As our society becomes ever more dependent on everything mobile, criminals are continually searching for and exploiting weaknesses in the digital ecosystem, causing significant harm to consumers, businesses and the economy.  In fact, according to our 2018 Global Fraud & Identity Report, 72 percent of business executives are more concerned than ever about the impact of fraud. Yet, despite the awareness and concern, 54 percent of businesses are only “somewhat confident” in their ability to detect fraud. That needs to change, and it needs to change right away.  Our industry has thrived by providing products and services that root out bad transactions and detect fraud with minimal consumer friction. We continue to innovate new ways to authenticate consumers, apply new cloud technologies, machine learning, self-service portals and biometrics. Yet, the fraud issue still exists. It hasn’t gone away. How do we provide effective means to prevent fraud without inconveniencing everyone in the process? That’s the conundrum. Unfortunately, a silver bullet doesn’t exist. As much as we would like to build a system that can detect all fraud, eliminate all consumer friction, we can’t. We’re not there yet. As long as money has changed hands, as long as there are opportunities to steal, criminals will find the weak points – the soft spots.  That said, we are making significant progress. Advances in technology and innovation help us bring new solutions to market more quickly, with more predictive power than ever, and the ability to help clients to turn  these services on in days and weeks. So, what is Experian doing? We’ve been in the business of fraud detection and identity verification for more than 30 years. We’ve seen fraud patterns evolve over time, and our product portfolio evolves in lock-step to counter the newest fraud vectors. Synthetic identity fraud, loan stacking, counterfeit, identity theft; the specific fraud attacks may change but our solution stack counters each of those threats. We are on a continuous innovation path, and we need to be. Our consumer and small business databases are unmatched in the industry for quality and coverage, and that is an invaluable asset in the fight against fraud. It used to be that knowing something about a person was the same as authenticating that same person. That’s just not the case today. But, just because I may not be the only person who knows where I live, doesn’t mean that identity information is obsolete. It is incredibly valuable, just in different ways today. And that’s where our scientists come into their own, providing complex predictive solutions that utilize a plethora of data and insight to create the ultimate in predictive performance. We go beyond traditional fraud detection methods, such as knowledge-based authentication, to offer a custom mix of passive and active authentication solutions that improve security and the customer experience. You want the latest deep learning techniques? We have them. You want custom models scored in milliseconds alongside your existing data requests. We can do that. You want a mix of cloud deployment, dedicated hosted services and on-premise? We can do that too. We have more than 20 partners across the globe, creating the most comprehensive identity management network anywhere. We also have teams of experts across the world with the know how to combine Experian and partner expertise to craft a bespoke solution that is unrivaled in detection performance. The results speak for themselves: Experian analyzes more than a billion credit applications per year for fraud and identity, and we’ve helped our clients save more than $2 billion in annual fraud losses globally. CrossCore™, our fraud prevention and identity management platform, leverages the full breadth of Experian data as well as the data assets of our partners. We execute machine learning models on every decision to help improve the accuracy and speed with which decisions are made. We’ve seen CrossCore machine learning result in a more than 40 percent improvement in fraud detection compared to rules-based systems. Our certified partner community for CrossCore includes only the most reputable leaders in the fraud industry. We also understand the need to expand our data to cover those who may not be credit active. We have the largest and most unique sets of alternative credit data among the credit bureaus, that includes our Clarity Services and RentBureau divisions. This rich data helps our clients verify an individual’s identity, even if they have a thin credit file. The data also helps us determine a credit applicant’s ability to pay, so that consumers are empowered to pursue the opportunities that are right for them. And in the background, our models are constantly checking for signs of fraud, so that consumers and clients feel protected. Fraud prevention and identity management are built upon a foundation of trust, innovation and keeping the consumer at the heart of every decision. This is where I’m proud to say that Experian stands apart. We realize that criminals will continue to look for new ways to commit fraud, and we are continually striving to stay one step ahead of them. Through our unparalleled scale of data, partnerships and commitment to innovation, we will help businesses become more confident in their ability to recognize good people and transactions, provide great experiences, and protect against fraud.

Every morning, I wake up and walk bleary eyed to the bathroom, pop in my contacts and start my usual routine. Did I always have contacts? No. But putting on my contacts and seeing clearly has become part of my routine. After getting used to contacts, wearing glasses pales in comparison. This is how I view alternative credit data in lending. Are you having qualms about using this new data set? I get it, it’s like sticking a contact into your eye for the first time: painful and frustrating because you’re not sure what to do. To relieve you of the guesswork, we’ve compiled the top four myths related to this new data set to provide an in-depth view as to why this data is an essential supplement to your traditional credit file. Myth 1: Alternative credit data is not relevant. As consumers are shifting to new ways of gaining credit, it’s important for the industry to keep up. These data types are being captured by specialty credit bureaus. Gone are the days when alternative financing only included the payday store on the street corner. Alternative financing now expands to loans such as online installment, rent-to-own, point-of-sale financing, and auto-title loans. Consumers automatically default to the financing source familiar to them – which doesn’t necessarily mean traditional financial institutions. For example, some consumers may not walk into a bank branch anymore to get a loan, instead they may search online for the best rates, find a completely digital experience and get approved without ever leaving their couches. Alternative credit data gives you a lens into this activity. Myth 2: Borrowers with little to no traditional credit history are high risk. A common misconception of a thin-file borrower is that they may be high risk. According to the CFPB, roughly 45 million Americans have little to no credit history and this group may contain minority consumers or those from low income neighborhoods. However, they also may contain recent immigrants or young consumers who haven’t had exposure to traditional credit products. According to recent findings, one in five U.S. consumers has an alternative financial services data hit– some of these are even in the exceptional or very good credit segments. Myth 3: Alternative credit data is inaccurate and has poor data quality. On the contrary, this data set is collected, aggregated and verified in the same way as traditional credit data. Some sources of data, such as rental payments, are monthly and create a consistent look at a consumer’s financial behaviors. Experian’s Clarity Services, the leading source of alternative finance data, reports their consumer information, which includes application information and bank account data, as 99.9% accurate. Myth 4: Using alternative credit data might be harmful to the consumer. This data enables a more complete view of a consumer’s credit behavior for lenders, and provides consumers the opportunity to establish and maintain a credit profile. As with all information, consumers will be assessed appropriately based on what the data shows about their credit worthiness. Alternative credit data provides a better risk lens to the lender and consumers may get more access and approval for products that they want and deserve. In fact, a recent Experian survey found 71% of lenders believe alternative credit data will help consumers who would have previously been declined. Like putting in a new pair of contact lenses the first time, it may be uncomfortable to figure out the best use for alternative credit data in your daily rhythm. But once it’s added, it’s undeniable the difference it makes in your day-to-day decisions and suddenly you wonder how you’ve survived without it so long. See your consumers clearly today with alternative credit data. Learn More About Alternative Credit Data

Synthetic identities come from accounts held not by actual individuals, but by fabricated identities created to perpetrate fraud. It often starts with stealing a child’s Social Security number (SSN) and then blending fictitious and factual data, such as a name, a mailing address and a telephone number. What’s interesting is the increase in consumer awareness about synthetic identities. Previously, synthetic identity was a lender concern, often showing itself in delinquent accounts since the individual was fabricated. Consumers are becoming aware of synthetic ID fraud because of who the victims are — children. Based on findings from a recent Experian survey, the average age of child victims is only 12 years old. Children are attractive victims since fraud that uses their personal identifying information can go for years before being detected. I recently was interviewed by Forbes about the increase of synthetic identities being used to open auto loans and how your child’s SSN could be used to get a phony auto loan. The article provides a good overview of this growing concern for parents and lenders. A recent Javelin study found that more than 1 million children were victims of fraud. Most upsetting is that children are often betrayed by people close to them -- while only 7 percent of adults are victimized by someone they know, 60 percent of victims under 18 know the fraudster. Unfortunately, when families are in a tight spot financially they often resort to using their child’s SSN to create a clean credit record. Fraud is an issue we all must deal with — lenders, consumers and even minors — and the best course of action is to protect ourselves and our organizations.

There are four reasons why the auto industry should be enthusiastic about the electric vehicle segment’s future.

Picking up where we left off, online fintech lenders face the same challenges as other financial institutions; however, they continue to push the speed of evolution and are early adopters across the board. Here’s a continuation of my conversation with Gavin Harding, Senior Business Consultant at Experian. (Be sure to read part 1.) Part two of a two-part series: As with many new innovations, fintechs are early adopters of alternative data. How are these firms using alt data and what are the results that are being achieved? In a competitive market, alternative data can be the key to helping fintechs lend deeper and better reach underserved consumers. By augmenting traditional credit data, a lender has access to greater insights on how a thin-file consumer will perform over time, and can then make a credit decision based on the identified risk. This is an important point. While alternative data often helps lenders expand their universe, it can also provide quantitative risk measures that traditional data doesn’t necessarily provide. For example, alternative data can recognize that a consumer who changes residences more than once every two years presents a higher credit risk. Another way fintechs are using alternative data is to screen for fraud. Fraudsters are digitally savvy and are using technology to initiate fraud attacks on a broader array of lenders, in bigger volumes than ever before. If I am a consumer who wants to get a loan through an online fintech lender, the first thing the lender wants to know is that I am who I say I am. The lender will ask me a series of questions and use traditional data to validate. Alternative data takes authentication a step further and allows lenders to not only identify what device I am using to complete the application, but whether the device is connected to my personal account records – giving them greater confidence in validating my identity. A second example of using alternative data to screen for fraud has to do with the way an application is actually completed. Most individuals who complete an online application will do so in a logical, sequential order. Fraudsters fall outside of these norms – and identifying these patterns can help lenders increase fraud detection. Lastly, alternative data can help fintech lenders with servicing and collections by way of utilizing behavioral analytics. If a consumer has a history of making payments on time, a lender may be apt to approve more credit, at better terms. As the consumer begins to pay back the credit advance, the lender can see the internal re-payment history and recommend incremental line increases. From your perspective, what is the future of data and what should fintechs consider as they evolve their products? The most sophisticated, most successful “think tanks” have two things that are evolving rapidly together: Data: Fintechs want all possible data, from a quality source, as close to real-time as possible. The industry has moved from “data sets” to “data lakes” to “data oceans,” and now to “data universes.” Analytics: Fintechs are creating ever-more sophisticated analytics and are incorporating machine learning and artificial intelligence into their strategies. Fintechs will continue to look for data assets that will help them reach the consumer. And to the degree that there is a return on the data investment, they will continue to capitalize on innovative solutions – such as alternative data.   In the competitive financial marketplace, insight is everything. Aite Group recently conducted a new report about alternative data that dives into new qualitative research collected by the firm. Join us to hear Aite Group’s findings about fintechs, banks, and credit unions at their webinar on December 4. Register today! Register for the Webinar Click here for more information about Experian’s Alternative Data solutions. Don’t forget to check out part one of this series here.   About Gavin Harding With more than 20 years in banking and finance Gavin leverages his expertise to develop sophisticated data and analytical solutions to problem solve and define strategies across the customer lifecycle for banking and fintech clients. For more than half of his career Gavin held senior leadership positions with a large regional bank, gaining experience in commercial and small business strategy, SBA lending, credit and risk management and sales. Gavin has guided organizations through strategic change initiatives and regulatory and supervisory oversight issues. Previously Gavin worked in the business leasing, agricultural and construction equipment sectors in sales and credit management roles.

Where are electric vehicles most popular? During the first half of the year, 3.6 percent of all new registrations in California were EVs.

In 2011, data scientists and credit risk managers finally found an appropriate analogy to explain what we do for a living. “You know Moneyball? What Paul DePodesta and Billy Beane did for the Oakland A’s, I do for XYZ Bank.” You probably remember the story: Oakland had to squeeze the most value out of its limited budget for hiring free agents, so it used analytics — the new baseball “sabermetrics” created by Bill James — to make data-driven decisions that were counterintuitive to the experienced scouts. Michael Lewis told the story in a book that was an incredible bestseller and led to a hit movie. The year after the movie was made, Harvard Business Review declared that data science was “the sexiest job of the 21st century.” Coincidence?   The importance of data Moneyball emphasized the recognition, through sabermetrics, that certain players’ abilities had been undervalued. In Travis Sawchik’s bestseller Big Data Baseball: Math, Miracles, and the End of a 20-Year Losing Streak, he notes that the analysis would not have been possible without the data. Early visionaries, including John Dewan, began collecting baseball data at games all over the country in a volunteer program called Project Scoresheet. Eventually they were collecting a million data points per season. In a similar fashion, credit data pioneers, such as TRW’s Simon Ramo, began systematically compiling basic credit information into credit files in the 1960s. Recognizing that data quality is the key to insights and decision-making and responding to the demand for objective data, Dewan formed two companies — Sports Team Analysis and Tracking Systems (STATS) and Baseball Info Solutions (BIS). It seems quaint now, but those companies collected and cleaned data using a small army of video scouts with stopwatches. Now data is collected in real time using systems from Pitch F/X and the radar tracking system Statcast to provide insights that were never possible before. It’s hard to find a news article about Game 1 of this year’s World Series that doesn’t discuss the launch angle or exit velocity of Eduardo Núñez’s home run, but just a couple of years ago, neither statistic was even measured. Teams use proprietary biometric data to keep players healthy for games. Even neurological monitoring promises to provide new insights and may lead to changes in the game. Similarly, lenders are finding that so-called “nontraditional data” can open up credit to consumers who might have been unable to borrow money in the past. This includes nontraditional Fair Credit Reporting Act (FCRA)–compliant data on recurring payments such as rent and utilities, checking and savings transactions, and payments to alternative lenders like payday and short-term loans. Newer fintech lenders are innovating constantly — using permissioned, behavioral and social data to make it easier for their customers to open accounts and borrow money. Similarly, some modern banks use techniques that go far beyond passwords and even multifactor authentication to verify their customers’ identities online. For example, identifying consumers through their mobile device can improve the user experience greatly. Some lenders are even using behavioral biometrics to improve their online and mobile customer service practices.   Continuously improving analytics Bill James and his colleagues developed a statistic called wins above replacement (WAR) that summarized the value of a player as a single number. WAR was never intended to be a perfect summary of a player’s value, but it’s very convenient to have a single number to rank players. Using the same mindset, early credit risk managers developed credit scores that summarized applicants’ risk based on their credit history at a single point in time. Just as WAR is only one measure of a player’s abilities, good credit managers understand that a traditional credit score is an imperfect summary of a borrower’s credit history. Newer scores, such as VantageScore® credit scores, are based on a broader view of applicants’ credit history, such as credit attributes that reflect how their financial situation has changed over time. More sophisticated financial institutions, though, don’t rely on a single score. They use a variety of data attributes and scores in their lending strategies. Just a few years ago, simply using data to choose players was a novel idea. Now new measures such as defense-independent pitching statistics drive changes on the field. Sabermetrics, once defined as the application of statistical analysis to evaluate and compare the performance of individual players, has evolved to be much more comprehensive. It now encompasses the statistical study of nearly all in-game baseball activities.   A wide variety of data-driven decisions Sabermetrics began being used for recruiting players in the 1980’s. Today it’s used on the field as well as in the back office. Big Data Baseball gives the example of the “Ted Williams shift,” a defensive technique that was seldom used between 1950 and 2010. In the world after Moneyball, it has become ubiquitous. Likewise, pitchers alter their arm positions and velocity based on data — not only to throw more strikes, but also to prevent injuries. Similarly, when credit scores were first introduced, they were used only in originations. Lenders established a credit score cutoff that was appropriate for their risk appetite and used it for approving and declining applications. Now lenders are using Experian’s advanced analytics in a variety of ways that the credit scoring pioneers might never have imagined: Improving the account opening experience — for example, by reducing friction online Detecting identity theft and synthetic identities Anticipating bust-out activity and other first-party fraud Issuing the right offer to each prescreened customer Optimizing interest rates Reviewing and adjusting credit lines Optimizing collections   Analytics is no substitute for wisdom Data scientists like those at Experian remind me that in banking, as in baseball, predictive analytics is never perfect. What keeps finance so interesting is the inherent unpredictability of the economy and human behavior. Likewise, the play on the field determines who wins each ball game: anything can happen. Rob Neyer’s book Power Ball: Anatomy of a Modern Baseball Game quotes the Houston Astros director of decision sciences: “Sometimes it’s just about reminding yourself that you’re not so smart.”  

While electric vehicles remain a relatively niche part of the market, with only 0.9 percent of the total vehicle registrations through June 2018, consumer demand has grown quite significantly over the past few years. As I mentioned in a previous blog post, electric vehicles held just 0.5 percent in 2016. Undoubtedly, manufacturers and retailers will look to capitalize on this growing segment of the population. But, it’s not enough to just dig into the sales number. If the automotive industry really wants to position itself for success, it’s important to understand the consumers most interested in electric vehicles. This level of data can help manufacturers and retailers make the right decisions and improve the bottom line. Based on our vehicle registration data, below is detailed look into the electric vehicle consumer. Home Value Somewhat unsurprisingly, the people most likely to purchase an electric vehicle tend to own more expensive homes. Consumers with homes valued between $450,000-$749,000 made up 25 percent of electric vehicle market share. And, as home values increase, these consumers still make up a significant portion of electric vehicle market. More than 15 percent of the electric vehicle market share was made up by those with homes valued between $750,000-$999,000, and 22.5 percent of the share was made up by those with home values of more than $1 million. In fact, consumers with home values of more than $1 million are 5.9 times more likely to purchase an electric vehicle than the general population.  Education Level Breaking down consumers by education level shows another distinct pattern. Individuals with a graduate degree are two times more likely to own an electric vehicle. Those with graduate degrees made up 28 percent of electric vehicle market share, compared to those with no college education, which made up just 11 percent. Consumer Lifestyle Segmentation Diving deeper into the lifestyles of individuals, we leveraged our Mosaic® USA consumer lifestyle segmentation system, which classifies every household and neighborhood in the U.S. into 71 unique types and 19 overachieving groups. Findings show American Royalty, who are described as wealthy, influential couples and families living in prestigious suburbs, led the way with a 17.8 percent share. Following them were Silver Sophisticates at 11.9 percent. Those in this category are described as mature couples and singles living an upscale lifestyle in suburban homes. Rounding out the top three were Cosmopolitan Achiever, described as affluent middle-aged and established couples and families who enjoy a dynamic lifestyle in metro areas. Their share was 10.1 percent. If manufacturers and retailers go beyond just the sales figures, a clearer picture of the electric vehicle market begins to form. They have an opportunity to understand that wealthier, more established individuals with higher levels of education and home values are much more likely to purchase electric vehicles. While these characteristics are consistent, the different segments represent a dynamic group of people who share similarities, but are still at different stages in life, leading different lifestyles and have different needs. As time wears on, the electric vehicle segment is poised for growth. If the industry wants to maximize its potential, they need to leverage data and insights to help make the right decisions and adapt to the evolving marketplace.

I believe it was George Bernard Shaw that once said something along the lines of, “If economists were laid end-to-end, they’d never come to a conclusion, at least not the same conclusion.” It often feels the same way when it comes to big data analytics around customer behavior. As you look at new tools to put your customer insights to work for your enterprise, you likely have questions coming from across your organization. Models always seem to take forever to develop, how sure are we that the results are still accurate? What data did we use in this analysis; do we need to worry about compliance or security? To answer these questions and in an effort to best utilize customer data, the most forward-thinking financial institutions are turning to analytical environments, or sandboxes, to solve their big data problems. But what functionality is right for your financial institution? In your search for a sandbox solution to solve the business problem of big data, make sure you keep these top four features in mind. Efficiency: Building an internal data archive with effective business intelligence tools is expensive, time-consuming and resource-intensive. That’s why investing in a sandbox makes the most sense when it comes to drawing the value out of your customer data.By providing immediate access to the data environment at all times, the best systems can reduce the time from data input to decision by at least 30%. Another way the right sandbox can help you achieve operational efficiencies is by direct integration with your production environment. Pretty charts and graphs are great and can be very insightful, but the best sandbox goes beyond just business intelligence and should allow you to immediately put models into action. Scalability and Flexibility: In implementing any new software system, scalability and flexibility are key when it comes to integration into your native systems and the system’s capabilities. This is even more imperative when implementing an enterprise-wide tool like an analytical sandbox. Look for systems that offer a hosted, cloud-based environment, like Amazon Web Services, that ensures operational redundancy, as well as browser-based access and system availability.The right sandbox will leverage a scalable software framework for efficient processing. It should also be programming language agnostic, allowing for use of all industry-standard programming languages and analytics tools like SAS, R Studio, H2O, Python, Hue and Tableau. Moreover, you shouldn’t have to pay for software suites that your analytics teams aren’t going to use. Support: Whether you have an entire analytics department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best sandbox solution for your company will have a robust support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. Look for solutions and data partners that also offer the consultative help of industry experts when your company needs it. Data, Data and More Data: Any analytical environment is only as good as the data you put into it. It should, of course, include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a sandbox solution, pick a system that will include the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data.The optimum solutions will have years of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a big data partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%. Solving the business problem around your big data can be a daunting task. However, investing in analytical environments or sandboxes can offer a solution. Finding the right solution and data partner are critical to your success. As you begin your search for the best sandbox for you, be sure to look for solutions that are the right combination of operational efficiency, flexibility and support all combined with the most robust national data, along with your own customer data. Are you interested in learning how companies are using sandboxes to make it easier, faster and more cost-effective to drive actionable insights from their data? Join us for this upcoming webinar. Register for the Webinar

This is an exciting time to work in big data analytics. Here at Experian, we have more than 2 petabytes of data in the United States alone. In the past few years, because of high data volume, more computing power and the availability of open-source code algorithms, my colleagues and I have watched excitedly as more and more companies are getting into machine learning. We’ve observed the growth of competition sites like Kaggle, open-source code sharing sites like GitHub and various machine learning (ML) data repositories. We’ve noticed that on Kaggle, two algorithms win over and over at supervised learning competitions: If the data is well-structured, teams that use Gradient Boosting Machines (GBM) seem to win. For unstructured data, teams that use neural networks win pretty often. Modeling is both an art and a science. Those winning teams tend to be good at what the machine learning people call feature generation and what we credit scoring people called attribute generation. We have nearly 1,000 expert data scientists in more than 12 countries, many of whom are experts in traditional consumer risk models — techniques such as linear regression, logistic regression, survival analysis, CART (classification and regression trees) and CHAID analysis. So naturally I’ve thought about how GBM could apply in our world. Credit scoring is not quite like a machine learning contest. We have to be sure our decisions are fair and explainable and that any scoring algorithm will generalize to new customer populations and stay stable over time. Increasingly, clients are sending us their data to see what we could do with newer machine learning techniques. We combine their data with our bureau data and even third-party data, we use our world-class attributes and develop custom attributes, and we see what comes out. It’s fun — like getting paid to enter a Kaggle competition! For one financial institution, GBM armed with our patented attributes found a nearly 5 percent lift in KS when compared with traditional statistics. At Experian, we use Extreme Gradient Boosting (XGBoost) implementation of GBM that, out of the box, has regularization features we use to prevent overfitting. But it’s missing some features that we and our clients count on in risk scoring. Our Experian DataLabs team worked with our Decision Analytics team to figure out how to make it work in the real world. We found answers for a couple of important issues: Monotonicity — Risk managers count on the ability to impose what we call monotonicity. In application scoring, applications with better attribute values should score as lower risk than applications with worse values. For example, if consumer Adrienne has fewer delinquent accounts on her credit report than consumer Bill, all other things being equal, Adrienne’s machine learning score should indicate lower risk than Bill’s score. Explainability — We were able to adapt a fairly standard “Adverse Action” methodology from logistic regression to work with GBM. There has been enough enthusiasm around our results that we’ve just turned it into a standard benchmarking service. We help clients appreciate the potential for these new machine learning algorithms by evaluating them on their own data. Over time, the acceptance and use of machine learning techniques will become commonplace among model developers as well as internal validation groups and regulators. Whether you’re a data scientist looking for a cool place to work or a risk manager who wants help evaluating the latest techniques, check out our weekly data science video chats and podcasts.

Electric vehicles are here to stay – and will likely gain market share as costs reduce, travel ranges increase and charging infrastructure grows.

There’s no shortage of buzz around fintechs shifting from marketplace challengers to industry collaborators. Regardless of fintech’s general reputation as market disruptors, a case can certainly be made for building partnerships with traditional financial institutions by leveraging the individual strengths of each organization. According to the World FinTech Report 2018, 75.5% of fintechs surveyed selected “collaborate with traditional firms” as their main objective. Whereas fintechs have agility, a singular focus on the customer, and an absence of legacy systems, traditional Financial Institutions have embedded infrastructure, scale, reach, and are well-versed with regulatory requirements. By partnering together, fintechs and other Financial Institutions can combine strengths to generate real business results and impact the customer experience. New stories are emerging – stories that illustrate positive outcomes beyond efforts exerted by one side alone. A recent report sponsored by Experian and conducted by the Filene Research Institute further explores the results of fintech and traditional FI partnerships by examining the experiences of six organizations: The outcomes of these relationships are sure to encourage more collaborative partnerships. And while leveraging each organization’s strength is a critical component, there’s much more to consider when developing a strategic approach. In the fast-moving, disruptive world of fintech, just what are the key elements to building a successful collaboration with traditional Financial Institutions? Click here to learn more. More Info on Marketplace Lending Read the Filene Report

Vehicle prices are going up, yet consumers seem unfazed. Despite consumers taking out larger loan amounts, they continue to make their monthly payments on time. But, affordability remains a point of industry interest.  As vehicle prices hit record highs, how long will consumers have an appetite for them? According to Experian’s latest State of Automotive Finance Market report, delinquency rates continued a downward trend, as 30- and 60-day delinquencies were 2.11 and 0.64 percent, respectively, at the end of Q2. Those numbers demonstrate that car owners are making timely payments despite rising vehicle costs, which is an encouraging sign for the market. The average loan amount for a new vehicle is now $30,958, a $724 increase from last year. Additionally, consumers are now making monthly payments of about $525 on a new car loan, an all-time high that has seen a $20 year over year increase. The auto market shows little to no sign of declining costs, but vehicles aren’t the only cost to consider – interest rates have increased by 56 basis points since last year.  When combined with the rising manufacturer costs, long-term affordability is a continued concern within the industry. The data points to consumers offsetting the expense by taking out longer loan terms. In Q2, the most common loan length was 72 months—which equates to six years—for both new and used financing. While this lowers the monthly payment, it leaves them subject to paying higher interest over time, as well as the potential for individuals to be upside down on their loan for a longer period of time. The key takeaway from this data is that costs continue to rise, but consumers appear to be doing a better job of managing their finances. This insight can help OEMs, dealers, and lenders make strategic decisions with a better understanding of consumer borrowing and credit habits, and think about how to make car ownership more inviting, through incentive or loyalty programs. For consumers, continuing to take steps to actively improve your credit score is one of the key ways to ensure that you’re able to negotiate the right deal when it comes to financing. Ultimately, for everyone involved, it comes down to leveraging the power of data to make more informed decisions, which can help make vehicle ownership more accessible and affordable. To learn more about the State of the Automotive Finance Market report, or to watch the webinar, click here.

Fintechs take on banks, technology, and finance as we know It. In the credit space, their reputation as a market disruptor precedes their definition. But now, as they infiltrate headlines and traditional finance as many know it – serving up consumer-centric, convenience-touting, access-for-all online marketplace lending – fintechs aren’t just becoming a mainstay within the financial spectrum’s vernacular. With their increasing foothold in the marketplace, they are here and they are gaining momentum. Since their initial entry to the marketplace in 2006, these technology-driven online platforms flaunt big data, actionable analytics and originations growing at exponential rates. Fintechs hang their hats on their ability to be the “anti-bank” of sorts. The brainchild of finance plus technology, their brands promise simple but powerful deliverables – all centered on innovation. And they market themselves as filling in the gaps commonly accepted as standard practices by traditional financial institutions. Think paperwork, less-than-instant turnaround times, a history of unwavering tradition, etc. Fintechs deliver a one-two punch, serving the marketplace as both lending companies and technology gurus – two pieces that financial institutions want and consumers crave. Now, as they grow more prominent within the marketplace, some are starting to pivot to test strategic partnerships and bring their strengths – technological infrastructure, speed and agility – to credit unions and other traditional financial institutions. According to the World FinTech Report 2018, 75.5% of fintechs surveyed want to collaborate with traditional financial services firms. The challenge, is that both fintechs and traditional financial institutions struggle with finding the right partners, efficiently working together and effectively scaling innovation. From competitors to collaborators, how can fintechs and traditional institutions strike a partnership balance? A recent report sponsored by Experian and conducted by the Filene Research Institute, explores this conundrum by examining the experiences of six financial institutions – some fintechs and some traditional FIs – as they seek to collaborate under the common goal of better serving customers. The results offer up key ingredients for fostering a successful collaboration between fintechs and traditional financial institutions – to generate real impact to the customer experience and the bottom-line. Rest assured, that in the fast-moving, disruptive world of fintech, effective partnerships such as these will continue to push boundaries and redefine the evolving financial services marketplace. Learn More About Online Marketplace Lending Download the Filene Report

How a business prices its products is a dynamic process that drives customer satisfaction and loyalty, as well as business success.  In the digital age, pricing is becoming even more complex.  For example, companies like Amazon may revise the price of a hot item several times per day. Dynamic pricing models for consumer financial products can be especially difficult for at least four reasons: A complex regulatory environment. Fair lending concerns. The potential for adverse selection by risky consumers and fraudsters. The direct impact the affordability of a loan may have on both the consumer’s ability to pay it and the likelihood that it will be prepaid. If a lender offered the same interest rate and terms to every customer for the same loan product, low-risk customers would secure better rates elsewhere, and high-risk customers would not. The end result? Only the higher-risk customers would select the product, which would increase losses and reduce profitability. For this reason, the lending industry has established risk-based pricing. This pricing method addresses the above issue, since customers with different risk profiles are offered different rates. But it’s limited. More advanced lenders also understand the price elasticity of customer demand, because there are diverse reasons why customers decide to take up differently priced loans. Customers have different needs and risk profiles, so they react to a loan offer in different ways. Many factors determine a customer’s propensity to take up an offer — for example, the competitive environment and availability of other lenders, how time-critical the decision is, and the loan terms offered. Understanding the customer’s price elasticity allows a business to offer the ideal price to each customer to maximize profitability. Pricing optimization is the superior method assuming the lender has a scientific, data-driven approach to predicting how different customers will respond to different prices. Optimization allows an organization to determine the best offer for each customer to meet business objectives while adhering to financial and operational constraints such as volume, margin and credit risk. The business can access trade-offs between competing objectives, such as maximizing revenue and maximizing volume, and determine the optimal decision to be made for each individual customer to best meet both objectives. In the table below, you can see five benefits lenders realize when they improve their pricing segmentation with an optimization strategy. Interested in learning more about pricing optimization? Click here to download our full white paper, Price optimization in retail consumer lending.