Financial Services

If you’re a credit risk manager or a data scientist responsible for modeling consumer credit risk at a lender, a fintech, a telecommunications company or even a utility company you’re certainly exploring how machine learning (ML) will make you even more successful with predictive analytics. You know your competition is looking beyond the algorithms that have long been used to predict consumer payment behavior: algorithms with names like regression, decision trees and cluster analysis. Perhaps you’re experimenting with or even building a few models with artificial intelligence (AI) algorithms that may be less familiar to your business: neural networks, support vector machines, gradient boosting machines or random forests. One recent survey found that 25 percent of financial services companies are ahead of the industry; they’re already implementing or scaling up adoption of advanced analytics and ML. My alma mater, the Virginia Cavaliers, recently won the 2019 NCAA national championship in nail-biting overtime. With the utmost respect to Coach Tony Bennett, this victory got me thinking more about John Wooden, perhaps the greatest college coach ever. In his book Coach Wooden and Me, Kareem Abdul-Jabbar recalled starting at UCLA in 1965 with what was probably the greatest freshman team in the history of basketball. What was their new coach’s secret as he transformed UCLA into the best college basketball program in the country? I can only imagine their surprise at the first practice when the coach told them, “Today we are going to learn how to put on our sneakers and socks correctly. … Wrinkles cause blisters. Blisters force players to sit on the sideline. And players sitting on the sideline lose games.” What’s that got to do with machine learning? Simply put, the financial services companies ready to move beyond the exploration stage with AI are those that have mastered the tasks that come before and after modeling with the new algorithms. Any ML library — whether it’s TensorFlow, PyTorch, extreme gradient boosting or your company’s in-house library — simply enables a computer to spot patterns in training data that can be generalized for new customers. To win in the ML game, the team and the process are more important than the algorithm. If you’ve assembled the wrong stakeholders, if your project is poorly defined or if you’ve got the wrong training data, you may as well be sitting on the sideline. Consider these important best practices before modeling: Careful project planning is a prerequisite — Assemble all the key project stakeholders, and insist they reach a consensus on specific and measurable project objectives. When during the project life cycle will the model be used? A wealth of new data sources are available. Which data sources and attributes are appropriate candidates for use in the modeling project? Does the final model need to be explainable, or is a black box good enough? If the model will be used to make real-time decisions, what data will be available at runtime? Good ML consultants (like those at Experian) use their experience to help their clients carefully define the model development parameters. Data collection and data preparation are incredibly important — Explore the data to determine not only how important and appropriate each candidate attribute is for your project, but also how you’ll handle missing or corrupt data during training and implementation. Carefully select the training and validation data samples and the performance definition. Any biases in the training data will be reflected in the patterns the algorithm learns and therefore in your future business decisions. When ML is used to build a credit scoring model for loan originations, a common source of bias is the difference between the application population and the population of booked accounts. ML experts from outside the credit risk industry may need to work with specialists to appreciate the variety of reject inference techniques available. Segmentation analysis — In most cases, more than one ML model needs to be built, because different segments of your population perform differently. The segmentation needs to be done in a way that makes sense — both statistically and from a business perspective. Intriguingly, some credit modeling experts have had success using an AI library to inform segmentation and then a more tried-and-true method, such as regression, to develop the actual models. During modeling: With a good plan and well-designed data sets, the modeling project has a very good chance of succeeding. But no automated tool can make the tough decisions that can make or break whether the model is suitable for use in your business — such as trade-offs between the ML model’s accuracy and its simplicity and transparency. Engaged leadership is important. After modeling: Model validation — Your project team should be sure the analysts and consultants appreciate and mitigate the risk of over fitting the model parameters to the training data set. Validate that any ML model is stable. Test it with samples from a different group of customers — preferably a different time period from which the training sample was taken. Documentation — AI models can have important impacts on people’s lives. In our industry, they determine whether someone gets a loan, a credit line increase or an unpleasant loss mitigation experience. Good model governance practice insists that a lender won’t make decisions based on an unexplained black box. In a globally transparent model, good documentation thoroughly explains the data sources and attributes and how the model considers those inputs. With a locally transparent model, you can further explain how a decision is reached for any specific individual — for example, by providing FCRA-compliant adverse action reasons. Model implementation — Plan ahead. How will your ML model be put into production? Will it be recoded into a new computer language, or can it be imported into one of your systems using a format such as the Predictive Model Markup Language (PMML)? How will you test that it works as designed? Post-implementation — Just as with an old-fashioned regression model, it’s important to monitor both the usage and the performance of the ML model. Your governance team should check periodically that the model is being used as it was intended. Audit the model periodically to know whether changing internal and external factors — which might range from a change in data definition to a new customer population to a shift in the economic environment — might impact the model’s strength and predictive power. Coach Wooden used to say, “It isn’t what you do. It’s how you do it.” Just like his players, the most successful ML practitioners understand that a process based on best practices is as important as the “game” itself.

So often a microscope is set on examining millennials and their behaviors – especially when it comes to their priorities and finances. But there’s a new generation entering the economy, with an entirely new set of preferences, behaviors and approach to money. Enter Gen Z. According to Bloomberg, this year, Generation Z becomes the biggest consumer cohort globally, “displacing millennials as a top obsession for investors.” This generation (falling between the ages of seven and 22) is 61 million strong and has a spending power of $143 billion in the U.S. alone. While much of the population that makes up Generation Z may still be in school, they are already creating their reputation as conscientious consumers. And lenders and financial institutions need to get in front of them if they want a chance at these meaningful investments. Because this generation has grown up in a world where the internet has always existed, everything can be ordered and delivered on demand, and communications occur over mobile platforms like Instagram and Snapchat, they view the world – and finances – through a different lens. Bloomberg suggests the following Gen Z broad trends; which investors should consider if they want this growing generation in their portfolios: They can be influenced. According to a recent Bloomberg survey, 52% of Gen Zers said they primarily find out about new products from social media. And they are 3 times more likely to purchase a product recommended by one of their favorite influencers than by a television or film celebrity. They have different vices – beyond just their smartphone addictions. As they are growing up in a world where screen time is eminent and cannabis is becoming legal (already legal in 10 U.S. states), they live with a different world view than many of the other generations. They don’t have to go to stores. Gen Z shops via clicks, not bricks. They choose their brand loyalties carefully. This generation is interested in environmental issues and ethical shopping, which drives their consumer activities, meaning it’s time for new considerations when it comes to marketing. They eat differently. Less likely to eat meat, we’re already seeing the shift that fast-food restaurants and packaged-food distributors are taking. What does this mean for financial institutions? You don’t have to be a social media influencer to get Gen Z in your portfolio – but it wouldn’t hurt. Many reports indicate that by 2020, Gen Z will command nearly 40% of all consumer shopping. With shopping driven by scrolling and purpose-driven purchases facilitated primarily by online transactions, gaining an understanding of these young consumers’ credit and charge card habits means you can better understand bankcard wallet share and target them as they start joining the workforce and beyond. In the not-too-distant future, there will be a need to examine high spend to increase interchange income. Trended data solutions can gain insight into these consumers as well as help you target and offer new lines of credit as they purchase with purpose – fueling them with credit to fund the ventures that matter to them most. Learn More  

A court in a Northern China province has developed a mobile app designed to enforce court rulings and create a socially credible environment. The app, which can be accessed via WeChat, China's most popular instant messaging platform, is designed to alert users when they are within a 500-meter radius of someone in debt. Users will get personal information about the debtors, including their exact location, names, national ID numbers, and why they have a low score. It's the latest innovation to become integrated into China's social credit system. What is a social credit system? China's social credit system, which will be enforced in 2020, aims to standardize the social reputation of citizens and businesses. It will rank citizens by attaching a score to various aspects of their social life - ranging from paying court fees to drinking alcohol to failing to pay bills. Although there are proposed consequences for low scorers, including travel bans and loan declines, 80% of citizens recently surveyed by the Washington Post support it. While the app seems like it could be a plotline from a "Black Mirror" episode, with its emphasis on reputation scoring and location-based activation, there are reasons it makes sense for the rather remote northern province. With many people in China still not having formal access to traditional banks, being able to alternatively assess their trustworthiness and risk could provide them the ability to access loans, rent houses, and even send their children to school. Additionally, to increase their scores, Chinese citizens are displaying improved behavior. China isn't the first country to attempt to gain a robust understanding of its consumers through alternative data sources. While U.S. financial institutions have experimented with using social media as a factor in determining a borrower's risk, Philippines-based Lenddo, a world leader in scoring and identity verification technology, is doing that and more. The company looks at social media, email, and mobile headset activity to determine repayment ability. Moreover, Discovery Bank in South Africa believes there's a correlation between fiscal responsibility and physical health. The South African bank plans to begin tracking the habits of its 4.4 million customers and offering better deals to those who are living a healthier lifestyle. For example, consumers can earn points for visiting the gym, getting a flu shot, or buying healthy groceries. The more points a consumer collects the better deals and savings they'll receive. The willingness to share data is not a characteristic unique to South African or Chinese citizens. A recent Accenture study of 47,000 banking and insurance customers showed that consumers are willing to share personal data in exchange for better customer assistance and discounts on products and services. The full extent of the impact on social credit to Chinese citizens is impossible to calculate, simply because the system doesn't fully exist yet. However, it does serve as an example of the many ways that credit scoring and the use of customer-permissioned data are evolving. Long gone are the days of mailing checks, ordering from a catalog, or even needing to carry cash. What's next?

At Experian, we know that fintechs don’t just need big data – they need the best data, and they need that data as quickly as possible. Successfully delivering on this need is one of the many reasons we’re proud to be selected as a Fintech Breakthrough Award winner for the second consecutive year. The Fintech Breakthrough Awards is the premier awards program founded to recognize fintech innovators, leaders and visionaries from around the world. The 2019 Fintech Breakthrough Award program received more than 3,500 nominations from across the globe. Last year, Experian took home the Consumer Lending Innovation Award for our Text for Credit Solution – a powerful tool for providing consumers the convenience to securely bypass the standard-length ‘pen & paper’ or keystroke intensive credit application process while helping lenders make smart, fraud protected lending decisions. This year, we are excited to announce that Experian’s Ascend Analytical Sandbox™ has been selected as winner in the Best Overall Analytics Platform category. “We are thrilled to be recognized by Fintech Breakthrough for the second year in a row and that our Ascend Analytical Sandbox has been recognized as the best overall analytics platform in 2019,” said Vijay Mehta, Experian’s Chief Innovation Officer. “We understand the challenges fintechs face - to stay ahead of constantly changing market conditions and customer demands,” said Mehta. “The Ascend Analytical Sandbox is the answer, giving financial institutions the fastest access to the freshest data so they can leverage the most out of their analytics and engage their customers with the best decisions.” Debuting in 2018, Experian’s Ascend Analytical Sandbox is a first-to-market analytics environment that moved companies beyond just business intelligence and data visualization to data insights and answers they could actually use. In addition to thousands of scores and attributes, the Ascend Analytical Sandbox offers users industry-standard analytics and data visualization tools like SAS, R Studio, Python, Hue and Tableau, all backed by a network of industry and support experts to drive the most answers and value out of their data and analytics. Less than a year post-launch, the groundbreaking solution is being used by 15 of the top financial institutions globally. Early Access Program Experian is committed to developing leading-edge solutions to power fintechs, knowing they are some of the best innovators in the marketplace. Fintechs are changing the industry, empowering consumers and driving customer engagement like never before. To connect fintechs with the competitive edge,  Experian launched an Early Access Program, which fast-tracks onboarding to an exclusive market test of the Ascend Analytical Sandbox. In less than 10 days, our fintech partners can leverage the power, breadth and depth of Experian’s data, attributes and models. With endless use cases and easy delivery of portfolio monitoring, benchmarking, wallet share analysis, model development, and market entry, the Ascend Analytical Sandbox gives fintechs the fastest access to the freshest data so they can leverage the most out of their analytics and engage their customers with the best decisions. A Game Changer for the Industry In a recent IDC customer spotlight, OneMain Financial reported the Ascend Analytical Sandbox had helped them reduce their archive process from a few months to 1-2 weeks, a nearly 75% time savings. “Imagine having the ability to have access to every single tradeline for every single person in the United States for the past almost 20 years and have your own tradelines be identified among them. Imagine what that can do,” said OneMain Financial’s senior managing director and head of model development. For more information, download the Ascend Analytical Sandbox™ Early Access Program product sheet here, or visit Experian.com/Sandbox.

With the number of consumer visits to bank branches having declined from 52% of people visiting their bank branch on a monthly basis to 32% since 2015, the shift in banking to digital is apparent. Rather than face-to-face interaction, today’s financial consumers value remote, on-demand, services. They expect instant credit decisioning, immediate account funding, and around-the-clock customer assistance. To adapt, financial service providers see the necessity to respond to consumers’ growing expectations and become part of their overall digital lifestyle. Here are a few ways that financial services can adjust to changing consumer behavior: Drive mobile app activity With more than 50% of the world’s population actively using smartphones, the popularity of mobile banking apps has soared. Mobile apps have revolutionized the banking sector by facilitating easier communication between clients and institutions, offering value-added services, and introducing blockchain technologies. Consumers use mobile banking apps to pay bills, transfer funds, deposit checks, and make person-to-person payments. In fact, according to a study by Bank of America, more than 60% of millennials use mobile apps to make person-to-person payments on a regular basis! Financial institutions who launch new, or invest in enhancing existing mobile apps, can lower their overall costs, increase ROI, and maintain customer loyalty. Provide convenience and rewards CGI conducted a survey on emerging financial consumer trends, focusing on bank customers’ top requirements. Results confirmed that 81% of respondents expected to receive some form of an incentive from their primary banks. Today’s financial consumers may reasonably be won over by service offerings. They want rewards, limited fees, and convenience. As an example, Experian’s Text for CreditTM simplifies the credit process by providing customers with instant credit decisioning through their mobile devices. Personalized offers based on customer behavior can help enhance your brand and attract new customers. Stay connected Today’s consumers expect instant service and gratification. Consumers prefer to work with banks who offer accessible and responsive customer service. According to a recent NGDATA consumer banking survey, 41% of banking customers report that poor customer service is the primary reason they would leave their bank. Mintel suggests developing an omnichannel experience aligned with consumer media consumption. Stay connected with consumers through mobile apps, chatbots, social media, and email. Ensure that all interactions are relevant and helpful and immediately alert customers of any institutional issues or changes. The growing digital demands of consumers are influencing how people purchase banking, lending, and credit services. These changes are driving increased urgency for financial service institutions to adopt real-time financial processes that meet demands for convenience and speed. Interested in more best practices? Watch our On-Demand Webinar

Be warned. I’m a Philadelphia sports fan, and even after 13 months, I still relish in the only Super Bowl victory I’ve ever known as a fan. Having spent more than two decades in fraud prevention, I find that Super Bowl LII is coalescing in my mind with fraud prevention and lessons in defense more and more. Let me explain: It’s fourth-down-and-goal from the one-yard line. With less than a minute on the clock in the first half, the Eagles lead, 15 to 12. The easy option is to kick the field goal, take the three points and come back with a six-point advantage. Instead of sending out the kicking squad, the Eagles offense stays on the field to go for a touchdown. Broadcaster Cris Collingsworth memorably says, “Are they really going to go for this? You have to take the three!” On the other side are the New England Patriots, winners of two of the last three Super Bowls. Love them or hate them, the Patriots under coach Bill Belichick are more likely than any team in league history to prevent the Eagles from scoring at this moment. After the offense sets up, quarterback Nick Foles walks away from his position in the backfield to shout instructions to his offensive line. The Patriots are licking their chops. The play starts, and the ball is snapped — not to Foles as everyone expects, but to running back Corey Clement. Clement takes two steps to his left and tosses the ball the tight end Trey Burton, who’s running in the opposite direction. Meanwhile, Foles pauses as if he’s not part of the play, then trots lazily toward the end zone. Burton lobs a pass over pursuing defenders into Foles’ outstretched hands. This is the “Philly Special” — touchdown! Let me break this down: A third-string rookie running back takes the snap, makes a perfect toss — on the run — to an undrafted tight end. The tight end, who hasn’t thrown a pass in a game since college, then throws a touchdown pass to a backup quarterback who hasn’t caught a ball in any athletic event since he played basketball in high school. A play that has never been run by the Eagles, led by a coach who was criticized as the worst in pro football just a year before, is perfectly executed under the biggest spotlight against the most dominant team in NFL history. So what does this have to do with fraud? There’s currently an outbreak of breach-fueled credential stuffing. In the past couple of months, billions of usernames and passwords stolen in various high-profile data breaches have been compiled and made available to criminals in data sets described as “Collections 1 through 5.” Criminals acquire credentials in large numbers and attack websites by attempting to login with each set — effectively “stuffing” the server with login requests. Based on consumer propensity to reuse login credentials, the criminals succeed and get access to a customer account between 1 in 1,000 and 1 in 50 attempts. Using readily available tools, basic information like IP address and browser version are easy enough to alter/conceal making the attack harder to detect. Credential stuffing is like the Philly Special: Credential stuffing doesn’t require a group of elite all-stars. Like the Eagles’ players with relatively little experience executing their roles in the Philly Special, criminals with some computer skills, some initiative and the guts to try credential stuffing can score. The best-prepared defense isn’t always enough. The Patriots surely did their homework. They set up their defense to stop what they expected the Eagles to do based on extensive research. They knew the threats posed by every Eagle on the field. They knew what the Eagles’ coaches had done in similar circumstances throughout their careers. The defense wasn’t guessing. They were as prepared as they could have been. It’s the second point that worries me when I think of credential stuffing. Consumers reuse online credentials with alarming frequency, so a stolen set of credentials is likely to work across multiple organizations, possibly even yours. On top of that, traditional device recognition like cookies can’t identify and stop today’s sophisticated fraudsters. The best-prepared organizations feel great about their ability to stop the threats they’re aware of. Once they’ve seen a scheme, they make investments, improve their defenses, and position their players to recognize a risk and stop it. Sometimes past expertise won’t stop the play you can’t see coming.  

The lending market has seen a significant shift from traditional financial institutions to fintech companies providing alternative business lending. Fintech companies are changing the brick-and-mortar landscape of lending by utilizing data and technology. Here are four ways fintech has changed the lending process and how traditional financial institutions and lenders can keep up: 1. They introduced alternative lending models In a traditional lending model, lenders accept deposits from customers to extend loan offers to other customers. One way that fintech companies disrupted the lending process is by introducing peer-to-peer lending. With peer-to-peer lending, there is no need to take a deposit at all. Instead, individuals can earn interest by lending to others. Banks who collaborate with peer-to-peer lenders can improve their credit appraisal models, enhance their online lending strategy, and offer new products at a lower cost to their customers. 2. They offer fast approvals and funding In certain situations, it can take banks and credit card providers weeks to months to process and approve a loan. Conversely, fintech lenders typically approve and fund loans in less than 24 hours. According to Mintel, only 30% of consumers find various banking features easy-to-use. Financial experts at Toptal suggest that banks consider speeding up the loan application and funding process within their online lending platforms to keep up with high-tech companies, such as Amazon, that offer customers an overall faster lending process from applications to approval, to payments.   3. They're making use of data Typically, fintech lenders pull data from several different alternative sources to quickly determine how likely a borrow is to pay back the loan. The data is collected and analyzed within seconds to create a snapshot of the consumer's creditworthiness and risk. The information can include utility, rent. auto payments, among other sources. To keep up, financial institutions have begun to implement alternative credit data to get a more comprehensive picture of a consumer, instead of relying solely upon the traditional credit score. 4. They offer perks and savings By enacting smoother automated processes, fintech lenders can save money on overhead costs, such as personnel, rent, and administrative expenses. These savings can then be passed onto the customer in the form of competitive interest rates. While traditional financial institutions generally have low overall interest rates, the current high demand for loans could help push their rates even lower. Additionally, financial institutions have started to offer more customer perks. For example, Goldman Sachs recently created an online lending platform, called Marcus, that offers unsecured consumer loans with no fees. Financial institutions may feel stuck in legacy systems and unable to accomplish the agile environments and instant-gratification that today's consumers expect. However, by leveraging new data sets and innovation, financial institutions may be able to improve their product offerings and service more customers. Looking to take the next step? We can help. Learn More About Banks Learn More About Fintechs

While it’s a word that has only recently made its way into financial circles, consumers and businesses alike have been enjoying life in a platform world. Digital platforms connect riders with drivers, friends with family, manufacturers with buyers and sellers, and the list goes on. Digital platforms are technology-enabled business models that work to enhance efficiency, flexibility, scalability, integration, and ultimately user engagement. They’re integral to the operation and success of some of the most valuable companies in the world, including Google, Facebook, and Amazon. While digital platforms have made their way beyond high-tech to other industries, like supply chain management and logistics, financial institutions have fallen behind. The reasons why are understandable: a quickly evolving marketplace, regulatory induced risk aversion, and the need to protect data and privacy. Most of the digital platform adoption that has occurred in the financial industry has revolved around open banking, with a focus on enriching the customer experience. BBVA, for instance, recently launched a platform to enable their business clients to use white-labeled versions of BBVA products and services on-demand. But the value of digital platforms for the financial industry can go beyond how the consumer interfaces with his or her bank or credit union. Financial institutions could see the same efficiency, flexibility, and integration benefits by implementing technology platforms into their internal systems. Traditionally, financial institutions have used contrasting technology and systems across their customers’ lifecycle. From financial marketing and targeting, to acquisition and underwriting, there is ample opportunity to streamline and integrate these systems by adopting a platform architecture. The most future-forward platforms not only enable financial institutions to integrate their internal systems, but they also allow companies to seamlessly integrate their customer data with third-party data resources. The powers of data-driven answers combined with platform technology can help overcome business challenges and satisfy consumer and client demands. Is it time you and your company stepped up to the platform?

There’s recently been a significant amount of discussion about the stability of the automotive finance industry. Many fear the increase in the volume of delinquent U.S. automotive loans may be an early stage harbinger of the downfall of the automotive industry. But, the fact is, that’s not entirely true. While we certainly want to keep a close eye on the volume of delinquent loans, it’s important to put these trends into context. We’ve seen a steady increase in the volume of outstanding loan balances for the past several years – though the growth has slowed the past few quarters. And while much of the increase is driven by higher loan amounts, it also means there’s been an overall higher volume of vehicle buyers leaning on automotive lenders to finance vehicles. In fact, findings from our Q4 2018 State of the Automotive Finance Market Report show 85.1 percent of all new vehicle purchases were financed in Q4 2018 – compare that to 81.4 percent in Q4 2010 and 78.2 percent in Q4 2006. Suffice it to say, more financed vehicles will undoubtedly lead to more delinquent loans. But that also means, there is a high volume of car buyers who continue to pay their automotive loans in a timely manner. Through Q4 2018, there were nearly 86 million automotive loans and leases that were in good standing. With a higher volume of automotive loans than in the past, we should pay close attention to the percentage of delinquent loans compared to the overall market and compare that to previous years. And when we examine findings from our report, the percentage of automotive loans and leases that were 30-days past due dropped from 2.36 percent to 2.32 percent compared to a year ago. When we look at loans and leases that were 60-days past due, the percentages are relatively stable (up slightly from 0.76 percent to 0.78 percent compared to a year ago). It’s worth noting, these percentages are well below the high-water mark set during Q4 2009 when 3.30 percent of loans were 30-days delinquent and 0.94 percent of loans were 60-days delinquent. But, while the rate of delinquency is down and/or relatively stable year-over-year, it has trended upward since Q4 2015 – we’ll want to stay close to these trends. That said, much of the increase in the percentage of 60-day delinquent automotive loans is a result of a higher percentage of deep subprime loans from previous years – high-risk originations that become delinquent often occur more than 16 months after the origination. Additionally, the percentage of deep subprime originations has steadily decreased over the past two years, which could lead to a positive impact on the percentage of delinquent automotive loans. Despite rising automotive loan amounts and monthly payments, the data shows consumers appear to be making their payments on-time – an encouraging sign for automotive lenders. That said, lenders will want to continue to keep a close eye on all facets of car buyers’ payment performance moving forward – but it is important to put it into context. A clear understanding of these trends will better position lenders to make the right decisions when analyzing risk and provide consumers with comprehensive automotive financing options. To learn more about the State of the Automotive Finance Market report, or to watch the webinar, click here.    

When it comes to relationships and significant others, debt is topping lists of what people look for - or don't look for - in their partner. Where looks, pedigree, or career trajectory were previous motivation drivers for mate selection (or at least companionship), recent studies indicate debt is a deal-breaker for many looking for love. Late payments from lifestyles past, less-than-stellar credit scores, and cancelled credit cards are all exhibits of debt and destruction influencing personal relationships, not to mention the relationship financial institutions have with these consumers. Are certain relationships – or rather, specific partners – more likely to carry debt? Women were found to be more financially vulnerable, according to the Survey of Consumer Finances, conducted by the Federal Reserve, that examined how men and women who had never been married felt about debt. Recent Experian data found that while both men and women share the same amount of revolving utilization at 30%, men carry more debt than women, $27,067 compared to $23,881 for women. Men are also more likely to have larger mortgage debt at $214,908 compared to $198,622 for women. Women have more credit cards and more retail cards but lower balances than men on both. From a generational viewpoint, Gen X and Boomer generations have a higher than average number of credit cards and higher than average number of retail cards (and the highest average balance on credit cards and retail cards). Gen X also has the highest average debt by generation for both non-mortgage and mortgage debt. While Boomer and Silent Generations have lower than average mortgage debt, the boomer generation still has higher than average non-mortgage debt. With nearly 3 in 4 American adults saying they would reconsider their romantic relationship because of their partner’s debt, consumers should consider revamping their balance sheets before updating their online dating profiles. For the hopeless romantics, the star-crossed lovers, and those instead celebrating Singles Awareness Day whose finances could use a little love, perhaps a digital collections portal or personalized options to consolidate debt might speak to their love language. Or, in the meantime, maybe a list of the top cities for singles with the best credit scores could be a start.

How can fintech companies ensure they’re one step ahead of fraudsters? Kathleen Peters discusses how fintechs can prepare for success in fraud prevention.

From a capricious economic environment to increased competition from new market entrants and a customer base that expects a seamless, customized experience, there are a host of evolving factors that are changing the way financial institutions operate. Now more than ever, financial institutions are turning to their data for insights into their customers and market opportunities. But to be effective, this data must be accurate and fresh; otherwise, the resulting strategies and decisions become stale and less effective. This was the challenge facing OneMain Financial, a large provider of personal installment loans serving 10 million total customers across more than 1,700 branches—creating accurate, timely and robust insights, models and strategies to manage their credit portfolios. Traditionally, the archive process had been an expensive, time-consuming, and labor-intensive process; it can take months from start to finish. OneMain Financial needed a solution to reduce expenses and the time involved in order to improve their core risk modeling.   In this recent IDC Customer Spotlight, sponsored by Experian, "Improving Core Risk Modeling with Better Data Analysis," Steven D’Alfonso, Research Director spoke with the Senior Managing Director and head of model development at OneMain Financial who turned to Experian’s Ascend Analytical Sandbox to improve its core risk modeling through reject inferencing. But OneMain Financial also realized additional benefits and opportunities with the solution including compliance and economic stress testing. Read the customer spotlight to learn more about the explore how OneMain Financial: Reduced expense and effort associated with its archive process Improved risk model development timing from several months to 1-2 weeks Used Sandbox to gain additional market insight including: market share, benchmarking and trends, etc. Read the Case Study

Perhaps more than ever before, technology is changing how companies operate, produce and deliver products and services to their customers. Similarly, technology is also driving a shift in customer expectation in how, when and where they consume products and services. But these changes aren’t just relegated to the arenas where tech giants with household names, like Amazon and Google, play. Likewise, financial institutions of every size are also fielding the changes brought on by innovations to the industry in recent years. According to this report by PWC, 77% of firms plan on dedicating time and budgets to increase innovation. But what areas make the most sense for your business? With a seemingly constant shift in consumer and corporate focus, it can be difficult to know which technological advancements are imperative to your company’s success and which are just the latest fizzling buzzword. As you evaluate innovation investments for your organization in 2019 and beyond, here’s a list of four technology innovations that are already changing the financial sector or will change the banking landscape in the near future. The APIs of Open Banking Ok, it’s not a singular innovation, so I’m cheating a bit here, but it’s a great place to begin the conversation because it comprises and sets the stage for many of the innovations and technologies that are in use today or will be implemented in the future. Created in 2015, the Open Banking Standard defined how a bank’s system data or consumer-permissioned financial data should be created, accessed and shared through the use of application programming interfaces or APIs. When financial institutions open their systems up to third-party developer partners, they can respond to the global trends driving change within the industry while greatly improving the customer experience. With the ability to securely share their financial data with other lenders, greater transparency into the banking process, and more opportunities to compare product offerings, consumers get the frictionless experience they’ve come to expect in just about every aspect of life – just not necessarily one that lenders are known for. But the benefits of open banking are not solely consumer-centric. Financial institutions are able to digitize their product offerings and thus expand their market and more easily share data with partners, all while meeting clients’ individualized needs in the most cost-effective way. Biometrically speaking…and smiling Verifying the identity of a customer is perhaps one of the most fundamental elements to a financial transaction. This ‘Know Your Customer’ (KYC) process is integral to preventing fraud, identity theft, money laundering, etc., but it’s also time-consuming and inconvenient to customers. Technology is changing that. From thumbprint and, now, facial recognition through Apple Pay, consumers have been using biometrics to engage with and authorize financial transactions for some time now. As such, the use of biometrics to authenticate identity and remove friction from the financial process is becoming more mainstream, moving from smartphones to more direct interaction. Chase has now implemented voice biometrics to verify a consumer’s identity in customer service situations, allowing the company to more quickly meet a customer’s needs. Meanwhile, in the US and Europe, Visa is testing biometric credit cards that have a fingerprint reader embedded in the card that stores his or her fingerprint in order to authenticate their identity during a financial transaction. In China, companies like Alipay are taking this to the next level by allowing customers to bypass the phone entirely with its ‘pay with a smile’ service. First launched in KFC restaurants in China, the service  is now being offered at hospitals as well. How, when and where a consumer accesses their financial institution data actually creates a digital fingerprint that can be verified. While facial and vocal matching are key components to identity verification and protecting the consumer, behavioral biometrics have also become an important part of the fraud prevention arsenal for many financial institutions. These are key components of Experian’s CrossCore solution, the first open fraud and identity platform partners with a variety of companies, through open APIs discussed above. Not so New Kid on the Block(chain) The first Bitcoin transaction took place on January 12, 2009. And for a number of years, all was quiet. Then in 2017, Bitcoin started to blow up, creating a scene reminiscent of the 1850s California gold rush. Growing at a seemingly exponential rate, the cryptocurrency topped out at a per unit price of more than $20,000. By design cryptocurrencies are decentralized, meaning they are not controlled or regulated by a single entity, reducing the need for central third-party institutions, i.e. banks and other financial institutions to function as central authorities of trust. Volatility and regulation aside, it’s understandable why financial institutions were uneasy, if not skeptical of the innovation. But perhaps the most unique characteristic of cryptocurrencies is the technology on which they are built: blockchain. Essentially, a blockchain is just a special kind of database. The database stores, validates, transfers and keeps a ledger of transfers of encrypted data—records of financial transfers in the case of Bitcoin. But these records aren’t stored on one computer as is the case with traditional databases. Blockchain leverages a distributed ledger or distributed trust approach where a full copy of the database is stored across many distributed processing nodes and the system is constantly checking and validating the contents of the database. But a blockchain can store any type of data, making it useful in a wide variety of applications including tracking the ownership digital or physical assets or the provenance of documents, etc. From clearing and settlements, payments, trade finance, identity and fraud prevention, we’re already seeing financial institutions explore and/or utilize the technology. Santander was the first UK bank to utilize blockchain for their international payments app One Pay FX. Similarly, other banks and industry groups are forming consortiums to test the technology for other various uses. With all this activity, it’s clear that blockchain will become an integral part of financial institutions technology and operations on some level in the coming years. Robot Uprising Rise in Robots While Artificial Intelligence seems to have only recently crept into pop-culture and business vernacular, it was actually coined in 1956 by John McCarthy, a researcher at Dartmouth who thought that any aspect of learning or intelligence could essentially be taught to a machine. AI allows machines to learn from experience, adjust to new inputs and carry out human-like tasks. It’s the result of becoming ‘human-like’ or the potential to become superior to humans that creeps out people like my father, and also worries others like Elon Musk. Doomsday scenarios a la Terminator aside, it’s easy to see how the tech can and is useful to society. In fact, much of the AI development done today uses human-style reasoning as a model, but not necessarily the ultimate aim, to deliver better products and services. It’s this subset of AI, machine learning, that allows companies like Amazon to provide everything from services like automatic encryption in AWS to products like Amazon Echo. While it’s much more complex, a simple way to think about AI is that it functions like billions of conditional if-then-else statements working in a random, varied environment typically towards a set goal. Whereas in the past, programmers would have to code these statements and input reference data themselves, machine learning systems learn, modify and map between inputs and outputs to create new actions based on their learning. It works by combining the large amounts of data created on a daily basis with fast, iterative processing and intelligent algorithms, allowing the program to learn from patterns in the data and make decisions. It’s this type of machine learning that banks are already using to automate routine, rule-based tasks like fraud monitoring and also drive the analytical environments used in their risk modeling and other predictive analytics. Whether or not you’ve implemented AI, machine learning or bot technology into your operations, it’s highly likely your customers are already leveraging AI in their home lives, with smart home devices like Amazon Echo and Google Home. Conversational AI is the next juncture in how people interface with each other, companies and life in general. We’re already seeing previews of what’s possible with technologies like Google Duplex. This has huge implication for the financial services industry, from removing friction at a transaction level to creating a stickier, more engaging customer experience. To that end, according to this report from Accenture, AI may begin to provide in-the-moment, holistic financial advice that is in a customer’s best interest.   It goes without saying that the market will continue to evolve, competition will only grow more fierce, consumer expectation will continue to shift, and regulation will likely become more complex. It’s clear technology can be a mitigating factor, even a competitive differentiator, with these changing industry variables. Financial institutions must evolve corporate mindsets in their approach to prioritize innovations that will have the greatest enterprise-wide impact. By putting together an intelligent mix of people, process, and the right technology, financial institutions can better predict consumer need and expectation while modernizing their business models.

Alternative credit data and trended data each have advantages to lenders and financial institutions. Is there such a thing as the MVD (Most Valuable Data)? Get Started Today When it comes to the big game, we can all agree the score is the last thing standing; however, how the two teams arrived at that score is arguably the more important part of the story. The same goes for consumers’ credit scores. The teams’ past records and highlight reels give insight into their actual past performance, while game day factors beyond the stat sheets – think weather, injury rehab and personal lives – also play a part. Similarly, consumers’ credit scores according to the traditional credit file may be the dependable source for determining credit worthiness. But, while the traditional credit file is extensive, there is a playbook of other, additional information you can arm yourself with for easier, faster and better lending decisions. We’ve outlined what you need to create a win-win data strategy: Alternative credit data and trended data each have unique advantages over traditional credit data for both lenders and consumers alike. How do you formulate a winning strategy? By making sure you have both powerhouses on your roster. The results? Better than that game-winning touchdown and hoisting the trophy above your head – universe expansion and the ability to lend deeper. Get Started Today

Are You #TeamTrended or #TeamAlternative? There’s no such thing as too much data, but when put head to head, differences between the data sets are apparent. Which team are you on? Here’s what we know: With the entry and incorporation of alternative credit data into the data arena, traditional credit data is no longer the sole determinant for credit worthiness, granting more people credit access. Built for the factors influencing financial health today, alternative credit data essentially fills the gaps of the traditional credit file, including alternative financial services data, rental payments, asset ownership, utility payments, full file public records, and consumer-permissioned data – all FCRA-regulated data. Watch this video to see more:    Trended data, on the other hand shows actual, historical credit data. It provides key balance and payment data for the previous 24 months to allow lenders to leverage behavior trends to determine how individuals are utilizing their credit. Different splices of that information reveal particular behavior patterns, empowering lenders to then act on that behavior. Insights include a consumer’s spend on all general purpose credit and charge cards and predictive metrics that identify consumers who will be in the market for a specific type of credit product. In the head-to-head between alternative credit data and trended data, both have clear advantages. You need both on your roster to supplement traditional credit data and elevate your game to the next level when it comes to your data universe. Compared to the traditional credit file, alternative credit data can reveal information differentiating two consumers. In the examples below, both consumers have moderate limits and have making timely credit card payments according to their traditional credit reports. However, alternative data gives insight into their alternative financial services information. In Example 1, Robert Smith is currently past due on his personal loan, whereas Michelle Lee in Example 2 is current on her personal loan, indicating she may be the consumer with stronger creditworthiness.   Similarly, trended data reveals that all credit scores are not created equal. Here is an example of how trended data can differentiate two consumers with the same score. Different historical trends can show completely different trajectories between seemingly similar consumers.   While the traditional credit score is a reliable indication of a consumer’s creditworthiness, it does not offer the full picture. What insights are you missing out on? Go to Infographic Get Started Today