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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

Published: October 15, 2020 by Guest Contributor

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  

Published: October 7, 2020 by Guest Contributor

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.

Published: October 6, 2020 by Eric Johnson

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

Published: October 5, 2020 by Laura Burrows

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

Published: September 30, 2020 by Victoria Soriano

Consumers are taking advantage of new car incentives, low interest rates and longer-term loans in order to ensure that their vehicle purchase is manageable.

Published: September 23, 2020 by Melinda Zabritski

Big data is bringing changes to the way credit scores are reported and making it easier for lenders to find creditworthy consumers, and for consumers to qualify for the financing they need. Since last year’s annual report, alternative credit data1 has continued to gain in popularity. In Experian’s latest 2020 State of Alternative Credit Data report, we take a closer look at why alternative credit data is supplemental and essential to consumer lending and how it’s being adopted by both consumers and financial institutions. While the topic of alternative credit data has become more well known, its capabilities and benefits are still not widely discussed. For instance, did you know that … 89% of lenders agree that alternative credit data allows them to extend credit to more consumers. 96% of lenders agree that in times of economic stress, alternative credit data allows them to more closely evaluate consumer’s creditworthiness and reduce their credit risk exposure. 3 out of 4 consumers believe they are a better borrower than their credit score represents. Not only do consumers believe they’re more financially astute than their credit score depicts – but they’re happy to prove it, with 80% saying they would share various types of financial information with lenders if it meant increased chances for approval or improved interest rates. This year’s report provides a deeper look into lenders’ and consumers’ perceptions of alternative credit data, as well as an overview of the regulatory landscape and how alternative credit data is being used across the lending marketplace. Lenders who incorporate alternative credit data and machine learning techniques into their current processes can harness the data to unlock their portfolio’s growth potential, make smarter lending decisions and mitigate risk. Learn more in the 2020 State of Alternative Credit Data white paper. Download now

Published: September 17, 2020 by Laura Burrows

Staying ahead of the trends and adjusting will support sales growth, while also supporting consumers as they begin to recover from the impact of COVID-19.

Published: September 17, 2020 by Guest Contributor

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.

Published: September 15, 2020 by Guest Contributor

As subprime originations decrease, some think that subprime consumers are being locked out of the automotive finance market, but that’s not the whole story.

Published: September 15, 2020 by Melinda Zabritski

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

Published: September 2, 2020 by Jim Bander

Since the start of the COVID-19 health crisis, gross domestic product (GDP) has continued to fall in the U.S. In fact, the GDP collapsed at a 32.9% annualized rate last quarter, which is the deepest decline since 1947. But as some states throughout the U.S. begin to relax their stay-at-home orders and start to reopen businesses, economists are taking note of how this will affect the nation’s recovery as a whole. When it comes to tracking the nation’s economic recovery, economists and policymakers need to account for all of the factors that will influence the outcome. This includes tracking the performance of individual states and understanding each state’s trajectory and recovery prospects. There are many factors that will impact each state’s trajectory for recovery. One example, in particular, can be seen in a state’s preparedness level and rainy day fund that’s set aside for emergencies. At the onset of the pandemic, many states were unprepared for the financial crisis. The Government Finance Officers Association recommends that states set aside at least two months of operating expenses in their rainy day funds – or roughly 16% of their general fund. However, although some states had set aside some budget to prepare for a recession, it was simply not enough. Only a few states were able to fulfill this requirement. Other factors that will impact each state’s recovery include: the efficiency of its unemployment program, state lockdown measures, and the concentration of jobs in vulnerable industries. Our new white paper, featuring key insights from Joseph Mayans, Principal Economist with Advantage Economics, provides a deep dive on: The economic landscape at the onset of the pandemic Statewide discrepancies for unemployment programs, lockdown measures, and labor markets Underlying factors that determine a state’s recovery prospects Why tracking state-level economies is critical for national recovery Listen in as he describes the importance of having a different perspective when tracking the national economy and download the white paper for greater insights. Download White Paper Now

Published: August 25, 2020 by Kelly Nguyen

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

Published: August 19, 2020 by Guest Contributor

The early assessment for the automotive industry is that despite significant challenges at the onset of the pandemic, the industry continues to rebound.

Published: August 17, 2020 by Melinda Zabritski

Achieving collection results within the subprime population was challenging enough before the current COVID-19 pandemic and will likely become more difficult now that the protections of the Coronavirus Aid, Relief, and Economic Security (CARES) Act have expired. To improve results within the subprime space, lenders need to have a well-established pre-delinquent contact optimization approach. While debt collection often elicits mixed feelings in consumers, it’s important to remember that lenders share the same goal of settling owed debts as quickly as possible, or better yet, avoiding collections altogether. The subprime lending population requires a distinct and nuanced approach. Often, this group includes consumers that are either new to credit as well as consumers that have fallen delinquent in the past suggesting more credit education, communication and support would be beneficial. Communication with subprime consumers should take place before their account is in arrears and be viewed as a “friendly reminder” rather than collection communication. This approach has several benefits, including: The communication is perceived as non-threatening, as it’s a simple notice of an upcoming payment. Subprime consumers often appreciate the reminder, as they have likely had difficulty qualifying for financing in the past and want to improve their credit score. It allows for confirmation of a consumer’s contact information (mainly their mobile number), so lenders can collect faster while reducing expenses and mitigating risk. When executed correctly, it would facilitate the resolution of any issues associated with the delivery of product or billing by offering a communication touchpoint. Additionally, touchpoints offer an opportunity to educate consumers on the importance of maintaining their credit. Customer segmentation is critical, as the way lenders approach the subprime population may not be perceived as positively with other borrowers. To enhance targeting efforts, lenders should leverage both internal and external attributes. Internal payment patterns can provide a more comprehensive view of how a customer manages their account. External bureau scores, like the VantageScore® credit score, and attribute sets that provide valuable insights into credit usage patterns, can significantly improve targeting. Additionally, the execution of the strategy in a test vs. control design, with progression to successive champion vs. challenger designs is critical to success and improved performance. Execution of the strategy should also be tested using various communication channels, including digital. From an efficiency standpoint, text and phone calls leveraging pre-recorded messages work well. If a consumer wishes to participate in settling their debt, they should be presented with self-service options. Another alternative is to leverage live operators, who can help with an uptick in collection activity. Testing different tranches of accounts based on segmentation criteria with the type of channel leveraged can significantly improve results, lower costs and increase customer retention. Learn About Trended Attributes Learn About Premier Attributes

Published: August 17, 2020 by Guest Contributor

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