Data & Analytics

Experian has been named one of the 10 participants, and only credit bureau, in the initial rollout of the SSA's new eCBSV service.

Experian Boost provides a unique opportunity to help dealers build loyalty while helping consumers.

What do movie actors Adam Sandler and Hugh Grant, jazz singer Michael Bublé, Russian literary giant Leo Tolstoy, and Colonel Sanders, the founder of KFC, have in common? Hint, it’s not a Nobel Prize for Literature, a Golden Globe, a Grammy Award, a trademark goatee, or a “finger-lickin’ good” bucket of chicken. Instead, they were all born on September 9, the most common birth date in the U.S. Baby Boom According to real birth data compiled from 20 years of American births, September is the most popular month to give birth to a child in America – and December, the most popular time to make one. With nine of the top 10 days to give birth falling between September 9 and September 20, one may wonder why the birth month is so common. Here are some theories: Those who get to choose their child’s birthday due to induced and elective births tend to stay away from the hospital during understaffed holiday periods and may plan their birth date around the start of the school year. Several of the most common birth dates in September correspond with average conception periods around the holidays, where couples likely have more time to spend together. Some studies within the scientific community suggest that our bodies may actually be biologically disposed to winter conceptions. While you may not be feeling that special if you were born in September, the actual differences in birth numbers between common and less common birthdays are often within just a few thousand babies. For example, September 10, the fifth most common birthday of the year, has an average birth rate of 12,143 babies. Meanwhile, April 20, the 328th most common birthday, has an average birth rate of 10,714 newborns. Surprisingly, the least common birthdays fall on Christmas Eve, Christmas Day and New Year’s Day, with Thanksgiving and Independence Day also ranking low on the list. Time to Celebrate – but Watch out! Statistically, there’s a pretty good chance that someone reading this article will soon be celebrating their birthday. And while you should be getting ready to party, you should also be on the lookout for fraudsters attempting to ruin your big day. It’s a well-known fact that cybercriminals can use your birth date as a piece of the puzzle to capture your identity and commit identity theft – which becomes a lot easier when it’s being advertised all over social media. It’s also important for employers to safeguard their organization from fraudsters who may use this information to break into corporate accounts. While sharing your birthday with a lot of people could be a good or bad thing depending on how much undivided attention you enjoy – you’re in great company! Not only can you plan a joint party with Michelle Williams, Afrojack, Cam from Modern Family, four people I went to high school with on Facebook and a handful of YouTube stars that I’m too old to know anything about, but there will be more people ringing in your birthday than any other day of the year! And that’s pretty cool.

Pickups are the most common vehicle in operation at 20% share today and hold 16.5% of new vehicle registrations in the market in Q1 2019.

Many companies rely on attributes for decisioning but lack the resources needed to invest in developing, managing, and updating the attributes themselves. Experian is there to guide you every step of the way with our Attribute Toolbox – our source independent solution that provides maximum flexibility and multiple data sources you can use in the calculation and management of attributes. To create and manage our attributes, Experian has established development principles and created a set methodology to ensure that our attribute management system works across the attribute life cycle. Here’s how it works: Develop Attributes The attribute development process includes: discovery, exploratory data analysis, filter leveling, and the development of attributes. When we create attributes, Experian takes great care to ensure that we: Analyze the available data elements and how they are populated (the frequencies of fields). Determine a “sensible” definition of the attribute. Evaluate attribute frequencies. Review consumer credit reports, where possible. Refine the definition and assess more frequencies and examples. Test Attributes Before implementing, Experian performs an internal audit of filters and attributes. Defining, coding and auditing filters is 80% of the attribute development process. The main objective of the auditing process is to ensure both programming and logical accuracy. This involves electronic and manual auditing and requires a thorough review of all data elements used in development. Deploy Attributes Deployment is very similar to attribute testing. However, in this case, the primary objective of the deployment audit is to ensure both the programming and logical accuracy of the output is executing correctly on various platforms. We aim to maintain consistency among various business lines and products, between batch and online environments across the life cycle, and wherever your models are deployed: on premises, in the cloud, and off-site in your partners’ systems. Govern Attributes Experian places a robust attribute governance process in place to ensure that our attributes remains up-to-date and on track with internal and external compliance regulations and audits. New learnings, industry and regulatory changes can lead to updated attributes or new attributes over time. Because attributes are ever-changing, we take great care to expand, update and add new attributes over time based on three types of external changes: economic, bureau, and reporting changes.   Fetch Data While we gather the data, we ensure that you can integrate a variety of external data sources, including: consumer bureau, business, fraud, and other data sources. Attributes need to be: Highly accurate. Suitable for use across the Customer Life Cycle. Suitable for use in credit decisioning and model development. Available and consistent across multiple platforms. Supportive and adaptable to ever-evolving regulatory considerations. Thoroughly documented and monitored. Monitor Performance We generate attribute distribution reports and can perform custom validations using data from credit reporting agencies (CRAs) and other data providers. This is based on monthly monitoring to ensure continued integrity and stability to stand up to regulatory scrutiny and compliance regulations. Variations that exceed predetermined thresholds are identified, quantified, and explained. If new fields or data values within existing fields are announced, we assess the impact and important of these values on attributes – to determine if revisions are needed. Maintain Attributes Credit bureau data updates, new attributes in response to market needs, compliance requirements, corrections in logic where errors are identified or improvements to logic often lead to new version releases of attributes. With each new version release, Experian takes care to conduct thorough analyses comparing the previous and current set of attributes. We also make sure to create detailed documentation on what’s changed between versions, the rationale for changes and the impact on existing attributes.   Experian Attributes are the key to unlocking consistent, enhanced and more profitable decisions. Our data analysts and statisticians have helped hundreds of clients build custom attributes and custom models to solve their business problems. Our Attribute Toolbox makes it easier to deploy and manage attributes across the customer lifecycle. We give companies the power to code, manage, test, and deploy all types of attributes, including: Premier AttributesSM, Trended 3DTM, and custom attributes – without relying on a third-party. We do the heavy lifting so that you don’t have to. Learn More    

Today is National Fintech Day – a day that recognizes the ever-important role that fintech companies play in revolutionizing the customer experience and altering the financial services landscape. Fintech. The word itself has become synonymous with constant innovation, agile technology structures and being on the cusp of the future of finance. Fintech challengers are disrupting existing financial models by leveraging data, advanced analytics and technology – both inspiring traditional financial institutions in their digital transformation strategies and giving consumers access to a variety of innovative financial products and services. But to us at Experian, National Fintech Day means more than just financial disruption. National Fintech Day represents the partnerships we have carefully fostered with our fintech clients to drive financial inclusion for millions of people around the globe and provide consumers with greater control and more opportunities to access the quality credit they deserve. “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,” said Experian North American CEO, Craig Boundy in a recent blog article on Experian’s fintech partnerships. “That’s why over the last year, we have built out an entire team 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 people around the world.” At Experian, we understand the challenges fintechs face – and our real-world solutions help fintech clients stay ahead of constantly changing market conditions and demands. “Experian’s pace of innovation is very impressive – we are helping both lenders and consumers by delivering technological solutions that make the lending ecosystem more efficient,” said Experian Senior Account Executive Warren Linde. “Financial technology is arguably the most important type of tech out there, it is an honor to be a part of Experian’s fintech team and help to create a better tomorrow.” If you’d like to learn more about Experian’s fintech solutions, visit us at Experian.com/Fintech.

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.

The fact that the last recession started right as smartphones were introduced to the world gives some perspective into how technology has changed over the past decade. Organizations need to leverage the same technological advancements, such as artificial intelligence and machine learning, to improve their collections strategies. These advanced analytics platforms and technologies can be used to gauge customer preferences, as well as automate the collections process. When faced with higher volumes of delinquent loans, some organizations rapidly hire inexperienced staff. With new analytical advancements, organizations can reduce overhead and maintain compliance through the collections process. Additionally, advanced analytics and technology can help manage customers throughout the customer life cycle. Let’s explore further: Why use advanced analytics in collections? Collections strategies demand diverse approaches, which is where analytics-based strategies and collections models come into play. As each customer and situation differs, machine learning techniques and constraint-based optimization can open doors for your organization. By rethinking collections outreach beyond static classifications (such as the stage of account delinquency) and instead prioritizing accounts most likely to respond to each collections treatment, you can create an improved collections experience. How does collections analytics empower your customers? Customer engagement, carefully considered, perhaps comprises the most critical aspect of a collections program—especially given historical perceptions of the collections process. Experian recently analyzed the impact of traditional collections methods and found that three percent of card portfolios closed their accounts after paying their balances in full. And 75 percent of those closures occurred shortly after the account became current. Under traditional methods, a bank may collect outstanding debt but will probably miss out on long-term customer loyalty and future revenue opportunities. Only effective technology, modeling and analytics can move us from a linear collections approach towards a more customer-focused treatment while controlling costs and meeting other business objectives. Advanced analytics and machine learning represent the most important advances in collections. Furthermore, powerful digital innovations such as better criteria for customer segmentation and more effective contact strategies can transform collections operations, while improving performance and raising customer service standards at a lower cost. Empowering consumers in a digital, safe and consumer-centric environment affects the complete collections agenda—beginning with prevention and management of bad debt and extending through internal and external account resolution. When should I get started? It’s never too early to assess and modernize technology within collections—as well as customer engagement strategies—to produce an efficient, innovative game plan. Smarter decisions lead to higher recovery rates, automation and self-service tools reduce costs and a more comprehensive customer view enhances relationships. An investment today can minimize the negative impacts of the delinquency challenges posed by a potential recession. Collections transformation has already begun, with organizations assembling data and developing algorithms to improve their existing collections processes. In advance of the next recession, two options present themselves: to scramble in a reactive manner or approach collections proactively. Which do you choose? Get started

Today, Experian and Oliver Wyman announced the launch of Ascend CECL ForecasterTM, a solution built to help financial institutions of all sizes more quickly and accurately forecast lifetime credit losses. The Financial Accounting Standards Board’s current expected credit loss (CECL) model has been a hot discussion topic throughout the financial services industry - first when it was announced (and considered one of the most significant accounting changes in decades), and most recently with the FASB’s delay for implementation for smaller lenders. As the compliance deadlines approach, Experian and Oliver Wyman have joined forces to help financial institutions adhere their loan portfolios to the new guidelines. Delivered through Experian’s Ascend Technology PlatformTM, Ascend CECL Forecaster is a new user-friendly, web-based application that combines Experian’s vast loan-level data and Premier AttributesSM, third-party macroeconomic data, valuation data and Oliver Wyman’s industry-leading CECL modeling methodology to accurately calculate potential losses over the life of a loan. “Ascend CECL Forecaster is a critical capability needed urgently by all lending and financial institutions,” said Ash Gupta, a Senior Advisor to Oliver Wyman and former Chief Risk Officer for American Express, in a press release. “The collaboration between Experian and Oliver Wyman allows a frictionless synthesis of industry data, capabilities and experience to serve customers in both first and second line of defense.” The premise behind the model, which will need access to more data than that used to calculate reserves under the incurred loss model, Allowance for Loan and Lease Losses (ALLL), is for financial institutions to estimate the expected loss over the life of a loan by using historical information, current conditions and reasonable forecasts. Built using advanced machine learning and statistical techniques, the web-based application maximizes the more than 15 years of historical credit data spanning previous economic cycles to help financial institutions gauge loan portfolio performance under various scenarios. Ascend CECL Forecaster does not require additional data nor does it require a secondary integration from the financial institution and enables organizations to more quickly test their portfolios under different economic factors. Moreover, financial institutions receive guidance from industry experts to assist with implementation and strategy. Additionally, Experian and Oliver Wyman will host a webinar to help financial institutions better understand and prepare for the upcoming CECL standards. Register today! Read the Press Release Register for Webinar

Consumer behavior is constantly evolving — from the channels they prefer to the economic trends spurring varying interest and activity. It’s no surprise that businesses find it challenging to know what their customers want today or tomorrow. But knowing and understanding this information is essential to growing your bottom line. Through years of working with businesses across every vertical, we’ve found that a solid approach to growing your business revolves around your customers. The better you know your customers, the better you can achieve your goals. Seeing the future. How well can you identify and rank your current customer population? Are you leveraging that insight to acquire new customers, manage current customers and prioritize collections efforts? If so, you’re probably using custom models in your business strategy. But if your organization is like many businesses, you may use a more traditional approach. In our highly competitive market, strategy and decisions must be based on the right data and insights. No excuses. The data is there, and we can help you turn it into actionable insights. Implementing a custom model can maximize your return on investment and help you make more profitable business decisions — now and in the future. No palm reading required. Without visiting your local fortuneteller, you still can predict the future. You need a model, but not the “runway” type. What constitutes a highly predictive and effective model? Many factors. A highly predictive custom model should incorporate robust data, advanced modeling methodologies, analytical expertise and attributes. Having these foundational components is essential to knowing your customers and making confident decisions. Models aren’t one-size-fits-all. When you take an innovative approach to model development, the model is targeted to support your specific business goals while providing the documentation required for regulatory reviews. Consider these items as you develop your custom model: Data — It all starts with the right data. Combining multiple data assets — your master-file data, our credit data and any additional data sources — is key to developing a robust model development sample. In other words, a model development sample should represent your future through-the-door population. Model design — To ensure the custom model is designed to help you achieve your specific goals, you’ll want to incorporate the latest analytics and modeling methodologies. An experienced analytics team will be essential here. Segmentation — With the right model development and segmentation strategies, you can identify optimal segments that will result in a more predictive custom model. This way, each consumer is scored on a scorecard developed using a credit profile similar to theirs. Validation — To ensure the model’s predictive ability and longevity, validate each custom model on a holdout sample and compare it with other scores to ensure it accounts for the current and future (through-the-door) consumer populations, as well as policy rule and behavioral changes. Regulatory review — Don’t forget about the documentation needed for compliance. While audits are unpleasant , fines and extensive scrutiny can significantly impact your business. Take your fortunetelling to the next level. Machine learning is all the rage. This cutting-edge technology can be embedded in your predictive models to help uncover patterns in data that may not be apparent otherwise. This can be done by comparing the performance of the machine learning model with your existing models. Once you know that machine learning can add the lift you’re looking for, you can apply that methodology to develop a custom model focused on stability, cost-efficiency, transparency and predictive performance. Predicting behavior across the Customer Life Cycle. How can a custom model benefit you? From improving baseline performance and increasing profitability by approving more good accounts to uncovering opportunities within your target market, custom models can provide the confidence needed to grow your business. Which one of these models can help you achieve your business goals? When it comes to accurately predicting customer behavior, you don’t need a crystal ball. You need a well-built, highly predictive custom model. Use the data that’s available to gain insight into your customers and grow your bottom line. If you need help, we’re here. We have the data, analytics and expertise to help you get started.

If you’ve seen an uptick in photos of friends and celebrities looking older with wrinkles on your social media feeds, you’re not alone. A new free photo editor has taken the internet by a storm, featuring an AI-powered image-altering application that allows users to see their “future self.” All you have to do is upload a single photo (or few) from your camera roll to be enhanced. While this may seem like harmless fun, the app is now making headlines over increased privacy concerns about what occurs behind the scenes once users submit their selfies. Red flags were raised when multiple alleged negative implications were connected to the app – including the app’s ownership and the potential risk that the app downloaded a user’s entire photo album onto their database. In fact, the privacy concerns also prompted Democratic Party officials to implore federal agencies, including the FBI, “to look into the potential national security and privacy risks the phone app poses to the United States.” Since then, the app’s creators have addressed these concerns, stating most of the photo processing occurs in the cloud and most photos are deleted within 48 hours. Additionally, the only photos uploaded are ones that have been personally submitted by the user. Regardless, a database of user-submitted photos could be seen as a goldmine to fraudsters. In a time where personal and biometric data (including facial recognition) are some of the key ways to validate security, it’s important for consumers to be aware of how and where they’re sharing their data, whether it’s for an age-progression photo app, or their financial accounts. Consumers, businesses, financial institutions – everyone – should exhibit caution and take measures to ensure personal information remains secure and is not being used for nefarious reasons. While consumers may be aware that businesses are collecting data, companies should take steps to form digital trust with transparency. This could be achieved by: Educating consumers on how their data is being used Effectively communicating privacy policies and service terms more concisely Helping consumers feel in control of their information To learn more about research that indicates a shift to advanced authentication methods (including biometrics), fraud trends and how to combat them, download our e-book. Download Now

Would you hire a new employee strictly by their resume? Surely not – there’s so much more to a candidate than what’s written on paper. With that being said, why would you determine your consumers’ creditworthiness based only on their traditional credit score? Resumes don’t always give you the full picture behind an applicant and can only tell a part of someone’s story, just as a traditional credit score can also be a limited view of your consumers. And lenders agree – findings from Experian’s 2019 State of Alternative Credit Data revealed that 65% of lenders are already leveraging information beyond the traditional credit report to make lending decisions. So in addition to the resume, hiring managers should look into a candidate’s references, which are typically used to confirm a candidate’s positive attributes and qualities. For lenders, this is alternative credit data. References are supplemental but essential to the resume, and allow you to gain new information to expand your view into a candidate – synonymous to alternative credit data’s role when it comes to lending. Lenders are tasked with evaluating their consumers to determine their stability and creditworthiness in an effort to prevent and reduce risk. While traditional credit data contains core information about a consumer’s credit data, it may not be enough for a lender to formulate a full and complete evaluation of the consumer. And for over 45 million Americans, the issue of having no credit history or a “thin” credit history is the equivalent of having a resume with little to no listed work experience. Alternative credit data helps to fill in the gaps, which has benefits for both lenders and consumers. In fact, 61% of consumers believe adding payment history would have a positive impact on their credit score, and therefore are willing to share their data with lenders. Alternative credit data is FCRA-compliant and includes information like alternative finance data, rental payments, utility payments, bank account information, consumer-permissioned data and full-file public records. Because this data shows a holistic view of the customer, it helps to determine their ability to repay debts and reveals any delinquent behaviors. These insights help lenders to expand their consumer lending universe– all while mitigating and preventing risk. The benefits can also be seen for home-based and small businesses. Fifty percent of all US small businesses are home-based, but many small business owners lack visibility due to their thin-file nature – making it extremely difficult to secure bank loans and capital to fund their businesses. And, younger generations and small business owners account for 58% of business owners who rely on short term lending. By leveraging alternative credit data, lenders can get greater insights into a small business owner’s credit profile and gauge risk. Entrepreneurs can also benefit from this information being used to build their credit profiles – making it easier for them to gain access to investment capital to fund their new ventures. Like a hiring manager, it’s important for lenders to get a comprehensive view to find the most qualified candidates. Using alternative credit data can expand your choices  – read our 2019 State of Alternative Credit Data Whitepaper to learn more and register for our upcoming webinar. Register Now

There were 276 million vehicles on the road in Q1 2019.

Once you have kids, your bank accounts will never be the same. From child care to college, American parents spend, on average, over $233,000 raising a child from birth through age 17. While moms and dads are facing the same pile of bills, they often don’t see eye to eye on financial matters. In lieu of Father’s Day, where spending falls $8 million behind Mother’s Day (sorry dads!), we’re breaking down the different spending habits of each parent: Who pays the bills? With 80% of mothers working full time, the days of traditional gender roles are behind us. As both parents share the task of caring for the children, they also both take on the burden of paying household bills. According to Pew Research, when asked to name their kids’ main financial provider, 45% of parents agreed they split the role equally. Many partners are finding it more logical to evenly contribute to shared joint expenses to avoid fighting over finances. However, others feel costs should be divvied up according to how much each partner makes. What do they splurge on? While most parents agree that they rarely spend money on themselves, splurge items between moms and dads differ. When they do indulge, moms often purchase clothes, meals out and beauty treatments. Dads, on the other hand, are more likely to binge on gadgets and entertainment. According to a recent survey on millennial dads, there’s a strong correlation between the domestic tasks they’re taking on and how they’re spending their money. For instance, most dads are involved in buying their children’s books, toys and electronics, as well as footing the bill for their leisure activities. Who are they more likely to spend on? No parent wants to admit favoritism. However, research from the Journal of Consumer Psychology found that you’re more likely to spend money on your daughter if you’re a woman and more likely to spend on your son if you’re a man. The suggested reasoning behind this is that women can more easily identify with their daughters and men with their sons. Overall, parents today are spending more on their children than previous generations as the cost of having children in the U.S. has exponentially grown. How are they spending? When it comes to money management both moms and dads claim to be the “saver” and label the other as the “spender.” However, according to Experian research, there are financial health gaps between men and women, specifically when it pertains to credit. For example, women have 11% less average debt than men, a higher average VantageScore® credit score and the same revolving debt utilization of 30%. Even with more credit cards, women have fewer overall debts and are managing to pay those debts on time. There’s no definite way to say whether moms are spending “better” than dads, or vice versa. Rather, each parent has their own strengths and weaknesses when it comes to allocating money and managing expenses.

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