Data & Analytics

Millions of consumers lack credit history and/or have difficulty obtaining credit from mainstream financial institutions. As a result, the use of expanded Fair Credit Reporting Act (FCRA) – or alternative – data has continued to gain popularity among lenders and financial intuitions to enrich decisions across the entire lending lifecycle to meet the financial needs of their consumers. Experian presented in a recent webinar hosted by AFSA, where Alpa Lally, Vice President of Product Management, and David Elmore, Automotive Solutions Consultant, had a chance to speak about the benefits of FCRA data, and ways lenders can leverage this data to ease access to credit for “invisible” and below prime consumers. Watch the full webinar, “FCRA Data: The Key to Unlocking Credit Universe” and learn more about: How expanded FCRA data is being used throughout the lending lifecycle The benefits of leveraging FCRA data including providing a more holistic view of a consumer’s credit profile and behavior beyond financial services, leading to smarter, more informed lending decisions The lift FCRA data can offer when augmented with traditional credit data This webinar is a part of AFSA’s partner webinar series. To learn more about FCRA data and explore related content, please visit our FCRA Alternative Credit Data Resources Page. Learn More About FCRA-Alternative Credit Data

Lately, I’ve been surprised by the emphasis that some fraud prevention practitioners still place on manual fraud reviews and treatment. With the market’s intense focus on real-time decisions and customer experience, it seems that fraud processing isn’t always keeping up with the trends. I’ve been involved in several lively discussions on this topic. On one side of the argument sit the analytical experts who are incredibly good at distilling mountains of detailed information into the most accurate fraud risk prediction possible. Their work is intended to relieve users from the burden of scrutinizing all of that data. On the other side of the argument sits the human side of the debate. Their position is that only a human being is able to balance the complexity of judging risk with the sensitivity of handling a potential customer. All of this has led me to consider the pros and cons of manual fraud reviews. The Pros of Manual Review When we consider the requirements for review, it certainly seems that there could be a strong case for using a manual process rather than artificial intelligence. Human beings can bring knowledge and experience that is outside of the data that an analytical decision can see. Knowing what type of product or service the customer is asking for and whether or not it’s attractive to criminals leaps to mind. Or perhaps the customer is part of a small community where they’re known to the institution through other types of relationships—like a credit union with a community- or employer-based field of membership. In cases like these, there are valuable insights that come from the reviewer’s knowledge of the world outside of the data that’s available for analytics. The Cons of Manual Review When we look at the cons of manual fraud review, there’s a lot to consider. First, the costs can be high. This goes beyond the dollars paid to people who handle the review to the good customers that are lost because of delays and friction that occurs as part of the review process. In a past webinar, we asked approximately 150 practitioners how often an application flagged for identity discrepancies resulted in that application being abandoned. Half of the audience indicated that more than 50% of those customers were lost. Another 30% didn’t know what the impact was. Those potentially good customers were lost because the manual review process took too long. Additionally, the results are subjective. Two reviewers with different levels of skill and expertise could look at the same information and choose a different course of action or make a different decision. A single reviewer can be inconsistent, too—especially if they’re expected to meet productivity measures. Finally, manual fraud review doesn’t support policy development. In another webinar earlier this year, a fraud prevention practitioner mentioned that her organization’s past reliance on manual review left them unable to review fraud cases and figure out how the criminals were able to succeed. Her organization simply couldn’t recreate the reviewer’s thought process and find the mistake that lead to a fraud loss. To Review or Not to Review? With compelling arguments on both sides, what is the best practice for manually reviewing cases of fraud risk? Hopefully, the following list will help: DO: Get comfortable with what analytics tell you. Analytics divide events into groups that share a measurable level of fraud risk. Use the analytics to define different tiers of risk and assign each tier to a set of next steps. Start simple, breaking the accounts that need scrutiny into high, medium and low risk groups. Perhaps the high risk group includes one instance of fraud out of every five cases. Have a plan for how these will be handled. You might require additional identity documentation that would be hard for a criminal to falsify or some other action. Another group might include one instance in every 20 cases. A less burdensome treatment can be used here – like a one-time-passcode (OTP) sent to a confirmed mobile number. Any cases that remain unverified might then be asked for the same verification you used on the high-risk group. DON’T: Rely on a single analytical score threshold or risk indicator to create one giant pile of work that has to be sorted out manually. This approach usually results in a poor experience for a large number of customers, and a strong possibility that the next steps are not aligned to the level of risk. DO: Reserve manual review for situations where the reviewer can bring some new information or knowledge to the cases they review. DON’T: Use the same underlying data that generated the analytics as the basis of a review. Consider two simplistic cases that use a new address with no past association to the individual. In one case, there are several other people with different surnames that have recently been using the same address. In the other, there are only two, and they share the same surname. In the best possible case, the reviewer recognizes how the other information affects the risk, and they duplicate what the analytics have already done – flagging the first application as suspicious. In other cases, connections will be missed, resulting in a costly mistake. In real situations, automated reviews are able to compare each piece of information to thousands of others, making it more likely that second-guessing the analytics using the same data will be problematic. DO: Focus your most experienced and talented reviewers on creating fraud strategies. The best way to use their time and skill is to create a cycle where risk groups are defined (using analytics), a verification treatment is prescribed and used consistently, and the results are measured. With this approach, the outcome of every case is the result of deliberate action. When fraud occurs, it’s either because the case was miscategorized and received treatment that was too easy to discourage the criminal—or it was categorized correctly and the treatment wasn’t challenging enough. Gaining Value While there is a middle ground where manual review and skill can be a force-multiplier for strong analytics, my sense is that many organizations aren’t getting the best value from their most talented fraud practitioners. To improve this, businesses can start by understanding how analytics can help group customers based on levels of risk—not just one group but a few—where the number of good vs. fraudulent cases are understood. Decide how you want to handle each of those groups and reserve challenging treatments for the riskiest groups while applying easier treatments when the number of good customers per fraud attempt is very high. Set up a consistent waterfall process where customers either successfully verify, cascade to a more challenging treatment, or abandon the process. Focus your manual efforts on monitoring the process you’ve put in place. Start collecting data that shows you how both good and bad cases flow through the process. Know what types of challenges the bad guys are outsmarting so you can route them to challenges that they won’t beat so easily. Most importantly, have a plan and be consistent. Be sure to keep an eye out for a new post where we’ll talk about how this analytical approach can also help you grow your business. Contact us

Earlier this year, we shared our predictions for five fraud threats facing businesses in 2021. Now that we’ve reached the midpoint of the year and economic recovery is underway, we’re taking another look at how these threats can impact businesses and consumers.   Putting a Face to Frankenstein IDs: Synthetic identity fraudsters will attempt to bypass fraud detection methods by using AI to combine facial characteristics from different people to form a new identity. Overexposure: As many as 80% of SSNs may have been exposed on the dark web, creating opportunities for account application fraud. The Heist: Surges in data breaches, advances in automation, expanded online banking services and vulnerabilities exposed from social engineering mistakes have lead to rises in account takeover fraud. Overstimulated: Opportunistic fraudsters may take advantage of ongoing relief payments by using stolen data from consumers. Behind the Times: Businesses with lackluster fraud prevention tools and insufficient online security technology will likely experience more attacks and suffer larger losses.   To learn more about upcoming fraud threats and how to protect your business, download our new infographic and check out Experian’s fraud prevention solutions. Download infographic Request a call

Establishing a strong digital strategy remains a top priority for most financial institutions. With more eyes on screens and electronic devices, the pandemic-induced shift to digital has increased the need to meet consumers where they are. New Innovations As a Result of an Accelerated Shift to Digital  In Ernst & Young’s 2019 biannual Global Fintech Adoption Index, 46% of American respondents indicated they were using at least one fintech service. Fast forward, COVID-19 has accelerated the American adoption rate to 59%, according to a September survey conducted by Plaid, a leading digital payments infrastructure company. This shift to digital also resulted in an uptick in the creation of banking and savings processes that leverage advanced technologies. For example, digital-first technologies and artificial intelligence (AI) are changing the prescreen landscape as never before. For financial institutions, smart prescreen marketing solutions, coupled with a traditional approach to personalized service, present vast opportunities to build deeper consumer relationships. However, implementing an effective strategy can be challenging. In a recent webinar, Experian’s Vice President of Product Management Jacob Kong tackled the topic of using new analytics and AI to create a digital-first strategy. Joined by Mark Sievewright, founder of Sievewright & Associates and co-author of Digital Life, and Devon Kinkead, CEO of Micronotes.ai, they explored the evolution of banking and the possibilities offered by pairing data with technology in our new digital world. Watch the full webinar, 'Digital-First Strategies: New Analytics and Artificial Intelligence for Marketing,' and learn more about: The shift to digital life and banking, new analytics and AI How data and information value empowers prescreen marketing Emerging technologies and new tools that can support aggressive growth and marketing initiatives while mitigating risk How Experian is joining forces with Micronotes.ai to launch Micronotes ReFI powered by Experian, to help lower customers’ or members’ borrowing costs by refinancing mispriced debt Learn more about Micronotes ReFI powered by Experian To explore how Experian’s solutions and capabilities can power your prescreen and marketing strategies, please visit our solutions page or contact us for more information. Contact Us

Over the past year and a half, the development of digital identity has shifted the ways businesses interact with consumers. Companies across every industry have incorporated digital services, biometrics, and other verification tools to enhance the consumer experience without increasing risk.   Changing consumer expectations   A digital identity strategy is no longer a nice-to-have, it’s table stakes. Consumers expect to be recognized across platforms and have a seamless experience every time.   89% of consumers use mobile banking 80% of companies now have a customer recognition strategy in place 55% of banking customers say they plan to visit the bank branch less often moving forward   Businesses are responding to these changing expectations while working to grow during the economic recovery – trying to balance consumer experience with risk appetite and bottom-line goals. The present state of digital identity   Digital identity strategies require both standardization and interoperability. The first provides the ability to consistently capture data and characteristics that can be used to recognize a specific individual. The second allows businesses to resolve an identity to a specific person – recognizing a phone number, user ID and password, or a device – and use that information to determine if the user of the identity is in fact the identity owner.   There are some roadblocks on the road to a seamless digital identity strategy. Issues include a lack of consumer trust and an ambiguous regulatory landscape – creating friction on both ends of the equation.   Recipe for success   To succeed, businesses need a framework that can reliably use different combinations of physical and digital identity data to determine that the person behind the identity is a known, verified, and unique individual. A one-size-fits-all solution doesn’t exist. However, a layered approach allows businesses to modernize identity, providing the services consumers want and expect while remaining agile in an ever-changing environment.   In our newest white paper, developed in partnership with One World Identity, we explore the obstacles hindering digital identity management, and the best way to build a layered solution that is flexible, trustworthy, and inclusive.   To learn more, download our “Capturing the Digital Evolution Through a Layered Approach” white paper. Download white paper

Premier Awards Program Recognizes Breakthrough Financial Technology Products and Companies Experian’s Ascend Intelligence Services was selected as a winner of the “Consumer Lending Innovation Award” category in the fifth annual Fintech Breakthrough Awards conducted by Fintech Breakthrough, an independent market intelligence organization that recognizes the top companies, technologies and products in the global fintech market today. The Fintech Breakthrough Awards is the premier awards program founded to recognize the fintech innovators, leaders and visionaries from around the world in a range of categories, including digital banking, personal finance, lending, payments, investments, RegTech, InsurTech and many more. The 2021 Fintech Breakthrough Awards attracted more than 3,850 nominations from across the globe. One of the latest developments on Experian's trusted, award-winning Ascend platform, Ascend Intelligence Services empowers financial services firms with Experian’s revolutionary managed analytics solutions and services, delivered on a modern-tech AI platform. Ascend Intelligence Services includes rapid model development, seamless deployment, optimized decision strategies, ongoing performance monitoring and continuous retraining. The technology-enabled service uses a secure cloud-based AI platform to harness the power of machine learning, and deliver unique capabilities covering the entire credit lifecycle, through an easy-to-use web portal. “To stay ahead of the latest economic conditions, fintechs need high-quality analytical models running on large and varied data sets that empower them to act quickly and decisively. The breakthrough Ascend Intelligence Services platform answers this immediate market need,” said James Johnson, Managing Director, Fintech Breakthrough. “Congratulations to Experian and the Ascend team on winning our ‘Consumer Lending Innovation Award’ for 2021 with this game-changing solution.” “Data scientists are spending too much time on manual, repetitive and low value-add tasks, and organizations cannot afford to do this is in a state of constant change,” said Srikanth Geedipalli, Experian’s SVP Global Analytics/AI Products. “While building and deploying high-quality analytical models can be time-consuming and expensive, Ascend Intelligence Services streamlines this process by harnessing the power of machine learning and Experian’s rich data assets to drive better, faster and smarter decisions. We have been able to deliver analytical solutions to clients up to 4X faster, significantly improving decision automation rates and increasing approval rates by double digits. We are proud that Ascend Intelligence Services is being recognized as a breakthrough solution in the 2021 Fintech Breakthrough Awards program,” he said. Ascend Intelligence Services is comprised of four modules: Ascend Intelligence Services Challenger™ is a powerful, dynamic and collaborative model development service that enables Experian to rapidly build a model and quantify the benefit to business. Businesses can review, comment on and approve the model, all from within the web portal, while it’s being built. The resulting score is available for testing through an API endpoint and can be deployed in production with a few easy steps. Reports are customizable, downloadable and regulatory compliant. Ascend Intelligence Services Pulse™ is a proactive model monitoring and validation service, which aids companies in monitoring the health of models that drive their business decisions. Pulse, provides convenient dashboards that include a model health index, performance summary, stress-testing results, model risk management reporting, model health alerts and more. Additionally, Pulse automatically builds challengers for champion models, providing an estimated performance lift and financial benefit. Ascend Intelligence Services Strategy Advance™ is a powerful business strategy development service, enabling clients to make optimal lending decisions on their applicants. Strategy Advance uses Experian’s powerful optimization engine to build the right credit policy for clients, including sophisticated decision rules, model overlays and client specified knock-out rules. The resulting decision is available for testing through an API endpoint and can be deployed in production with a few easy steps. Ascend Intelligence Services Limit™ is a credit limit optimization service, enabling clients to make the right credit limit decisions at account origination and during account management. Limit uses Experian’s data, predictive risk and balance models and our powerful optimization engine to design the right credit limit strategy that maximizes product usage, while keeping losses low. The limit decision is available for testing through an API endpoint and can be deployed in production with a few easy steps. To learn more about how Ascend Intelligence Services can support your business, please explore our solutions page. Learn more For a list of all award winners selected for the Fintech Breakthrough Awards, read the full press release here.

The pandemic changed nearly everything – and consumer credit is no exception. Data, analytics, and credit risk decisioning are gaining an even more significant role as we grow closer to the end of the global crisis. Consumers face uneven roads to recovery, and while some are ready to spend again, others are still dealing with pandemic-related financial stress. We surveyed nearly 9,000 consumers and 2,700 businesses worldwide about how consumers are stabilizing their finances and businesses are returning to growth for our new Global Decisioning Report. In this report, we dive into: Key business priorities in 2021 Financial concerns for consumers How to navigate an uneven recovery Business priorities for the year ahead The importance of the online experience As we begin to near the end of the pandemic, businesses need to prioritize technology that enables a responsive, flexible, efficient and confident approach. This can be done by leveraging advanced data and analytics and integrating machine learning tools into model development. By investing in the right credit risk decisioning tools now, you can help ensure your future. Download the report

As quarantine restrictions lift and businesses reopen, there is still uncertainty in the mortgage market. Research shows that more than two million households face foreclosure as moratoriums expire. And with regulators, like the Consumer Financial Protection Bureau (CFPB), urging mortgage servicers to prepare for an expected surge in homeowners needing assistance, lenders need the right resources as well. One of the resources mortgage lenders rely on to help gain greater insight into their borrower’s financial picture is income and employment verification. The challenge, however, is striking the right balance between gaining the insights needed to support lending decisions and creating a streamlined, frictionless mortgage process. There are three main barriers on the path to a seamless and digital verification process. Legacy infrastructure Traditional verification solutions tend to rely on old technology or processes. Whether a lender’s verification strategy is centered around a solution built on older technology or a manual process, the time to complete a borrower verification can vary from taking a day to weeks. Borrowers have grown accustomed to digital experiences that are simple and frictionless and experiencing a drawn out, manual verification process is likely to impact loyalty to the lender’s brand. Stale employment and income data The alternative to a manual process is an instant hit verification solution, with the aim to create a more seamless borrower experience. However, lenders may receive stale borrower income and employment data back as a match. Consumer circumstances can change frequently in today’s economic environment and, depending on the data source the lender is accessing, data may be out of date or simply incorrect. Decisioning based on old information is problematic since it can increase origination risk. Cost and complexity Lenders that use manual processes to verify information are adding to their time to close and ultimately, their bottom line by way of time and resources. Coupled with pricing increases, lenders are paying more to put their borrowers through a cumbersome and sometimes lengthy process to verify employment and income information. How can mortgage lenders avoid these common pitfalls in their verification strategy? By seeking verification solutions focused on innovation, quality of data, and that are customer-centric. The right tool, such as Experian VerifyTM, can help provide a seamless customer experience, reduce risk, and streamline the verification process. Learn more

The tax gap—the difference between what taxpayers should pay and what they actually pay on time—can have a substantial impact on states’ budgets. Tax agencies and other state departments are responsible for helping states manage their budgets by minimizing expected revenue shortfalls. Underreported income is a significant budget complication that continues to frustrate even the most effective tax agencies, until the right tools are brought into play.   The Problem Underreporting is a large, complex issue for agencies. The IRS currently estimates the annual tax gap at $441 billion. There are multiple factors that comprise that total, but the most prevalent is underreporting, which represents 80% of the total tax gap. Of that, 54% is due to underreporting of individual income tax. In addition to being the largest contributor to the tax gap, underreporting is also extremely challenging to identify out of the millions of returns being filed. With 85% of taxes owed correctly reported and paid, finding underreporting can be like trying to locate a needle in the proverbial haystack. Making this even more challenging is the limited resources available for auditing returns, which makes efficiency key. The Solution Data, combined with artificial intelligence (AI) equals efficient detection. The problem with trying to detect which returns are most likely to have underreported income is similar to many other challenges Experian has solved with AI. Partnerships between Experian and state agencies combine what we know about consumers with what their agency knows about their population. We can take the data and use AI to separate the signal from the noise, finding opportunities to recoup lost revenue. Read our case study on how Experian was able to help an agency identify instances of underreporting, detecting an estimated $80 million annual lost revenue from underreported income. Download case study Contact us

The COVID-19 pandemic has created shifting economic conditions and rapidly evolving consumer preferences. Lenders must keep up by re-evaluating their strategies to accelerate growth and beat the competition. Here's how AI/ML can help your organization evolve post-COVID-19: With the democratization of AI/ML, lenders of all sizes can now use this technology to grow their lending and optimize for strategic growth. Register for our upcoming webinar to see how lenders like Elevate have incorporated this new technology into their business processes. Register now

For credit unions of all sizes, choosing a strategic partner with the right tools, capabilities, and industry expertise to support growth while minimizing expenses is a decision critical to the bottom line. This is especially important, since the goal of achieving sustainable growth has continued to be a trending topic for credit unions since the start of the pandemic. According to this CU Times analysis of NCUA data, the fourth quarter of 2020 showed that high overhead per assets was the main factor holding down net income, and credit unions with less than $1 billion in assets fared the worst. These high overhead costs kept margins low and served to be a key contributing factor in gauging a credit union’s profitability. Overcoming this problem lies not only in improving operational efficiency, but in seeking out partners that can provide innovative insight and “right-sized,” scalable solutions to help credit unions effectively grow at a strategic pace. The less money a credit union spends earning each dollar, the more operationally efficient and resource-savvy it becomes—which in turn generates more value for both the credit union and its members. So how can a credit union successfully assess a potential partner’s ability to help them achieve goals for sustainable growth? Asking three key questions can reveal a potential partner’s operational prowess and their ability to understand and offer the right solutions tailored for an individual credit union’s need. Minimize Overhead with a Partner Who Can Help Accelerate and Support Sustainable Growth: Evaluation Questions to Ask 1. Does my potential partner offer solutions to ease the strain on staff, or help automate time-consuming, repetitive tasks and processes? Automation is not only for large credit unions. Employees at credit unions with $4 billion and less in assets often wear many hats and manage the full spectrum of credit activities, leaving leaders to ponder how much time staff is spending on rote, manual tasks throughout the end-to-end member lifecycle. As a result, credit unions are turning to automated decisioning to streamline repetitive tasks and meet increasing member expectations, while also reducing risk. To drive sustainable growth, credit unions will want to look at current processes as a means of measuring efficiency. Can existing programs handle growth to scale in all areas of the business? How can digital lending automation be increased and free up more time for staff to focus attention where it is needed most, such as high-value engagements with members and delivering a personalized member experience? Can self-service tools save your credit union valuable time and increase employee satisfaction? 2. Does my potential partner have access to the right data, advanced analytics and technology to help optimize credit decisioning? As credit unions consider different ways to minimize overhead and accelerate growth, the last few years have shown that automation, coupled with advanced analytics and technology, has taken on a second wave of focus and intense interest. A significant opportunity pertaining to automation is supporting decisioning throughout the member lifecycle, again, eliminating the need for manual processes that cannibalize time and resources. For example, access to advanced analytics and data at the onset of account acquisition can quickly inform a lender as to whether a new account should be approved or declined. Furthermore, it also presents an opportunity to lend deeper. Credit unions can leverage expanded datasets to perform an analysis on rejected applicants and make more predictive decisions – leading to incremental loans. Additionally, lenders have identified other areas where automated decisioning could speed up processes that once required manual evaluation – from account and portfolio management, to marketing and prescreening efforts, to managing early and late-stage delinquent accounts. By leveraging a partner who can support optimizing credit decisioning with the freshest data and analytics, credit unions can routinely and consistently be sure they’re making the right offers and decisions to the right customer at the right time. 3. Does my potential partner offer digital-first strategies and solutions that help reduce friction and improve the member experience? More and more members are interacting and engaging with their credit unions via digital channels. To meet their demands, credit unions – who have historically prioritized other initiatives over digital transformation– are quickly pivoting and rethinking their digital strategy to offer best-in-class digital banking and borrowing experiences, while also reducing friction. Part of this strategy includes smart, easy and well-designed applications that support sustainable growth simply by streamlining offers and reducing abandonment. When considering a potential partner, take into consideration their ability to assist with digital-first solutions, including: Real-time income and employment verification, and fraud tools to quickly and accurately confirm important factors, including the legitimacy of members, and streamline the borrowing process with minimal friction. Instant prescreen, self-service prequalification and instant credit to offer fast, easy, and convenient real-time credit decisions for members. Additionally, improving lending economics with a digital-first pre-qualification tool can not only better serve members, but also drive more apps and grow loans. Artificial intelligence, machine learning and other innovative technologies to enhance underwriting and decrease both hard inquiries on applications and the need for extensive underwriter review. Prequalification tools powered by innovative technology solutions can lead to efficient use of underwriter resources and act as a filter in front of the LOS to remove unqualified applications from hard inquiries. Technology that integrates with multiple lending and core systems and delivers solutions that integrate with multiple systems and channels. For example, to help improve conversion, the borrower experience can be offered a simple application that is designed to “get to offer” as fast as possible. This helps reduce abandonment. The process can be further streamlined by integrating data sources for ID verification, auto fill assistance and adding integrations with existing lending and core systems. To learn more about Experian and how our solutions can support and grow your credit union, contact us now. Contact Us

Forrester recently named Experian to their Programs of the Year awards, which recognize outstanding achievements in a particular area in sales, marketing and product functions. Forrester gives this award to companies who achieve the successful implementation of Forrester’s research, frameworks and best practices to improve functional performance. At Experian, innovation is at the heart of what we do. We strive for continuous improvement, and look for ways to progress our products and services to better serve businesses and consumers. Over the last year, Experian’s Decision Analytics Portfolio Marketing team engaged with Forrester’s SiriusDecisions group to refine the programs they employ to assess and respond to market needs while meeting their stated growth and performance goals. Experian’s Keir Breitenfeld, Vice President, Portfolio Marketing, Experian Decision Analytics, who presented the team’s results at the recent Forrester B2B summit said, “I’m proud of the Decision Analytics Portfolio Marketing team for what they accomplished while working alongside Forrester SiriusDecisions. We were able to reframe how we assess market opportunities for increased impact as we highlight Experian’s areas of expertise to better serve businesses and the consumers that rely on them.” To learn more about the Programs of the Year award and how Experian innovation helps businesses achieve their goals, visit us or request a call. Contact us

To grow in today’s economic climate and beat the competition, financial institutions need to update their acquisition and cross-sell strategies. By doing so, they are able to drive up conversions, minimize risk, and ultimately connect consumers with the right offers at the right time. Businesses and consumers are spending more time online than ever before, with 40% of consumers increasing the number of businesses they visit online. They’ve also made it clear that they expect easy, frictionless transactions with their providers. This includes new accounts and offers of credit – creating the need for better delivery systems. Effective targeting and conversion come down to more than just direct mail and email subject lines, especially now in a volatile economy where consumers are seeking appropriate products for their current situation. Be the first to meet consumers’ needs by leveraging the freshest data, advanced analytics, and automated decision systems. For example, when a consumer tries to open a checking account, the system can initiate a “behind-the-scenes” real-time prescreen request while assessing information needed to open the deposit account. The financial institution can then see if the consumer qualifies for overdraft protection, refinancing offers, loans, credit cards, and more. By performing the pre-approval process in seconds, financial institutions can be sure that they're making the right offers to the right customer, and doing it at the right time. All of this helps to increase the offer acceptance rate, improving customer retention, and maximizing customer account life-time value. The pandemic upended a lot of the ways that your businesses run day-to-day – from where you work to how you (better) engage with customers. Arguably, some of the changes have been long overdue, particularly the acceleration to digital and better customer acquisition strategies. Ahead lies the opportunity to grow – strategies enacted now will determine the extent of that opportunity. To learn more about how Experian can help you assess your prescreen strategy and grow, contact us today. Request a call

Recently, I wrote about how Experian is assisting NASWA (National Association of State Workforce Agencies) with identity verification to help mitigate the spike in fraudulent unemployment insurance claims. Because of this I was not all that surprised when I found a letter in my mailbox from the Texas Workforce Commission with a fraudulent claim using my identity, inspiring me to follow up on this topic with a focus on fraud prevention best practices. Identity theft is on the rise According to Experian data analysis and a recent study on unemployment insurance fraud, at least 25% of new claims are a result of identity theft. This is 50 times higher than what we have traditionally seen in the highest ID theft fraud use case, new credit card applications, which generally amounts to less than 0.5% of new applications. Increasing digitization of the last few years—culminating in the huge leap forward in 2020—has resulted in a massive amount of information available online. Of that information, a reported 1.03 billion records were exposed between 2016 and 2020. There are currently approximately 330 million Americans, so on average more than three records per person have been exposed, creating an environment ripe for identity theft. In fact, a complete identity consisting of name, address, date of birth, and Social Security number (SSN) can be purchased for as little as $8. This stolen data is then often leveraged by both criminal rings who are able to perpetrate fraud on a large scale and smaller scale opportunists – like the ones in Riverside, CA leveraging access to identities of prison inmates. Fraud prevention through layered identity controls In the 20 years that I have been combatting ID theft both in the private and public sectors, I’ve learned that the most effective identity proofing goes beyond traditional identity resolution, validation, and verification. To be successful, you must take advantage of all available data and incorporate it into a layered and risk-based approach that utilizes device details, user behavior, biometrics, and more. Below, I outline three key layers to design an effective process for ID proofing new unemployment insurance claims. Layer 1: Resolve and Validate Identities Traditional identity data consists of the same basic information—name, address, date of birth, telephone number, and SSN—which is now readily available to fraudsters. These have been the foundation for ID proofing in the past and are still critical to resolving the identity in question. The key is to also include additional identity elements like email address and phone number to gain a more holistic view of the applicant. Layer 2: Assess Fraud Risk Determining an identity belongs to a real-life subject is not sufficient to mitigate the risk of ID theft associated with a new unemployment insurance claim. You must go beyond identity validation to assess the risk associated with their claim. Risk assessment risk falls into two categories – identity and digital risk. Identity Risk When assessing a claim, it’s important to check the identity for: Velocity: How often have you (or other states) seen the information being presented with this application? Has the information been associated with multiple identities? Recency of change: How long has the identity been associated with the contact information (phone, email, address, etc.)? Red flags: Has the subject been a recent victim of ID theft, or are they reported as deceased? Synthetic Identity: Are there signs that the identity itself is fictitious or manipulated and does not belong to a real-life person? Digital Risk Similar to the identity risk layer above, the device itself and how the subject interacts with the device are significantly important in identifying the likelihood a new claim is fraudulent. Device risk can be assessed by utilizing geolocation and checking for inconsistent settings or high-risk browsers, while behavioral risk might check for mouse movement, typing speed, or screen pressure. Layer 3: Verify Highest Risk Subjects The final stage in this process is to require additional verification for the highest risk claims, which helps to balance the experience of your valid subjects while minimizing the impact of fraud. Additional steps might include: Document verification: Scanning a government-issued ID (driver’s license, passport, or similar), which includes assessing for document security features and biometric comparison to the applicant. One-time passcode (OTP): It is key to deploy this sparingly only to phone numbers that have been associated with the subject for a significant time frame and incorporate checks to determine if it is at high risk (e.g., recently ported or forwarded). Knowledge-based verification (KBV): Leveraging non-public information from a variety of sources. By adding additional, context-based identity elements, it becomes possible to improve the three main objectives of most agencies’ identity proofing process – get good constituents through the first time, protect the agency and citizens from fraud, and deliver a smooth and secure customer experience in online channels. While there’s no quick fix to prevent unemployment insurance fraud, a layered identity strategy can help prevent it. Finding a partner that has a single, holistic solution empowers agencies to defend against unemployment insurance fraud while minimizing friction for the end-user, and preparing for future fraud schemes. To learn more about how you can protect your constituents and your agency from unemployment insurance fraud request a call today. Contact us

At some point a lender may need to issue an RFI or an RFP for a credit decisioning system. In this latest installment of “working with vendors” let’s dive into some best practices for writing RFIs and RFPs that will help you more quickly and efficiently understand the capabilities of a vendor. First, have one person (or at most a very small group) review the document before it goes out to vendors. Too often these kinds of documents seem like they’re just cut and pasted together without any concern if they paint a coherent picture. If it’s worth the time to write an RFI/RFP, then it’s worth the time to get it right so that the vendor responses make sense. If your document paints an inconsistent picture, a vendor may not know what products will best serve your requirements. In turn, precious time will be wasted in discussions around what’s being proposed. Here are some things to make clear in the document: For what part of the credit life cycle does this RFI/RFP apply (prospecting, origination, account management or collections)? If the request covers more than one part of the life cycle, make clear which questions apply to which part of the life cycle. Do you need a system that processes in batch or real-time requests (or both)? For example, a credit card account management solution can process accounts in batch (for proactive line management), in real time (for reactive requests) or possibly even both. Let the vendor know what it is you’re trying to do, as there may be different systems involved in processing these requests. Do you want this system hosted at the vendor, a third party (like AWS, Azure, etc.) or installed on premises? If you have a preference, let the vendor know. If you have no preference, ask the vendor what they can support. In general, consider playing down or skip detailed pricing questions. There’s nothing wrong with asking for a price range. For credit decisioning systems, detailed pricing is difficult for the vendor since there are often high levels of unknown customization to do. A better question might be, “What things will the vendor have to know in order to accurately price the solution? What are the logical next steps to get more accurate pricing? What’s the typical range of pricing in a solution such as this and what drives that range?” Will you be acting as an aggregator? Sometimes systems are created as front ends to several lenders. For example, a client may want to create a website where a borrower can “shop” among several lenders. This is certainly doable but carries with it a whole host of legal, compliance, business and technical questions. In my opinion, I’d skip the RFI/RFP in this situation and have a robust sit down directly with the vendors. This option will likely be far more productive. Ask more open-ended questions. “How does the solution perform task X?” as opposed to, “Do you support Y?” Often, there’s more than one way to accomplish a task. Asking more open-ended questions will yield a more comprehensive answer from the vendor rather than a simple yes or no response. It also gives you the opportunity to learn about the latest decisioning techniques. Be careful that you have not copied old RFP questions that are no longer relevant. I’ve had clients ask if we support Bernoulli Boxes (a mid-80s kind of floppy disk), or whether we support OS/2, etc. I’ve even had questions about supporting a particular printer. These kinds of questions are centered on the support of the operating system and not a particular vendor’s credit decisioning software. Instead of asking yes/no technology questions, ask for a typical sample architecture. Ask what kinds of APIs are supported (REST, SOAP/XML, etc.). Ask about the solution’s capabilities to call third-party systems (both internal and external). Ask fewer, but more in-depth questions. If the solution needs screens, be clear which screens you’re talking about. Do you need screens to make rule adjustments or configuration changes? Do you need screens for manual review or some sort of case management? Do you need consumer-facing screens where borrowers can type in their application data? If you need screens, be clear on the task the screens should perform. If you have particular concerns, ask them in an open-ended way. For example, “The solution will have to exchange file-based data with a mainframe. How can your solution best satisfy this requirement?” In general, state your requirement not the technology to use. A preamble or brief executive summary is useful to get the big picture across before the vendor delves into any questions. A paragraph or two can go a long way to help the vendor better assess your requirements and provide more meaningful answers to you. This works well because it’s easier to give the big picture in a few paragraphs as opposed to sprinkled around in multiple questions. To summarize, be clear on your requirements and provide a more open-ended format for the vendor to respond. This will save both you and the vendor a lot of time. In section three, I’ll cover evaluating vendors.