, one group may be more likely to spend on children’s clothing, child care services, and theme parks while another spends on expensive restaurants, airlines, and golf courses. If these two consumers happened to spend approximately the same each month on your card, you’d probably treat them as category. But understanding one is a young family and their other is jet setter allows you to tailor messaging, offers, and terms to their needs and use of your products. Further, you can ensure they have the best product based on their lifestyle to minimize silent attrition as their needs evolve. But it’s not just about marketing. When your latest attrition dashboard is updated, what period are you measuring? Do you analyze account closures from the previous month? Maybe a few months back? Understanding churn is important, but it’s inherently reactive and backward looking. You wouldn’t drive a car looking in the rearview mirror, would you? Experian enables clients to actively monitor the portfolio for attrition risk by analyzing usage patterns and predicting future spend. Transactions are then monitored up to daily and, when spend doesn’t occur as expected, an alert is sent so you can proactively attempt to save the account before it closes. These algorithms are finely tuned to reduce false positives that can come from seasonality or predictable gaps in spend such as only using a card at certain times during the week. Most importantly, it gives you an opportunity to manage each account and address changing customer needs instead of waiting for customers to call to cancel. So how well do you know your customers? If you’re still looking at them as numbers, it may be time to explore new capabilities that allow you to act small, no matter how large your portfolio. Transaction Data Insights brings cutting-edge machine learning capabilities to lenders of all sizes. By digging into behavioral segments and having tools to monitor and send alerts when a consumer is showing signs of attrition risk, card portfolios can suddenly treat customers like people, providing the customized experience they increasingly expect.
Our national survey found that consumers struggle to find a credit card that meets their needs. They say there are too many options and it’s too time-consuming to research. What do consumers want? With 53% of survey respondents not satisfied with their current cards and 1 in 3 saying they’re likely to get a new card within 6 months, now’s the time to start personalizing offers and growing your portfolio. Start personalizing offers today>
We regularly hear from clients that charge-offs are increasing and they’re struggling to keep up with the credit loss. Many clients use the same debt collection strategy they’ve used for years – when businesses or consumers can’t repay a loan, the creditor or collection agency aggressively contacts them via phone or mail to obtain repayment – never considering the customer experience for the debtor. Our data shows that consumers accounted for $37.24 billion in bankcard charge-offs in Q2 2017, a 17.1 percent increase from Q2 2016. Absorbing credit losses at such a high rate can impact the sustainability of the institution. Clearly the process could use some adjusting. Traditionally, debt collection has been solely about the money. The priority was ensuring that as much of the outstanding debt as possible was repaid. But collecting needs to be about more than that. It also should focus on the customer and his or her individual situation. When it comes to debt collection, customers should not all be treated the same way. I recently shared some tips in Credit Union Business Magazine about how to actively engage and collect from members. The same holds true for other financial institutions – they need to know the difference between a customer who has simply forgotten to make a payment and one who is dealing with financial hardship. As an example, if a person is current on his or her mortgage payment but has slipped behind on his or her credit card payment, that doesn’t necessarily signify financial hardship. It’s an opportunity to work with the customer to manage the debt and get back to current. Modern financial institutions build acquisition and customer management strategies targeted at individuals, so why should the collection process be any different? The challenge is keeping the customer at the center while also managing against potential increases in delinquencies. This holistic approach may be slightly more complex, but technology and analytics will simplify the process and bring about a more engaging experience for customers. The Power of Data and Technology Instead of relying on the same outdated collections approach – which results in uncomfortable exchanges on the phone that don’t ensure repayment –leverage data to your advantage. The data and technology exists to help you make more informed decisions, such as: What’s the most effective communication channel to reach the defaulting customer? When should you contact him or her? How often? The best course of action could be high-touch outreach, but sometimes doing nothing is the right approach. It all depends on the situation. Data and analytics can help uncover which customers are most likely to pay on their own and those who may need a little more help, allowing you to adjust your treatment strategy accordingly. By catering to the preferences of the customer, there’s a greater chance for a positive experience on both sides. The results: less charge-off debt, higher customer satisfaction and a stronger relationship. Explore the Digital Age In 2016, 36 million Americans made some form of mobile payment—paying a bill, purchasing something online, or paying for fast food, or making a Mobile Wallet purchase at a retailer. By 2020, nearly 184 million consumers will have done so, according to Aite. Consumers expect and deserve convenience. In the digital world, financial institutions have an opportunity to provide that expectation and then some. Imagine a customer being able to negotiate and manage his or her past-due account virtually, in the privacy of his or her own home, when it’s most convenient, to set their payment dates and terms. Luckily, the technology exists to make this vision a reality. Customers, not money, need to be at the heart of every debt collections strategy. Gone are the days of mass phone calls to debtors. That strategy made consumers unhappy, embarrassed and resentful. Successful debt collection comes down to a basic philosophy: Treat customers and his or her unique situation individually rather than as a portfolio profile. The creditors who live by that philosophy have an opportunity to reap the rewards on the back-end.
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.
There’s no shortage of headlines alluding to a student loan crisis. But is there a crisis brewing or is this just a headline grab? Let’s look at the data over the past 4 years to find out. Outstanding student loan (should be loan) debt grew 21%, reaching a high of $1.49 trillion in Q4 2016. Over the past 4 years, student loan trades grew 4%, with a slight decline from 2015 to 2016. Average balance per trade grew 17% to reach $8,210. Number of overall student loan trades per consumer is down 5% to just 3.85. The average person with a student loan balance had just over $32,000 outstanding at the end of 2016 — a rise of 15%. While we’re seeing some increases, the data tells us this is a media headline grab. If students are educated about the debt they’re acquiring and are confident they can repay it, student loan debt shouldn’t be a crippling burden. More student loan insights
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