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In the wake of unprecedented unemployment fraud since the start of COVID-19, Experian announced it was selected as the exclusive partner for identity and fraud verification for the Unemployment Insurance (UI) Integrity Center’s centralized Identity Verification (IDV) capability. IDV is available to state agencies at no cost through UI Integrity Center, which is operated by the National Association of Workforce Agencies (NASWA) in partnership with the U.S. Department of Labor.   With the Federal Bureau of Investigations (FBI) reporting a spike in fraudulent unemployment insurance claims complaints related to COVID-19, it’s more important than ever for state agencies to use innovative solutions to verify identities that are applying for unemployment insurance to protect consumers. If improper unemployment insurance payments are made to fraudsters, the efforts of the CARES Act could be largely wasted.   The IDV capability leverages Experian’s Precise IDTM to provide a centralized identity verification and proofing solution. Precise ID combines identity analytics with advanced fraud risk models to distinguish various types of fraud, which can help state agencies maximize time and resources. When state agencies submit claims, the IDV solution will return ID theft scoring and associated cause codes, enabling them to assess whether a claim may be fraudulent.   “Due to the COVID-19 health crisis, unemployment is high, with over roughly 60 million Americans filing for unemployment since March,” said Robert Boxberger, president of Experian’s Decision Analytics in North America. “At Experian, we’re proud to have a strong culture dedicated to continuous innovation that helps protect consumers’ financial health. We’re taking that same consumer focus and helping make the unemployment insurance application process more efficient and safer for constituents.”   The Integrity Data Hub (IDH) is a robust, multi-state data system that contains a continuously expanding set of sources to provide advanced cross-matching and analytic capabilities to states. It is designed to be easily implemented by any state Unemployment Insurance agency, regardless of claim volume, technology, or access to internal resources. The IDH was designed and built using the latest National Institute of Standards and Technology IT security standards, including the use of asymmetric encryption and other techniques to ensure the security of sensitive data.   “We’re excited to partner with Experian and utilize its Precise ID solution to assist states in mitigating fraud during these unprecedented times,” said Scott Sanders, NASWA Executive Director. “States are finding this to be a very valuable tool and we are pleased that we can offer this solution to states through our partnership with the U.S. Department of Labor.” Read Press Release Learn More About Precise ID

Synthetic identity fraud, otherwise known as SID fraud, is reportedly the fastest-growing type of financial crime. One reason for its rapid growth is the fact that it’s so hard to detect, and thus prevent. This allows the SIDs to embed within business portfolios, building up lines of credit to run up charges or take large loans before “busting out” or disappearing with the funds. In Experian’s recent perspective paper, Preventing synthetic identity fraud, we explore how SID differs from other types of fraud, and the unique steps required to prevent it. The paper also examines the financial risks of SID, including: $15,000 is the average charge-off balance per SID attack Up to 15% of credit card losses are due to SID 18% - the increase in global card losses every year since 2013 SID is unlike any other type of fraud and standard fraud protection isn’t sufficient. Download the paper to learn more about Experian’s new toolset in the fight against SID. Download the paper

The CU Times recently reported on a nationwide synthetic identity fraud ring impacting several major credit unions and banks. Investigators for the Federal and New York governments charged 13 people and three businesses in connection to the nationwide scheme. The members of the crime ring were able to fraudulently obtain more than $1 million in loans and credit cards from 10 credit unions and nine banks. Synthetic Identity Fraud Can’t Be Ignored Fraud was on an upward trend before the pandemic and does not show signs of slowing. Opportunistic criminals have taken advantage of the shift to digital interactions, loosening of some controls in online transactions, and the desire of financial institutions to maintain their portfolios – seeking new ways to perpetrate fraud. At the onset of the COVID-19 pandemic, many financial institutions shifted their attention from existing plans for the year. In some cases they deprioritized plans to review and revise their fraud prevention strategy. Over the last several months, the focus swung to moving processes online, maintaining portfolios, easing customer friction, and dealing with IT resource constraints. While these shifts made sense due to rapidly changing conditions, they may have created a more enticing environment for fraudsters. This recent synthetic identity fraud ring was in place long before COVID-19. That said, it still highlights the need to have a prevention and detection plan in place. Financial institutions want to maintain their portfolios and their customer or member experience. However, they can’t afford to table fraud plans in the meantime. “72% of FI executives surveyed believe synthetic identity fraud to be more challenging than identity theft. This is due to the fact that it is harder to detect—either crime rings nurture accounts for months or years before busting out with six-figure losses, or they are misconstrued as credit losses, and valuable agent time is spent trying to collect from someone who doesn’t exist,” says Julie Conroy, Research Director at Aite Group. Prevention and Detection Putting the fraud strategy discussion on hold—even in the short term—could open up a financial institution to potential risk at time when cost control and portfolio maintenance are watch words. Canny fraudsters are on the lookout for financial institutions with fewer protections. Waiting to implement or update a fraud strategy could open a business up to increased fraud losses. Now is the time to review your synthetic identity fraud prevention and detection strategies, and Experian can help. Our innovative new tool in the fight against synthetic identity fraud helps financial institutions stop fraudsters at the door. Learn more  

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.

Consumer behavior and payment trends are constantly evolving, particularly in a rapidly changing economic environment. Faced with changing demands, including an accelerated shift to digital communications, and new regulatory rules, debt collectors must adapt to advance in the new collections landscape. According to Experian research, as of August, the U.S. unemployment rate was at 8.4%, with numerous states still having employment declines over 10%. These triggers, along with other recent statistics, signal a greater likelihood of consumers falling delinquent on loans and credit card payments. The issue for debt collectors? Many debt collection departments and agencies are not equipped to properly handle the uptick in collection volumes. By refining your process and capabilities to meet today’s demands, you can increase the success rate of your debt collection efforts. Join Denise McKendall, Experian’s Director of Collection Solutions, and Craig Wilson, Senior Director of Decision Analytics, during our live webinar, "Adapting to the New Collections Landscape," on October 21 at 10:00 a.m. PT. Our expert speakers will provide a view of the current collections environment and share insights on how to best adapt. The agenda includes: Meeting today’s collections challenges A Look at the state of the market Devising strategies and solving collections problems across the debt lifecycle Register now

Profitability analysis is one of the most powerful analytics tools in business and strategy development. Yet it’s underrated, deemed too complicated and often ignored. A chief lending officer may state that the goal of strategy development is to increase approvals or to reduce losses. Each one of these goals has an impact generally inversely on each other. That impact may be consequential, and evaluating the effects requires deeper thought and discipline. I propose that the benefits of a profitability analysis in strategy development are worth the additional effort, time and cost. Profitability analysis provides a disciplined framework for making business decisions. For financial companies, a simple profit and loss (P&L) statement will identify interest income, subtract losses and arrive at a risk-adjusted yield. A more robust P&L statement will include interest expense, loss reserves, recovery, fees and other income, operating expenses, other cost per account, and net income. Whether simplified or fully loaded, a P&L analysis used in strategy development must provide a clear and informative representation of key performance metrics and risks. The most important benefit of a profitability analysis is its inherent ability to quantify the trade-offs between risk and rewards. In the P&L terminology, we mean the trade-off between expenses and revenue or losses and interest income. Understanding trade-offs allows companies to make informed decisions and explore serious alternatives. The net income is a concise and elegant metric that captures the impact of various and sometimes competing business objectives. Consider different divisions within a financial organization. Each division has its own specific and measurable objective. Marketing’s goal is to increase loan approvals while Risk is tasked with managing losses. Operations looks to improve efficiencies while IT aims to provide stable, reliable and accurate systems infrastructure. Legal and Compliance ensure regulatory compliance across the entire organization. Each division working to achieve its objectives creates externalities — each division’s actions may not fully incorporate costs imposed on other divisions. For example, targeting highly responsive consumers for a loan product achieves higher loan approvals and may in turn lead to higher credit risk losses. A P&L analysis imposes the discipline for each division to internalize costs and lead to a favorable and efficient outcome for the organization. The challenge with profitability analysis in strategy development is how to develop a good P&L statement. We look to historical data to define assumptions and calibrate inputs to the P&L. There will be uncertainty and concerns regarding the reliability and quality of such data. Organizations don’t regularly conduct test and control experiments or champion and challenger strategies that provide actual performance information on specific areas of studies. Though imperfect, historical data provides a starting foundation for profitability analysis. We augment historical data with predictive credit attributes, industry experience and understanding consumer behavior and incentives. For example, to estimate interest income we may utilize estimated interest rates combined with balance propensity behavior, such as a balance revolver or transactor. To estimate losses on declined population that may be considered for approval, we infer on-us performance using off-us performance with other lenders. Defining assumptions is tedious, hard work and full of uncertainty. This exercise once again imposes the discipline required of organizations to know in detail the characteristics of their products and businesses that make them relevant to consumers. We generate P&L simulations using a set of assumptions, acknowledge the data limitations and evaluate recommendations. A profitability analysis is useful in both times of economic expansion and contraction. A P&L analysis is valuable when evaluating strategies across the customer life cycle. Remember, we live in a world of trade-offs and choices are inevitable. In the prospecting and acquisition life cycle, a P&L analysis provides insights on approval expansion and the consequences of higher credit losses. Alternatively, tighter lending criteria will have a direct impact on balance growth and interest income with lower losses. In account management, a P&L analysis provides estimates on expanded account authorization limits and the effect on activation and usage. In collections, a P&L analysis provides valuation on recoveries and operational costs. These various assessments are quantified in the P&L and allows the organization to identify other mechanisms such as marketing campaigns, customer services or technology investments in support of the organization’s goals and mission. Organizations face a full spectrum of opportunities and risks. We propose a profitability analysis to evaluate business trade-offs, navigate the marketplace, and continue to provide relevant financial products and services to consumers and businesses. Learn more

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.

This is the fourth 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 second with predicting consumer payment behavior, and the third with validating consumer credit scores. This post describes some specific Experian solutions that are especially timely for lenders strategizing their response to the COVID Recession. Will the US economy recover from the pandemic recession?  Certainly yes. When will the economy recover? There is a lot more uncertainty around that question. Many people are encouraged by positive indicators, such as the initial rebound of the stock market, a return of many of the jobs lost at the beginning of the pandemic, and a significant increase in housing starts. August’s retail spending and homebuilder confidence are very encouraging economic indicators. Other experts doubt that the “V-shaped” recovery can survive flare-ups of the virus in various parts of the US and the world, and are calling for a “W-shaped” recovery.  Employment indicators are alarming: many people remain out of work, some job losses are permanent, and there are more initial jobless claims each week now than at the height of the Great Recession. Serious hurdles to economic recovery may remain until a vaccine is widely available: childcare, urban transportation, and global trade, for example. I’m encouraged by the resilience of many of our country’s consumer lenders. They are generally responding well to these challenges. If past recessions are a guide, some lenders will not survive these turbulent times. This time, many lenders—whether or not they have already adopted the CECL accounting standards—have been increasing allowances for their anticipated credit losses. At least one rating agency believes major banks are prepared to absorb those losses from earnings.  The lenders who are most prepared for the eventual recovery will be those that make good decisions during these volatile times and take action to put themselves in the best position in anticipation of the recovery that will certainly follow. The best lenders are making smart investments now to be prepared to capitalize on future opportunities. Experian’s analytics and consulting experts are continuously improving our suite of solutions that help consumer lenders and others assess consumer behavior and respond quickly to the rapidly fluctuating market conditions as well as changing regulations and credit reporting practices. Our newly announced Economic Response and Recovery Suite includes the ABCD’s that lenders need to be resilient and competitive now and to prepare to thrive during the eventual recovery: A – Analytics. As I’ve written about in prior blog posts, data is a prerequisite to making good business decisions, but data alone is not enough. To make wise, insightful decisions, lenders need to use the most appropriate analytical techniques, whether that means more meaningful attributes, more predictive and compliant credit scores, more accurate and defensible loss forecasting solutions, or optimization systems that help develop strategies in a world where budgets, regulations, and other constraints are changing. For example, Experian has released a set of Spotlight 2020 Attributes that help consumer lenders create a positive experience for customers who have received an accommodation during the pandemic. In many cases motivated by the new race to improve customer experience online, and in other cases as a reaction to new and creative fraud schemes, some clients are using this period as an opportunity to explore or deploy ethical and explainable Artificial Intelligence. B – Business Intelligence. Credit bureaus like Experian are uniquely situated to understand the impact of the COVID recession on America’s consumers. With impact reports, dashboards, and custom business intelligence solutions, lenders are working during the recession to gain an even better understanding of their current and prospective customers. We’re helping many of them to proactively help consumers when they need it most. For example, lenders have turned to us to understand their customer’s payment hierarchy—which bills they pay first when times are tough. Our free COVID-19 US Business Risk Index helps make lending options available to the businesses who need them most. And we’ve armed lenders with recommendations for which of our pre-existing attributes and scores are most helpful during trying times. Additional reporting tools such as the Auto Market Tracker, Ascend Market Insights Dashboard, and the weekly economic update video provide businesses with information on new market trends—information that helps them respond during the recession and promises to help them grow during the eventual recovery. C – Consulting. It’s good to turn data into information and information into insight, but how do these lenders incorporate these insights in their business strategies? Lenders and other businesses have been turning to Experian’s analytics and Advisory services consultants to unlock the information hidden in credit and other data sources—finding ways to make their business processes more efficient and more effective while developing quick response plans and more long-term recovery strategies. D – Delivery.  Decision science is the practice of using advanced analytics, artificial intelligence, and other techniques to determine the best decision based on available data and resources. But putting those decisions into action can be a challenge. (Organizations like IBM and Gartner estimate that a great majority of data science projects are never put into production.) Experian technologies—from our analytics platform to our attribute integration and decision management solutions ensure that data-driven decisions can be quickly implemented to make a real difference. Treating each customer optimally has a number of benefits—whether you are trying to responsibly grow your portfolio, reduce credit losses and allowances, control servicing costs, or simply staying in compliance during dynamic times. In the age of COVID, IT departments have placed increased priority on agility, security, customer experience, and cost control, and appreciate cloud-first approach to deploying analytics. It’s too early to know how long this period of extreme uncertainty will last. But one thing is certain: it will come to an end, and the economy will recover someday. I predict that many of the companies that make the best use of data now will be the ones who do the best during the recovery. To hear more ways your organization can navigate this downturn and the recovery to follow, please watch our on-demand webinar and check out our Economic Response and Recovery Suite. Watch the Webinar

In 2015, U.S. card issuers raced to start issuing EMV (Europay, Mastercard, and Visa) payment cards to take advantage of the new fraud prevention technology. Counterfeit credit card fraud rose by nearly 40% from 2014 to 2016, (Aite Group, 2017) fueled by bad actors trying to maximize their return on compromised payment card data. Today, we anticipate a similar tsunami of fraud ahead of the Social Security Administration (SSA) rollout of electronic Consent Based Social Security Number Verification (eCBSV). Synthetic identities, defined as fictitious identities existing only on paper, have been a continual challenge for financial institutions. These identities slip past traditional account opening identity checks and can sit silently in portfolios performing exceptionally well, maximizing credit exposure over time. As synthetic identities mature, they may be used to farm new synthetics through authorized user additions, increasing the overall exposure and potential for financial gain. This cycle continues until the bad actor decides to cash out, often aggressively using entire credit lines and overdrawing deposit accounts, before disappearing without a trace. The ongoing challenges faced by financial institutions have been recognized and the SSA has created an electronic Consent Based Social Security Number Verification process to protect vulnerable populations. This process allows financial institutions to verify that the Social Security number (SSN) being used by an applicant or customer matches the name. This emerging capability to verify SSN issuance will drastically improve the ability to detect synthetic identities. In response, it is expected that bad actors who have spent months, if not years, creating and maturing synthetic identities will look to monetize these efforts in the upcoming months, before eCBSV is more widely adopted. Compounding the anticipated synthetic identity fraud spike resulting from eCBSV, financial institutions’ consumer-friendly responses to COVID-19 may prove to be a lucrative incentive for bad actors to cash out on their existing synthetic identities. A combination of expanded allowances for exceeding credit limits, more generous overdraft policies, loosened payment strategies, and relaxed collection efforts provide the opportunity for more financial gain. Deteriorating performance may be disguised by the anticipation of increased credit risk, allowing these accounts to remain undetected on their path to bust out. While responding to consumers’ requests for assistance and implementing new, consumer-friendly policies and practices to aid in impacts from COVID-19, financial institutions should not overlook opportunities to layer in fraud risk detection and mitigation efforts. Practicing synthetic identity detection and risk mitigation begins in account opening. But it doesn’t stop there. A strong synthetic identity protection plan continues throughout the account life cycle. Portfolio management efforts that include synthetic identity risk evaluation at key control points are critical for detecting accounts that are on the verge of going bad. Financial institutions can protect themselves by incorporating a balance of detection efforts with appropriate risk actions and authentication measures. Understanding their portfolio is a critical first step, allowing them to find patterns of identity evolution, usage, and connections to other consumers that can indicate potential risk of fraud. Once risk tiers are established within the portfolio, existing controls can help catch bad accounts and minimize the resulting losses. For example, including scores designed to determine the risk of synthetic identity, and bust out scores, can identify seemingly good customers who are beginning to display risky tendencies or attempting to farm new synthetic identities. While we continue to see financial institutions focus on customer experience, especially in times of uncertainty, it is paramount that these efforts are not undermined by bad actors looking to exploit assistance programs. Layering in contextual risk assessments throughout the lifecycle of financial accounts will allow organizations to continue to provide excellent service to good customers while reducing the increasing risk of synthetic identity fraud loss. Prevent SID

The COVID-19 pandemic created a global shift in the volume of online activity and experiences over the past several months. Not only are consumers increasing their usage of mobile and digital channels to bank, shop, work and socialize — and anticipating more of the same in the coming months — they’re closely watching how businesses respond to their needs.   Between late June and early July of this year, Experian surveyed 3,000 consumers and 900 businesses to explore the shifts in consumer behavior and business strategy pre- and post-COVID-19.   More than half of businesses surveyed believe their operational processes have mostly or completely recovered since COVID-19 began. However, many consumers fear that a second wave of COVID-19 will further deplete their already strained finances. They are looking to businesses for reassurance as they shift their behaviors by:   Reducing discretionary spending Building up emergency savings Tapping into financial reserves Increasing online spending   Moving forward, businesses are focusing on short-term investments in security, managing credit risk with artificial intelligence, and increasing online customer engagement.   Download the full report to get all of the insights into global business and consumer needs and priorities and keep visiting the Insights blog in the coming weeks for a deeper dive into US-specific findings. Download the report

Do consumers pay certain types of credit accounts before others during financial distress? For instance, do they prioritize paying mortgage bills over credit card bills or personal loans? During the Great Recession, the traditional notion of payment priority among multiple credit accounts was upended, throwing strategies employed by financial institutions into disarray. Similarly, current circumstances in the context of COVID-19 might cause sudden shifts in prioritization of payments which might have a dramatic impact on your credit portfolio. Financial institutions would be better able to forecast and control exposure to credit risk, and to optimize servicing practices such as forbearance and collections treatments if they could understand changing customer payment behaviors and priorities of their existing customers across all open trades.  Unfortunately, financial institutions’ data—including their own behavioral data and refreshed credit bureau data--are limited to information about their own portfolio. Experian data provides insight which complements the financial institutions’ data expanding understanding of consumer payment behavior and priorities spanning all trades. Experian recently completed a study aimed at providing financial institutions valuable insights about their customer portfolios prior to COVID-19 and during the initial months of COVID-19. Using the Experian Ascend Technology Platform™, our data scientists evaluated a random 10% sample of U.S. consumers from its national credit file. Data from multiple vintages were pulled (June 2006, June 2008 and February 2018) and the payment trends were studied over the subsequent performance period. Experian tabulated the counts of consumers who had various combinations of open and active trade types and selected several trade type combinations with volume to differentiate performance by trade type. The selected combinations collectively span a variety of scenarios involving six trade types (Auto Loans, Bankcard, Student Loan, Unsecured Personal Loans, Retail Cards and First Mortgages). The trade combinations selected accommodate a variety of lenders offering different products. For each of the consumer groups identified, Experian calculated default rates associated with each trade type across several performance periods. For brevity, this blog will focus on customers identified as of February 2018 and their subsequent performance through February 2020. As the recession evolves and when the economy eventually recovers, we will continue to monitor the impacts of COVID-19 on consumer payment behavior and priorities and share updates to this analysis. Consumers with Bankcard, Mortgage, Auto and Retail accounts Among consumers having open and recently active Bankcard, Mortgage, Auto and Retail accounts, bankcard delinquency was highest throughout the 24-month performance window, followed by Retail.  Delinquency rates for Auto and Mortgage were the lowest. During the pre-COVID-19 period, consumers paid their secured loans before their unsecured loans. As demonstrated in the table below, customer payment priority was stable across the entire 24-month period, with no significant shift in payment priorities between trade types. Consumers with Unsecured Personal Loan, Retail Card and Bankcard accounts. Among consumers having open and recently active Unsecured Personal Loan, Retail Card and Bankcard accounts, consumers are likely to pay unsecured personal loans first when in financial distress. Retail is the second priority, followed by Bankcard. KEY FINDINGS From February 2018 through April 2020, relative payment priority by trade type has been stable Auto and Mortgage trades, when present, show very high payment priority Download the full Payment Hierarchy Report here. Download Now Learn more about how Experian can create a custom payment hierarchy for the customers in your own portfolio, contact your Experian Account Executive, or visit our website.

Experian’s Chris Ryan and Bobbie Paul recently re-joined David Mattei from Aite to discuss how emerging fraud trends and changes in consumer behavior will have long-term impacts on businesses. Chris, Bobbie, and David have combined experience of more than 60 years in the world of fraud prevention. In this discussion, they bring that experience to bear as they review how businesses should revise their long-term fraud strategy in response to COVID-19 and the subsequent economic shifts, including: The requirements to authenticate a digital customer Businesses’ technology challenges Differentiating between first party and third party fraud The importance of businesses’ technology investment How to build a roadmap for the next 90 days and beyond Experian · Make Your Fraud Plan Recession-Ready: Your 90 Day and Beyond Plan

Pre COVID-19, operations functions for retailers and financial institutions had not typically consisted of a remote (stay at home) workforce. Some organizations were better prepared than others, but there is a firm belief that retail and banking have changed for good as a result of the pandemic and resulting economic and workforce shifts. Market trends and implications When stay at home orders were issued, non-essential brick and mortar businesses closed unexpectedly. What were retailers to do with no traffic coming through the doors at their physical locations? The impact on big-box retailers like Best Buy, Dick’s Sporting goods, Sears, JCPenney, Nike, Starbucks, Macy’s, Neiman Marcus, Nordstrom, Kohl’s to name a few, has been unprecedented; some have had to shut their doors for good. Over the past several months global retail has seen e-commerce sales grow over 81% compared to the same period last year, according to Card Not Present. Some sectors have seen triple-digit growth year over year. Most online retailers have been ill-prepared to handle this increase in transactional volume in such a short amount of time, which has resulted in rapid fraud loss increases. A recent white paper from Aite Group reported that prior to COVID-19, a large financial institution forecasted an 8% decrease in fraud for 2020, but has since revised the projection to increase 10-15%. What does this all mean?  Bad actors are taking advantage of the pandemic to exploit the online retail channel. The increased remote channel usage—online, mobile, and contact centers in particular—continues to be an area where retailers are exposed. Account takeover, through phishing and relaxed call center controls, is rising as well. Increases in phishing attacks are leading to compromised and stolen identities and synthetic identity fraud. Account takeover (ATO) fraud has increased 347% since 2019 according to PYMNTS.com. A recent survey found more than a quarter of merchants (27%) admit that they don’t have measures to prevent ATO. 24% of merchants can’t identify an ATO during a purchase. 14% of merchants say they are not even aware that an ATO has occurred unless a customer contacts them. When criminals use these compromised accounts to make fraudulent purchases, the merchant loses revenue and the value of the goods. They can also suffer from damage to brand reputation and a loss of customer confidence. A lack of account security can have lasting effects as 65% of customers surveyed say they would likely stop buying from a merchant if their account was compromised, according to that same Card Not Present study. So how can retailers start to identify bad actors with malicious intent? This will be a constant struggle for retailers. Rather than a one size fits all solution, retailers must move toward a strategy that is nimble and dynamic and can address multiple areas of exposure. A fraudster could easily slip by one verification method—for instance with a stolen credential—only to be foiled by a secondary authentication tactic like device identity. A layered fraud strategy continues to be the industry best practice, where both passive and active authentication methods are leveraged to frustrate fraudsters without applying undue friction to “good” consumers. The layered solution should also utilize device risk, identity verification and fraud analytics, with tailoring to each businesses’ needs, risk tolerance, and customer profiles. Learn more about how to build a layered fraud strategy today. Learn more

Every few months we hear in the news about a fraud ring that has been busted here in the U.S. or in another part of the world. In May, I read about a fraud ring based in Georgia and Louisiana that bought 13,000 stolen identities of children who were on the Louisiana Medicaid program and billed the government for services not rendered. This group defrauded the Medicaid program of more than $500,000.   This is just one of many stories that we hear about fraud rings, and given the rapidly changing economic environment, now is the time for businesses to think about how to protect against fraud rings. There are a number of challenges that organizations may have when it comes to sharing trends and collaborations, understanding the ways to tie fraud rings together, creating treatments for identifying fraud rings and ways to store and catalogue fraud ring experiences so they can be easily recognized.   The trouble with identifying fraud rings   It’s important to understand the challenges that organizations have because they see the fraud rings through their own internal lens. Here are a few of the top things businesses should work on:   Think like a fraudster. This will help businesses become more creative in their approach to fraud prevention. Facilitate internal collaboration. Share with in-organization partners. Sometimes this can be difficult due to organizational structure. Promote external collaboration. Intel-sharing groups are a great way for businesses to network within their industries and learn about the fraud that others are seeing. An organization that I’ve worked with in the past is the National Cyber Forensic and Training Alliance (NCFTA).   Putting the pieces together   How do businesses identify a fraud ring? There are three steps to get started. The first is reviewing and understanding the data. Fraudsters are lazy and want to replicate the process over and over again, and because of this there is always some piece of information that is repeated. It could be a name, an email address, device fingerprint, or similar.   The second step is tying the fraud ring together. This is done by creating rules to help identify the trends. Having rules in place to identify fraud rings allows businesses to easily pull stats together for their leadership.   Lastly, applying an acronym or name to the particular fraud ring and adding comments to the cases associated with a particular ring will help with post-investigation analysis.   Learning from the past   Before I became a consultant, I remember identifying a fraud ring that was submitting events with the same language pack and where the device fingerprint was staying consistent. Those events were being referred out for review and marked with the same note. At a post-mortem review, I was able to talk to the fraud ring we had seen, and it was easy to pull all events associated with this fraud ring because my team had marked the events with the same comments.   Another fraud ring example happened a few years ago. A client called me and said that they were under a fraud attack and this fraud ring was rotating the email handle. I reviewed the data and came up with a rule to catch this activity. Fraud rings will use email handle rotation to help them keep track of accounts that are opened or what emails they used in the past. By coupling the email handle rotation with an email verification service like Emailage, this insight could be very telling. I would assume that when fraud rings use email handle rotation these emails are new and have just been created.   These are just a few of the many fraud rings that I’ve encountered over the course of my career and I’m sure there will be a lot more in the years to come. The best advice I can give to anyone that reads this post is to understand the data that you are reviewing, look for anomalies within the data, ask questions and test your theories by running queries on the data that you’re reviewing. I would love to hear about the different fraud rings that you’ve encountered over your career.   Stay safe.   Contact us

Experian’s own Chris Ryan and Bobbie Paul recently joined David Mattei from Aite to discuss the latest research and insights into emerging fraud schemes and how businesses can combat them in light of COVID-19 and the resulting economic changes. Between them, Chris, Bobbie, and David have more than 60 years of experience in the world of fraud prevention. Listen in as they discuss how businesses can shape their fraud prevention plan in the short term, including: The impacts of the health crisis and physical distancing The rise of e-commerce and consumer digital engagement Changes in criminal activity Fraud attack vectors 2020 fraud loss projections Critical next steps for the 30-60 day time frame Experian · Make Your Fraud Plan Recession-Ready: 2020 Fraud Trends