Fintech

2018 was a whirlwind of a year – though it was not surprising when Google’s 2018 “most-searched” list showed Fornite GIFs ruled the internet, Black Panther was the most-Googled movie, and the Keto diet was trending (particularly in late December and early January, go figure). But, while Google’s most-searched terms of 2018 present pure pop-culture entertainment, they miss the mark on the trends we find most meaningful being principals of the financial services industry. What about the latest news in fintech? According to Business Insider, fintech companies secured $57.9 billion in funding in the first half of 2018 alone, nearing the previous annual record of $62.5 billion set in 2015. Taking it a step further, CBInsights reports that 24 of 39 fintech unicorns are based in North America. We won’t blame Google for this oversight. Faced with the harsh reality that the “most-searched” results are based on raw-data, perhaps it’s possible that people really do find Fortnite more exciting than financial services trends – but not us at Experian. We have been closely following disruption in the financial services space all while leading the charge in data innovation. When competing in environments where financial institutions vie for customer acquisition and brand loyalty, digital experience is not enough. Today’s world demands finance redefined – and fintechs have answered the call. Fintechs are, by far, among the most innovative technology and data-driven companies in the financial services industry. That’s why we built a team of seasoned consultants, veteran account executives and other support staff that are 100% dedicated to supporting our fintech partners. With our expert team and a data accuracy rate of 99.9%, there isn’t a more reliable fintech source. Perhaps this is one financial services trend that Google can’t ignore (we see you Google)! For more information regarding Experian’s fintech solutions, check out our video below and visit Experian.com/fintech.

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.

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

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

If your company is like many financial institutions, it’s likely the discussion around big data and financial analytics has been an ongoing conversation. For many financial institutions, data isn’t the problem, but rather what could or should be done with it. Research has shown that only about 30% of financial institutions are successfully leveraging their data to generate actionable insights, and customers are noticing. According to a recent study from Capgemini,  30% of US customers and 26% of UK customers feel like their financial institutions understand their needs. No matter how much data you have, it’s essentially just ones and zeroes if you’re not using it. So how do banks, credit unions, and other financial institutions who capture and consume vast amounts of data use that data to innovate, improve the customer experience and stay competitive? The answer, you could say, is written in the sand. The most forward-thinking financial institutions are turning to analytical environments, also known as a sandbox, to solve the business problem of big data. Like the name suggests, a sandbox is an environment that contains all the materials and tools one might need to create, build, and collaborate around their data. A sandbox gives data-savvy banks, credit unions and FinTechs access to depersonalized credit data from across the country. Using custom dashboards and data visualization tools, they can manipulate the data with predictive models for different micro and macro-level scenarios. The added value of a sandbox is that it becomes a one-stop shop data tool for the entire enterprise. This saves the time normally required in the back and forth of acquiring data for a specific to a project or particular data sets. The best systems utilize the latest open source technology in artificial intelligence and machine learning to deliver intelligence that can inform regional trends, consumer insights and highlight market opportunities. From industry benchmarking to market entry and expansion research and campaign performance to vintage analysis, reject inferencing and much more. An analytical sandbox gives you the data to create actionable analytics and insights across the enterprise right when you need it, not months later. The result is the ability to empower your customers to make financial decisions when, where and how they want. Keeping them happy keeps your financial institution relevant and competitive. Isn’t it time to put your data to work for you? Learn more about how Experian can solve your big data problems. >> Interested to see a live demo of the Ascend Sandbox? Register today for our webinar “Big Data Can Lead to Even Bigger ROI with the Ascend Sandbox.”

Machine learning (ML), the newest buzzword, has swept into the lexicon and captured the interest of us all. Its recent, widespread popularity has stemmed mainly from the consumer perspective. Whether it’s virtual assistants, self-driving cars or romantic matchmaking, ML has rapidly positioned itself into the mainstream. Though ML may appear to be a new technology, its use in commercial applications has been around for some time. In fact, many of the data scientists and statisticians at Experian are considered pioneers in the field of ML, going back decades. Our team has developed numerous products and processes leveraging ML, from our world-class consumer fraud and ID protection to producing credit data products like our Trended 3DTM attributes. In fact, we were just highlighted in the Wall Street Journal for how we’re using machine learning to improve our internal IT performance. ML’s ability to consume vast amounts of data to uncover patterns and deliver results that are not humanly possible otherwise is what makes it unique and applicable to so many fields. This predictive power has now sparked interest in the credit risk industry. Unlike fraud detection, where ML is well-established and used extensively, credit risk modeling has until recently taken a cautionary approach to adopting newer ML algorithms. Because of regulatory scrutiny and perceived lack of transparency, ML hasn’t experienced the broad acceptance as some of credit risk modeling’s more utilized applications. When it comes to credit risk models, delivering the most predictive score is not the only consideration for a model’s viability. Modelers must be able to explain and detail the model’s logic, or its “thought process,” for calculating the final score. This means taking steps to ensure the model’s compliance with the Equal Credit Opportunity Act, which forbids discriminatory lending practices. Federal laws also require adverse action responses to be sent by the lender if a consumer’s credit application has been declined. This requires the model must be able to highlight the top reasons for a less than optimal score. And so, while ML may be able to deliver the best predictive accuracy, its ability to explain how the results are generated has always been a concern. ML has been stigmatized as a “black box,” where data mysteriously gets transformed into the final predictions without a clear explanation of how. However, this is changing. Depending on the ML algorithm applied to credit risk modeling, we’ve found risk models can offer the same transparency as more traditional methods such as logistic regression. For example, gradient boosting machines (GBMs) are designed as a predictive model built from a sequence of several decision tree submodels. The very nature of GBMs’ decision tree design allows statisticians to explain the logic behind the model’s predictive behavior. We believe model governance teams and regulators in the United States may become comfortable with this approach more quickly than with deep learning or neural network algorithms. Since GBMs are represented as sets of decision trees that can be explained, while neural networks are represented as long sets of cryptic numbers that are much harder to document, manage and understand. In future blog posts, we’ll discuss the GBM algorithm in more detail and how we’re using its predictability and transparency to maximize credit risk decisioning for our clients.

The August 2018 LinkedIn Workforce Report states some interesting facts about data science and the current workforce in the United States. Demand for data scientists is off the charts, but there is a data science skills shortage in almost every U.S. city — particularly in the New York, San Francisco and Los Angeles areas. Nationally, there is a shortage of more than 150,000 people with data science skills. One way companies in financial services and other industries have coped with the skills gap in analytics is by using outside vendors. A 2017 Dun & Bradstreet and Forbes survey reported that 27 percent of respondents cited a skills gap as a major obstacle to their data and analytics efforts. Outsourcing data science work makes it easier to scale up and scale down as needs arise. But surprisingly, more than half of respondents said the third-party work was superior to their in-house analytics. At Experian, we have participated in quite a few outsourced analytics projects. Here are a few of the lessons we’ve learned along the way: Manage expectations: Everyone has their own management style, but to be successful, you must be proactively involved in managing the partnership with your provider. Doing so will keep them aligned with your objectives and prevent quality degradation or cost increases as you become more tied to them. Communication: Creating open and honest communication between executive management and your resource partner is key. You need to be able to discuss what is working well and what isn’t. This will help to ensure your partner has a thorough understanding of your goals and objectives and will properly manage any bumps in the road. Help external resources feel like a part of the team: When you’re working with external resources, either offshore or onshore, they are typically in an alternate location. This can make them feel like they aren’t a part of the team and therefore not directly tied to the business goals of the project. To help bridge the gap, performing regular status meetings via video conference can help everyone feel like a part of the team. Within these meetings, providing information on the goals and objectives of the project is key. This way, they can hear the message directly from you, which will make them feel more involved and provide a clear understanding of what they need to do to be successful. Being able to put faces to names, as well as having direct communication with you, will help external employees feel included. Drive engagement through recognition programs: Research has shown that employees are more engaged in their work when they receive recognition for their efforts. While you may not be able to provide a monetary award, recognition is still a big driver for engagement. It can be as simple as recognizing a job well done during your video conference meetings, providing certificates of excellence or sending a simple thank-you card to those who are performing well. Either way, taking the extra time to make your external workforce feel appreciated will produce engaged resources that will help drive your business goals forward. Industry training: Your external resources may have the necessary skills needed to perform the job successfully, but they may not have specific industry knowledge geared towards your business. Work with your partner to determine where they have expertise and where you can work together to providing training. Ensure your external workforce will have a solid understanding of the business line they will be supporting. If you’ve decided to augment your staff for your next big project, Experian® can help. Our Analytics on DemandTM service provides senior-level analysts, either onshore or offshore, who can help with analytical data science and modeling work for your organization.

Customer Identification Program (CIP) solution through CrossCore® Every day, I work closely with clients to reduce the negative side effects of fraud prevention. I hear the need for lower false-positive rates; maximum fraud detection in populations; and simple, streamlined verification processes. Lately, more conversations have turned toward ID verification needs for Customer Information Program (CIP) administration. As it turns out, barriers to growth, high customer friction and high costs dominate the CIP landscape. While the marketplace struggles to manage the impact of fraud prevention, CIP routinely disrupts more than 10 percent of new customer acquisitions. Internally at Experian, we talk about this as the biggest ID problem our customers aren’t solving. Think about this: The fight for business in the CIP space quickly turned to price, and price was defined by unit cost. But what’s the real cost? One of the dominant CIP solutions uses a series of hyperlinks to connect identity data. Every click is a new charge. Their website invites users to dig into the data — manually. Users keep digging, and they keep paying. And the challenges don’t stop there. Consider the data sources used for these solutions. The winners of the price fight built CIP solutions around credit bureau header data. What does that do for growth? If the identity wasn’t sufficiently verified when a credit report was pulled, does it make sense to go back to the same data source? Keep digging. Cha-ching, cha-ching. Right about now, you might be feeling like there’s some sleight of hand going on. The true cost of CIP administration is much more than a single unit price. It’s many units, manual effort, recycled data and frustrated customers — and it impacts far more clients than fraud prevention. CIP needs have moved far beyond the demand for a low-cost solution. We’re thrilled to be leading the move toward more robust data and decision capabilities to CIP through CrossCore®. With its open architecture and flexible decision structure, our CrossCore platform enables access to a diverse and robust set of data sources to meet these needs. CrossCore unites Experian data, client data and a growing list of available partner data to deliver an intelligent and cost-conscious approach to managing fraud and identity challenges. The next step will unify CIP administration, fraud analytics and a range of verification treatment options together on the CrossCore platform as well. Spoiler alert. We’ve already taken that step.

As more financial institutions express interest and leverage alternative credit data sources to decision and assess consumers, lenders want to be assured of how they can best utilize this data source and maintain compliance. Experian recently interviewed Philip Bohi, Vice President for Compliance Education for the American Financial Services Association (AFSA), to learn more about his perspective on this topic, as well as to gain insights on what lenders should consider as they dive into the world of alternative credit data. Alternative data continues to be a hot topic in the financial services space. How have you seen it evolve over the past few years? It’s hard to pinpoint where it began, but it has been interesting to observe how technology firms and people have changed our perceptions of the value and use of data in recent years. Earlier, a company’s data was just the information needed to conduct business. It seems like people are waking up to the realization that their business data can be useful internally, as well as to others.  And we have come to understand how previously disregarded data can be profoundly valuable. These insights provide a lot of new opportunities, but also new questions.  I would also say that the scope of alternative credit data use has changed.  A few years ago, alternative credit data was a tool to largely address the thin- and no-file consumer. More recently, we’ve seen it can provide a lift across the credit spectrum. We recently conducted a survey with lenders and 23% of respondents cited “complying with laws and regulations” as the top barrier to utilizing alternative data. Why do you think this is the case? What are the top concerns you hear from lenders as it relates to compliance on this topic? The consumer finance industry is very focused on compliance, because failure to maintain compliance can kill a business, either directly through fines and expenses, or through reputation damage. Concerns about alternative data come from a lack of familiarity. There is uncertainty about acquiring the data, using the data, safeguarding the data, selling the data, etc. Companies want to feel confident that they know where the limits are in creating, acquiring, using, storing and selling data. Alternative data is a broad term. When it comes to utilizing it for making a credit decision, what types of alternative data can actually be used?  Currently the scope is somewhat limited. I would describe the alternative data elements as being analogous to traditional credit data. Alternative data includes rent payments, utility payments, cell phone payments, bank deposits, and similar records. These provide important insights into whether a given consumer is keeping up with financial obligations. And most importantly, we are seeing that the particular types of obligations reflected in alternative data reflect the spending habits of people whose traditional credit files are thin or non-existent.  This is a good thing, as alternative data captures consumers who are paying their bills consistently earlier than traditional data does. Serving those customers is a great opportunity. If a lender wants to begin utilizing alternative credit data, what must they know from a compliance standpoint? I would begin with considering what the lender’s goal is and letting that guide how it will explore using alternative data. For some companies, accessing credit scores that include some degree of alternative data along with traditional data elements is enough. Just doing that provides a good business benefit without introducing a lot of additional risk as compared to using traditional credit score information. If the company wants to start leveraging its own customer data for its own purposes, or making it available to third parties, that becomes complex very quickly.  A company can find itself subject to all the regulatory burdens of a credit-reporting agency very quickly. In any case, the entire lifecycle of the data has to be considered, along with how the data will be protected when the data is “at rest,” “in use,” or “in transit.” Alternative data used for credit assessment should additionally be FCRA-compliant. How do you see alternative credit data evolving in the future? I cannot predict where it will go, but the unfettered potential is dizzying. Think about how DNA-based genealogy has taken off, telling folks they have family members they did not know and providing information to solve old crimes. I think we need to carefully balance personal privacy and prudent uses of customer data. There is also another issue with wide-ranging uses of new data. I contend it takes time to discern whether an element of data is accurately predictive.  Consider for a moment a person’s utility bills. If electricity usage in a household goes down when the bills in the neighborhood are going up, what does that tell us? Does it mean the family is under some financial strain and using the air conditioning less? Or does it tell us they had solar panels installed? Or they’ve been on vacation?  Figuring out what a particular piece of data means about someone’s circumstances can be difficult. About Philip Bohi Philip joined  AFSA in 2017 as Vice President, Compliance Education. He is responsible for providing strategic direction and leadership for the Association’s compliance activities, including AFSA University, and is the staff liaison to the Operations and Regulatory Compliance Committee and Technology Task Forces. He brings significant consumer finance legal and compliance experience to AFSA, having served as in-house counsel at Toyota Motor Credit Corporation and Fannie Mae. At those companies, Philip worked closely with compliance staff supporting technology projects, legislative tracking, and vendor management. His private practice included work on manufactured housing, residential mortgage compliance, and consumer finance matters at McGlinchey Stafford, PLLC and Lotstein Buckman, LLP. He is a member of the Virginia State Bar and the District of Columbia Bar. Learn more about the array of alternative credit data sources available to financial institutions.

The early stages of establishing a startup are some of the most difficult. In fact, it is said 90 percent of startups fail. Challenges include forming the right team, raising capital, and constructing a business model. But no one will deny that one of the most important parts of a startup’s business strategy is the data and technology that underpin its solution. On the one hand, new startups don’t benefit from a wealth of historic data on their clients, prospects, and partners like their more established competitors. While this isn’t the end of the world, it does emphasize the importance of finding a trusted data partner to build those data insights into the design for their application or platform. By using a trusted third-party data provider, companies can ensure they receive reliable and accurate data to utilize in their products and services. On the other hand, startups have the luxury of not being bogged down and burdened by legacy systems and older tech. While building a solution from the ground up is never an easy feat, startups can generally move faster. They can benefit from the latest technology to build new apps and products, making them nimbler than the incumbents in the space. Cloud technology enables organizations to quickly get their business up and running. In addition, companies are exposing many of their data assets and services through application programming interfaces (APIs), allowing others to more easily create their own solutions. Rather than reinventing the wheel, companies can leverage existing services to build more complex solutions and launch faster. “We’ve talked to countless startups and businesses and know they want easy, fast, and secure access to our data assets and services,” said Alpa Jain, vice president of Experian’s API Center of Excellence. “That’s why we’ve launched our API Developer Portal.” The list of APIs available through Experian’s Developer Portal includes solutions like consumer credit data, commercial credit data, commercial public record information, data quality, vehicle history information, and more. Companies can browse the list of available APIs, create an account, and start utilizing the APIs for building out a product within minutes. “Our goal is to help companies unlock untapped market opportunities and grow,” said Jain. “Success with APIs requires a successful developer program and portal to accelerate developer productivity – we believe we’ve created both with our new portal experience."

An introduction to the different types of validation samples Model validation is an essential step in evaluating and verifying a model’s performance during development before finalizing the design and proceeding with implementation. More specifically, during a predictive model’s development, the objective of a model validation is to measure the model’s accuracy in predicting the expected outcome. For a credit risk model, this may be predicting the likelihood of good or bad payment behavior, depending on the predefined outcome. Two general types of data samples can be used to complete a model validation. The first is known as the in-time, or holdout, validation sample and the second is known as the out-of-time validation sample. So, what’s the difference between an in-time and an out-of-time validation sample? An in-time validation sample sets aside part of the total sample made available for the model development. Random partitioning of the total sample is completed upfront, generally separating the data into a portion used for development and the remaining portion used for validation. For instance, the data may be randomly split, with 70 percent used for development and the other 30 percent used for validation. Other common data subset schemes include an 80/20, a 60/40 or even a 50/50 partitioning of the data, depending on the quantity of records available within each segment of your performance definition. Before selecting a data subset scheme to be used for model development, you should evaluate the number of records available in your target performance group, such as number of bad accounts. If you have too few records in your target performance group, a 50/50 split can leave you with insufficient performance data for use during model development. A separate blog post will present a few common options for creating alternative validation samples through a technique known as resampling. Once the data has been partitioned, the model is created using the development sample. The model is then applied to the holdout validation sample to determine the model’s predictive accuracy on data that wasn’t used to develop the model. The model’s predictive strength and accuracy can be measured in various ways by comparing the known and predefined performance outcome to the model’s predicted performance outcome. The out-of-time validation sample contains data from an entirely different time period or customer campaign than what was used for model development. Validating model performance on a different time period is beneficial to further evaluate the model’s robustness. Selecting a data sample from a more recent time period having a fully mature set of performance data allows the modeler to evaluate model performance on a data set that may more closely align with the current environment in which the model will be used. In this case, a more recent time period can be used to establish expectations and set baseline parameters for model performance, such as population stability indices and performance monitoring. Learn more about how Experian Decision Analytics can help you with your custom model development needs.

Consumers and businesses alike have been hyper-focused on all things data over the past several months. From the headlines surrounding social media privacy, to the flurry of spring emails we’ve all received from numerous brands due to the recent General Data Protection Regulation (GDPR) going into effect in Europe, many are trying to assess the data “sweet spot.” In the financial services space, lenders and businesses are increasingly seeking to leverage enhanced digital marketing channels and methods to deliver offers and invitations to apply. But again, many want to know, what are the data rules and how can they ensure they are playing it safe in such a highly regulated environment. In an Experian-hosted webinar, Credit Marketing in the Digital Age, the company recently featured a team of attorneys from Venable LLP’s award-winning privacy and advertising practice. There’s no question today’s consumers expect hyper-targeted messages and user experiences, but with the number of data breaches on the rise, there is also the concern around data access. Who has my data? Is it safe? Are companies using it in the appropriate way? As financial services companies wrestle with the laws and consumer expectations, the Venable legal team provided a few insights to consider. While the digital delivery channels may be new, the underlying credit product remains the same. A prescreened offer is a prescreened offer, and an application for credit is still an application for credit. The marketing of these and other credit products is governed by an array of pre-existing laws, regulations, and self-regulatory principles that combine to form a unique compliance framework for each of the marketing channels. Adhere to credit regulations, but build in enhanced policies and technological protocols with digital delivery. With digital delivery of the offer, lenders should be thinking about the additional compliance aspects attached to those varying formats. For example, in the case of digital display advertising, you should pay close attention to ensuring delivery of the ad to the correct consumer, with suitable protections in place for sharing data with vendors. Lenders and service providers also should think about using authentication measures to match the correct consumer with a landing page containing the firm offer along with the appropriate disclosures and opt-outs. Strong compliance policies are important for all participants in this process. Working with a trusted vendor that has a commitment to data security, compliance by design, and one that maintains an integrated system of decisioning and delivery, with the ability to scrub for FCRA opt-outs, is essential. Consult your legal, risk and compliance teams. The digital channels raise questions that can and must be addressed by these expert audiences. It is so important to partner with service providers that have thought this through and can demonstrate a compliance framework. Embrace the multitude of delivery methods. Yes, there are additional considerations to think about to ensure compliance, but businesses should seek opportunities to reach their consumers via email, text, digital display and beyond. Also, digital credit offers need not replace mail and phone and traditional channels. Rather, emerging digital channels can supplement a campaign to drive the response rates higher. In Mary Meeker’s annual tech industry report, she touched on a phenomenon called the “privacy paradox” in which companies must balance the need to personalize their products and services, but at the same time remain in good favor with consumers, watchdog groups and regulators. So, while financial services players have much to consider in the regulatory space, the expectation is they embrace the latest technology advancements to interact with their consumers. It can be done and the delivery methods exist today. Just ensure you are working with the right partners to respect the data and consumer privacy laws.  

The second full day of Experian Vision 2018 kicked off with an inspirational message from keynote speakers Capt. Mark Kelly and Former Congresswomen Gabby Giffords, rolled into a series of diverse breakout sessions, and concluded with Super Bowl-winning quarterback Aaron Rodgers sharing tales of sports, leadership and winning. Need a recap of some of the headlines from the day? Here you go ... Retail Apocalypse? Not so fast alarmists. Yes, there are media headlines around mergers, closings and consumers adopting new ways to shop, but let me give you three reasons as to why the retail sky is not falling. There were more store openings last year than closings, and that trend is expected to continue this year with an estimated 5,500 openings by December. There continues to be a positive sales trajectory. E-commerce sales are increasing. Big department stores have seen pains, but if brands are focused on connection, relevance and convenience, there is hope. Consumers continue to spend. Subprime auto bubble? Nope. Malinda Zabritski, Sr. Director of Experian Automotive Sales, says the media likes to fixate on the subprime, but subprime financing has been on the decline, reaching record lows. Deep subprime is at .65%. Additionally, delinquency rates have also tapered. The real message? Consumers are relying on auto lenders for financing, largely due to consumer preferences to lease. The market is healthy, and while it has slowed slightly, the market is still at 7% year-over-year growth. Consumer-permissioned data is not just a value-add for thin-file consumers. Take for instance the inclusion of demand deposit accounts (DDAs). David Shellenberger, Sr. Director of Scoring and Predictive Analytics for FICO, says people who have had long relationships with their checking accounts tend to be more stable and generally sport higher credit scores. Consumers with thick, mature files can also benefit with DDA data. Consumer-permissioned data is not just about turning a “no” to a “yes.” It can also take a consumer from near-prime to prime, or from prime to super-prime. Would you want to make a credit decision with less information or more? This was the question Paul DeSaulniers, Experian Sr. Director of Product, posed to the audience as he kicked off the session on alternative data. With an estimated 100 million U.S. consumers falling below “thick-file” credit status, there is a definite need to learn more about these individuals. By leveraging alternative credit data – like short-term lending product use, rental data, public records and consumer-permissioned data – a more holistic view of these consumers is available. A few more facts: While alternative finance users tend to be more subprime, 20% are prime or better. A recent data pull revealed 20% of approved credit card users also had alternative finance data on them as well. About 2/3 of households headed by young adults are rentals. Imagine a world where the mortgage journey takes only seven to 10 days. With data and technology, we are closer than you think. Future products are underway that could master the underwriting phase in just one day, leaving the remaining days dedicated for signing disclosures, documents and wiring funds. Processes need to be firmed up, but a vision has been set. The average 30- to 45-day mortgage journey could soon be a distant memory. 97% of online banking applications that are started are abandoned. Why? Filling out lengthy forms, especially on a mobile device, is not fun. New technology, such as Experian’s Instant Form Fill, is allowing consumers to provide a name, zip and last four numbers of their social security number for an instant form fill of the rest of the application. Additionally, voice assistants are expected to increasingly facilitate research on purchases big and small. A recent study revealed nearly half of consumers perceive voice assistants to be useful. Businesses have more fraud losses than ever before. Not surprising. What is scary? An estimated 54% of businesses said they are not confident in their ability to detect fraud. Another session reported that approximately 20% of credit charge-offs are synthetic IDs, a growing pain point for all businesses. Consumers, on the other hand, say they “want visible signs of security” and “no friction.” Tough to balance, but those are today’s expectations. More Vision 2018 insights can be accessed on #ExperianVision twitter feed. Vision 2019 will be in San Antonio, Texas next May 5-8.

Alternative credit data. Enhanced digital credit marketing. Faster, integrated decisioning. Fraud and identity protections. The latest in technology innovation. These were the themes Craig Boundy, Experian’s CEO of North America, imparted to an audience of 800-plus Vision guests on Monday morning. “Technology, innovation and new sources of data are fusing to create an unprecedented number of new ways to solve pressing business challenges,” said Boundy. “We’re leveraging the power of data to help people and businesses thrive in the digital economy.” Main stage product demos took the shape of dark web scans, data visualization, and the latest in biometric fraud scanning. Additionally, a diverse group of breakout sessions showcased all-new technology solutions and telling stats about how the economy is faring in 2018, as well as consumer credit trends and preferences. A few interesting storylines of the day … Regulatory Under the Trump administration, everyone is talking about deregulation, but how far will the pendulum swing? Experian Sr. Director of Regulatory Affairs Liz Oesterle told audience members that Congress will likely pass a bill within the next few days, offering relief to small and mid-sized banks and credit unions. Under the new regulations, these smaller players will no longer have to hold as much capital to cover losses on their balance sheets, nor will they be required to have plans in place to be safely dismantled if they fail. That trigger, now set at $50 billion in assets, is expected to rise to $250 billion. Fraud Alex Lintner, Experian’s President of Consumer Information Services, reported there were 16.7 million identity theft victims in 2017, resulting in $16.8 billion in losses. Need more to fear? There is also a reported 323k new malware samples found each day. Multiple sessions touched on evolving best practices in authentication, which are quickly shifting to biometrics-based solutions. Personal identifiable information (PII) must be strengthened. Driver’s licenses, social security numbers, date of birth – these formats are no longer enough. Get ready for eye scans, as well as voice and photo recognition. Emerging Consumers The quest to understand the up-and-coming Millennials continues. Several noteworthy stats: 42% of Millennials said they would conduct more online transactions if there weren’t so many security hurdles to overcome. So, while businesses and lenders are trying to do more to authenticate and strengthen security, it’s a delicate balance for Millennials who still expect an easy and turnkey customer experience. Gen Z, also known as Centennials, are now the largest generation with 28% of the population. While they are just coming onto the credit scene, these digital natives will shape the credit scene for decades to come. More than ever, think mobile-first. And consider this … it's estimated that 25% of shopping malls will be closed within five years. Gen Z isn’t shopping the mall scene. Retail is changing rapidly! Economy Mortgage originations are trending up. Consumer confidence, investor confidence, interest rates and home sales are all positive. Unemployment remains low. Bankcard originations have now surpassed the 2007 peak. Experian’s Vice President of Analytics Michele Raneri had glowing remarks on the U.S. economy, with all signs pointing to a positive 2018 across the board. Small business loan volumes are also up 10% year-to-date versus the same time last year. Keynote presenters speculate there could be three to four rate hikes within the year, but after years of no hikes, it’s time. Data There are 2.5 quintillion pieces of data created daily. And 80% of what we know about a consumer today is the result of data generated within the past year. While there is no denying there is a LOT of data, presenters throughout the day talked about the importance of access and speed. Value comes with more APIs to seamlessly connect, as well as data visualization solutions like Tableau to make the data easier to understand. More Vision news to come. Gain insights and news throughout the day by following #ExperianVision on Twitter.    

The traditional credit score has ruled the financial services space for decades, but it‘s clear the way in which consumers are managing their money and credit has evolved. Today’s consumers are utilizing different types of credit via various channels. Think fintech. Think short-term loans. Think cash-checking services and payday. So, how do lenders gain more visibility to a consumer’s credit worthiness in 2018? Alternative credit data has surfaced to provide a more holistic view of all consumers – those on the traditional file and those who are credit invisibles and emerging. In an all-new report, Experian dives into “The State of Alternative Credit Data,” providing in-depth coverage on how alternative credit data is defined, regulatory implications, consumer personas attached to the alternative financial services industry, and how this data complements traditional credit data files. “Alternative credit data can take the shape of alternative finance data, rental, utility and telecom payments, and various other data sources,” said Paul DeSaulniers, Experian’s senior director of Risk Scoring and Trended/Alternative Data and attributes. “What we’ve seen is that when this data becomes visible to a lender, suddenly a much more comprehensive consumer profile is formed. In some instances, this helps them offer consumers new credit opportunities, and in other cases it might illuminate risk.” In a national Experian survey, 53% of consumers said they believe some of these alternative sources like utility bill payment history, savings and checking account transactions, and mobile phone payments would have a positive effect on their credit score. Of the lenders surveyed, 80% said they rely on a credit report, plus additional information when making a lending decision. They cited assessing a consumer’s ability to pay, underwriting insights and being able to expand their lending universe as the top three benefits to using alternative credit data. The paper goes on to show how layering in alternative finance data could allow lenders to identify the consumers they would like to target, as well as suppress those that are higher risk. “Additional data fields prove to deliver a more complete view of today’s credit consumer,” said DeSaulniers. “For the credit invisible, the data can show lenders should take a chance on them. They may suddenly see a steady payment behavior that indicates they are worthy of expanded credit opportunities.” An “unscoreable” individual is not necessarily a high credit risk — rather they are an unknown credit risk. Many of these individuals pay rent on time and in full each month and could be great candidates for traditional credit. They just don’t have a credit history yet. The in-depth report also explores the future of alternative credit data. With more than 90 percent of the data in the world having been generated in just the past five years, there is no doubt more data sources will emerge in the coming years. Not all will make sense in assessing credit decisions, but there will definitely be new ways to capture consumer-permissioned data to benefit both consumer and lender. Read Full Report