Tag: credit attributes

Customer retention is crucial for lenders to maximize lifetime value, especially during economic uncertainty. Increasing customer retention rates by just 5% can boost profits by 25% to 95%. However, many lenders struggle with loyalty, as seen in Q2 2024 when mortgage servicers’ retention rates for refinances dropped to 20%, the second lowest in 17 years. Nonbanks and banks also saw significant declines. This is due to increased competition, changing economic conditions, and a lack of personalization. Key strategies for improving customer retention Lenders can improve retention by leveraging data for personalization, maintaining consistent communication, offering loyalty rewards, and utilizing retention triggers. Leverage data for personalization. Use customer data to offer tailored products and refinancing options based on financial behaviors. Using credit attributes, trended data and alternative credit data (alternative financial services data,  cashflow attributes, etc.) can help provide deeper insights of your customers. Maintain consistent communication. Keep customers informed with regular updates about interest rate changes or new loan products. Use a variety of communication channels, including email and in-app messaging, to ensure customers are kept in the loop. Ensure your customer service team is always available and responsive, offering clear answers to any financial concerns. Offer loyalty rewards. Develop programs that reward repeat business and referrals. Offer special rates or discounts for returning customers or for those who refer friends and family to your services. Increase customer lifetime value (LTV) by offering additional services like financial planning or credit score monitoring. Utilize retention triggers. Identify key events for engagement with automated retention triggers. For example, a borrower who has a mortgage with a fixed rate may be less likely to consider refinancing unless prompted. Experian’s Retention TriggersSM can notify lenders when refinancing might be beneficial to their customer, offering them personalized incentives or new product options at the right time. Why Experian’s Retention Triggers? By integrating Experian’s Retention Triggers, lenders can keep borrowers engaged, increase retention, and boost profitability even in tough economic times. Advanced data insights: Gain deeper insights into your customers’ behavior to identify those at risk of leaving and take proactive action. Personalized engagement: Automate personalized communications based on customer behaviors, ensuring timely engagement. Increased revenue: By offering personalized, timely and relevant offers, you can increase the likelihood of retaining your customers and growing your revenue. Make customer retention a priority In today’s challenging economic climate, lenders who focus on personalized experiences, consistent communication, and relevant offers will stand out and retain borrowers. Leverage tools like Experian’s Retention Triggers to proactively engage customers, reduce churn, and foster long-term relationships for increased profitability and success. Learn more

Getting customers to respond to your credit offers can be difficult. With the advent of artificial intelligence (AI) and machine learning (ML), optimizing credit prescreen campaigns has never been easier or more efficient. In this post, we'll explore the basics of prescreen and how AI and ML can enhance your strategy.  What is prescreen?  Prescreen involves evaluating potential customers to determine their eligibility for credit offers. This process takes place without the consumer’s knowledge and without any negative impact on their credit score.  Why optimize your prescreen strategy?  In today's financial landscape, having an optimized prescreen strategy is crucial. Some reasons include:  Increased competition: Financial institutions face stiff competition in acquiring new customers. An optimized prescreen strategy helps you stand out by targeting the right individuals with tailored offers, increasing the chances of conversion.  Customer expectations: Modern customers expect personalized and relevant offers. An effective prescreen strategy ensures that your offers resonate with the specific needs and preferences of potential customers.  Strict budgets: Organizations today are faced with a limited marketing budget. By determining the right consumers for your offers, you can minimize prescreen costs and maximize the ROI of your campaigns.  Regulatory compliance: Compliance with regulations such as the Fair Credit Reporting Act (FCRA) is essential. An optimized prescreen strategy helps you stay compliant by ensuring that only eligible individuals are targeted for credit offers.  Financial inclusion: 49 million American adults don’t have conventional credit scores. An optimized prescreen strategy allows you to send offers to creditworthy consumers who you may have missed due to a lack of traditional credit history.  How AI and ML can enhance your strategy  AI and ML can revolutionize your prescreen strategy by offering advanced analytics and custom response modeling capabilities.  AI-driven data analytics  AI analytics allow financial institutions to analyze vast amounts of data quickly and accurately. This enables you to identify patterns and trends that may not be apparent through traditional analysis. By leveraging data-centric AI, you can gain deeper insights into customer behavior and preferences, allowing for more precise targeting and increased response rates.  LEARN MORE: Explore the benefits of AI for credit unions.  Custom response modeling  Custom response models enable you to better identify individuals who fall within your credit criteria and are more likely to respond to your credit offers. These models consider various factors such as credit history, spending habits, and demographic information to predict future behavior. By incorporating custom response models into your prescreen strategy, you can select the best consumers to engage, including those you may have previously overlooked.  LEARN MORE: AI can be leveraged for numerous business needs. Learn about generative AI fraud detection.   Get started today  Incorporating AI and ML into your prescreen campaigns can significantly enhance their effectiveness and efficiency. By leveraging Experian's Ascend Intelligence Services™ Target, you can better target potential customers and maximize your marketing spend.   Our optimized prescreen solution leverages:  Full-file credit bureau data on over 245 million consumers and over 2,100 industry-leading credit attributes.  Exclusive access to the industry's largest alternative datasets from nontraditional lenders, rental data inputs, full-file public records, and more.  24 months of trended data showing payment patterns over time and over 2,000 attributes that help determine your next best action.  When it comes to compliance, Experian leverages decades of regulatory experience to provide the documentation needed to explain lending practices to regulators. We use patent-pending ML explainability to understand what contributed most to a decision and generate adverse action codes directly from the model.  For more insights into Ascend Intelligence Services Target, view our infographic or contact us at 855 339 3990. View infographic This article includes content created by an AI language model and is intended to provide general information. 

Rising balances and delinquency rates are causing lenders to proactively minimize credit risk through pre-delinquency treatments. However, the success of these types of account management strategies depends on timely and predictive data. Credit attributes summarize credit data into specific characteristics or variables to provide a more granular view of a consumer’s behavior. Credit attributes give context about a consumer’s behavior at a specific point in time, such as their current revolving credit utilization ratio or their total available credit. Trended credit attributes analyze credit history data for consumer behavior patterns over time, including changes in utilization rates or how often a balance exceeded an account’s credit limit during the previous 12 months. In a recent analysis, we found that credit attributes related to utilization were highly predictive of future delinquencies in bankcard accounts, with many lenders better managing their credit risk when incorporating these attributes into their account management processes. READ: Find out how custom attributes and models can help you stay ahead of your competitors in the "Build a profitable portfolio with credit attributes" e-book. Using attributes to manage credit risk An enhanced understanding of credit attributes can be leveraged to manage risk throughout the customer lifecycle. They can be important when you want to: Improve credit strategies and efficiencies: Overlay attributes and incorporate them into credit policy rules, such as knockout criteria, to expand your lending population and increase automation without taking on more credit risk. Better understand customers' credit trends: Experian’s wide range of credit data, including trended credit attributes, can help you quickly understand how consumers are faring off-book for visibility into other lending relationships and if they’ll likely experience financial stress in the future. Credit attributes can also help precisely segment populations. For example, attributes can help you distinguish between two people who have similar credit risk scores — but very different trajectories — and will better determine who's the least risky customer. Predicting 60+ day delinquencies with credit attributes To evaluate the effectiveness of credit attributes during account review, we looked at 2.9 million open and active bankcard accounts to see which attributes best predicted the likelihood of an account reaching 60 days past due. For this analysis, we used snapshots of bankcard accounts that were reported in October 2022 and April 2023. Additionally, we analyzed the predictive power of over 4,000 attributes from Experian Premier AttributesSM and Trended 3DTM. Key findings Nine of the top 20 most predictive credit attributes were related to credit utilization rates. Delinquency-related attributes were predictive but weren’t part of the top 10. Three of the top 10 attributes were related to available credit. Turning insight into action While we analyzed credit attributes for account review, determining attribute effectiveness for other use cases will depend on your own portfolio and goals. However, you can use a similar approach to finding the predictive power of attributes. Once you identify the most predictive credit attributes for your population, you can also create an account review program to track these metrics, such as changes in utilization rates or available credit balances. Using Experian’s Risk and Retention Triggers℠ can immediately notify you of customers' daily credit activity to monitor those changes. Ongoing monitoring of attributes and triggers can help you identify customers who are facing financial stress and are headed toward delinquency. You can then proactively take steps to reduce your risk exposure, prioritize accounts, and modify pre-collections strategy based on triggering events. Experian offers credit attributes and the tools to use them Creating and managing credit attributes can be a complex and never-ending task. You need to regularly monitor attributes for performance drift and to address changing regulatory requirements. You may also want to develop new attributes based on expanding data sources and industry trends. Many organizations don’t have the resources to create, manage, and update credit attributes on their own. That’s where Experian’s 4,500+ attributes and tools can help to save time and money. Premier Attributes includes our core attributes and subsets for over 50 industries. Trended 3D attributes can help you better understand changes in consumer behavior and creditworthiness. Clear View AttributesTM offers insights from expanded FCRA data* that generally isn’t reported to consumer credit bureaus. You can easily review and manage your portfolios with Experian’s Ascend Quest™ platform. The always-on access allows you to request thousands of data elements, including credit attributes, risk scores, income models, segmentation data, and payment history, at any time. Use insights from the data and leverage Ascend Quest to quickly identify accounts that may be experiencing financial stress to limit your credit risk — and target others with retention and up-selling opportunities. Watch the Ascend Quest demo to see it in action, or contact us to learn more about Experian’s credit attributes and account review solutions. Watch demo Contact us

Today’s changing economy is directly impacting consumers’ financial behaviors, with some individuals doing well and some showing signs of payment stress. And while these trends may pose challenges to financial institutions, such as how to expand their customer base without taking on additional risk, the right credit attributes can help them drive smarter and more profitable lending decisions. With Experian’s industry-leading credit attributes, organizations can develop precise and explainable acquisition models and strategies. As a result, they can: Expand into new segments: By gaining deeper insights into consumer trends and behaviors, organizations can better assess an individual’s creditworthiness and approve populations who might have been overlooked due to limited or no credit history. Improve the customer experience: Having a wider view of consumer credit behavior and patterns allows organizations to apply the best treatment at the right time based on each consumer’s specific needs. Save time and resources: With an ongoing managed set of base attributes, organizations don’t have to invest significant resources to develop the attributes themselves. Additionally, existing attributes are regularly updated and new attributes are added to keep pace with industry and regulatory changes. Case study: Enhance decision-making and segmentation strategies A large retail credit card issuer was looking to grow their portfolio by identifying and engaging more consumers who met their credit criteria. To do this, they needed to replace their existing custom acquisition model with one that provided a granular view of consumer behavior. By partnering with Experian, the company was able to implement an advanced custom acquisition model powered by our proprietary Trended 3DTM and Premier AttributesSM. Trended 3D analyzes consumers’ behavior patterns over time, while Premier Attributes aggregates and summarizes findings from credit report data, enabling the company to make faster and more strategic lending decisions. Validations of the new model showed up to 10 percent improvement in performance across all segments, helping the company design more effective segmentation strategies, lower their risk exposure and approve more accounts. To learn how Experian can help your organization make the best data-driven decisions, read the full case study or visit us. Download case study Visit us

With consumers having more credit options than ever before, it’s imperative for lenders to get their message in front of ideal customers at the right time and place. But without clear insights into their interests, credit behaviors or financial capacity, you may risk extending preapproved credit offers to individuals who are unqualified or have already committed to another lender. To increase response rates and reduce wasted marketing spend, you must develop an effective customer targeting strategy. What makes an effective customer targeting strategy? A customer targeting strategy is only as good as the data that informs it. To create a strategy that’s truly effective, you’ll need data that’s relevant, regularly updated, and comprehensive. Alternative data and credit-based attributes allow you to identify financially stressed consumers by providing insight into their ability to pay, whether their debt or spending has increased, and their propensity to transfer balances and consolidate loans. With a more granular view of consumers’ credit behaviors over time, you can avoid high-risk accounts and focus only on targeting individuals that meet your credit criteria. While leveraging additional data sources can help you better identify creditworthy consumers, how can you improve the chances of them converting? At the end of the day, it’s also the consumer that’s making the decision to engage, and if you aren’t sending the right offer at the precise moment of interest, you may lose high-value prospects to competitors who will. To effectively target consumers who are most likely to respond to your credit offers, you must take a customer-centric approach by learning about where they’ve been, what their goals are, and how to best cater to their needs and interests. Some types of data that can help make your targeting strategy more customer-centric include: Demographic data like age, gender, occupation and marital status, give you an idea of who your customers are as individuals, allowing you to enhance your segmentation strategies. Lifestyle and interest data allow you to create more personalized credit offers by providing insight into your consumers’ hobbies and pastimes. Life event data, such as new homeowners or new parents, helps you connect with consumers who have experienced a major life event and may be receptive to event-based marketing campaigns during these milestones. Channel preference data enables you to reach consumers with the right message at the right time on their preferred channel. Target high-potential, high-value prospects By using an effective customer targeting strategy, you can identify and engage creditworthy consumers with the greatest propensity to accept your credit offer. To see if your current strategy has what it takes and what Experian can do to help, view this interactive checklist or visit us today. Review your customer targeting strategy Visit us

Credit reports and conventional credit scores give lenders a strong starting point for evaluating applicants and managing risk. But today's competitive environment often requires deeper insights, such as credit attributes. Experian develops industry-leading credit attributes and models using traditional methods, as well as the latest techniques in machine learning, advanced analytics and alternative credit data — or expanded Fair Credit Reporting Act (FCRA)-regulated data)1 to unlock valuable consumer spending and payment information so businesses can drive better outcomes, optimize risk management and better serve consumers READ MORE: Using Alternative Credit Data for Credit Underwriting Turning credit data into digestible credit attributes Lenders rely on credit attributes — specific characteristics or variables based on the underlying data — to better understand the potentially overwhelming flow of data from traditional and non-traditional sources. However, choosing, testing, monitoring, maintaining and updating attributes can be a time- and resource-intensive process. Experian has over 45 years of experience with data analytics, modeling and helping clients develop and manage credit attributes and risk management. Currently, we offer over 4,500 attributes to lenders, including core attributes and subsets for specific industries. These are continually monitored, and new attributes are released based on consumer trends and regulatory requirements. Lenders can use these credit attributes to develop precise and explainable scoring models and strategies. As a result, they can more consistently identify qualified prospects that might otherwise be missed, set initial limits, manage credit lines, improve loyalty by applying appropriate treatments and limit credit losses. Using expanded credit data effectively Leveraging credit attributes is critical for portfolio growth, and businesses can use their expanding access to credit data and insights to improve their credit decisioning. A few examples: Spot trends in consumer behavior: Going beyond a snapshot of a credit report, Trended 3DTM attributes reveal and make it easier to understand customers' behavioral patterns. Use these insights to determine when a customer will likely revolve, transact, transfer a balance or fall into distress. Dig deeper into credit data: Making sense of vast amounts of credit report data can be difficult, but Premier AttributesSM  aggregates and summarizes findings. Lenders use the 2,100-plus attributes to segment populations and define policy rules. From prospecting to collections, businesses can save time and make more informed decisions across the customer lifecycle. Get a clear and complete picture: Businesses may be able to more accurately assess and approve applicants, simply by incorporating attributes overlooked by traditional credit bureau reports into their decisioning process. Clear View AttributesTM uses data from the largest alternative financial services specialty bureau, Clarity Services, to show how customers have used non-traditional lenders, including auto title lenders, rent-to-own and small-dollar credit lenders. The additional credit attributes and analysis help lenders make more strategic approval and credit limit decisions, leading to increased customer loyalty, reduced risk and business growth. Additionally, many organizations find that using credit attributes and customized strategies can be important for measuring and reaching financial inclusion goals. Many consumers have a thin credit file (fewer than five credit accounts), don’t have a credit file or don’t have information for conventional scoring models to score them. Expanded credit data and attributes can help lenders accurately evaluate many of these consumers and remove barriers that keep them from accessing mainstream financial services. There's no time to wait Businesses can expand their customer base while reducing risk by looking beyond traditional credit bureau data and scores. Download our latest e-book on credit attributes to learn more about what Experian offers and how we can help you stay ahead of the competition.  Download e-book  Learn more 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.

As industry experts are still unsure when the economy will fully recover, re-entry into marketing preapproved credit offers seems like a far-off proposal. However, several of the top credit card issuers are already mailing prescreen offers, with many other lenders following suit. When the time comes for organizations to resume, or even expand this type of targeting, odds are that the marketing budget will be tighter than in the past. To make the most of the limited available marketing spend, lenders will need to be more prescriptive with their selection process to increase response rates on fewer delivered offers. Choosing the best candidates to receive these offers, from a credit risk perspective, will be critical. With delinquencies being suppressed due to CARES Act reporting guidelines, identifying consumers with the ability to repay will require additional assessment of recent credit behavior metrics, such as actual payment amounts and balance migration. Along with the presence of explicit indicators of accommodated trades (trades affected by natural disaster, trades with a balance but no scheduled payment amount) on a prospect’s credit file, their recent trends in payments and balance shifts can be integral in determining whether a prospect has been adversely impacted by today’s economic environment. Once risk criteria have been developed using a mix of bureau scores (like the VantageScore® credit score), traditional credit attributes and trended attributes measuring recent activity, additional targeting will be critical for selecting a population that’s most likely to open the relevant trade type. For credit cards and personal installment loans, response performance can be greatly improved by aligning product offers with prospects based on their propensity to revolve, pay in full each month or consolidate balances. Additionally, the process to select final prospects should integrate a propensity to open/respond assessment for the specific offering. While many lenders have custom models developed on previous internal response performance, off-the-shelf propensity to open models are also available to provide an assessment of a prospect’s likelihood to open a particular type of trade in the coming months. These models can act as a fast-start for lenders that intend to develop internal custom models, but don’t have the response performance within a particular product/geography/risk profile. They are also commonly used as a long-term solution for lenders without an internal model development team or budget for an outsourced model. Prescreen selection best practices Identify geography and traditional credit risk assessment of the prospect universe. Overlay attributes measuring accommodated trades and recent payment/balance trends to identify prospects with indications of ability to pay. Segment the prospect universe by recent credit usage to determine products that would resonate. Make final selections using propensity to open model scores to increase response rates by only making offers to consumers who are likely looking for new credit offers. While the best practices listed above don’t represent a risk-free approach in these uncertain times, they do provide a framework for identifying prospects with mitigated repayment risk and insights into the appropriate credit offer to make and when to make it. Learn about in the market models Learn about trended attributes VantageScore® is a registered trademark of VantageScore Solutions, LLC.

Changing consumer behaviors caused by the COVID-19 pandemic have made it difficult for businesses to make good lending decisions. Maintaining a consistent lending portfolio and differentiating good customers who are facing financial struggles from bad actors with criminal intent is getting more difficult, highlighting the need for effective decisioning tools. As part of our ongoing Q&A perspective series, Jim Bander, Experian’s Market Lead, Analytics and Optimization, discusses the importance of automated decisions in today’s uncertain lending environment. Check out what he had to say: Q: What trends and challenges have emerged in the decisioning space since March? JB: In the age of COVID-19, many businesses are facing several challenges simultaneously. First, customers have moved online, and there is a critical need to provide a seamless digital-first experience. Second, there are operational challenges as employees have moved to work from home; IT departments in particular have to place increase priority on agility, security, and cost-control. Note that all of these priorities are well-served by a cloud-first approach to decisioning. Third, the pandemic has led to changes in customer behavior and credit reporting practices. Q: Are automated decisioning tools still effective, given the changes in consumer behaviors and spending? JB: Many businesses are finding automated decisioning tools more important than ever. For example, there are up-sell and cross-sell opportunities when an at-home bank employee speaks with a customer over the phone that simply were not happening in the branch environment. Automated prequalification and instant credit decisions empower these employees to meet consumer needs. Some financial institutions are ready to attract new customers but they have tight marketing budgets. They can make the most of their budget by combining predictive models with automated prescreen decisioning to provide the right customers with the right offers. And, of course, decisioning is a key part of a debt management strategy. As consumers show signs of distress and become delinquent on some of their accounts, lenders need data-driven decisioning systems to treat those customers fairly and effectively. Q: How does automated decisioning differentiate customers who may have missed a payment due to COVID-19 from those with a history of missed payments? JB: Using a variety of credit attributes in an automated decision is the key to understanding a consumer’s financial situation. We have been helping businesses understand that during a downturn, it is important for a decisioning system to look at a consumer through several different lenses to identify financially stressed consumers with early-warning indicators, respond quickly to change, predict future customer behavior, and deliver the best treatment at the right time based on customer specific situations or behaviors.  In addition to traditional credit attributes that reflect a consumer’s credit behavior at a single point in time, trended attributes can highlight changes in a consumer’s behavior. Furthermore, Experian was the first lender to release new attributes specifically created to address new challenges that have arisen since the onset of COVID. These attributes help lenders gain a broader view of each consumer in the current environment to better support them. For example, lenders can use decisioning to proactively identify consumers who may need assistance. Q: What should financial institutions do next? JB: Financial institutions have rarely faced so much uncertainty, but they are generally rising to the occasion. Some had already adopted the CECL accounting standard, and all financial institutions were planning for it. That regulation has encouraged them to set aside loss reserves so they will be in better financial shape during and after the COVID-19 Recession than they were during the Great Recession. The best lenders are making smart investments now—in cloud technology, automated decisioning, and even Ethical and Explainable Artificial Intelligence—that will allow them to survive the COVID Recession and to be even more competitive during an eventual recovery. Financial institutions should also look for tools like Experian’s In the Market Model and Trended 3D Attributes to maximize efficiency and decisioning tactics – helping good customers remain that way while protecting the bottom line. In the Market Models Trended 3D Attributes  About our Expert: [avatar user="jim.bander" /] Jim Bander, PhD, Market Lead, Analytics and Optimization, Experian Decision Analytics Jim joined Experian in April 2018 and is responsible for solutions and value propositions applying analytics for financial institutions and other Experian business-to-business clients throughout North America. He has over 20 years of analytics, software, engineering and risk management experience across a variety of industries and disciplines. Jim has applied decision science to many industries, including banking, transportation and the public sector.

Last week, the unemployment rate soared past 20%, with over 30 million job losses attributed to the COVID-19 pandemic. As a result, many consumers are facing financial stress, which has raised many questions and discussions around how credit history and reporting should be treated at this time. Since the initial start of the pandemic, credit reporting companies and data furnishers have been put under the spotlight to ensure that consumers are able to get the assistance that they need. Numerous questions and concerns have also been raised around the extent of which consumers have access to fair and affordable credit. On March 27th, 2020, Congress signed the Coronavirus Aid, Relief, and Economic Security (CARES) Act into law, which was a bill created to provide support and relief for American workers, families, and small businesses. This newly proposed Act also provides guidelines on how creditors and data furnishers should report information to credit bureaus, to ensure that lenders remain flexible as consumers navigate the current pandemic. The Act requires that creditors must provide “accommodations” to consumers affected by COVID-19 during “covered periods.” According to the National Credit Union Administration, “The CARES Act requires credit reporting agency data providers, including credit unions, to report loan modifications resulting from the COVID-19 pandemic as ‘current’ or as the status reported before the accommodation unless the consumer becomes current,” as stated in Section 4021. Section 4021 of the CARES Act also provides other guidelines for accurate data reporting. During this time, lenders can use attributes to determine risk during COVID-19. Attributes within custom scores can also capture consumer behavior and help lenders determine the best treatments. Payment attributes, debt burden attributes, inquiry attributes, credit extensions and originations are all key indicators to keep an eye on at this time as lenders monitor risk in their portfolios. Listen in as our panel of experts explore the areas related to data reporting that impact you the most. In addition to a regulatory update and discussions around programs to help support consumers and businesses, we’ll also review what other lenders are doing and early indicators of credit trends. You’ll also be able to walk away with key strategies around what your organization can do right now. Discover the latest information on: Data reporting and CDIA regulations Regulatory updates, including the CARES Act, a breakdown of Section 4021, and guidelines to remember Credit attribute trends and highlights, treatment of scores and attributes, as well as recommended attributes Watch the webinar

As financial institutions and other organizations scramble to formulate crisis response plans, it’s important to consider the power of data and analytics. Jim Bander, PhD, Experian’s Analytics and Optimization Market Lead discusses the ways that data, analytics and models can help during a crisis. Check out what he had to say: What implications does the global pandemic have on financial institutions’ analytical needs?  JB: COVID-19 is a humanitarian crisis, one that parallels Hurricanes Sandy and Katrina and other natural disasters but which far exceeds their magnitude. It is difficult to predict the impact as huge parts of the global economy have shut down. Another dimension of this disaster is the financial impact: in the US alone, more than 17 million people applied for unemployment in the first 6 weeks of the COVID-19 crisis. That compares to 15 million people in 18 months during the Great Recession. Data and analytics are more important than ever as financial institutions formulate their responses to this crisis. Those institutions need to focus on three key things: safety, soundness, and compliance. Safety: Financial institutions are taking immediate action to mitigate safety risks for their employees and their customers. Soundness: Organizations need to mitigate credit and fraud risk and to evaluate capital and liquidity. Some executives may need a better understanding of how their bank’s stress scenarios were calculated in the past to understand how they must be updated for the future. Important analytic functions include performing portfolio monitoring and benchmarking—quantifying the effects not only of consumer distress, but also of low interest rates. Compliance: Understanding and meeting complex regulatory and compliance requirements is crucial at this time. Companies have to adapt to new credit reporting guidelines. CECL requirements have been relaxed but lenders should assess the effects of COVID, and not only during their annual stress tests. As more consumers seek credit, from an analytics perspective, what considerations should financial institutions make during this time?  JB: During this volatile time, analytics will help financial institutions: Identify financially stressed consumers with early warning indicators Predict future consumer behavior Respond quickly to changes Deliver the best treatments at the right time for individual customers given their specific situations and their specific behavior. Financial institutions should be reevaluating where their organizations have the most vulnerability and should be taking immediate action to mitigate these risks. Some important areas to keep an eye on include early warning indicators, changes in fraudulent behavior (with the increase in digital engagements), and changes in customer behavior. Banks are already offering payment flexibility, deferments, and credit reporting accommodations. If volatility continues or increases, they may need to offer debt forgiveness plans. These organizations should also be prepared to understand their own changing constraints—such as budget, staffing levels, and liquidity requirements— especially as consumers accelerate their move to digital channels. In the near future, lenders should be optimizing their operations, servicing treatments, and lending policies to meet a number of possibly conflicting objectives in the presence of changing constraints and somewhat unpredictable transaction volumes.   What is the smartest next play for financial institutions?  JB: I see our smartest clients doing four things: Adapting to the new normal Maintaining engagement with existing customers by refreshing data that companies have on-hand for these consumers, and obtain additional views of these customers for analytics and data-driven decisioning Reallocating operational resources and anticipating the need for increased capacity in various servicing departments in the future Improving their risk management practices   What is Experian doing to help clients improve their risk management? JB: During this time, banks and other financial institutions are searching for ways to predict consumer behavior, especially during a crisis that combines aspects of a natural disaster with characteristics of a global recession. It is more important than ever to use analytics and optimization. But some of the details of the methodology is different now than during a time of economic expansion. For example, while credit scores (like FICO® and VantageScore® credit scores) will continue to rank consumers in terms of their probability to pay, those scores must be interpreted differently. Furthermore, those scores should be combined with other views of the consumer—such as trends in consumer behavior and with expanded FCRA-compliant data (data that isn’t reported to traditional credit bureaus). One way we’re helping clients improve their credit risk management is to provide them with a list of 140 consumer credit data attributes in 10 categories. With this list, companies will be able to better manage portfolio risk, to better understand consumer behavior, and to select the next best action for each consumer. Four other things we’re doing: We’re quickly updating our loss forecasting and liquidity management offerings to account for new stress scenarios. We’re helping clients review their statistical models’ performance and their customer segmentation practices, and helping to update the models that need refreshing. Our consulting team—Experian Advisory Services—has been meeting with clients virtually--helping them update, execute their crisis and downturn responses, and whiteboard new or updated tactical plans. Last but not least, we’re helping lenders and consumers defend themselves against a variety of fraud and identity theft schemes. Experian is committed to helping your organization during these uncertain times. For more resources, visit our Look Ahead 2020 Hub. Learn more Jim Bander, PhD, Analytics and Optimization Market Lead, Decision Analytics, Experian North America Jim Bander, PhD joined Experian in April 2018 and is responsible for solutions and value propositions applying analytics for financial institutions and other Experian business-to-business clients throughout North America. Jim has over 20 years of analytics, software, engineering and risk management experience across a variety of industries and disciplines. He has applied decision science to many industries including banking, transportation and the public sector. He is a consultant and frequent speaker on topics ranging from artificial intelligence and machine learning to debt management and recession readiness. Prior to joining Experian, he led the Decision Sciences team in the Risk Management department at Toyota Financial Services.

This is the second in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty. The word "unprecedented" gets thrown around pretty carelessly these days. When I hear that word, I think fondly of my high school history teacher.  Mr. Fuller had a sign on his wall quoting the philosopher-poet George Santayana: "Those who cannot remember the past are condemned to repeat it." Some of us thought it meant we had to memorize as many facts as possible so we wouldn't have to go to summer school. The COVID-19 crisis--with not only health consequences but also accompanying economic and financial impacts--certainly breaks with all precedents.  The bankers and other businesspeople I've been listening to are rightly worried that This Time is Different. While I'm sure there are history teachers who can name the last time a global disaster led to a widescale humanitarian crisis and an economic and financial downturn, I'm even more sure times have changed a lot since then. But there are plenty of recent precedents to guide business leaders and other policymakers through this crisis. Hurricanes Katrina and Sandy impacted large regions of the United States, with terrible human consequences followed by financial ones. Dozens of local disasters—floods, landslides, earthquakes—devastated smaller numbers of people in equally profound ways. The Great Recession, starting in 2008, put millions of Americans and others around the world out of work. Each of those disasters, like this one, broke with all precedents in various ways. Each of those events was in many ways a dress rehearsal, as bankers and other lenders learned to provide assistance to distressed businesses and consumers, while simultaneously planning for the inevitable changes to their balance sheets and income statements. Of course, the way we remember the past has changed. Just as most of us no longer memorize dates--we search for them on the web--businesspeople turn to their databases and use analytics to understand history. I've been following closely as the data engineers and data scientists here at Experian have worked on perhaps their most important problem ever. Using Experian's Ascend Analytical Sandbox--named last year as the Best Overall Analytics Platform, they combed through over eighteen years of anonymized historical data covering every credit report in the United States. They asked--using historical experience, wisdom, time-consuming analytics, a little artificial intelligence, and a lot of hard work--whether predicting credit performance during and after a crisis is possible. They even considered scenarios regarding what happens as creditors change the way they report consumer delinquencies to the credit bureaus. After weeks of sleepless nights, they wrote down their conclusions.  I've read their analysis carefully and I’m pleased to report that it says…Drumroll, please…Yes, but. Yes, it's possible to predict consumer behavior after a disaster. But not in precisely the same way those predictions are made during a period of economic growth. For a credit risk manager to review a lending portfolio and to predict its credit losses after a crisis requires looking at more data--and looking at it a little differently--than during other periods. Yes, after each disaster, credit scores like FICO® and VantageScore® credit scores continued to rank consumers from most likely to least likely to repay debts. But the interpretation of the score changes. Technically speaking, there is a substantial shift in the odds ratio that is particularly pronounced when a score is applied to subprime consumers. To predict borrower behavior more accurately, our scientists found that it helps to look at ten additional categories of data attributes and a few additional types of mathematical models. Yes, there are attributes on the credit report that help lenders identify consumer distress, willingness, and ability to pay. But, the data engineers identified that during times like these it is especially helpful to look beyond a single point in time; trends in a consumer's payment history help understand whether that customer is changing their typical behavior. Yes, the data reported to the credit bureaus is predictive, especially over time. But when expanded FCRA data is available beyond what is traditionally reported to a bureau, that data further improves predictions. All told, the data engineers found over 140 data attributes that can help lenders and others better manage their portfolio risk, understand consumer behavior, appreciate how the market is changing, and choose their next best action. The list of attributes might be indispensable to a credit data specialist whose institution needs to weather the coming storm. Because Experian knows how important it is to learn from historical precedents, we're sharing the list at no charge with qualified risk managers. To get the latest Experian data and insights or to request the Crisis Response Attributes recommendation, visit our Look Ahead 2020 page. Learn more

A few months ago, I got a letter from the DMV reminding me that it was finally time to replace my driver’s license. I’ve had it since I was 21 and I’ve been dreading having to get a new one. I was especially apprehensive because this time around I’m not just getting a regular old driver’s license, I’m getting a REAL ID. For those of you who haven’t had this wonderful experience yet, a REAL ID is the new form of driver’s license (or state ID) that you’ll need to board a domestic flight starting October 1, 2020. Some states already offered compliant IDs, but others—like California, where I’m from—didn’t. This means that if I want to fly within the U.S. using my driver’s license next year, I can’t renew by mail. It’s Easier Than It Looks Imagine my surprise when I started the process to schedule my appointment, and the California DMV website made things really easy! There’s an online application you can fill out before you get to the DMV and they walk you through the documents to bring to the appointment (which I was able to schedule online). Despite common thought that the DMV and agencies like it are slow to adopt technology, the ease of this process may indicate a shift toward a digital-first mindset. As financial institutions embrace a similar shift, they’ll be better positioned to meet the needs of customers. Case in point, the electronic checklist the DMV provided to prepare me for my appointment. I sailed through the first two parts of the checklist, confirming that I’ll bring my proof of identity and social security number, but I paused when I got to the “Two Proofs of Residency” screen. Like many people my age—read: 85% of the millennial population, according to a recent Experian study—I don’t have a mortgage or any other documents relating to property ownership. I also don’t have my name on my utilities (thanks, roomie) or my cell phone bill (thanks Mom). I do however have a signed lease with my name on it, plus my renter’s insurance, both of which are acceptable as proof of residency. And just like that, I’m all set to get my REAL ID, even though I don’t have some of the basic adulting documents you might expect, because the DMV took into account the fact that not all REAL ID applicants are alike. Imagine if lenders could adopt that same flexibility and create opportunities for the more than 45 million U.S. consumers1 who lack a credit report or have too little information to generate a credit score. The Bigger Picture By removing some of the usual barriers to entry, the DMV made the process of getting my REAL ID much easier than it might have been and corrected my assumptions about how difficult the process would be. Experian has the same line of thought when it comes to helping you determine whether a borrower is credit-worthy. Just because someone doesn’t have a credit card, auto loan or other traditional credit score contributor doesn’t mean they should be written off. That’s why we created Experian BoostTM, a product that lets consumers give read-only access to their bank accounts and add in positive utility and telecommunications bill payments to their credit file to change their scores in real time and demonstrate their stability, ability and willingness to repay. It’s a win-win for lenders and consumers. 2 out of 3 users of Experian Boost see an increase in their FICO Score and of those who saw an increase, 13% moved up a credit tier. This gives lenders a wider pool to market to, and thanks to their improved credit scores, those borrowers are eligible for more attractive rates. Increasing your flexibility and removing barriers to entry can greatly expand your potential pool of borrowers without increasing your exposure to risk. Learn more about how Experian can help you leverage alternative credit data and expand your customer base in our 2019 State of Alternative Data Whitepaper. Read Full Report 1Kenneth P. Brevoort, Philipp Grimm, Michelle Kambara. “Data Point: Credit Invisibles.” The Consumer Financial Protection Bureau Office of Research. May 2015.

According to our recent research for the State of Alternative Credit Data, more lenders are using alternative credit data to determine if a consumer is a good or bad credit risk. In fact, when it comes to making decisions: More than 50% of lenders verify income, employment and assets as well as check public records before making a credit decision. 78% of lenders believe factoring in alternative data allows them to extend credit to consumers who otherwise would be declined. 70% of consumers are willing to provide additional financial information to a lender if it increases their chance for approval or improves their interest rate. The alternative financial services space continues to grow with products like payday loans, rent-to-own products, short-term loans and more. By including alternative financial data, all types of lenders can explore both universe expansion and risk mitigation. State of Alternative Credit Data

Trended attributes and consumer lending Digging deeper into consumer credit data can help provide new insights into trending behavior, providing more than just point-in-time credit evaluation. The information derived through trended attributes can help you understand your customers’: Payment rates and account migration behavior. Slope of balance changes. Delinquency patterns over time. Today’s consumer lending environment is more dynamic and competitive than ever. Trended attributes can give additional lift in your segmentation strategies and custom models and provides a high-definition lens that opens a world of opportunity. Learn more

With 81% of Americans having a social media profile, you may wonder if social media insights can be used to assess credit risk. When considering social media data as it pertains to financial decisions, there are 3 key concerns to consider. The ECOA requires that credit must be extended to all creditworthy applicants regardless of race, religion, gender, marital status, age and other personal characteristics. Social media can reveal these characteristics and inadvertently affect decisions. Social media data can be manipulated. Individuals can represent themselves as financially responsible when they’re not. On the flip side, consumers can’t manipulate their payment history. When it comes to credit decisions, always remember that the FCRA trumps everything. Data is essential for all aspects of the financial services industry, but it’s still too early to click the “like” button for social media. Make more insightful decisions with credit attributes>