Tag: 2019 trends

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

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?

2019 is here — with new technology, new regulations and new opportunities on the docket. What does that mean for the financial services space? Here are the five trends you should keep your eye on and how these affect your credit universe.   1. Credit access is at an all-time high With 121 million Americans categorized as credit-challenged (subprime scores and a thin or nonexistent credit file) and 45 million considered credit-invisible (no credit history), the credit access many consumers take for granted has appeared elusive to others. Until now. The recent launch of Experian BoostTM empowers consumers to improve their credit instantly using payment history from their utility and phone bills, giving them more control over their credit scores and making them more visible to lenders and financial institutions. This means more opportunities for more people. Coupled with alternative credit data, which includes alternative financial services data, rental payments, and full-file public records, lenders and financial institutions can see a whole new universe. In 2019, inclusion is key when it comes to universe expansion goals. Both alternative credit and consumer-permissioned data will continue to be an important part of the conversation.   2. Machine learning for the masses The financial services industry has long been notorious for being founded on arguably antiquated systems and steeped in compliance and regulations. But the industry’s recent speed of disruption, including drastic changes fueled by technology and innovation, may suggest a changing of the guard. Digital transformation is an industry hot topic, but defining what that is — and navigating legacy systems — can be challenging. Successfully integrating innovation is the convergence at the center of the Venn diagram of strategy, technology and operations. The key, according to Deloitte, is getting “a better handle on data to extract the greatest value from technology investments.” How do you get the most value? Risk managers need big data, machine learning and artificial intelligence strategies to deliver market insights and risk evaluation. Between the difficulty of leveraging data sets and significant investment in time and money, it’s impossible for many to justify. To combat this challenge, the availability and access to an analytical sandbox (which contains depersonalized consumer data and comparative industry intel) is crucial to better serve clients and act on opportunities in lenders’ credit universe and beyond. “Making information analysis easily accessible also creates distinct competitive advantages,” said Vijay Mehta, Chief Innovation Officer for Experian’s Consumer Information Services, in a recent article for BAI Banking Strategies. “Identifying shifts in markets, changes in regulations or unexpected demand allows for quick course corrections. Tightening the analytic life cycle permits organizations to reach new markets and quickly respond to competitor moves.” This year is about meaningful metrics for action, not just data visualization.   3. How to fit into the digital-first ecosystem With so many things available on demand, the need for instant gratification continues to skyrocket. It’s no secret that the financial services industry needs to compete for attention across consumers’ multiple screens and hours of screen time. What’s in the queue for 2019? Personalization, digitalization and monetization. Consumers’ top banking priorities include customized solutions, omnichannel experience improvement and enhancing the mobile channel (as in, can we “Amazonize” everything?). Financial services leaders’ priorities include some of the same things, such as enhancing the mobile channel and delivering options to customize consumer solutions (BAI Banking Strategies). From geolocation targeting to microinteractions in the user experience journey to leveraging new strategies and consumer data to send personalized credit offers, there’s no shortage of need for consumer hyper-relevance. 33 percent of consumers who abandon business relationships do so because personalization is lacking, according to Accenture data for The Financial Brand. This expectation spans all channels, emphasizing the need for a seamless experience across all devices.   4. Keeping fraudsters out Many IT professionals regard biometric authentication as the most secure authentication method currently available. We see this technology on our personal devices, and many companies have implemented it as well. Biometric hacking is among the predicted threats for 2019, according to Experian’s Data Breach Industry Forecast, released last month. “Sensors can be manipulated and spoofed or deteriorate with too much use. ... Expect hackers to take advantage of not only the flaws found in biometric authentication hardware and devices, but also the collection and storage of data,” according to the report.   5. Regulatory changes and continued trends  Under the Trump Administration, the regulatory front has been relatively quiet. But according to the Wall Street Journal, as Democrats gain control of the House of Representatives, lawmakers may be setting their sights on the financial services industry — specifically on legislation in response to the credit data breach in 2017. The Democratic Party leadership has indicated that the House Financial Services Committee will be focused on protecting consumers and investors, preserving sector stability, and encouraging responsible innovation in financial technology, according to Deloitte. In other news, the focus on improving accuracy in data reporting, transparency for consumers in credit scoring and other automated decisions can be expected to continue. Consumer compliance, and specifically the fair and responsible treatment of consumers, will remain a top priority.   For all your needs in 2019 and beyond, Experian has you covered. Learn more