Data & Analytics

For financial marketers, long gone are the days of branded coffee mugs, teddy bears and the occasional print ad. Financial marketers are charged with customizing messaging and offerings at a customer level, increasing conversion rates, and moving beyond digital while keeping an eye on traditional channels. Additionally, financial marketing teams are having to do it all with less; according to CMO Survey, marketing budgets have remained stagnant for the last 6 years. Accordingly, competing in today’s world requires transforming your organization to address rapidly increasing complexity while containing costs.  Here are four tactics leading-edge firms are using to respond to changes in the market and better serve customers. More data, fewer problems Financial institutions ingest a mind-boggling variety of data, transaction details, transaction history, credit scores, customer preferences, etc. It can be difficult to know where to start or what to do with what is often terabytes of data. But the savviest teams are mining their unique data, along with bureau data, and other alternative and third-party data for rich decision making that drives differentiation. Getting analytical In financial institutions, advanced analytics has traditionally lived with lenders, underwriters, risk and fraud, departments, etc. But marketers too can find the value in the volume, velocity and variety of new data sources available to financial institutions. Using advanced analytics allows the most forward-thinking financial marketers to better target customers, personalize experiences, respond in near-real-time or even predict actions, and measure the impact of marketing investments. Customized quality time with customers Thanks to the likes of Google and Amazon, consumers have become accustomed to individualized interactions with firms they utilize. And this desire is just as present when it comes to their financial institution. But banks, credit unions and fintechs have been historically slow to respond. According to a recent Capgemini study, 70% of US consumers feel like their financial institution doesn’t understand their needs. The most dynamic financial marketing teams tailor quality experiences that increase consumer engagement and long-term relationships. All the channels, all the time The financial marketer’s job doesn’t stop at creating bespoke experiences for customers. Firms are also having to leverage an omnichannel approach to reach these clients, across an ever-growing number of channels and touchpoints. If that wasn’t enough, campaign cycles are shortening to match consumers changing demands and need for instant gratification—again, thanks Amazon. But the best teams determine which media or interaction resonates most effectively with clients, whether face-to-face, via an app, chatbot, or social media and have conversations across all of them seamlessly. It’s clear, financial firms must transform their approach to address increasing market complexity without increasing costs. Financial marketers are saddled with stagnant marketing budgets, proliferating media channels and shorter campaign cycles, with an expectation to continue delivering results. It’s a very tall order, especially if your financial institution is not leveraging data, analytics and insights as the differentiators they could be. CMOs and their marketing teams must invest in new technologies, strategies and data sources that best reflect the expectations of their customers. How is your bank or credit union responding to these financial marketing challenges? Watch our 2020 Credit Marketing Trends On-Demand Webinar  

Do you have 20/20 vision when it comes to the readiness of your organization? How financially healthy are your customers today? They are likely facing some challenges and difficult choices. Based on a study by the Center for Financial Services Innovation (CFSI), almost half of the US adult population - that’s 112.5 million - say they do not have enough savings to cover at least three months of living expenses. With debt rising and a possible recession on the horizon, it’s crucial to have a solid strategy in place for your organization. Here are three easy steps to help you prepare: Anticipate the recession before it arrives Gathering a complete view of your customers can be difficult if you have multiple systems, which can result in subjective, costly and inefficient processes. If you don’t have a full picture of your customers, it’s hard to understand their risk, behavior and ability to pay and to determine the most effective treatment decisions. Having the right data is only the first step. Using analytics to make sense of the data helps you better understand your customers at an individual level, which will increase recovery rates and improve the customer experience. Analytics can provide early-warning indicators that identify customers most likely to miss payments, predict future behavior, and deliver the best treatment option based on a customer’s specific situation or behavior. With a deeper understanding of at-risk customers, you can apply more targeted interventions that are specific to each customer, so you can be confident your collections process is individualized, efficient and fair. The result? A cost-effective, compliant process focused on retaining valuable customers and reducing losses.   What to look for: ✔ Know when customers are experiencing negative credit events ✔ View consumer credit trends that may not yet be visible on your own account base ✔ Watch for payment stress – understand the actual payment consumers are making. Is it changing? ✔ See individual trends and take action – are your customers sliding down to a lower score band? ✔ Understand how your client-base is performing within your own portfolio and with other organizations   Take immediate and impactful actions around risk mitigation and staffing Every interaction with consumers needs optimizing, from target marketing through to collections and recovery. Organizations that proactively modernize their business to scale and increase effectiveness before the next economic downturn may avoid struggling to address rising delinquencies when the economy corrects itself. This may improve portfolio performance and collection capabilities — significantly increasing recoveries, containing costs and sustaining returns. Identify underperforming products and inefficient processes by staff. Consider reassessing the data used and the manual processes required for making decisions. Optimize product pricing and areas where organizations or staff could automate the decision processes.   Areas to focus: ✔ Identity theft protection and account takeover awareness ✔ Improve underwriting strategy and automation ✔ Maximize profitability — drive spend, optimize approvals, line assignment and pricing ✔ Evaluate collection risk strategies and operational efficiencies   Design and deploy a strategy to be organizationally and technologically ready for change Communication is key in debt recovery. Failing to contact customers via their preferred channel can cause frustration and reduce the likelihood of recovery. Your customers are looking for a convenient and discreet way to negotiate or repay debt, and if you aren’t providing one, you’re incurring higher collections costs and lower recovery rates. With developments in the digital world, consumer interactions have changed. Most people prefer to communicate via mobile or online, with little to no human interaction. Behavioral analytics help to automate and decide the next best action, so you contact the right customer at the right time through the right channel. In addition, offering a convenient, discreet way to negotiate or repay debt can result in customers who are more engaged and more likely to pay. Online and self-service portals along with AI-powered chatbots use the latest technology to provide a safe and customer-centric experience, creating less time-consuming interactions and higher customer satisfaction. Your digital collections process is more convenient and less stressful for consumers and more profitable and compliant for you. Visualize the future... ✔ Superior customer service is embraced at the end of the customer life cycle as it is in the beginning ✔ Leverage data, analytics, software, and industry expertise to drive an automated collections process with fewer manual interventions ✔ Meet the growing expectation for digital consumer self-service by providing the ability to proactively negotiate and manage debt through preferred contact channels ✔ When economy and market conditions change for the worst, have the right data, analytics, software in place and be prepared to implement relevant collections strategies to remain competitive in the market   Don’t wait until the next recession hits. Our collaborative approach to problem solving ensures you have the right solution in place to solve your most complex problems and are ready for market changes. The combination of our data, analytics, fraud tools, decisioning software and consulting services will help you proactively manage your portfolio to minimize the flow of accounts into collections and modernize your collections and recovery processes. Learn More

It may be a new decade of disruption, but one thing remains constant – the consumer is king. As such, customer experience (and continually evolving digital transformations necessary to keep up), digital expansion and all things identity will also reign supreme as we enter this new set of Roaring 20s. Here are seven of the top trends to keep tabs of through 2020 and beyond. 1. Data that does more – 100 million borrowers and counting Traditional, alternative, public record, consumer-permissioned, small business, big business, big, bigger, best – data has a lot of adjectives preceding it. But no matter how we define, categorize and collate data, the truth is there’s a lot of it that’s untapped, which is keeping financial institutions from operating at their max efficiency levels. Looking for ways to be bigger and bolder? Start with data to engage your credit-worthy consumer universe and beyond. Across the entire lending lifecycle, data offers endless opportunities – from prospecting and acquisitions to fraud and risk management. It fuels any technology solution you have or may want to implement over the coming year. Additionally, Experian is doing their part to create a more holistic picture of consumer creditworthiness with the launch of Experian LiftTM in November. The new suite of credit score products combines exclusive traditional credit, alternative credit and trended data assets, intended to help credit invisible and thin-file consumers gain access to fair and affordable credit. "We're committed to improving financial access while helping lenders make more informed decisions. Experian Lift is our latest example of this commitment brought to life,” said Greg Wright, Executive Vice President and Chief Product Officer for Experian Consumer Information Services. “Through Experian Boost, we're empowering consumers to play an active role in building their credit histories. And, with Experian Lift, we're empowering lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem,” he said. 2. Identity boom for the next generation Increasingly digital lifestyles have put personalization and frictionless transactions on hyperdrive. They are the expectation, not a nice-to-have. Having customer intelligence will become a necessary survival strategy for those in the market wanting to compete. Identity is not just for marketing purposes; it must be leveraged across the lending lifecycle and every customer interaction. Fragmented customer identities are more than flawed for decisioning purposes, which could potentially lead to losses. And, of course, the conversation around identity would be incomplete without a nod to privacy and security considerations. With the roll-out of the California Consumer Privacy Act (CCPA) earlier this month, we will wait to see if the other states follow suit. Regardless, consumers will continue to demand security and trust. 3. All about artificial intelligence and machine learning We get it – we all want the fastest, smartest, most efficient processes on limited – and/or shrinking – budgets. But implementing advanced analytics for your financial institution doesn’t have to break the bank. And, when it comes to delivering services and messaging to customers the way they want it, how to do that means digital transformation – specifically, leveraging big data and actionable analytics to evaluate risk, uncover industry intel and improve decisioning. One thing’s for certain, financial institutions looking to compete, gain traction and pull away from the competition in this next decade will need to do so by leveraging a future-facing partner’s expertise, platforms and data. AI and machine learning model development will go into hyperdrive to add accuracy, efficiency, and all-out speed. Real-time transactional processing is where it’s at. 4. Customer experience drives decisioning and everything Faster, better, more frictionless. 2020 and the decade will be all about making better decisions faster, catering to the continually quickening pace of consumer attention and need. Platforms and computing language aside, how do you increase processing speed at the same time as increasing risk mitigation? Implementing decisioning environments that cater to consumer preferences, coupled with best-in-class data are the first two steps to making this happen. This can facilitate instant decisioning within financial institutions. Looking beyond digital transformation, the next frontier is digital expansion. Open platforms enable financial institutions to readily add solutions from numerous providers so that they can connect, access and orchestrate decisions across multiple systems. Flexible APIs, single integrations and better strategy and design build the foundation of the framework to be implemented to enhance and elevate customer experience as it’s known today. 5. Credit marketing that keeps up with the digital, instant-gratification age Know your customer may be a common acronym for the financial services industry, but it should also be a baseline for determining whether to send a specific message to clients and prospects. From the basics, like prescreen, to omni-channel marketing campaigns, financial institutions need to leverage the communication channels that consumers prefer. From point of sale to mobile – there are endless possibilities to fit into your consumers’ credit journey. Marketing is clearly not a one-and-done tactic, and therefore multi-channel prequalification offers and other strategies will light the path for acquisitions and cross-sell/up-sell opportunities to come. By developing insights from customer data, financial institutions have a clear line of sight into determining optimal strategies for customer acquisition and increasing customer lifetime value. And, at the pinnacle, the modern customer acquisition engine will continue to help financial institutions best build, test and optimize their customer channel targeting strategies faster than ever before. From segmentation to deployment, and the right data across it all, today and tomorrow’s technology can solve many of financial organizations’ age-old customer acquisition challenges. 6. Three Rs: Recession, regulatory and residents of the White House Last March, the yield curve inverted for the first time since 2007. Though the timing of the next economic correction is debated, messaging is consistent around making a plan of action now. Whether it’s arming your collections department, building new systems, updating existing systems, or adjusting rules and strategy, there are gaps every organization needs to fill. By leveraging the stability of the economy now, financial institutions can put strategies in place to maximize profitability, manage risk, reduce bad debt/charge-offs, and ensure regulatory compliance among their list of to-do’s, ultimately resulting in a more efficient, better-performing program. Also, as we near the election later this year, the regulatory landscape will likely change more than the usual amount. Additionally, we will witness the first accounts of what CECL looks like for SEC-filing financial institutions (and if that will suggest anything for how non-SEC-filing institutions may fare as their deadline inches closer), as well as see the initial implications of the CCPA roll out and whether it will pave a path for other states to follow. As system sophistication continues to evolve, so do the risks (like security breaches) and new regulatory standards (like GDPR and CCPA) which provide reasons for organizations to transform. 7. Focus on fraud (in all forms) With evolving technology, comes evolved fraudsters. Whether it’s loyalty and rewards programs, account openings, breaches, there are so many angles and entry points. Synthetic identity fraud is the fastest-growing type of financial crime in the United States. The cost to businesses is estimated to grow to $1.2 billion by 2020, according to the Aite Group. To ensure the best protection for your business and your customers, a layered, risk-based approach to fraud management provides the highest levels of confidence in the industry. Balance is key – while being compliant with regulatory requirements and conscious of user experience, ensuring consumers’ peace of mind is priority one. Not a new trend, but recognizing fraud and recognizing good consumers will save continue to save financial institutions money and reputational harm, driving significant improvement in key performance indicators. Using the right data (and aggregating multiple data sets) and digital device intelligence tools is the one-two punch to protect your bottom line. For all your needs in 2020 and throughout the next decade, Experian has you covered. Learn more

According to Experian’s Q3 2019 State of the Automotive Finance Market report, used vehicle financing increased across all credit tiers.

Electric vehicles have 2.08 percent of total VIO share through September 30, 2019

The challenges facing today’s marketers seem to be mounting and they can feel more pronounced for financial institutions. From customizing messaging and offerings at an individual customer level, increasing conversion rates, moving beyond digital while keeping an eye on traditional channels, and more, financial marketers are having to modernize their approach to customer acquisition. The most forward-thinking financial firms are turning to customer acquisition engines to help them best build, test and optimize their custom channel targeting strategies faster than ever before. But what functionality is right for your company? Here are 5 capabilities you should look for in a modern customer acquisition engine. Advanced Segmentation It’s without question that targeting and segmentation are vital to a successful financial marketing strategy. Make sure you select a tool that allows for advanced segmentation, ensuring the ability to uncover lookalike groups with similar attributes or behaviors and then customize messages or offerings accordingly. With the right customer acquisition engine, you should be able to build filters for targeted segments using a range of data including demographic, past behavior, loyalty or transaction history, offer response and then repurpose these segments across future campaigns. Campaign Design With the right campaign design, your team has the ability to greatly affect customer engagement. The right customer acquisition engine will allow your team to design a specific, optimized customer journey and content for each of the segments you create. When you’re ready to apply your credit criteria to the audience to generate a pre-screen, the best tools will allow you to view the size of your list adjusted in real-time. Make sure to look for an acquisition engine that can do all of this easily with a drag and drop user experience for faster and efficient campaign design. Rapid Deployment Once you finalize your audience for each channel or offer, the clock starts ticking. From bureau processing, data aggregation, targeting and deployment, the data that many firms are currently using for prospecting can be at least 60-days. When searching for a modern customer acquisition engine, make sure you choose a tool that gives you the option to fetch the freshest data (24-48 hours) before you deploy. If you’re sending the campaign to an outside firm to execute, timing is even more important. You’ll also want a system that can encrypt and decrypt lists to send to preferred partners to execute your marketing campaign. Support Whether you have an entire marketing department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best customer acquisition solution for your company will have a robust onboarding and support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. The best customer acquisition tool should be able to take your data and get you up and running in less than 30 days. Data, Data and more Data Any customer acquisition engine is only as good as the data you put into it. It should, of course, be able to include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a customer acquisition engine, pick a system that gives your company access to the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data. The optimum solutions can be fueled by the analytical power of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a marketing and technology partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%.

As consumers prepare for the next decade, we look at how we’re rounding out this year. The results? The average American credit score is 682, an eight-year high. Experian released the 10th annual state of credit report, which provides a comprehensive look at the credit performance of consumers across America by highlighting consumer credit scores and borrowing behaviors. And while the data is spliced to show men vs. women, as well as provides commentary at the state and generational level, the overarching trend is up. Even with the next anticipated economic correction often top of mind for financial institutions, businesses and consumers alike, 2019 was a year marked by more access, more spending and decreasing delinquencies. Things are looking up. “We are seeing a promising trend in terms of how Americans are managing their credit as we head into a new decade with average credit scores increasing two points since 2018 to 682 – the highest we’ve seen since 2011,” said Shannon Lois, Senior Vice President and Head of EAS, Analytics, Consulting & Operations for Experian Decision Analytics. “Average credit card balances and debt are up year over year, yet utilization rates remain consistent at 30 percent, indicating consumers are using credit as a financial tool and managing their debts responsibly.”   Highlights of Experian’s State of Credit report: 3-year comparison 2017 2018 2019   Average number of credit cards 3.06 3.04 3.07 Average credit card balances $6,354 $6,506 $6,629 Average number of retail credit cards 2.48 2.59 2.51 Average retail credit card balances $1,841 $1,901 $1,942 Average VantageScore® credit score[1, 2] 675 680 682 Average revolving utilization 30% 30% 30% Average nonmortgage debt[3] $24,706 $25,104 $25,386 Average mortgage debt $201,811 $208,180 $231,599 Average 30 days past due delinquency rates 4.0% 3.9% 3.9% Average 60 days past due delinquency rates 1.9% 1.9% 1.9% Average 90+ days past due delinquency rates 7.3% 6.7% 6.8%   In the scope of the credit score battle of the sexes, women have a four-point lead over men with an average credit score of 686 compared to 682. Their lead is a continued trend since 2017 where they’ve bested their male counterparts. According to the report, while men carry more non-mortgage and mortgage debt than women, women have more credit cards and retail cards (albeit they carry lower balances). Generationally, Generations X, Y and Z tend to carry more debt, including mortgage, non-mortgage, credit card and retail card, than older generations with higher delinquency and utilization rates. Segmented by state and gender, Minnesota had the highest credit scores for both men and women, while Mississippi was the state with the lowest average credit score for females and Louisiana was the lowest average credit score state for males.     As we round out the decade and head full-force into 2020, we can reflect on the changes in the past year alone that are helping consumers improve their financial health. Just to name a few: Experian launched Experian BoostTM in March, allowing millions of consumers to add positive payment history directly to their credit file for an opportunity to instantly increase their credit score. Since then, there has been over 13 million points boosted across America. Experian LiftTM was launched in November, designed to help credit invisible and thin-file consumers gain access to fair and affordable credit. Long-standing commitments to consumer education, including the Ask Experian Blog and volunteer work by Experian’s Education Ambassadors, continue to offer assistance to the community and help consumers better understand their financial actions. From what we can tell, this is just the beginning. “Understanding the factors that influence their overall credit profile can help consumers improve and maintain their financial health,” said Rod Griffin, Experian’s director of consumer education and awareness. “Credit can be used as a financial tool. Through this report, we hope to provide insights that will help consumers make more informed decisions about credit use as we prepare to head into a new decade.” Learn more 1 VantageScore® is a registered trademark of VantageScore Solutions, LLC. 2 VantageScore® credit score range is 300 to 850. 3 Average debt for this study includes all credit cards, auto loans and personal loans/student loans.

It’s December, and if you’re like most credit union leaders, your strategic plan is distributed, and the 2020 budget is approved. Before you know it, you and your team will be off and running to pursue the New Year’s goals. Another thing most of us have in common is a strategic membership growth priority. New members are needed to help us take loan and deposit growth to the next level. Specifically, who are you looking for? It’s surprising how many credit union leaders have a difficult time clarifying their ideal member(s). They usually come up short after they have called out younger borrowers, active checking account users, prime credit, middle income, homeowners, etc. The reality is in today’s competitive market, these general audiences are not definitive enough. Many then go to market with a limited universe that is too generic to be highly effective. Savvy marketers have a much deeper understanding of who they are reaching and why. First, they have clearly defined the ideal member i.e. product profitability, relationship profitability, referrals, how they access the credit union, etc. Second, they use data, analytics and demographic segmenting to refine their search further to reveal the ideal member. They leverage information to understand what drives the potential members decision making. They understand that every potential member does not live the same type of life. They segment markets into groups to understand their shared values and life experiences. These segments include geographic, demographic, financial behavior, and motivation that includes psychographics and social values. Thus, armed with this information, they align the consumer’s needs with the credit union’s products, purpose and strategic goals. This clarity allows them to invest their marketing dollars for the best possible result. Most credit unions would identify “younger borrowers” as a desired member, so we’ve laid out two examples of just how different this member can look. Ambitious Singles – is a demographic segment comprised of younger cutting-edge singles living in mid-scale, metro areas that balance work and leisure lifestyles. Annual Median income $75k - $100K Highly educated First time home buyers Professionals, upwardly mobile Channel preferences for engaging with brands (and their offers) is while watching or streaming TV, listening to their favorite radio apps or while browsing the web on their phones. They are also quite email receptive (but subject lines must be compelling) Families Matter Most – This segment is comprised of young middle-class families in scenic suburbs, leading family focused lives. Annual Median income $75K - $99K Have children 4-6 yrs. old Educated Homeowners Child-related purchases Credit revolver and auto borrowers (larger vehicles) Go online for banking, telecommuting and shopping Both segments represent younger borrowers with similar incomes, but they have different loan needs, lifestyle priorities and preferences for engaging with a marketing offer. These are just two examples of the segmentation data that is available from Experian. The segmentation solution provides a framework to help credit unions identify the optimal customer investment strategy for each member segment. This framework helps the credit union optimize their marketing between differentiating segments. For some segments the investment may be directed toward finding the ideal member. Others may be made to find depositors. While many credit unions don’t have infinite marketing budgets or analytical resources, segmentation help marketers more efficiently and effectively pursue the best member or develop member personas to better resonate with existing members. The feedback we have heard from credit union leaders is that the solution is the best segmentation tool they have seen. Learn more about it here. What your team is up against Today, credit unions face national competitors that are using state-of-the-art data analytics, first-rate technology and in-depth market segmentation to promote very attractive offers to win new members, deposits, checking accounts and loans. Their offers have a look, feel, message and offer that are relevant to the person receiving the offer. Here are a few recent “offer” examples that we have heard of that should give you pause: Fintech companies, like the Lending Club offering auto loan refinances (the offer provides an estimate of refinance interest savings). The ad we saw had an estimated monthly payment of $80. PayPal Cashback Mastercard® – with a $300 early use cash bonus and 3% cash back on purchases. High limit personal loans that take minutes to apply and to be funded. Banks acting alone or in partnership with a fintech to offer online checking accounts with new account opening bonuses ranging from $300-$600. and of course, Quicken® Mortgage promoting low rates and fast and seamless origination. These are just a few recent examples from thousands of offers that are reaching your ideal member. Besides offering great rates, cash back, low fees and seamless service – these offers are guided by robust data analytics and consumer segmentation to reach and engage a well-defined, ideal consumer. Why it matters The 2020 race is on. Hopefully your team has clarity of the member(s) they want to reach, access to robust data analytics, in depth consumer insights, reliable credit resources and marketing tools they will need to compete in the toughest financial market any of us have likely ever seen. If you’re afraid that you can’t afford the right tools when it comes to marketing, consider what the dealer fee is for purchasing an indirect auto loan. What if the 2% or more fee was reallocated to finding organic loan growth with consumers you’re more likely to build a relationship with? Or consider the cost of consistently marketing to the wrong consumer segments with the wrong message, at the wrong time and in the wrong channels. What if you could increase your market engagement rate from 5% to 10%? Perhaps the best strategic question is can you afford NOT to have the best tools that support future membership growth? If you don’t win your ideal member, somebody else will. Learn More About Scott Butterfield, CUDE, CCUE Principal, Your Credit Union Partner Scott Butterfield is a trusted advisor to the leaders of more than 170 credit unions located throughout the United States. A respected veteran of the CU Movement, he understands the challenges and opportunities facing credit unions today. Scott believes that credit unions matter, and that consumers and small businesses need credit unions to now more than ever.

In today’s ever-changing and hypercompetitive environment, the customer experience has taken center-stage – highlighting new expectations in the ways businesses interact with their customers. But studies show financial institutions are falling short. In fact, a recent study revealed that 94% of banking firms can’t deliver on the “personalization promise.” It’s not difficult to see why. Consumer preferences have changed, with many now preferring digital interactions. This has made it difficult for financial institutions to engage with consumers on a personal level. Nevertheless, customers expect seamless, consistent, and personalized experiences – that’s where the power of advanced analytics comes into play. It’s no secret that using advanced analytics can enable businesses to turn rich data into insights that lead to confident business decisions and strategy development. But these business tools can actually help financial institutions deliver on that promise of personalization. According to an Experian study, 90% of organizations say that embracing advanced analytics is critical to their ability to provide an excellent customer experience. By using data and analytics to anticipate and respond to customer behavior, companies can develop new and creative ways to cater to their audiences – revolutionizing the customer experience as a whole. It All Starts With Data Data is the foundation for a successful digital transformation – the lack of clean and cohesive datasets can hinder the ability to implement advanced analytic capabilities. However,  89% of organizations face challenges on how to effectively manage and consolidate their data, according to Experian’s Global Data Management Research Benchmark Report of 2019. Because consumers prefer digital interactions, companies have been able to gather a vast amount of customer data. Technology that uses advanced analytic capabilities (like machine learning and artificial intelligence) are capable of uncovering patterns in this data that may not otherwise be apparent, therefore opening doors to new avenues for companies to generate revenue. To start, companies need a strategy to access all customer data from all channels in a cohesive ecosystem – including data from their own data warehouses and a variety of different data sources. Depending on their needs, the data elements can come from a third party data provider such as: a credit bureau, alternative data, marketing data, data gathered during each customer contact, survey data and more. Once compiled, companies can achieve a more holistic and single view of their customer. With this single view, companies will be able to deliver more relevant and tailored experiences that are in-line with rising customer expectations. From Personalized Experiences to Predicting the Future The most progressive financial institutions have found that using analytics and machine learning to conquer the wide variety of customer data has made it easier to master the customer experience. With advanced analytics, these companies gain deeper insights into their customers and deliver highly relevant and beneficial offers based on the holistic views of their customers. When data is provided, technology with advanced analytic capabilities can transform this information into intelligent outputs, allowing companies to optimize and automate business processes with the customer in mind. Data, analytics and automation are the keys to delivering better customer experiences. Analytics is the process of converting data into actionable information so firms can understand their customers and take decisive action. By leveraging this business intelligence, companies can quickly adapt to consumer demand. Predictive models and forecasts, increasingly powered by machine learning, help lenders and other businesses understand risks and predict future trends and consumer responses. Prescriptive analytics help offer the right products to the right customer at the right time and price. By mastering all of these, businesses can be wherever their customers are. The Experian Advantage With insights into over 270 million customers and a wealth of traditional credit and alternative data, we’re able to drive prescriptive solutions to solve your most complex market and portfolio problems across the customer lifecycle – while reinventing and maintaining an excellent customer experience. If your company is ready for an advanced analytical transformation, Experian can help get you there. Learn More

Article written by Melanie Smith, Senior Copywriter, Experian Clarity Services, Inc. It’s been almost a decade since the Great Recession in the United States ended, but consumers continue to feel its effects. During the recession, millions of Americans lost their jobs, retirement savings decreased, real estate reduced in value and credit scores plummeted. Consumers that found themselves impacted by the financial crisis often turned to alternative financial services (AFS). Since the end of the recession, customer loyalty and retention has been a focus for lenders, given that there are more options than ever before for AFS borrowers. To determine what this looks like in the current climate, we examined today’s non-prime consumers, what their traditional scores look like and if they are migrating to traditional lending. What are alternative financial services (AFS)? Alternative financial services (AFS) is a term often used to describe the array of financial services offered by providers that operate outside of traditional financial institutions. In contrast to traditional banks and credit unions, alternative service providers often make it easier for consumers to apply and qualify for lines of credit but may charge higher interest rates and fees. More than 50% of new online AFS borrowers were first seen in 2018 To determine customer loyalty and fluidity, we looked extensively at the borrowing behavior of AFS consumers in the online marketplace. We found half of all online borrowers were new to the space as of 2018, which could be happening for a few different reasons. Over the last five years, there has been a growing preference to the online space over storefront. For example, in our trends report from 2018, we found that 17% of new online customers migrated from the storefront single pay channel in 2017, with more than one-third of these borrowers from 2013 and 2014 moving to online overall. There was also an increase in AFS utilization by all generations in 2018. Additionally, customers who used AFS in previous years are now moving towards traditional credit sources. 2017 AFS borrowers are migrating to traditional credit As we examined the borrowing behavior of AFS consumers in relation to customer loyalty, we found less than half of consumers who used AFS in 2017 borrowed from an AFS lender again in 2018. Looking into this further, about 35% applied for a loan but did not move forward with securing the loan and nearly 24% had no AFS activity in 2018. We furthered our research to determine why these consumers dropped off. After analyzing the national credit database to see if any of these consumers were borrowing in the traditional credit space, we found that 34% of 2017 borrowers who had no AFS activity in 2018 used traditional credit services, meaning 7% of 2017 borrowers migrated to traditional lending in 2018. Traditional credit scores of non-prime borrowers are growing After discovering that 7% of 2017 online borrowers used traditional credit services in 2018 instead of AFS, we wanted to find out if there had also been an improvement in their credit scores. Historically, if someone is considered non-prime, they don’t have the same access to traditional credit services as their prime counterparts. A traditional credit score for non-prime consumers is less than 600. Using the VantageScore® credit score, we examined the credit scores of consumers who used and did not use AFS in 2018. We found about 23% of consumers who switched to traditional lending had a near-prime credit score, while only 8% of those who continued in the AFS space were classified as near-prime. Close to 10% of consumers who switched to traditional lending in 2018 were classified in the prime category. Considering it takes much longer to improve a traditional credit rating, it’s likely that some of these borrowers may have been directly impacted by the recession and improved their scores enough to utilize traditional credit sources again. Key takeaways AFS remains a viable option for consumers who do not use traditional credit or have a credit score that doesn’t allow them to utilize traditional credit services. New AFS borrowers continue to appear even though some borrowers from previous years have improved their credit scores enough to migrate to traditional credit services. Customers who are considered non-prime still use AFS, as well as some near-prime and prime customers, which indicates customer loyalty and retention in this space. For more information about customer loyalty and other recently identified trends, download our recent reports. State of Alternative Data 2019 Lending Report

AI, machine learning, and Big Data – these are no longer just buzzwords. The advanced analytics techniques and analytics-based tools that are available to financial institutions today are powerful but underutilized. And the 30% of banks, credit unions and fintechs successfully deploying them are driving better data-driven decisions, more positive customer experiences and stronger profitability. As the opportunities surrounding advanced analytics continue to grow, more lenders are eager to adopt these capabilities to make the most of their datasets. And it’s understandable that financial institution are excited at the possibilities and insights that advanced analytics can bring to their business. However, there are some key considerations to keep in mind as you begin this important digital transformation. Here are three things you should do as your financial institution begins its advanced analytics journey. Ensure consistent and clean data quality Companies have a plethora of data and information on their customers. The main hurdles that many organizations face is being able to turn this information into a clean and cohesive dataset and formulating an effective and long-term data management strategy. Trying to implement advanced analytic capabilities while lacking an effective data governance strategy is like building a house on a poor foundation – likely to fail. Data quality issues, such as inconsistent data, data gaps, and incomplete and duplicated data, also haunt many organizations, making it difficult to complete their analytics objectives. Ensuring that issues in data quality are managed is the key to gaining the correct insights for your business.   Establish and maintain a single view of customers The power of advanced analytics can only be as strong as the data provided. Unfortunately, many companies don’t realize that advanced analytics is much more powerful when companies are able to establish a single view of their customers. Companies need to establish and maintain a single view of customers in order to begin implementing advanced analytic capabilities. According to Experian research, a single customer view is a consistent, accurate and holistic view of your organization’s customers, prospects, and their data. Having full visibility and a 360 view into your customers paves the way for companies to make personalized, relevant, timely and precise decisions. But as many companies have begun to realize, getting this single view of customers is easier said than done. Organizations need to make sure that data should always be up-to-date, unique and available in order to begin a complete digital transformation.   Ensure the right resources and commitment for your advanced analytics initiative It’s important to have the top-down commitment within your organization for advanced analytics. From the C-suite down, everyone should be on the same page as to the value analytics will bring and the investment the project might require. Organizations that want to move forward with implementing advanced analytic capabilities need to make sure to set aside the right financial and human resources that will be needed for the journey. This may seem daunting, but it doesn’t have to be. A common myth is that the costs of new hardware, new hires and the costs required to maintain, configure, and set up new technology will make advanced analytics implementation far too expensive and difficult to maintain. However, many organizations don’t realize that it’s not necessary to allocate large capital expenses to implement advanced analytics. All it takes is finding the right-sized solution with configurations to fit the team size and skill level in your organization. Moreover, finding the right partner and team (whether internal or external) can be an efficient way to fill temporary skills gaps on your team. No digital transformation initiative is without its challenges. However, beginning your advanced analytics journey on the right footing can deliver unparalleled growth, profitability and opportunities. Still not sure where to begin? At Experian, we offer a wide range of solutions to help you harness the full power and potential of data and analytics. Our consultants and development teams have been a game-changer for financial institutions, helping them get more value, insight and profitability out of their data and modeling than ever before. Learn More

Fintech is quickly changing. The word itself is synonymous with constant innovation, agile technology structures and being on the cusp of the future of finance. The rapid rate at which fintech challengers are becoming established, is in turn, allowing for greater consumer awareness and adoption of fintech platforms. It would be easy to assume that fintech adoption is predominately driven by millennials. However, according to a recent market trend analysis by Experian, adoption is happening across multiple generational segments. That said, it’s important to note the generational segments that represent the largest adoption rates and growth opportunities for fintechs. Here are a few key stats: Members of Gen Y (between 24-37 years old) account for 34.9% of all fintech personal loans, compared to just 24.9% for traditional financial institutions. A similar trend is seen for Gen Z (between 18-23 years old). This group accounts for 5% of all fintech personal loans as compared to 3.1% for traditional Let’s take a closer look at these generational segments… Gen Y represents approximately 19% of the U.S. population. These consumers, often referred to as “millennials,” can be described as digital-centric, raised on the web and luxury shoppers. In total, millennials spend about $600 billion a year. This group has shown a strong desire to improve their credit standing and are continuously increasing their credit utilization. Gen Z represents approximately 26% of the U.S. population. These consumers can be described as digital centric, raised on the social web and frugal. The Gen Z credit universe is growing, presenting a large opportunity to lenders, as the youngest Gen Zers become credit eligible and the oldest start to enter homeownership. What about the underbanked as a fintech opportunity? The CFPB estimates that up to 45 million people, or 24.2 million households, are “thin-filed” or underbanked, meaning they manage their finances through cash transactions and not through financial services such as checking and savings accounts, credit cards or loans. According to Angela Strange, a general partner at Andreessen Horowitz, traditional financial institutions have done a poor job at serving underbanked consumers affordable products. This has, in turn, created a trillion-dollar market opportunity for fintechs offering low-cost, high-tech financial services. Why does all this matter? Fintechs have a unique opportunity to engage, nurture and grow these market segments early on. As the fintech marketplace heats up and the overall economy begins to soften, diversifying revenue streams, building loyalty and tapping into new markets is a strategic move. But what are the best practices for fintechs looking to build trust, engage and retain these unique consumer groups? Join us for a live webinar on November 12 at 10:00 a.m. PST to hear Experian experts discuss financial inclusion trends shaping the fintech industry and tactical tips to create, convert and extend the value of your ideal customers. Register now

Retailers are already starting to display their Christmas decorations in stores and it’s only early November. Some might think they are putting the cart ahead of the horse, but as I see this happening, I’m reminded of the quote by the New York Yankee’s Yogi Berra who famously said, “It gets late early out there.” It may never be too early to get ready for the next big thing, especially when what’s coming might set the course for years to come. As 2019 comes to an end and we prepare for the excitement and challenges of a new decade, the same can be true for all of us working in the lending and credit space, especially when it comes to how we will approach the use of alternative data in the next decade. Over the last year, alternative data has been a hot topic of discussion. If you typed “alternative data and credit” into a Google search today, you would get more than 200 million results. That’s a lot of conversations, but while nearly everyone seems to be talking about alternative data, we may not have a clear view of how alternative data will be used in the credit economy. How we approach the use of alternative data in the coming decade is going to be one of the most important decisions the lending industry makes. Inaction is not an option, and the time for testing new approaches is starting to run out – as Yogi said, it’s getting late early. And here’s why: millennials. We already know that millennials tend to make up a significant percentage of consumers with so-called “thin-file” credit reports. They “grew up” during the Great Recession and that has had a profound impact on their financial behavior. Unlike their parents, they tend to have only one or two credit cards, they keep a majority of their savings in cash and, in general, they distrust financial institutions. However, they currently account for more than 21 percent of discretionary spend in the U.S. economy, and that percentage is going to expand exponentially in the coming decade. The recession fundamentally changed how lending happens, resulting in more regulation and a snowball effect of other economic challenges. As a result, millennials must work harder to catch up financially and are putting off major life milestones that past generations have historically done earlier in life, such as homeownership. They more often choose to rent and, while they pay their bills, rent and other factors such as utility and phone bill payments are traditionally not calculated in credit scores, ultimately leaving this generation thin-filed or worse, credit invisible. This is not a sustainable scenario as we enter the next decade. One of the biggest market dynamics we can expect to see over the next decade is consumer control. Consumers, especially millennials, want to be in the driver’s seat of their “credit journey” and play an active role in improving their financial situations. We are seeing a greater openness to providing data, which in turn enables lenders to make more informed decisions. This change is disrupting the status quo and bringing new, innovative solutions to the table. At Experian, we have been testing how advanced analytics and machine learning can help accelerate the use of alternative data in credit and lending decisions. And we continue to work to make the process of analyzing this data as simple as possible, making it available to all lenders in all verticals. To help credit invisible and thin-file consumers gain access to fair and affordable credit, we’ve recently announced Experian Lift, a new suite of credit score products that combines exclusive traditional credit, alternative credit and trended data assets to create a more holistic picture of consumer creditworthiness that will be available to lenders in early 2020. This new Experian credit score may improve access to credit for more than 40 million credit invisibles. There are more than 100 million consumers who are restricted by the traditional scoring methods used today. Experian Lift is another step in our commitment to helping improve financial health of consumers everywhere and empowers lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. This isn’t just a trend in the United States. Brazil is using positive data to help drive financial inclusion, as are others around the world. As I said, it’s getting late early. Things are moving fast. Already we are seeing technology companies playing a bigger role in the push for alternative data – often powered by fintech startups. At the same time, there also has been a strong uptick in tech companies entering the banking space. Have you signed up for your Apple credit card yet? It will take all of 15 seconds to apply, and that’s expected to continue over the next decade. All of this is changing how the lending and credit industry must approach decision making, while also creating real-time frictionless experiences that empower the consumer. We saw this with the launch of Experian Boost earlier this year. The results speak for themselves: hundreds of thousands of previously thin-file consumers have seen their credit scores instantly increase. We have also empowered millions of consumers to get more control of their credit by using Experian Boost to contribute new, positive phone, cable and utility payment histories. Through Experian Boost, we’re empowering consumers to play an active role in building their credit histories. And, with Experian Lift, we’re empowering lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. That’s game-changing. Disruptions like Experian Boost and newly announced Experian Lift are going to define the coming decade in credit and lending. Our industry needs to be ready because while it may seem early, it’s getting late.

It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?

Over the years, businesses have gathered a plethora of datasets on their customers. However, there is no value in data alone. The true value comes from the insights gained and actions that can be derived from these datasets. Advanced analytics is the key to understanding the data and extracting the critical information needed to unlock these insights. AI and machine learning in particular, are two emerging technologies with advanced analytics capabilities that can help companies achieve their business goals. According to an IBM survey, 61% of company executives indicated that machine learning and AI are their company’s most significant data initiatives in 2019. These leaders recognize that advanced analytics is transforming the way companies traditionally operate. It is no longer just a want, but a must. With a proper strategy, advanced analytics can be a competitive differentiator for your financial institution. Here are some ways that advanced analytics can empower your organization: Provide Personalized Customer Experiences Business leaders know that their customers want personalized, frictionless and enhanced experiences. That’s why improving the customer experience is the number one priority for 80 percent of executives globally, according to an Experian study. The data is already there – companies have insights into what products their customers like, the channels they use to communicate, and other preferences. By utilizing the capabilities of advanced analytics, companies can extract more value from this data and gain better insights to help create more meaningful, personalized and profitable lending decisions. Reduce Costs Advanced analytics allows companies to deploy new models and strategies more efficiently – reducing expenses associated with managing models for multiple lending products and bureaus. For example, OneMain Financial, was able to successfully drive down risk modeling expenses after implementing a solution with advanced analytics capabilities. Improve Accuracy and Speed to Market To stay ahead of the competition, companies need to maintain fast-moving environments. The speed, accuracy and power of a company’s predictive models and forecasts are crucial for success. Being able to respond to changing market conditions with insights derived from advanced analytics is a key differentiator for future-forward companies. Advanced analytic capabilities empower companies to anticipate new trends and drive rapid development and deployment, creating an agile environment of continual improvement. Drive Growth and Expand Your Customer Base With the rise of AI, machine learning and big data, the opportunities to expand the credit universe is greater than ever. Advanced analytic capabilities allow companies to scale datasets and get a bird’s eye view into a consumer’s true financial position – regardless of whether they have a credit history. The insights derived from advanced analytics opens doors for thin file or credit invisible customers to be seen – effectively allowing lenders to expand their customer base. Meet Compliance Requirements Staying on top of model risk and governance should always remain top of mind for any institution. Analytical processing aggregates and pulls new information from a wide range of data sources, allowing your institution to make more accurate and faster decisions. This enables lenders to lend more fairly, manage models that stand up to regulatory scrutiny, and keep up with changes in reporting practices and regulations. Better, faster and smarter decisions. It all starts with advanced analytics. Businesses must take advantage of the opportunities that come with implementing advanced analytics, or risk losing their customers to more future-forward organizations. At Experian, we believe that using big data can help power opportunities for your company. Learn how we can help you leverage your data faster and more effectively. Learn More