All posts by Jesse Hoggard
Experian is proud to announce, for the second year in a row, we have been named to the global Fintech Leaders list, placing in the top 20 for 2021. The list and adjoining report are released annually by international research organization, the Center for Financial Professionals (CeFPro). In addition to placing 19th on the list, Experian also placed in the Credit Risk category. The Center for Financial Professionals’ Fintech Leaders 2021 Report is one of the most rigorous programs that rank fintech industry leaders. The report’s coverage includes evaluating top fintech companies, solution providers, and vendors. The results are usually based on gathered surveys from end-users, practitioners, and subject matter experts. CeFPro’s report comes from the group’s market analysis and original research, which are backed by an advisory board that consists of 60 international industry professionals. Andreas Simou, CeFPro’s Managing Director, shared that the CeFPro board and voting members recognized Experian within the fintech survey as leaders for their data, decisioning and analytical capabilities. Simou said, \"Experian cements its place on the Fintech Leaders List, and has once again been very highly regarded, as a leading player within credit risk, most notably for their subject-matter expertise and excelling within the areas of data management and modelling,” he said. “We are honored to once again be recognized as a Fintech Leader by CeFPro and the global Fintech marketplace,” said Jon Bailey, Vice President for Fintech at Experian. “We are committed to supporting the Fintech community and we will continue to invest and innovate to help our clients solve problems, create opportunities, and promote financial inclusion,” Bailey said.
With 2020 firmly behind us and multiple COVID-19 vaccines being dispersed across the globe, many of us are entering 2021 with a bit of, dare we say it, optimism. But with consumer spending and consumer confidence dipping at the end of the year, along with an inversely proportional spike in coronavirus cases, it’s apparent there’s still some uncertainty to come. This leaves businesses and consumers alike, along with fintechs and their peer financial institutions, wondering when the world’s largest economy will truly rebound. But based on the most recent numbers available from Experian, fintechs have many reasons to be bullish. In this unprecedented year, marked by a global pandemic and a number of economic and personal challenges for both businesses and consumers, Americans are maintaining healthy credit profiles and responsible spending habits. While growth expectedly slowed towards the end of the year, Q4 of 2020 saw solid job gains in the US labor market, with 883,000 jobs added through November and the US unemployment rate falling to 6.7%. Promisingly, one of the sectors hit hardest by the pandemic, the leisure and hospitality industry added back the most jobs of all sectors in October: 271,000. Additionally, US home sales hit a 14-year high fueled by record low mortgage rates. And finally, consumer sentiment rose to the highest level (81.4%) since March 2020. Not only are these promising signs of continued recovery, they illustrate there are ample market opportunities now for fintechs and other financial institutions. “It’s been encouraging to see many of our fintech partners getting back to their pre-COVID marketing levels,” said Experian Account Executive for Fintech Neil Conway. “Perhaps more promising, these fintechs are telling me that not only are response rates up but so is the credit quality of those applicants,” he said. More plainly, if your company isn’t in the market now, you’re missing out. Here are the four steps fintechs should take to reenter the lending marketing intelligently, while mitigating as much risk as possible. Re-do Your Portfolio Review Periodic portfolio reviews are standard practice for financial institutions. But the health crisis has posted unique challenges that necessitate increased focus on the health and performance of your credit portfolio. If you haven’t done so already, doing an analysis of your current lending portfolio is imperative to ensure you are minimizing risk and maximizing profitability. It’s important to understand if your portfolio is overexposed to customers in a particularly hard-hit industry, i.e. entertainment, or bars and restaurants. At the account level there may be opportunities to reevaluate customers based on a different risk appetite or credit criteria and a portfolio review will help identify which of your customers could benefit from second chance opportunities they may not have otherwise been able to receive. Retool Your Data, Analytics and Models As the pandemic has raged on, fintechs have realized many of the traditional data inputs that informed credit models and underwriting may not be giving the complete picture of a consumer. Essentially, a 720 in June 2020 may not mean the same as it does today and forbearance periods have made payment history and delinquency less predictive of future ability to pay. To stay competitive, fintechs must make sure they have access to the freshest, most predictive data. This means adding alternative data and attributes to your data-driven decisioning strategies as much as possible. Alternative data, like income and employment data, works to enhance your ability to see a consumer’s entire credit portfolio, which gives lenders the confidence to continue to lend – as well as the ability to track and monitor a consumer’s historical performance (which is a good indicator of whether or not a consumer has both the intention and ability to repay a loan). Re-Model Your Lending Criteria One of the many things the global health crisis has affirmed is the ongoing need for the freshest, most predictable data inputs. But even with the right data, analytics can still be tedious, prolonging deployment when time is of the essence. Traditional models are too slow to develop and deploy, and they underperform during sudden economic upheavals. To stay ahead in times recovery or growth, fintechs need high-quality analytics models, running on large and varied data sets that they can deploy quickly and decisively. Unlike many banks and traditional financial institutions, fintechs are positioned to nimbly take advantage of market opportunities. Once your models are performing well, they should be deployed into the market to actualize on credit-worthy current and future borrowers. Advertising/Prescreening for Intentional Acquisition As fintechs look to re-enter the market or ramp up their prescreen volumes to pre-COVID levels, it’s imperative to reach the right prospects, with the right offer, based on where and how they’re browsing. More consumers than ever are relying on their phones for browsing and mobile banking, but aligning messaging and offers across devices and platforms is still important. Here’s where data-driven advertising becomes imperative to create a more relevant experience for consumers, while protecting privacy. As 2021 rolls forward, there will be ample chance for fintechs to capitalize on new market opportunities. Through up-to-date analysis of your portfolio, ensuring you have the freshest, predictive data, adjusting your lending criteria and tweaking your approach to advertising and prescreen, you can be ready for the opportunities brought on by the economic recovery. How is your fintech gearing up to re-enter the market? Learn more
Experian recently announced the new members named to its Fintech Advisory Board. The board and its members provide Experian with valuable insights and key perspectives into the unique and quickly evolving needs of the fintech industry. “For years Experian has been committed to partnering with innovators in the fintech industry to bring better opportunities to businesses and consumers alike,” said Experian North American CEO Craig Boundy. “We appreciate the thought leadership we get from our Fintech Advisory Board members and the challenge and the push that comes along with it,” he said. The board met virtually last month, welcoming representatives from across the fintech ecosystem representing payments, personal and secured loan lenders, credit card issuers, investors and others. “This was my first board meeting with Experian, and I’m very pleased to see the investment Experian has put into being the best of the three major bureaus in having the best technology to enable us to turnaround our models more quickly, and better data and alternative data sources like Boost,” said one of the new executives appointed to the board. “We are delighted to gather this group of innovators together to ensure we are consistently meeting the needs of our fintech partners,” said Experian Vice President Jon Bailey, who oversees the fintech vertical. “Now more than ever it’s important that we work alongside them in shaping the industry and helping them meet their goals for the future,” he said. Experian’s fintech vertical provides leading-edge solutions and data across the credit lifecycle specifically designed to impact Fintech and marketplace lending companies and their customers. For more information on Experian’s fintech services or the advisory board, click here.
The housing industry seems to be one of the more visible sectors impacted by the global health crisis. According to a recent U.S. Census Household Pulse Survey, at the end of October, 9.9 million Americans were not up-to-date on their rent or mortgage payments and were not confident that they could pay next month’s rent or mortgage on time. Meanwhile, the CDC’s moratorium on evictions is set to last through December 31, 2020. This has left landlords, property management companies and other companies involved in the housing industry wondering what the long-term effects might be to their bottom lines and strategic direction. As companies continue to reevaluate their approach, they should look for strategies they can implement today that will work as the pandemic continues but will also pay dividends as the rental market reopens and expands. Make sure these three strategies are part of your rental industry solutions playbook. Customer Experience Perhaps one of the first complications brought on by shelter-in-place orders and social distancing was their effect on customer experience. Seemingly overnight, property owners and in-markets renters had to rethink the traditional rental process. From viewing, application and contract-signing, every aspect of the leasing lifecycle needed to go digital. Digital applications and identity verification, along with touchless viewing can minimize leasing staff and applicant exposure in the near term. However, property management companies should think of these capabilities as long-term investments as they create an opportunity to improve the rental customer experience by reducing friction in the rental process: allowing quick and efficient application submission, leasing decisions, and deposit and rent collection. Risk Reduction Operational difficulties, along with the uncertainty created by eviction moratoriums, have put the need for risk reduction front and center for rental industry and property management professionals. During the health crisis and beyond, companies should develop strategies that help to maintain occupancy rates, reduce losses and help maintain compliance. In addition to clearly stating processes and procedures to prospective renters, this starts with accessing insightful data and verification services that ensure the best tenants are being selected. The data and tools implemented should also predict or identify the likelihood of non-payment and reduce disclosure risk. Together, these rental risk mitigation tactics not only verify identity, background information and employment, but also help property managers and landlords avoid the rising application fraud associated with the health crisis. Reducing Cost; Increasing Efficiencies Along with the risks and uncertainty brought on by COVID-19, the rental industry has also seen new expenses brought on by the health crisis, i.e. cleaning requirements and staff safety protocols. Rental industry professionals and landlords should look for every opportunity to reduce costs and realize efficiencies. The good news is that many of the tools and tactics implemented to improve the renter experience and reduce risk also create efficiencies and cost-savings in the process. Using online tools to eliminates the time, resources, and paperwork required to process applications and verify applicant information. Leveraging the right data and insights to prioritize the right applicants avoids future potential complications and loss of income from future evictions. (Evictions cost an average of $7,685, according to the National Association of Realtors). It’s clear COVID-19 will be a part of everyday life for the foreseeable future. However, like the saying goes, there’s opportunity in every crisis. Rental industry professionals have the opportunity to implement meaningful strategies that can help shepherd them through the health crisis and also future-proof their portfolios, all while reducing friction and improving the customer experience across the leasing lifecycle. For more information on tools you can use now to future-proof your rental portfolio, visit Experian’s Rental Industry Solutions hub.
The financial services industry is not always synonymous with innovation and forward-thinking. While there are some exceptions with top-10 banks and some savvy regionals, as a whole, the sector tends to fall on the latter half of the diffusion of innovation curve, usually slotting in the late majority or laggard phase. Conversely, the opposite is true for fintechs who have been an enormously disruptive force of change in financial services over the past 10 years. For many businesses, the pandemic has created uncertainty and an inability to conduct or generate business. However, the silver lining with COVID-19 might just be that it’s driving digital innovation across industries. Andreesen Horowitz, a venture capital firm, estimates businesses of all kinds are experiencing at least two years’ worth of digitization compressed into the last six months. And while they have been significantly impacted, for fintechs who were already pushing the envelope and challenging existing business models, COVID-19 suddenly accelerated financial services innovation into overdrive. Here are three challenges fintechs are answering in the wake of the COVID-19 health crisis. Digital Banking The first lockdowns flipped the digital switch in financial services. Seemingly overnight, banking moved digital. In April, new mobile banking registrations increased 200%, while mobile banking traffic rose 85%. Likewise, Deloitte reported online banking activity has increased 35% since the pandemic started. Being mobile-first or digital-only has allowed many fintechs to win in offering presentment, activation, underwriting, and a contextual digital interface, all capabilities that will only become more relevant as the pandemic stretches on. At Square, direct deposit volumes grew by three times from March to April, up to $1.3 billion; Chime saw record signups. Continued social distancing will only serve to accelerate customers’ use of mobile and online platforms to manage their finances. Contactless Payments Similar to digital banking as a whole, the health crisis has accelerated the necessity for contactless payments. Whereas convenience and a seamless customer experience may have been drivers for payments innovation in the past, now, many customers may view it as a life or death health concern. Phones, wearables and even connected vehicles are empowering customers to participate in commerce while avoiding handling cash or coming in contact with an infected surface. Through their adoption of IOT-powered contactless payments, fintechs are accelerating this area of financial services to keep customers safe. Financial Inclusion and Speeding Economic Relief Any disaster disproportionally affects the underbanked and those living at the poverty line, and COVID-19 is no different. While it will undoubtedly contribute to an increase in unbanked households, the pandemic may also provide an opportunity to innovate through this problem. Financial inclusion was already a focus for many fintechs, who’ve made it their mission to bring equity by offering basic financial services in a transparent way. Unencumbered by legacy systems and business models, fintechs are well positioned to work across the financial ecosystem, from financial services, retail and government to efficiently and more quickly distribute benefits to at-risk groups and impacted businesses. From their ability to quickly ingest new and novel data sources, to a focus on using a digital-first approach to delight customers, fintechs will continue to harness their strengths to disrupt financial services, even during the pandemic. How is your fintech driving innovation and customer experience during the health crisis? Learn more
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
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%.
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?
Big Data, once thought to be overhyped consultant-speak, is now a term and business model so ubiquitous it underpins billions of dollars in revenue across nearly every industry. Similarly, the advanced analytics derived from big data are key to staying relevant in an everchanging global economy and to consumers with expanding expectations. But for many financial institutions, using big data and advanced analytics seemed to only be in reach for big banks with large, advanced data teams. With the expansion of the Experian Ascend Technology PlatformTM, the conversation is changing. Financial institutions of all sizes can now leverage advanced analytics, artificial intelligence and machine learning with new configurations in the award-winning platform. In a release earlier this week, Experian announced new tools and configurations in the Ascend Analytical SandboxTM to fit teams of every size and skill level. Now fintechs, banks and credit unions of every size can have access to Experian’s one-stop source for advanced analytics, business intelligence and ultimately, better decisions. The secure hybrid-cloud environment allows users to combine their own data sets with Experian’s exclusive data assets, including credit, alternative, commercial, auto and more. From there, users can build and test models across different stages of the lending cycle, including originations, prescreen, account management and collections, and seamlessly put their models into production. Experian’s Ascend Analytical Sandbox also allows users to benchmark their portfolios against the industry, identify credit trends and explore new product opportunities. All the insights gathered through the Ascend Analytical Sandbox can be viewed and shared through interactive dashboards and customizable reports that can be pulled in near real time. Additional use cases include: Reject inferencing – refine models, scorecards and strategies by analyzing trades opened by previous applicants who were rejected or approved but did not move forward Prescreen campaigns – design prescreen campaigns, evaluate results and improve strategies Cross-sell – identify cross-sell opportunities for existing customers and identify how they may be working with other lenders Collections strategies, stress testing and loss forecasting – build stronger models to identify customers that have ability and willingness to pay debts, stress test and forecast loss Peer benchmarking and industry trends – compare current portfolio against peers and the industry Recession planning – identify areas to adjust your portfolio to prepare for an economic downturn OneMain Financial, a large provider of personal installment loans serving 10 million total customers across more than 1,700 branches, turned to Experian to improve its risk modeling and credit portfolio management capabilities with the Ascend Analytical Sandbox. Since using the solution, the company has seen significant improvements in reject inferencing – a process that is traditionally expensive, manually-intensive and time consuming. According to OneMain Financial, the Ascend Analytical Sandbox has shortened the process to less than two weeks from up to 180 days. \"Experian\'s Ascend Technology Platform and Analytical Sandbox is an industry gamechanger,\" said Michael Kortering, OneMain Financial\'s Senior Managing Director and Head of Model Development. \"We\'re completing analyses that just weren\'t possible before and we\'re getting decisions to our clients faster, without compromising risk.” For more information on Ascend Analytical Sandbox SX – the latest solution for financial institutions of all sizes – or other enterprise-wide capabilities of the Experian Ascend Technology Platform, click here.
You’ve Got Mail! Probably a lot of it. Birthday cards from Mom, a graduation announcement from your third cousin’s kid whose name you can’t remember and a postcard from your dentist reminding you you’re overdue for a cleaning. Adding to your pile, are the nearly 850 pieces of unsolicited mail Americans receive annually, according to Reader’s Digest. Many of these are pre-approval offers or invitations to apply for credit cards or personal loans. While many of these offers are getting to the right mailbox, they’re hitting a changing consumer at the wrong time. The digital revolution, along with the proliferation and availability of technology, has empowered consumers. They now not only have access to an abundance of choices but also a litany of new tools and channels, which results in them making faster, sometimes subconscious, decisions. Three Months Too Late The need to consistently stay in front of customers and prospects with the right message at the right time has caused a shortening of campaign cycles across industries. However, for some financial institutions, the customer acquisition process can take up to 120 days! While this timeframe is extreme, customer prospecting can still take around 45-60 days for most financial institutions and includes: Bureau processing: Regularly takes 10-15 days depending on the number of data sources and each time they are requested from a bureau. Data aggregation: Typically takes anywhere from 20-30 days. Targeting and selection: Generally, takes two to five days. Processing and campaign deployment: Usually takes anywhere from three days, if the firm handles it internally, or up to 10 days if an outside company handles the mailing. A Better Way That means for many firms, the data their customer acquisition campaigns are based off is at least 60 days old. Often, they are now dealing with a completely different consumer. With new card originations up 20% year-over-year in 2019 alone, it’s likely they’ve moved on, perhaps to one of your competitors. It’s time financial institutions make the move to a more modern form of prospecting and targeting that leverages the power of cloud technology, machine learning and artificial intelligence to accelerate and improve the marketing process. Financial marketing systems of the future will allow for advanced segmentation and targeting, dynamic campaign design and immediate deployment all based on the freshest data (no more than 24-48 hours old). These systems will allow firms to do ongoing analytics and modeling so their campaign testing and learning results can immediately influence next cycle decisions. Your customers are changing, isn’t it time the way you market to them changes as well?
Do more with less. Once the mantra of the life-hacking movement, it seems to be the charge given to marketers across the globe. Reduce waste; increase conversion rates; customize messages at a customer level; and do it all faster and more efficiently (read cheaper) than you did last quarter. The marketing challenges facing all companies seem to be more pronounced for financial institutions – not surprising for an industry with a reputation for late adoption. But doing more with less is not just a catchphrase thrown around by lean-obsessed consultants, it’s a response to key changes and challenges in the market. Here are 3 of the top marketing challenges creating business problems for financial institutions today. Budget constraints and misalignment As someone charged with the marketing remit in your firm, this probably comes as no surprise to you. Marketing budgets are stagnant, if not shrinking. Based on a 2018 report from CMO Survey, marketing budgets represent just over 11% of firm expenditures, a level which has remained largely constant over the last six years.Meanwhile, budgets at many financial firms appear to be out-of-touch with today’s ever-evolving market. In this Financial Brand report, virtually no financial institution committed more than 40% of their budget to mobile marketing, a stat unchanged from the prior two years. More channels mean even more segmentation Gone are the days where a company can rely heavily on traditional media to reach targets and clients. Now more than ever, your customers have access to a compounding amount of media on a proliferating number of channels. Some examples: In 2018, the Pew Research Center found most Americans (68%) get their news from social media. Cable companies recently followed streaming services to offer seamless service and experience across TV, desktop and mobile. Apple and Disney are two of several media juggernauts who are throwing their new streaming services and networks into the ring.This level of access is driving a shift in customers’ expectations for how, when and where they consume content. They want custom messages delivered in a seamless experience across the various channels they use. Shorter campaign cycles According to a recent study by Microsoft, humans now have shorter attention spans, at 8 seconds, than goldfish at 9 seconds. This isn’t surprising considering the levels of digital reach and access your customers are presented with. But this is also forcing a shortening of content and campaign cycles in response. Marketers are now expected to plan, launch and analyze engaging campaigns to meet and stay ahead of customer need and expectation. Ironically, while there’s an intentional shortening of campaign cycles, there’s also a corporate focus to prolong and grow the customer relationship. It’s clear, competing in today’s world requires transforming your organization to address rapidly increasing complexity while containing costs. Competing against stagnant marketing budgets, proliferating media channels and shorter campaign cycles while delivering results is a formidable task, especially if your financial institution is not effectively leveraging data and analytics as differentiators. CMOs and their marketing teams must invest in new technologies and revisit product and channel strategies that reflect the expectations of their customers. How is your bank or credit union responding to these financial marketing challenges? Download Customer Acquisition eBook
While it’s a word that has only recently made its way into financial circles, consumers and businesses alike have been enjoying life in a platform world. Digital platforms connect riders with drivers, friends with family, manufacturers with buyers and sellers, and the list goes on. Digital platforms are technology-enabled business models that work to enhance efficiency, flexibility, scalability, integration, and ultimately user engagement. They’re integral to the operation and success of some of the most valuable companies in the world, including Google, Facebook, and Amazon. While digital platforms have made their way beyond high-tech to other industries, like supply chain management and logistics, financial institutions have fallen behind. The reasons why are understandable: a quickly evolving marketplace, regulatory induced risk aversion, and the need to protect data and privacy. Most of the digital platform adoption that has occurred in the financial industry has revolved around open banking, with a focus on enriching the customer experience. BBVA, for instance, recently launched a platform to enable their business clients to use white-labeled versions of BBVA products and services on-demand. But the value of digital platforms for the financial industry can go beyond how the consumer interfaces with his or her bank or credit union. Financial institutions could see the same efficiency, flexibility, and integration benefits by implementing technology platforms into their internal systems. Traditionally, financial institutions have used contrasting technology and systems across their customers’ lifecycle. From financial marketing and targeting, to acquisition and underwriting, there is ample opportunity to streamline and integrate these systems by adopting a platform architecture. The most future-forward platforms not only enable financial institutions to integrate their internal systems, but they also allow companies to seamlessly integrate their customer data with third-party data resources. The powers of data-driven answers combined with platform technology can help overcome business challenges and satisfy consumer and client demands. Is it time you and your company stepped up to the platform?
From a capricious economic environment to increased competition from new market entrants and a customer base that expects a seamless, customized experience, there are a host of evolving factors that are changing the way financial institutions operate. Now more than ever, financial institutions are turning to their data for insights into their customers and market opportunities. But to be effective, this data must be accurate and fresh; otherwise, the resulting strategies and decisions become stale and less effective. This was the challenge facing OneMain Financial, a large provider of personal installment loans serving 10 million total customers across more than 1,700 branches—creating accurate, timely and robust insights, models and strategies to manage their credit portfolios. Traditionally, the archive process had been an expensive, time-consuming, and labor-intensive process; it can take months from start to finish. OneMain Financial needed a solution to reduce expenses and the time involved in order to improve their core risk modeling. In this recent IDC Customer Spotlight, sponsored by Experian, \"Improving Core Risk Modeling with Better Data Analysis,\" Steven D’Alfonso, Research Director spoke with the Senior Managing Director and head of model development at OneMain Financial who turned to Experian’s Ascend Analytical Sandbox to improve its core risk modeling through reject inferencing. But OneMain Financial also realized additional benefits and opportunities with the solution including compliance and economic stress testing. Read the customer spotlight to learn more about the explore how OneMain Financial: Reduced expense and effort associated with its archive process Improved risk model development timing from several months to 1-2 weeks Used Sandbox to gain additional market insight including: market share, benchmarking and trends, etc. Read the Case Study
Perhaps more than ever before, technology is changing how companies operate, produce and deliver products and services to their customers. Similarly, technology is also driving a shift in customer expectation in how, when and where they consume products and services. But these changes aren’t just relegated to the arenas where tech giants with household names, like Amazon and Google, play. Likewise, financial institutions of every size are also fielding the changes brought on by innovations to the industry in recent years. According to this report by PWC, 77% of firms plan on dedicating time and budgets to increase innovation. But what areas make the most sense for your business? With a seemingly constant shift in consumer and corporate focus, it can be difficult to know which technological advancements are imperative to your company’s success and which are just the latest fizzling buzzword. As you evaluate innovation investments for your organization in 2019 and beyond, here’s a list of four technology innovations that are already changing the financial sector or will change the banking landscape in the near future. The APIs of Open Banking Ok, it’s not a singular innovation, so I’m cheating a bit here, but it’s a great place to begin the conversation because it comprises and sets the stage for many of the innovations and technologies that are in use today or will be implemented in the future. Created in 2015, the Open Banking Standard defined how a bank’s system data or consumer-permissioned financial data should be created, accessed and shared through the use of application programming interfaces or APIs. When financial institutions open their systems up to third-party developer partners, they can respond to the global trends driving change within the industry while greatly improving the customer experience. With the ability to securely share their financial data with other lenders, greater transparency into the banking process, and more opportunities to compare product offerings, consumers get the frictionless experience they’ve come to expect in just about every aspect of life – just not necessarily one that lenders are known for. But the benefits of open banking are not solely consumer-centric. Financial institutions are able to digitize their product offerings and thus expand their market and more easily share data with partners, all while meeting clients’ individualized needs in the most cost-effective way. Biometrically speaking…and smiling Verifying the identity of a customer is perhaps one of the most fundamental elements to a financial transaction. This ‘Know Your Customer’ (KYC) process is integral to preventing fraud, identity theft, money laundering, etc., but it’s also time-consuming and inconvenient to customers. Technology is changing that. From thumbprint and, now, facial recognition through Apple Pay, consumers have been using biometrics to engage with and authorize financial transactions for some time now. As such, the use of biometrics to authenticate identity and remove friction from the financial process is becoming more mainstream, moving from smartphones to more direct interaction. Chase has now implemented voice biometrics to verify a consumer’s identity in customer service situations, allowing the company to more quickly meet a customer’s needs. Meanwhile, in the US and Europe, Visa is testing biometric credit cards that have a fingerprint reader embedded in the card that stores his or her fingerprint in order to authenticate their identity during a financial transaction. In China, companies like Alipay are taking this to the next level by allowing customers to bypass the phone entirely with its ‘pay with a smile’ service. First launched in KFC restaurants in China, the service is now being offered at hospitals as well. How, when and where a consumer accesses their financial institution data actually creates a digital fingerprint that can be verified. While facial and vocal matching are key components to identity verification and protecting the consumer, behavioral biometrics have also become an important part of the fraud prevention arsenal for many financial institutions. These are key components of Experian’s CrossCore solution, the first open fraud and identity platform partners with a variety of companies, through open APIs discussed above. Not so New Kid on the Block(chain) The first Bitcoin transaction took place on January 12, 2009. And for a number of years, all was quiet. Then in 2017, Bitcoin started to blow up, creating a scene reminiscent of the 1850s California gold rush. Growing at a seemingly exponential rate, the cryptocurrency topped out at a per unit price of more than $20,000. By design cryptocurrencies are decentralized, meaning they are not controlled or regulated by a single entity, reducing the need for central third-party institutions, i.e. banks and other financial institutions to function as central authorities of trust. Volatility and regulation aside, it’s understandable why financial institutions were uneasy, if not skeptical of the innovation. But perhaps the most unique characteristic of cryptocurrencies is the technology on which they are built: blockchain. Essentially, a blockchain is just a special kind of database. The database stores, validates, transfers and keeps a ledger of transfers of encrypted data—records of financial transfers in the case of Bitcoin. But these records aren’t stored on one computer as is the case with traditional databases. Blockchain leverages a distributed ledger or distributed trust approach where a full copy of the database is stored across many distributed processing nodes and the system is constantly checking and validating the contents of the database. But a blockchain can store any type of data, making it useful in a wide variety of applications including tracking the ownership digital or physical assets or the provenance of documents, etc. From clearing and settlements, payments, trade finance, identity and fraud prevention, we’re already seeing financial institutions explore and/or utilize the technology. Santander was the first UK bank to utilize blockchain for their international payments app One Pay FX. Similarly, other banks and industry groups are forming consortiums to test the technology for other various uses. With all this activity, it’s clear that blockchain will become an integral part of financial institutions technology and operations on some level in the coming years. Robot Uprising Rise in Robots While Artificial Intelligence seems to have only recently crept into pop-culture and business vernacular, it was actually coined in 1956 by John McCarthy, a researcher at Dartmouth who thought that any aspect of learning or intelligence could essentially be taught to a machine. AI allows machines to learn from experience, adjust to new inputs and carry out human-like tasks. It’s the result of becoming ‘human-like’ or the potential to become superior to humans that creeps out people like my father, and also worries others like Elon Musk. Doomsday scenarios a la Terminator aside, it’s easy to see how the tech can and is useful to society. In fact, much of the AI development done today uses human-style reasoning as a model, but not necessarily the ultimate aim, to deliver better products and services. It’s this subset of AI, machine learning, that allows companies like Amazon to provide everything from services like automatic encryption in AWS to products like Amazon Echo. While it’s much more complex, a simple way to think about AI is that it functions like billions of conditional if-then-else statements working in a random, varied environment typically towards a set goal. Whereas in the past, programmers would have to code these statements and input reference data themselves, machine learning systems learn, modify and map between inputs and outputs to create new actions based on their learning. It works by combining the large amounts of data created on a daily basis with fast, iterative processing and intelligent algorithms, allowing the program to learn from patterns in the data and make decisions. It’s this type of machine learning that banks are already using to automate routine, rule-based tasks like fraud monitoring and also drive the analytical environments used in their risk modeling and other predictive analytics. Whether or not you’ve implemented AI, machine learning or bot technology into your operations, it’s highly likely your customers are already leveraging AI in their home lives, with smart home devices like Amazon Echo and Google Home. Conversational AI is the next juncture in how people interface with each other, companies and life in general. We’re already seeing previews of what’s possible with technologies like Google Duplex. This has huge implication for the financial services industry, from removing friction at a transaction level to creating a stickier, more engaging customer experience. To that end, according to this report from Accenture, AI may begin to provide in-the-moment, holistic financial advice that is in a customer’s best interest. It goes without saying that the market will continue to evolve, competition will only grow more fierce, consumer expectation will continue to shift, and regulation will likely become more complex. It’s clear technology can be a mitigating factor, even a competitive differentiator, with these changing industry variables. Financial institutions must evolve corporate mindsets in their approach to prioritize innovations that will have the greatest enterprise-wide impact. By putting together an intelligent mix of people, process, and the right technology, financial institutions can better predict consumer need and expectation while modernizing their business models.
“We don’t know what we don’t know.” It’s a truth that seems to be on the minds of just about every financial institution these days. The market, not-to-mention the customer base, seems to be evolving more quickly now than ever before. Mergers, acquisitions and partnerships, along with new competitors entering the space, are a daily headline. Customers expect the same seamless user experience and instant gratification they’ve come to expect from companies like Amazon in just about every interaction they have, including with their financial institutions. Broadly, financial institutions have been slow to respond both in the products they offer their customers and prospects, and in how they present those products. Not surprisingly, only 26% of customers feel like their financial institutions understand and appreciate their needs. So, it’s not hard to see why there might be uncertainty as to how a financial institution should respond or what they should do next. But what if you could know what you don’t know about your customer and industry data? Sound too good to be true? It’s not—it’s exactly what Experian’s Ascend Analytical Sandbox was built to do. “At OneMain we’ve used Sandbox for a lot of exploratory analysis and feature development,” said Ryland Ely, a modeler at Experian partner client, OneMain Financial and a Sandbox user. For example, “we’ve used a loan amount model built on Sandbox data to try and flag applications where we might be comfortable with the assigned risk grade but we’re concerned we might be extending too much or too little credit,” he said. The first product built on Experian’s big data platform, Ascend, the Analytical Sandbox is an analytics environment that can have enterprise-wide impact. It provides users instant access to near real-time customer data, actionable analytics and intelligence tools, along with a network of industry and support experts to drive the most value out of their data and analytics. Developed with scalability, flexibility, efficiency and security at top-of-mind, the Sandbox is a hybrid-cloud system that leverages the high availability and security of Amazon Web Services. This eliminates the need, time and infrastructure costs associated with creating an internally hosted environment. Additionally, our web-based interface speeds access to data and tools in your dedicated Sandbox all behind the protection of Experian’s firewall. In addition to being supported by a revolutionized tech stack backed by an $825 million annual investment, Sandbox enables use of industry-leading business intelligence tools like SAS, RStudio, H2O, Python, Hue and Tableau. Where the Ascend Sandbox really shines is in the amount and quality of the data that’s put into it. As the largest, global information services provider, the Sandbox brings the full power of Experian’s 17+ years of full-file historical tradeline data, boasting a data accuracy rate of 99.9%. The Sandbox also allows users the option to incorporate additional data sets including commercial small business data and soon real estate data, among others. Alternative data assets add to the 50 million consumers who use some sort of financial service, in addition to rental and utility payments. In addition to including Experian’s data on the 220+ million credit-active consumers, small business and other data sets, the Sandbox also allows companies to integrate their own customer data into the system. All data is depersonalized and pinned to allow companies to fully leverage the value of Experian’s patented attributes and scores and models. Ascend Sandbox allows companies to mine the data for business intelligence to define strategy and translate those findings into data visualizations to communicate and win buy-in throughout their organization. But here is where customers are really identifying the value in this big data solution, taking those business intelligence insights and being able to take the resulting models and strategies from the Sandbox directly into a production environment. After all, amassing data is worthless unless you’re able to use it. That’s why 15 of the top financial institutions globally are using the Experian Ascend Sandbox for more than just benchmarking and data visualization but also risk modeling, score migration, share of wallet, market entry, cross-sell and much more. Moreover, clients are seeing time-savings, deeper insights and reduced compliance concerns as a result of consolidating their production data and development platform inside Sandbox. “Sandbox is often presented as a tool for visualization or reporting, sort of creating summary statistics of what’s going on in the market. But as a modeler, my perspective is that it has application beyond just those things,” said Ely. To learn more about the Experian Ascend Analytical Sandbox and hear more about how OneMain Financial is getting value out of the Sandbox, watch this on-demand webinar.
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