Financial Services
Financial institutions preparing for the launch of the Financial Accounting Standard Board’s (FASB) new current expected credit loss model, or CECL, may have concerns when it comes to preparedness, implications and overall impact. Gavin Harding, Experian’s Senior Business Consultant and Jose Tagunicar, Director of Product Management, tackled some of the tough questions posed by the new accounting standard. Check out what they had to say: Q: How can financial institutions begin the CECL transition process? JT: To prepare for the CECL transition process, companies should conduct an operational readiness review, which includes: Analyzing your data for existing gaps. Determining important milestones and preparing for implementation with a detailed roadmap. Running different loss methods to compare results. Once losses are calculated, you’ll want to select the best methodology based on your portfolio. Q: What is required to comply with CECL? GH: Complying with CECL may require financial institutions to gather, store and calculate more data than before. To satisfy CECL requirements, financial institutions will need to focus on end-to-end management, determine estimation approaches that will produce reasonable and supportable forecasts and automate their technology and platforms. Additionally, well-documented CECL estimations will require integrated workflows and incremental governance. Q: What should organizations look for in a partner that assists in measuring expected credit losses under CECL? GH: It’s expected that many financial institutions will use third-party vendors to help them implement CECL. Third-party solutions can help institutions prepare for the organization and operation implications by developing an effective data strategy plan and quantifying the impact of various forecasted conditions. The right third-party partner will deliver an integrated framework that empowers clients to optimize their data, enhance their modeling expertise and ensure policies and procedures supporting model governance are regulatory compliant. Q: What is CECL’s impact on financial institutions? How does the impact for credit unions/smaller lenders differ (if at all)? GH: CECL will have a significant effect on financial institutions’ accounting, modeling and forecasting. It also heavily impacts their allowance for credit losses and financial statements. Financial institutions must educate their investors and shareholders about how CECL-driven disclosure and reporting changes could potentially alter their bottom line. CECL’s requirements entail data that most credit unions and smaller lenders haven’t been actively storing and saving, leaving them with historical data that may not have been recorded or will be inaccessible when it’s needed for a CECL calculation. Q: How can Experian help with CECL compliance? JT: At Experian, we have one simple goal in mind when it comes to CECL compliance: how can we make it easier for our clients? Our Ascend CECL ForecasterTM, in partnership with Oliver Wyman, allows our clients to create CECL forecasts in a fraction of the time it normally takes, using a simple, configurable application that accurately predicts expected losses. The Ascend CECL Forecaster enables you to: Fulfill data requirements: We don’t ask you to gather, prepare or submit any data. The application is comprised of Experian’s extensive historical data, delivered via the Ascend Technology PlatformTM, economic data from Oxford Economics, as well as the auto and home valuation data needed to generate CECL forecasts for each unsecured and secured lending product in your portfolio. Leverage innovative technology: The application uses advanced machine learning models built on 15 years of industry-leading credit data using high-quality Oliver Wyman loan level models. Simplify processes: One of the biggest challenges our clients face is the amount of time and analytical effort it takes to create one CECL forecast, much less several that can be compared for optimal results. With the Ascend CECL Forecaster, creating a forecast is a simple process that can be delivered quickly and accurately. Q: What are immediate next steps? JT: As mentioned, complying with CECL may require you to gather, store and calculate more data than before. Therefore, it’s important that companies act now to better prepare. Immediate next steps include: Establishing your loss forecast methodology: CECL will require a new methodology, making it essential to take advantage of advanced statistical techniques and third-party solutions. Making additional reserves available: It’s imperative to understand how CECL impacts both revenue and profit. According to some estimates, banks will need to increase their reserves by up to 50% to comply with CECL requirements. Preparing your board and investors: Make sure key stakeholders are aware of the potential costs and profit impacts that these changes will have on your bottom line. Speak with an expert
What is CECL? CECL (Current Expected Credit Loss) is a new credit loss model, to be leveraged by financial institutions, that estimates the expected loss over the life of a loan by using historical information, current conditions and reasonable forecasts. According to AccountingToday, CECL is considered one of the most significant accounting changes in decades to affect entities that borrow and lend money. To comply with CECL by the assigned deadline, financial institutions will need to access much more data than they’re currently using to calculate their reserves under the incurred loss model, Allowance for Loan and Lease Losses (ALLL). How does it impact your business? CECL introduces uncertainty into accounting and growth calculations, as it represents a significant change in the way credit losses are currently estimated. The new standard allows financial institutions to calculate allowances in a variety of ways, including discounted cash flow, loss rates, roll-rates and probability of default analyses. “Large banks with historically good loss performance are projecting increased reserve requirements in the billions of dollars,” says Experian Advisory Services Senior Business Consultant, Gavin Harding. Here are a few changes that you should expect: Larger allowances will be required for most products As allowances will increase, pricing of the products will change to reflect higher capital cost Losses modeling will change, impacting both data collection and modeling methodology There will be a lower return on equity, especially in products with a longer life expectancy How can you prepare? “CECL compliance is a journey, rather than a destination,” says Gavin. “The key is to develop a thoughtful, data-driven approach that is tested and refined over time.” Financial institutions who start preparing for CECL now will ultimately set their organizations up for success. Here are a few ways to begin to assess your readiness: Create a roadmap and initiative prioritization plan Calculate the impact of CECL on your bottom line Run altered scenarios based on new lending policy and credit decision rules Understand the impact CECL will have on your profitability Evaluate current portfolios based on CECL methodology Run different loss methods and compare results Additionally, there is required data to capture, including quarterly or monthly loan-level account performance metrics, multiple year data based on loan product type and historical data for the life of the loan. How much time do you have? Like most accounting standards, CECL has different effective dates based on the type of reporting entity. Public business entities that file financial statements with the Security and Exchange Commission will have to comply by 2020, non-public entity banks must comply by 2022 and non-SEC registered companies have until 2023 to adopt the new standard. How can we help: Complying with CECL may require you to gather, store and calculate more data than before. Experian can help you comply with CECL guidelines including data needs, consulting and loan loss calculation. Experian industry experts will help update your current strategies and establish an appropriate timeline to meet compliance dates. Leveraging our best-in-class industry data, we will help you gain CECL compliance quickly and effectively, understand the impacts to your business and use these findings to improve overall profitability. Learn more
Consumer credit trends are continuously changing, making it imperative to keep up with the latest developments in originations, delinquencies on mortgages, credit cards and auto loans. By monitoring consumer behavior and market trends over time, you can predict and prepare for potential issues within each market. In this 30-minute webinar, our featured speakers, Gavin Harding, Experian Senior Business Consultant, and Alan Ikemura, Experian Data Analytics Senior Product Manager, reveal Q1 2019 market intelligence data and explore recent advances in consumer credit trends. Watch our on-demand webinar
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?
The universe has been used as a metaphor for many things – vast, wide, intangible – much like the credit universe. However, while the man on the moon, a trip outside the ozone layer, and all things space from that perspective may seem out of touch, there is a new line of access to consumers. In Experian\'s latest 2019 State of Alternative Credit Data report, consumers and lenders alike weigh in on the growing data set and how they are leveraging the data in use cases across the lending lifecycle. While the topic of alternative credit data is no longer as unfamiliar as it may have been a year or two ago, the capabilities and benefits that can be experienced by financial institutions, small businesses and consumers are still not widely known. Did you know?: - 65% of lenders say they are using information beyond the traditional credit report to make a lending decision. - 58% of consumers agree that having the ability to contribute payment history to their credit file make them feel empowered. - 83% of lenders agree that digitally connecting financial account data will create efficiencies in the lending process. These and other consumer and lender perceptions of alternative credit data are now launched with the latest edition of the State of Alternative Credit Data whitepaper. This year’s report rounds up the different types of alternative credit data (from alternative financial services data to consumer-permissioned account data, think Experian BoostTM), as well as an overview of the regulatory landscape, and a number of use cases across consumer and small business lending. In addition, consumers also have a lot to say about alternative credit data: With the rise of machine learning and big data, lenders can collect more data than ever, facilitating smarter and more precise decisions. Unlock your portfolio’s growth potential by tapping into alternative credit data to expand your consumer universe. Learn more in the 2019 State of Alternative Credit Data Whitepaper. Read Full Report View our 2020 State of Alternative Credit Data Report for an updated look at how consumers and lenders are leveraging alternative credit data.
Earlier this month, Experian joined FinovateSpring in San Francisco, CA to demonstrate innovations impacting financial health to over 1,000 attendees. The Finovate conference promotes real-world solutions while highlighting short-form demos and key insights from thought-leaders on digital lending, banking, payments, artificial intelligence and the customer experience. With more than 100 million Americans lacking fair access to credit, it\'s more important than ever for companies to work to improve the financial health of consumers. In addition to the show\'s abundance of fintech-centered content, Experian hosted an exclusive, cutting-edge breakout series demonstrating innovations that are positively impacting the financial health of consumers across the nation. Finovate Day One Overview While fintechs, banks, venture capitalist, entrepreneurs and industry analysts ascended on the general conference floor for a fast-paced day of demos, a select subset gathered for a luncheon presented by Experian North America CEO, Craig Boundy, and Group President, Alex Lintner. Attendees were given an in-depth look at new, alternative credit data streams and tools that are helping to increase financial access. Demos included: Experian Boost™: a free, groundbreaking online platform that allows consumers to instantly boost their credit scores by adding telecommunications and utility bill payments to their credit file. More than half a million consumers have leveraged Experian Boost, increasing their score by an average of 13 points. Cumulatively, Experian Boost has helped add more than 2.8 million points to consumers’ credit scores. Ascend Analytical Sandbox™: A first-of-its-kind data and analytics platform that gives companies instant access to more than 17 years of depersonalized credit data on more than 220 million U.S. consumers. It has been the most successful product launch in Experian’s history and recently earned the title of “Best Overall Analytics Platform” at this year’s Fintech Breakthrough Awards. Alternative Credit Data: Comprised of data from alternative credit sources, this data helps lenders make smarter and more informed lending decisions. Additionally, Experian’s Clear Data Platform is next-level credit data that adds supplemental FCRA-compliant credit data to enrich decisions across the entire credit spectrum. This new platform features alternative credit data, rental data, public records, consumer-permissioned data and more Upon conclusion of the luncheon, Alpa Lally, Experian’s Vice President of Data Business at Consumer Information Services, was interviewed for the HousingWire Podcast with Jacob Gaffney, HousingWire Editor in Chief, to discuss how new forms of data streams are helping improve consumers’ access to credit by giving lenders a clearer picture of their creditworthiness and risk. “Alternative credit data is different than traditional credit data and helps us paint a fuller picture of the consumer in terms of their ability to pay, willingness to pay and stability. It helps consumers get better access overall to the credit they deserve so that they can actively participate in the economy,” said Lally. Finovate Day Two Overview On the last day of the conference, expert speakers took to the main stage to analyze the latest fintech trends, opportunities and challenges. Alex Lintner and Sandeep Bhandari, Chief Strategy Officer and Chief Risk Officer at Affirm, participated in a fireside chat titled “Improving the Financial Health of America’s 100 Million Credit Underserved Consumers.” Moderated by David Penn, Finovate Analyst, the session explored the latest innovations, trends and technologies – from machine learning to alternative data – that are making a difference in positively impacting the financial health of Americans and expanding financial opportunities for underserved consumers. The panel discussed the efforts made to put financial health at the center of their business and the impact it’s had on their organizations. Following the fireside chat, Experian hosted a second lunch briefing, presented by Vijay Mehta, Chief Innovation Officer, and Greg Wright, EVP Chief Product Officer. The lunch included exclusive table discussions and open conversations to help attendees leave with a better understanding of the importance of prioritizing financial health to build trust, reach new customers and ultimately grow their business. \"We are actively seeking out unresolved problems and creating products and technologies that will help transform the way businesses operate and consumers thrive in our society. But we know we can\'t do it alone,\" Experian North American CEO, Craig Boundy said in a recent blog post on Experian\'s fintech partnerships and Finovate participation. \"That\'s why over the last year, we have built out an entire time of account executives and other support staff that are fully dedicated to developing and supporting partnerships with leading fintech companies. We\'ve made significant strides that will help us pave the way for the next generation of lending while improving the financial health of more people around the world.\" For more information on how Experian is partnering with fintechs, visit experian.com/fintech or read our recent blog article on consumer-permissioned data for an in-depth discussion on Experian BoostTM.
It’s been over 10 years since the first rumblings of Great Recession started in 2008. Today, Americans are experiencing high levels of consumer confidence, marked by high employment rates and increasing credit balances over last year. What have we learned over the last decade? And how do we compare to our behaviors then? Experian released the 9th 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. Who’s faring the best since the recession? According to the data, younger consumers. “We’re continuing to see the positive effects of economic recovery, especially among younger consumers,” said Michele Raneri, Vice President of Analytics and Business Development at Experian. “Since the recession, responsible credit card behaviors and lower debt among younger consumers is driving an upward trend in average credit scores across the nation. Over the last ten years, those 18 – 21 increased their credit scores by 23 points on average compared to those 18-21 ten years ago.” As a whole, 2018 was a year marked by financial reform, consumer protection and the return of volatility for the financial markets. A large portion of the analytics from this year’s report took a close look at the credit behaviors of today and compared them to 2008, the year the US headed into the worst recession in 80 years. 10-Year Lookback 2008 2017 2018 Average number of credit cards 3.40 3.06 3.04 Average credit card balances $7,101 $6,354 $6,506 Average number of retail credit cards 3.03 2.48 2.59 Average retail credit card balances $1,759 $1,841 $1,901 Average VantageScore[1,2] 685 675 680 Average revolving utilization 28% 30% 30% Average non-mortgage debt $23,929 $24,706 $25,104 Average mortgage debt $191,357 $201,811 $208,180 Average 30 days past due delinquency rates 5.4% 4.0% 3.9% Average 60 days past due delinquency rates 2.9% 1.9% 1.9% Average 90+ days past due delinquency rates 7.1% 7.3% 6.7% In regards to credit scores, the average VantageScore increased 5 points from last year, reaching 680 , while still down from 2008. Segmented by state and gender, Minnesota had the highest credit scores for both men and women. Data also showed that women had higher credit scores than men, consistent with 2017 and 2008. The past year has been flooded with headlines illustrating increased spending for American consumers. How do the numbers compare with 2008 data? In comparison with 10 years ago, the number of retail trades since 2008 are down, while average balance is up, according to Experian’s State of Credit Report. Additionally, the number of credit cards is down for all age groups, and balance is also down for consumers 22-71 years of age. Average revolving utilization has creeped up in the past decade, but only two percentage points from 28% to 30%, while both average non-mortgage and mortgage debt has increased 5% and 9% respectively. Not surprisingly, the report reflects that delinquency rates have also increased over 20% since 2008, though down from last year. In conclusion, there’s a lot to learn from both 2008 and 2018. One of the most important and resonating takeaways might be that while fortune may not seem to favor the young, younger consumers are exhibiting responsible behaviors and higher credit scores, setting a precedence for consistent and better financial health in the future. Learn more Experian Boost can help consumers instantly improve their credit score by incorporating their positive payment history from utility and phone bills, among other consumer-permissioned data. [1] VantageScore is a registered trademark of VantageScore Solutions, LLC. [2] VantageScore range is 300-850 VantageScore Calculated on the VantageScore 3.0 model. Your VantageScore 3.0 from Experian indicates your credit risk level and is not used by all lenders, so don’t be surprised if your lender uses a score that’s different from your VantageScore 3.0.
Millions of consumers lack credit history and/or have difficulty obtaining credit from mainstream financial institutions. To ease access to credit for “invisible” and below prime consumers, financial institutions have sought ways to both extend and improve the methods by which they evaluate borrowers’ risk. This initiative to effectively score more consumers has involved the use of alternative credit data.1 Alternative credit data is FCRA-compliant data that is typically not included in a traditional credit report and is used to deliver a more complete view into a consumer’s creditworthiness. “Alternative credit data helps us paint a fuller picture of a consumer so they can get better access to the financial services they need and deserve,” said Alpa Lally, Vice President of Data Business at Experian. Experian recently sponsored the FinovateSpring conference in San Francisco, where Alpa had a chance to sit down with Jacob Gaffney, Editor-in-Chief of the HousingWire News Podcast, to discuss ways consumers can improve their credit scores. As an immigrant, Alpa spoke personally about the impact of having a limited credit history and how alternative credit data can help drive greater access to credit for consumers and profitable growth for lenders through more informed lending decisions. Highlights include: How alternative and traditional credit data differ Types of alternative credit data being used by lenders How “credit-invisibles” can best leverage alternative credit data Alternative credit data product solutions, including Experian BoostTM Listen now 1When we refer to “Alternative Credit Data,” this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data” may also apply in this instance and both can be used interchangeably.
Consumer credit trends and markets are constantly evolving, particularly when it comes to originations and delinquencies on mortgages, credit cards and auto loans. According to Experian research, over 2.7 million out of 89 million active automotive loans and leases are either 30 or 60 days delinquent. Triggers that signal a greater likelihood of consumers falling delinquent on loans, mortgages and credit card payments, include high-interest rates, a high utilization rate and recent derogatory trades. By tracking and forecasting consumer trends over time, you can more easily predict consumer behavior and better prepare for potential issues within each market. Join Gavin Harding, Experian Senior Business Consultant, and Alan Ikemura, Experian Data Analytics Senior Product Manager, during our live Quarterly Credit Trends webinar on May 30 at 2:00 p.m. ET. Our expert speakers will provide a view of the real estate market and share insights on the latest consumer credit trends. Highlights include: 2019 economic drivers Q1 2019 origination and delinquency trends Mortgage Home equity Bankcard Auto Register now
Day 2 at this year’s Vision conference was fueled with new technology and inspiration. The morning session opened with Robert Boxberger, Experian President, Decision Analytics, and also featured two live demos, one on Experian’s solution for the upcoming CECL compliance deadline and the second for mobile credit, including two use cases on instant issuance and lead generation, which has resulted in a 28% conversion rate of hot leads for one of Experian’s marquee clients. Keynote Speaker: Aimée Mullins \"Get comfortable with the uncomfortable\" was just one of the mantras shared on Tuesday morning by Aimée Mullins, an actor, Olympian, TEDTalk speaker, and one of the youngest honorees to be inducted into the National Women’s Hall of Fame, among many other accomplishments. “It is our uniqueness that’s our greatest asset that we can leverage for our greatest strength,” said Mullins during her keynote centered on achieving the “impossible.” As a bi-lateral amputee (or “double BK” also known as double below-the-knee amputee, as she referenced), Mullins had doctors and experts tell her and her parents what she would not be able to do. Instead, she encouraged Tuesday’s audience to never stop thinking like a child, to use their curiosity to find new ways where you want to go, and to practice curiosity like a sport to keep from getting comfortable, and therefore static. “It made my not knowing what I can do so much more powerful than an expert\'s presumption of what he thought I could do,” she said. Session Highlights – Day 2 Consumer Trust What engenders trust as consumers? And what does it take to build online trust? With 51% of new account fraud victims personally knowing the perpetrator and 3.4 billion total losses from fraudulent account openings (Javelin Feb 2019), there are five key components to building trust: digital adoption, transparency, fraud management, recognition and authentication. Today’s consumers want to use the digital channel, have both security and ease of access, be recognized, know how their personal information is being used, and engage and trust with biometrics. Artificial Intelligence – Chat Bots and Beyond According to Gartner, “\'Conversational AI-first\' will supersede ‘cloud-first,’ ‘mobile first’ as the most important high-level imperative for the next 10 years.” As evidenced by Google Duplex’s realistic conversations with humans, including the use of “uh” and “um,” conversational AI is positioned to redefine the next generation of human interface, aimed at achieving better customer satisfaction and elevate the customer relationship. Marketing Analytics The marketing analytics landscape is changing. Today’s marketing problems – including the always limited budget and need to produce greater ROI – require tactical strategies to target the right consumers. Enter Experian’s AscendTM marketing platform. Leveraging this tool, including its neural networks that were demonstrated Monday morning, helps gain new insights into consumer behavior. Fraud in the Digital Wild West A panel discussion featuring representatives from Merchant Risk Council, USAA and Alliance Data compared fighting fraud to herding cats. Challenges discussed included the ongoing struggle to find balance between limiting friction during the authentication process, while also protecting customers, as well as fraudsters’ tendencies to tap into victims’ emotions and curiosity (think phishing schemes). As one of the panelists offered as a piece of advice, “Fraudsters share best practices, so should we.” Visibility for the Invisibles People are more than the sum of their parts. The traditional credit score may show a consumer’s reputation, but layering trended and alternative data sets adds their character. Not only can trended data and alternative credit data – including leveraging education attributes – make invisible consumers visible, they can also reveal that a consumer with a presumably superlative credit score is actually a “credit zombie.” These data sets enable the opportunity to create first chances, drive second chances and re-evaluate risk, while also driving a strong growth strategy. CECL After reviewing the basics of CECL and the upcoming deadlines (ranging from Q1 2020 to Q1 2022), a review of CECL compliance challenges and potential product changes preceded a modeling techniques case study and a list of key impacts to businesses. Those impacts include: product profitability, loss forecasting methodology, data management and processes and capital ratios. Experian’s CECL forecasting solution leverages Experian’s extensive historical data and Ascend Analytical Sandbox. Using a best practice modeling pipeline to improve efficiency and reduce operational risks, the solution combines advanced machine learning, traditional model techniques and modeling experience to improve performance and reduce risk of overfitting. Keynote Speaker: Kobe Bryant Kobe Bryant closed out the day with stories from his highly-decorated 20-year career with the Los Angeles Lakers, some tips on trash talk and lessons in leadership. “I had to figure out how to be undeniable,” Bryant said, on competing for minutes at the start of his career. In addition to his basketball legacy, including wining five NBA championships, being named an NBA MVP, a two-time NBA Finals MVP and winning two Olympic gold medals, Bryant also launched the Kobe and Vanessa Bryant Family Foundation, hosts the Kobe Academy and has formed Kobe Inc. He’s a storyteller, an Oscar winner, and his name has become synonymous with standing for uncompromising excellence. How to be successful? “Make sure you have the right people on the team,” Bryant said. “Passionate. Borderline obsessive.” One of his key takeaways from his basketball career that translates to his leadership on and off the court happened when his pre-game and game time thinking shifted from internal to external. “You have to put yourself 2nd, 3rd, 4th…you have to put the team first,” Bryant said. For more coverage, follow #ExperianVision on Twitter or check the Experian Insights LinkedIn page.
“Experian is transforming our business from a traditional credit bureau to a true technology and software provider,” said Craig Boundy, CEO of Experian, North America, as part of his opening remarks Monday morning to kick off the 2019 Experian Vision Conference. “We are committed to working as a force of good.” Covering the themes of financial inclusion, giving consumers control of their lives and better outcomes, a digital-first society, and the latest trends in fraud and security, Boundy addressed a crowd of over 850. Alex Lintner, Experian’s Group President, gave a quick history of the past 3,000 years, from the first credit card, to the addition of wheels to a suitcase, to the iPhone and artificial intelligence. “Innovation is not invention,” Lintner said. He gave the example of the iPhone and how a tear down analysis revealed there were no new elements; however, it was the translation of an idea into a good or service that benefited everyone (as the entire audience raised their hand when asked who had a smart phone). Lintner’s mainstage presentation also featured three live demos, including how the Ascend Technology Platform takes complex model building and outputs from days and weeks to a few clicks, to the incorporation of Small Business Financial Exchange (SBFE) data into the Ascend Analytical Sandbox (incorporating more than 17 years of small business tradeline data and 150 predictive attributes) and lastly, Experian Boost, which according to a live tracker, has boosted consumer credit scores by a total of 3.2 million points, as of this morning, since its launch eight weeks ago. Keynote Speaker: Gary D. Cohn Gary D. Cohn, Former Director of the U.S. National Economic Council, was Monday morning’s keynote speaker. He weighed in on the domestic and global economy, policy issues, financial institutions’ responsibilities and some of his predictions. Cohn brought attention to the ever-changing financial services space, including new forms of encryption and the world of biometric security, calling the financial services industry the “tip of the spear” when it comes to the digitization of the world. Session Highlights - Day 1 Machine Learning From the building blocks of neural networks to artificial intelligence, machine learning has been used in the areas of financial services that do not have adverse actions – think fraud, ID, collections. As we look to harness machine learning for models and other spaces (including adverse action), it’s important to delineate descriptive data (what’s happening now); predictive data (what’s happening in the future); prescriptive data (what am I going to do now); and cognitive data (are we asking the right question?). In addition, it’s necessary to address the five advanced analytic drivers including customer experience, cost, risk and loss, growth and compliance. Home Equity & Lending US macroeconomic trends show consumer confidence is still on an upward trend. While investor confidence is a little volatile, the GDP remains strong (though slightly slowing down) and unemployment is low and forecasted to remain low. Since 2006, the US hasn’t returned in the HELOC space. Mortgage and personal loans are up 20% and 13% respectively, while mortgages have dropped 1% and HELOCs have dropped 2%. With an estimated market potential of over $700 billion, HELOCs may be an untapped credit line given the strength of the economy. Identity Evolution From dumpster divers, aka pulling receipts out of dumpsters behind businesses, to today’s identity-based authentication, there’s been an evolution of how identity is defined as well as its corresponding risks. According to Experian’s Global Fraud & Identity Report, 74% of consumers value security as the most important part of the online experience (over convenience and personalization). However, 74% of consumers abandoned a shopping session that required too much information, and 72% of consumers said they were willing to share more data if it meant a seamless experience. What does this mean? Consumers want it all. Identity today now includes proxies and activity, which can also mean greater risk. Because of aggregators and other associated entities acting on a consumer’s behalf, there are lots of nuances that will need to be looked through. Consumer-Permissioned Data In order to be more consumer-centric, there are four levers through which consumers are given control: data accuracy, knowing their financial profile, the ability to improve their scores (via Experian BoostTM and UltraFICOTM) and protecting consumers when they permission access to their identity credentials. Using Experian Boost, consumers have seen an average increase of 13 points for consumers with positive changes. Additionally, using alternative credit data, financial institutions can score more people and score more accurately. One hundred million consumers could gain greater access to credit with consumer-permissioned data sources. --- Meanwhile, the tech showcase featured over 20 demos covering alternative data, digital credit marketing, consumer empowerment, fraud and identity, integrated decisioning and technology. More insights from Vision to come. Follow @ExperianVision and #ExperianVision on Twitter to see more of the action.
Experian’s 38th annual Vision Conference kicks off on Sunday, May 5 in San Antonio, Texas. The sold-out thought leadership conference, is known for driving discussions on the industry’s hard-hitting topics as well as introducing the latest and greatest in technology, innovation and data science. “For 38 years, Experian’s Vision Conference has connected business leaders to new ideas and solutions through cutting edge data and insights. Our goal is to power opportunities for you to target new markets, grow existing customer bases, improve response rates, reduce fraud and increase profits by using our data, analytics and technology. The intimate setting of the conference allows for unique networking opportunities with the industry’s most sought-after thought leaders,” said Klaudette Christensen, Experian’s Chief Operations Officer. A few spotlight sessions include: Several sessions about machine learning and artificial intelligence, highlighting opportunities related to best practices, underwriting and fraud detection A deep dive into the modern mortgage, leveraging insights on home equity and how to leverage data and analytics to redefine the process as it’s known today Sessions on credit delinquency, collections and the Great Recession Marketing analytics and the latest releases from Experian’s Ascend Platform Sessions on advanced analytics and integrated decisioning as they relate to commercial and consumer insights The event, which runs through Tuesday evening, continues its tradition of featuring several noteworthy keynote speakers. On Monday, Gary D. Cohn, American business leader, philanthropist and former Director of the U.S. National Economic Council, will kick off the event. On Tuesday, Aimée Mullins will take the stage discussing what is “possible” by drawing from her experiences as a record-breaking Olympic athlete, model and actress. The closing keynote will feature five-time NBA Champion and two-time Olympic Gold Medalist, Kobe Bryant. The event will also include a Tech Showcase, featuring hands-on demos for attendees to experience. Stay tuned for additional highlights and insights on our social media platforms throughout the course of the conference. Follow Experian Insights on Twitter and LinkedIn and check out #ExperianVision.
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
Your consumers’ credit score plays an important role in how lenders and financial institutions measure their creditworthiness and risk. With a good credit score, which is generally defined as a score of 700 or above, they can quickly be approved for credit cards, qualify for a mortgage, and have easier access to loans with lower interest rates. In the spirit of Financial Literacy Month, we’ve rounded up what it takes for consumers to have a good credit score, in addition to some alternative considerations. Pay on Time Life gets busy and sometimes your consumers miss the “credit card payment due” note on their calendar squished between their work meetings and doctor’s appointment. However, payment history is one of the top factors in most credit scoring models and accounts for 35% of their credit score. As the primary objective of your consumers’ credit score is to illustrate to lenders just how likely they are to repay their debts, even one missed payment can be viewed negatively when reviewing their credit history. However, if there is a missed payment, consider checking their alternative financial services payments. They may have additional payment histories that will skew their creditworthiness more so than just their record according to traditional credit lines alone. Limit Credit Cards When your consumers apply for a new loan or credit card, lenders “pull” their credit report(s) to review their profile and weigh the risk of granting them credit or loan approval. The record of the access to their credit reports is known as a “hard” inquiry and has the potential to impact their credit score for up to 12 months. Plus, if they’re already having trouble using their card responsibly, taking on potential new revolving credit could impact their balance-to-limit ratio. For your customers that may be looking for new cards, Experian TAPSSM can be used to accurately estimate your consumers spend on all general-purpose credit and charge cards, so you can identify where there is additional wallet share and assign their credit lines based on actual spending need. Have a Lengthy Credit History The longer your consumers’ credit history, the more time they’ve spent successfully managing their credit obligations. When considering credit age, which makes up 21% of their credit score, credit scoring models evaluate the ages of your consumers’ oldest and newest accounts, along with the average age of all their accounts. Every time they open new credit cards or close an old account, the average age of their credit history is impacted. If your consumer’s score is being negatively affected by their credit history, consider adding information from alternative credit data sources for a more complete view. Manage Debt Wisely While some types of debt, such as a mortgage, can help build financial health, too much debt may lead to significant financial problems. By planning, budgeting, only borrowing when it makes sense, and setting themselves up for unexpected financial expenses, your consumers will be on the path to effective debt management. To get a better view of your consumers spending, consider Experian’s Trended3DTM, a trended attribute set that helps lenders unlock valuable insights hidden within their consumers’ credit scores. By using Trended3DTM data attributes, you’ll be able to see how much of your consumers’ credit line they typically utilize, whether they tend to revolve or transact, and if they are likely to transfer a balance. By adopting these habits and making smart financial decisions, your consumers will quickly realize that it’s never too late to rebuild their credit score. For example, they can potentially instantly improve their score with Experian Boost, an online tool that scans their bank account transactions to identify mobile phone and utility payments. The positive payments are then added to their Experian credit file and increase their FICO® Score in real time. Learn More About Experian Boost Learn More About Experian TAPS
If you’re a credit risk manager or a data scientist responsible for modeling consumer credit risk at a lender, a fintech, a telecommunications company or even a utility company you’re certainly exploring how machine learning (ML) will make you even more successful with predictive analytics. You know your competition is looking beyond the algorithms that have long been used to predict consumer payment behavior: algorithms with names like regression, decision trees and cluster analysis. Perhaps you’re experimenting with or even building a few models with artificial intelligence (AI) algorithms that may be less familiar to your business: neural networks, support vector machines, gradient boosting machines or random forests. One recent survey found that 25 percent of financial services companies are ahead of the industry; they’re already implementing or scaling up adoption of advanced analytics and ML. My alma mater, the Virginia Cavaliers, recently won the 2019 NCAA national championship in nail-biting overtime. With the utmost respect to Coach Tony Bennett, this victory got me thinking more about John Wooden, perhaps the greatest college coach ever. In his book Coach Wooden and Me, Kareem Abdul-Jabbar recalled starting at UCLA in 1965 with what was probably the greatest freshman team in the history of basketball. What was their new coach’s secret as he transformed UCLA into the best college basketball program in the country? I can only imagine their surprise at the first practice when the coach told them, “Today we are going to learn how to put on our sneakers and socks correctly. … Wrinkles cause blisters. Blisters force players to sit on the sideline. And players sitting on the sideline lose games.” What’s that got to do with machine learning? Simply put, the financial services companies ready to move beyond the exploration stage with AI are those that have mastered the tasks that come before and after modeling with the new algorithms. Any ML library — whether it’s TensorFlow, PyTorch, extreme gradient boosting or your company’s in-house library — simply enables a computer to spot patterns in training data that can be generalized for new customers. To win in the ML game, the team and the process are more important than the algorithm. If you’ve assembled the wrong stakeholders, if your project is poorly defined or if you’ve got the wrong training data, you may as well be sitting on the sideline. Consider these important best practices before modeling: Careful project planning is a prerequisite — Assemble all the key project stakeholders, and insist they reach a consensus on specific and measurable project objectives. When during the project life cycle will the model be used? A wealth of new data sources are available. Which data sources and attributes are appropriate candidates for use in the modeling project? Does the final model need to be explainable, or is a black box good enough? If the model will be used to make real-time decisions, what data will be available at runtime? Good ML consultants (like those at Experian) use their experience to help their clients carefully define the model development parameters. Data collection and data preparation are incredibly important — Explore the data to determine not only how important and appropriate each candidate attribute is for your project, but also how you’ll handle missing or corrupt data during training and implementation. Carefully select the training and validation data samples and the performance definition. Any biases in the training data will be reflected in the patterns the algorithm learns and therefore in your future business decisions. When ML is used to build a credit scoring model for loan originations, a common source of bias is the difference between the application population and the population of booked accounts. ML experts from outside the credit risk industry may need to work with specialists to appreciate the variety of reject inference techniques available. Segmentation analysis — In most cases, more than one ML model needs to be built, because different segments of your population perform differently. The segmentation needs to be done in a way that makes sense — both statistically and from a business perspective. Intriguingly, some credit modeling experts have had success using an AI library to inform segmentation and then a more tried-and-true method, such as regression, to develop the actual models. During modeling: With a good plan and well-designed data sets, the modeling project has a very good chance of succeeding. But no automated tool can make the tough decisions that can make or break whether the model is suitable for use in your business — such as trade-offs between the ML model’s accuracy and its simplicity and transparency. Engaged leadership is important. After modeling: Model validation — Your project team should be sure the analysts and consultants appreciate and mitigate the risk of over fitting the model parameters to the training data set. Validate that any ML model is stable. Test it with samples from a different group of customers — preferably a different time period from which the training sample was taken. Documentation — AI models can have important impacts on people’s lives. In our industry, they determine whether someone gets a loan, a credit line increase or an unpleasant loss mitigation experience. Good model governance practice insists that a lender won’t make decisions based on an unexplained black box. In a globally transparent model, good documentation thoroughly explains the data sources and attributes and how the model considers those inputs. With a locally transparent model, you can further explain how a decision is reached for any specific individual — for example, by providing FCRA-compliant adverse action reasons. Model implementation — Plan ahead. How will your ML model be put into production? Will it be recoded into a new computer language, or can it be imported into one of your systems using a format such as the Predictive Model Markup Language (PMML)? How will you test that it works as designed? Post-implementation — Just as with an old-fashioned regression model, it’s important to monitor both the usage and the performance of the ML model. Your governance team should check periodically that the model is being used as it was intended. Audit the model periodically to know whether changing internal and external factors — which might range from a change in data definition to a new customer population to a shift in the economic environment — might impact the model’s strength and predictive power. Coach Wooden used to say, “It isn’t what you do. It’s how you do it.” Just like his players, the most successful ML practitioners understand that a process based on best practices is as important as the “game” itself.
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