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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

The COVID-19 pandemic has created unprecedented challenges for the utilities industry. This includes the need to plan for – and be prepared to respond to – changing behaviors and a sudden uptick in collections activities. As part of our recently launched Q&A perspective series, Mark Soffietti, Experian’s Senior Manager of Analytics Consulting and Tom Hanson, Senior Energy Consultant, provided insight on how utility providers can evolve and refine their collections and recovery processes. Check out what they had to say: Q: How has COVID-19 impacted payment behavior and debt collections? TH: Consumer payment behavior is changing. For example, those who paid as agreed, may not currently have the means to pay and are now distressed borrowers. Or those who were sloppy payers before the pandemic may now be defaulting on a more consistent basis. MS: As we saw with the last recession when faced with economic stress, consumer and commercial payment behavior changes based on their needs and current cash flow. For example, people prioritize their car, as they need it to get to and from work, so they’ll likely pay their auto bills on time. The same goes for their credit cards, which they need to make ends meet. We expect this will also be true with COVID-19. The commercial segment will face more dramatic and challenging circumstances, where complete or partial business closures and lack of federal relief could have severe ramifications. Q: What new restrictions have been put in place surrounding debt collection efforts and outbound calls? TH: To protect consumers who may be experiencing financial distress, most states have imposed new, stringent restrictions to prevent utilities from engaging in certain collections activities. Utilities are currently not charging any late payment fees and are instead structuring payment plans. Additionally, all outbound collections efforts have been suspended and there is fieldwork being executed of services for both commercial and consumer properties. As of now, consumer and commercial fieldwork will likely not commence until after the first year or when the winter moratorium concludes. MS: The new restrictions imposed upon collections activities will likely drive consumer payment behavior. If consumers know that their utilities (i.e. energy and water) will not be shut off if they miss a payment, they will make these bills less of a priority. This will dramatically increase the amount owed when these restrictions are lifted next year. Q: Can we predict how the utilities industry will fare post-COVID-19? TH: The volume of accounts in collections and eligible for disconnect will be overwhelming. Many utility providers fear the unpaid balances consumers and commercial entities accumulate will be nearly impossible to fit into a repayment schedule. Both analyzing internal payment segments and overlaying external factors may be the best way to optimize the most critical go-forward plan. MS: The amount of people who fall into collections is going to greatly increase and utility providers need to start planning for it now to weather the storm. They will need to use data, analytics and tools to help them optimize their tasks, so they can be more efficient with their resources. Like many other industries, the utilities sector will look to increasing digitalization of their processes and having less social interaction where possible. This could mean the need and drive for expediting current smart meter programs where possible to enable remote fieldwork to assist in managing this unprecedented level of activity that is sure to overwhelm field operations (where allowed by state regulators). Q: What should utility providers be doing to plan for an uptick in collections activities post-COVID-19? TH: With regulatory mandated suspensions of collections activities for utility providers and self-selected reductions due to stay at home orders and staff protection, the backlog of payments, calls and inquiries once business resumes as normal is set to overwhelm existing capacity. More than ever, self-service options (text/web), Q&A and alternative communication methods will be needed to shepherd consumers through the collections process and minimize the strain on call center agents. Many utility providers are asking for external data points to segment their consumers by industry or by those whose employment would have been adversely impacted by COVID-19. MS: Utility providers should be monitoring consumer data in order to prepare for when they are able to collect. This will help them strategize the number of resources they will need in their call centers and out in the field performing shut off activities. Given that the rise in cases will be more volume than their call centers can handle, they will need to use their resources wisely and plan to use them efficiently when they are able to resume collections. Q: How can Experian help utility providers reduce collections costs and maximize recovery? TH: Experian can help revise collections tactics and segmentation strategies by providing insight on how consumers are paying other creditors and identifying new segmentation opportunities as we emerge from the freeze on collections activities. Collections cases will be complex, and many factors and constraints will need to balanced against changing goals, making optimization key. MS: Utilizing Experian’s credit data and models can help ensure that resources are being used efficiently (i.e. making successful calls). There is also a need to leverage ability to pay models as well as prioritization models. By using these models and tools, utility providers can optimize their treatment strategies, reduce costs and maximize dollars collected. Learn more About our Experts: Tom Hanson, Senior Energy Consultant, Experian CEM, North America Tom is a Senior Consultant within the Energy Vertical at Experian, supporting regulated energy companies throughout the U.S. He brings over 25 years of experience in the energy field and supports his clients throughout the customer lifecycle, providing expertise in ID verification, account treatment, fraud solutions, analytics, consulting and final bill/field optimization strategies and techniques. Mark Soffietti, Analytics Consulting Senior Manager, Experian Decision Analytics, North America Mark has over 15 years of experience transforming data into actionable knowledge for effective decision management. Mark’s expertise includes solution development for consumer and commercial lending across the credit spectrum – from marketing to collections.

When running a credit report on a new applicant, you must ensure Fair Credit Reporting Act (FCRA) compliance before accessing, using and sharing the collected data. The Coronavirus Aid, Relief, and Economic Security (CARES) Act has impacted credit reporting under the FCRA, as has new guidance from the Consumer Financial Protection Bureau (CFPB). Recent updates include: The CARES Act amended the FCRA to require furnishers who agree to an “accommodation,”1 to report the account as current, although it is permitted to continue to report the account as delinquent if the account was delinquent before the accommodation was made. Although not legally obligated, data furnishers should continue furnishing information to the credit reporting agencies (CRAs) during the COVID-19 crisis, and make sure that information reported is complete and accurate. Below is a brief FCRA-related compliance overview2 covering various FCRA requirements3 when requesting and using consumer credit reports for an extension of credit permissible purpose. For more information regarding your responsibilities under the FCRA as a user of consumer reports, please consult your Legal Counsel and the Notice to Users of Consumer Reports: Obligations of Users Under the FCRA handbook located on our website. Before obtaining a consumer report you have…  Reviewed your federal and state regulations and laws related to consumer reports, scores, decisions, etc.  Made sure you have a valid permissible purpose for pulling the consumer report.  Certified compliance to the CRA from which you are getting the consumer report. You have certified that you complied with all the federal and state requirements. After you take an adverse action based on a consumer report you… Provide the consumer with an oral, written or electronic notice of the adverse action. Provide written or electronic disclosure of the numerical credit score used to take the adverse action, or when providing a “risk-based pricing” notice. Provide the consumer with an oral, written or electronic notice, which includes the below information:  Name, address and telephone number of CRA that supplied the report, if nationwide. A statement that the CRA did not make the adverse decision and therefore can’t explain why the decision was made.  Notice of the consumer’s right to a free copy of their report from the CRA, if requested within 60 days.  Notice of the consumer’s right to dispute with the CRA the accuracy or completeness of any information in a consumer report provided by the CRA. Provide the consumer with a “risk-based pricing” notice if credit was granted but on less favorable terms based on information in their consumer report. We understand how challenging it is to understand and meet all your obligations as a data furnisher – we’re here to make it a little easier. Click below to speak with a representative and gain more insight on how the CARES Act impacts FCRA reporting. Download overview Speak with a representative 1An “accommodation” is defined as “an agreement to defer one or more payments, make a partial payment, forbear any delinquent amounts, modify a loan or contract, or any other assistance or relief” granted to a consumer affected by COVID-19 during the covered period. 2This FCRA overview is not legal guidance and does not enumerate all your requirements under the FCRA as a user of consumer reports. Additionally, this FCRA Overview is not intended to provide legal advice or counsel you regarding your obligations under the FCRA or any other federal or state law or regulation. Should you have any questions about your institution’s specific obligations under the FCRA or any other federal or state law or regulation, you should consult with your Legal Counsel. 3This FCRA overview is intended to be used solely by financial service providers when extending credit to consumers and does not include all FCRA regulatory obligations. You are responsible for regulatory compliance when requesting and using consumer reports, which includes adhering to all applicable federal and state statutes and regulations and ensuring that you have the correct policies and procedures in place.

Today’s lending market has seen a significant increase in alternative business lending, with companies utilizing new data assets and technology. As the lending landscape becomes increasingly competitive, consumers have more choices than ever when it comes to lending products. To drive profitable growth, lenders must find new ways to help applicants gain access to the loans they need. How Spring EQ is leveraging Experian BoostTM Home equity lender Spring EQ turned to Experian’s first-of-its-kind financial tool that empowers consumers to add positive payments directly into their credit file to assist applicants with attaining the best loan opportunities and rates. By using Experian BoostTM, which captures the value of consumer’s utility and telecom trade lines, in their current lending process, Spring EQ can help applicants near approval or risk thresholds move to higher risk tiers and qualify for better loan terms and conditions. Driving growth with consumer-permissioned data Over 40 million consumers in the U.S. either have no credit file or have insufficient information in their files to generate a traditional credit score. Consumer-permissioned data empowers these individuals to leverage their online financial data and payment histories to gain better access to loans and other financial services while providing lenders with a more comprehensive view of their creditworthiness. According to Experian research, 70% of consumers see the benefits of sharing additional financial information and contributing positive payment history to their credit file if it increases their odds of approval and helps them access more favorable credit terms. Read our case study for more insight on using Experian Boost to: Make better lending decisions Offer or underwrite credit to more people Promote the right credit products Increase conversion and utilization rates Read case study Learn more about Experian Boost

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

With new legislation, including the Coronavirus Aid, Relief, and Economic Security (CARES) Act impacting how data furnishers will report accounts, and government relief programs offering payment flexibility, data reporting under the coronavirus (COVID-19) outbreak can be complicated. Especially when it comes to small businesses, many of which are facing sharp declines in consumer demand and an increased need for capital. As part of our recently launched Q&A perspective series, Greg Carmean, Experian’s Director of Product Management and Matt Shubert, Director of Data Science and Modelling, provided insight on how data furnishers can help support small businesses amidst the pandemic while complying with recent regulations. Check out what they had to say: Q: How can data reporters best respond to the COVID-19 global pandemic? GC: Data reporters should make every effort to continue reporting their trade experiences, as losing visibility into account performance could lead to unintended consequences. For small businesses that have been negatively affected by the pandemic, we advise that when providing forbearance, deferrals be reported as “current”, meaning they should not adversely impact the credit scores of those small business accounts. We also recommend that our data reporters stay in close contact with their legal counsel to ensure they follow CARES Act guidelines. Q: How can financial institutions help small businesses during this time? GC: The most critical thing financial institutions can do is ensure that small businesses continue to have access to the capital they need. Financial institutions can help small businesses through deferral of payments on existing loans for businesses that have been most heavily impacted by the COVID-19 crisis. Small Business Administration (SBA) lenders can also help small businesses take advantage of government relief programs, like the Payment Protection Program (PPP), available through the CARES Act that provides forgiveness on up to 75% of payroll expenses and 25% of other qualifying expenses. Q: How do financial institutions maintain data accuracy while also protecting consumers and small businesses who may be undergoing financial stress at this time? GC: Following bureau recommendations regarding data reporting will be critical to ensure that businesses are being treated fairly and that the tools lenders depend on continue to provide value. The COVID-19 crisis also provides a great opportunity for lenders to educate their small business customers on their business credit. Experian has made free business credit reports available to every business across the country to help small business owners ensure the information lenders are using in their credit decisioning is up-to-date and accurate. Q: What is the smartest next play for financial institutions? GC: Experian has several resources that lenders can leverage, including Experian’s COVID-19 Business Risk Index which identifies the industries and geographies that have been most impacted by the COVID crisis. We also have scores and alerts that can help financial institutions gain greater insights into how the pandemic may impact their portfolios, especially for accounts with the greatest immediate exposure and need. MS: To help small businesses weather the storm, financial institutions should make it simple and efficient for them to access the loans and credit they need to survive. With cash flow to help bridge the gap or resume normal operations, small businesses can be more effective in their recovery processes and more easily comply with new legislation. Finances offer the support needed to augment currently reduced cash flows and provide the stability needed to be successful when a return to a more normal business environment occurs. At Experian, we’re closely monitoring the updates around the coronavirus outbreak and its widespread impact on both consumers and businesses. We will continue to share industry-leading insights to help data furnishers navigate and successfully respond to the current environment. Learn more About Our Experts Greg Carmean, Director of Product Management, Experian Business Information Services, North America Greg has over 20 years of experience in the information industry specializing in commercial risk management services. In his current role, he is responsible for managing multiple product initiatives including Experian’s Small Business Financial Exchange (SBFE), domestic and international commercial reports and Corporate Linkage. Recently, he managed the development and launch of Experian’s Global Data Network product line, a commercial data environment that provides a single source of up to date international credit and firmographic information from Experian commercial bureaus and Tier 1 partners across the globe. Matt Shubert, Director of Data Science and Modelling, Experian Data Analytics, North America Matt leads Experian’s Commercial Data Sciences Team which consists of a combination of data scientists, data engineers and statistical model developers. The Commercial Data Science Team is responsible for the development of attributes and models in support of Experian’s BIS business unit. Matt’s 15+ years of experience leading data science and model development efforts within some of the largest global financial institutions gives our clients access to a wealth of knowledge to discover the hidden ROI within their own data.  

In the face of severe financial stress, such as that brought about by an economic downturn, lenders seeking to reduce their credit risk exposure often resort to tactics executed at the portfolio level, such as raising credit score cut-offs for new loans or reducing credit limits on existing accounts. What if lenders could tune their portfolio throughout economic cycles so they don’t have to rely on abrupt measures when faced with current or future economic disruptions? Now they can. The impact of economic downturns on financial institutions Historically, economic hardships have directly impacted loan performance due to differences in demand, supply or a combination of both. For example, let’s explore the Great Recession of 2008, which challenged financial institutions with credit losses, declines in the value of investments and reductions in new business revenues. Over the short term, the financial crisis of 2008 affected the lending market by causing financial institutions to lose money on mortgage defaults and credit to consumers and businesses to dry up. For the much longer term, loan growth at commercial banks decreased substantially and remained negative for almost four years after the financial crisis. Additionally, lending from banks to small businesses decreased by 18 percent between 2008-2011. And – it was no walk in the park for consumers. Already faced with a rise in unemployment and a decline in stock values, they suddenly found it harder to qualify for an extension of credit, as lenders tightened their standards for both businesses and consumers. Are you prepared to navigate and successfully respond to the current environment? Those who prove adaptable to harsh economic conditions will be the ones most poised to lead when the economy picks up again. Introducing the FICO® Resilience Index The FICO® Resilience Index provides an additional way to evaluate the quality of portfolios at any point in an economic cycle. This allows financial institutions to discover and manage potential latent risk within groups of consumers bearing similar FICO® Scores, without cutting off access to credit for resilient consumers. By incorporating the FICO® Resilience Index into your lending strategies, you can gain deeper insight into consumer sensitivity for more precise credit decisioning. What are the benefits? The FICO® Resilience Index is designed to assess consumers with respect to their resilience or sensitivity to an economic downturn and provides insight into which consumers are more likely to default during periods of economic stress. It can be used by lenders as another input in credit decisions and account strategies across the credit lifecycle and can be delivered with a credit file, along with the FICO® Score. No matter what factors lead to an economic correction, downturns can result in unexpected stressors, affecting consumers’ ability or willingness to repay. The FICO® Resilience Index can easily be added to your current FICO® Score processes to become a key part of your resilience-building strategies. Learn more

In today’s rapidly changing economic environment, the looming question of how to reduce portfolio volatility while still meeting consumers' needs is on every lender’s mind. So, how can you better asses risk for unbanked consumers and prime borrowers? Look no further than alternative credit data. In the face of severe financial stress, when borrowers are increasingly being shut out of traditional credit offerings, the adoption of alternative credit data allows lenders to more closely evaluate consumer’s creditworthiness and reduce their credit risk exposure without unnecessarily impacting insensitive or more “resilient” consumers. What is alternative credit data? Millions of consumers lack credit history or have difficulty obtaining credit from mainstream financial institutions. To ease access to credit for “invisible” and subprime consumers, financial institutions have sought ways to both extend and improve the methods by which they evaluate borrowers’ risk. This initiative to effectively score more consumers has involved the use of alternative credit data.1 Alternative credit data is FCRA-regulated data that is typically not included in a traditional credit report and helps lenders paint a fuller picture of a consumer, so borrowers can get better access to the financial services they need and deserve. How can it help during a downturn? The economic environment impacts consumers’ financial behavior. And with more than 100 million consumers already restricted by the traditional scoring methods used today, lenders need to look beyond traditional credit information to make more informed decisions. By pulling in alternative credit data, such as consumer-permissioned data, rental payments and full-file public records, lenders can gain a holistic view of current and future customers. These insights help them expand their credit universe, identify potential fraud and determine an applicant’s ability to pay all while mitigating risk. Plus, many consumers are happy to share additional financial information. According to Experian research, 58% say that having the ability to contribute positive payment history to their credit files makes them feel more empowered. Likewise, many lenders are already expanding their sources for insights, with 65% using information beyond traditional credit report data in their current lending processes to make better decisions. By better assessing risk at the onset of the loan decisioning process, lenders can minimize credit losses while driving greater access to credit for consumers. Learn more 1When we refer to “Alternative Credit Data,” this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data” may also apply in this instance and both can be used interchangeably.

For the last several years, as the global economy flourished, the opportunities created by removing friction and driving growth guided business strategies governing identity and fraud. The amount of profitable business available in a low-friction environment simply outweighed the fraud that could be mitigated with more stringent verification methods. Now that we’re facing a global crisis, it’s time to reconsider the approach that drove the economic boom that defined that last decade. Recognizing how economic changes impact fraud At the highest level, we separate fraud into two types; third party fraud and first party fraud. In simple terms, third party fraud involves the misuse of a real customer’s identity or unauthorized access to a real customer’s accounts or assets. First party fraud involves the use of an identity that the fraudster controls—whether it’s their own identity, a manipulated version of their own identity, or a synthetic identity that they have created. The important difference in this case is that the methods of finding and stopping third party fraud remain constant even in the event of an economic downturn – establish contact with the owner of the identity and verify whether the events are legitimate. Fraud tactics will evolve, and volumes increase as perpetrators also face pressure to generate income, but at the end of the day, a real person is being impersonated, and a victim exists that will confirm when fraud is taking place. Changes in first party fraud during an economic downturn are dramatically different and much more problematic. The baseline level of first party fraud using synthetic, manipulated and the perpetrator’s own identity continue, but they are augmented by real people facing desperate circumstances and existing “good” customers who over-extend while awaiting a turn-around. The problem is that there is no “victim” to confirm fraud is occurring, and the line between fraud (which implies intent) and credit default (which does not) becomes very difficult to navigate. With limited resources and pressures of their own, at some point lenders must try to distinguish deliberate theft from good customers facing bad circumstances and manage cases accordingly. The new strategy When times are good, it’s easier to build up a solid book of business with good customers. Employment rates are high, incomes are stable, and the risks are manageable. Now, we’re experiencing rapidly changing conditions, entire industries are disrupted, unemployment claims have skyrocketed and customers will need assistance and support from their lenders to help them weather the storm. This is a reciprocal relationship – it behooves those same lenders to help their customers get through to the other side. Lenders will look to limit losses and strengthen relationships. At the same time, they’ll need to reassess their existing fraud and identity strategies (among others) as every interaction with a customer takes on new meaning. Unexpected losses We’ve all been bracing for a recession for a while. But no one expected it to show up quite like it did. Consumers who have been model customers are suddenly faced with a complete shift in their daily life. A job that seemed secure may be less so, investments are less lucrative in the short term, and small business owners are feeling the pressure of a change in day-to-day commerce. All of this can lead to unexpected losses from formerly low-risk customers. As this occurs, it becomes more critical than ever to identify and help good customers facing grim circumstances and find different ways to handle those that have malicious intent. Shifting priorities When the economy was strong, many businesses were able to accept higher losses because those losses were offset by immense growth. Unfortunately, the current crisis means that some of those policies could have unforeseen consequences. For instance – the loss of the ability to differentiate between a good customer who has fallen on hard times and someone who’s been a bad actor from the start. Additionally, businesses need to revise their risk management strategies to align with shifting customer needs. The demand for emergency loans and will likely rise, while loans for new purchases like cars and homes will fall as consumers look to keep their finances secure. As the need to assist customers in distress rises and internal resources are stressed, it’s critical that companies have the right tools in place to triage and help customers who are truly in need. The good news The tools businesses like yours need to screen first party fraud already exist. In fact, you may already have the necessary framework in place thanks to an existing partnership, and a relatively simple process could prepare your business to properly screen both new and existing customers at every touchpoint. This global crisis is nowhere near over, but with the right tools, your business can protect itself and your customers from increased fraud risks and losses of all sorts – first party, stolen identities, or synthetic identities, and come out on the other side even stronger. Contact Experian for a review of your current fraud strategy to help ensure you’re prepared to face upcoming challenges. Contact us

Sometimes, the best offense is a good defense. That’s certainly true when it comes to detecting synthetic identities, which by their very nature become harder to find the longer they’ve been around. To launch an offense against synthetic identity fraud, you need to defend yourself from it at the top of your new customer funnel. Once fraudsters embed their fake identity into your portfolio, they become nearly impossible to detect. The Challenge Synthetic identity fraud is the fastest-growing type of financial crime in the United States. The cost to businesses is hard to determine because it’s not always caught or reported, but the amounts are staggering. According to the Aite Group, it was estimated to total at least $820 million in 2017 and grow to $1.2 billion by 2020. This type of theft begins when individual thieves and large-scale crime rings use a combination of compromised personal information—like unused social security numbers—and fabricated data to stitch together increasingly sophisticated personas. These well-crafted synthetic identities are hard to differentiate from the real deal. They often pass Know Your Customer, Customer Identification Program and other onboarding checks both in person and online. This puts the burden on you to develop new defense strategies or pay the price. Additionally, increasing pressure to grow deposits and expand loan portfolios may coincide with the relaxation of new customer criteria, allowing even more fraudsters to slip through the cracks. Because fraudsters nurture their fake identities by making payments on time and don’t exhibit other risk factors as their credit limits increase, detecting synthetic identities becomes nearly impossible, as does defending against them. How This Impacts Your Bottom Line Synthetic identity theft is sometimes viewed as a victimless crime, since no single individual has their entire identity compromised. But it’s not victimless. When undetected fraudsters finally max out their credit lines before vanishing, the financial institution is usually stuck footing the bill. These same fraudsters know that many financial institutions will automatically settle fraud claims below a specific threshold. They capitalize on this by disputing transactions just below it, keeping the goods or services they purchased without paying. Fraudsters can double-dip on a single identity bust-out by claiming identity theft to have charges removed or by using fake checks to pay off balances before maxing out the credit again and defaulting. The cost of not detecting synthetic identities doesn’t stop at the initial loss. It flows outward like ripples, including: Damage to your reputation as a trusted organization Fines for noncompliance with Know Your Customer Account opening and maintenance costs that are not recouped as they would be with a legitimate customer Mistakenly classifying fraudsters as bad debt write offs Monetary loss from fraudsters’ unpaid balances Rising collections costs as you try to track down people who don’t exist Less advantageous rates for customers in the future as your margins grow thinner These losses add up, continuing to impact your bottom line over and over again. Defensive Strategies So what can you do? Tools like eCBSV that will assist with detecting synthetic identities are coming but they’re not here yet. And once they’re in place, they won’t be an instant fix. Implementing an overly cautious fraud detection strategy on your own will cause a high number of false positives, meaning you miss out on revenue from genuine customers. Your best defense requires finding a partner to help you implement a multi-layered fraud detection strategy throughout the customer lifecycle. Detecting synthetic identities entails looking at more than a single factor (like length of credit history). You need to aggregate multiple data sets and connect multiple customer characteristics to effectively defend against synthetic identity fraud. Experian’s synthetic identity prevention tools include Synthetic Identity High Risk Score to incorporate the history and past relationships between individuals to detect anomalies. Additionally, our digital device intelligence tools perform link analyses to connect identities that seem otherwise separate. We help our partners pinpoint false identities not associated with an actual person and decrease charge offs, protecting your bottom line and helping you let good customers in while keeping false personas out. Find out how to get your synthetic identity defense in place today.

Article written by Melanie Smith, Senior Copywriter, Experian Clarity Services, Inc. It’s been almost a decade since the Great Recession in the United States ended, but consumers continue to feel its effects. During the recession, millions of Americans lost their jobs, retirement savings decreased, real estate reduced in value and credit scores plummeted. Consumers that found themselves impacted by the financial crisis often turned to alternative financial services (AFS). Since the end of the recession, customer loyalty and retention has been a focus for lenders, given that there are more options than ever before for AFS borrowers. To determine what this looks like in the current climate, we examined today’s non-prime consumers, what their traditional scores look like and if they are migrating to traditional lending. What are alternative financial services (AFS)? Alternative financial services (AFS) is a term often used to describe the array of financial services offered by providers that operate outside of traditional financial institutions. In contrast to traditional banks and credit unions, alternative service providers often make it easier for consumers to apply and qualify for lines of credit but may charge higher interest rates and fees. More than 50% of new online AFS borrowers were first seen in 2018 To determine customer loyalty and fluidity, we looked extensively at the borrowing behavior of AFS consumers in the online marketplace. We found half of all online borrowers were new to the space as of 2018, which could be happening for a few different reasons. Over the last five years, there has been a growing preference to the online space over storefront. For example, in our trends report from 2018, we found that 17% of new online customers migrated from the storefront single pay channel in 2017, with more than one-third of these borrowers from 2013 and 2014 moving to online overall. There was also an increase in AFS utilization by all generations in 2018. Additionally, customers who used AFS in previous years are now moving towards traditional credit sources. 2017 AFS borrowers are migrating to traditional credit As we examined the borrowing behavior of AFS consumers in relation to customer loyalty, we found less than half of consumers who used AFS in 2017 borrowed from an AFS lender again in 2018. Looking into this further, about 35% applied for a loan but did not move forward with securing the loan and nearly 24% had no AFS activity in 2018. We furthered our research to determine why these consumers dropped off. After analyzing the national credit database to see if any of these consumers were borrowing in the traditional credit space, we found that 34% of 2017 borrowers who had no AFS activity in 2018 used traditional credit services, meaning 7% of 2017 borrowers migrated to traditional lending in 2018. Traditional credit scores of non-prime borrowers are growing After discovering that 7% of 2017 online borrowers used traditional credit services in 2018 instead of AFS, we wanted to find out if there had also been an improvement in their credit scores. Historically, if someone is considered non-prime, they don’t have the same access to traditional credit services as their prime counterparts. A traditional credit score for non-prime consumers is less than 600. Using the VantageScore® credit score, we examined the credit scores of consumers who used and did not use AFS in 2018. We found about 23% of consumers who switched to traditional lending had a near-prime credit score, while only 8% of those who continued in the AFS space were classified as near-prime. Close to 10% of consumers who switched to traditional lending in 2018 were classified in the prime category. Considering it takes much longer to improve a traditional credit rating, it’s likely that some of these borrowers may have been directly impacted by the recession and improved their scores enough to utilize traditional credit sources again. Key takeaways AFS remains a viable option for consumers who do not use traditional credit or have a credit score that doesn’t allow them to utilize traditional credit services. New AFS borrowers continue to appear even though some borrowers from previous years have improved their credit scores enough to migrate to traditional credit services. Customers who are considered non-prime still use AFS, as well as some near-prime and prime customers, which indicates customer loyalty and retention in this space. For more information about customer loyalty and other recently identified trends, download our recent reports. State of Alternative Data 2019 Lending Report

It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?

Last month, Kenneth Blanco, Director of the Financial Crimes Enforcement Network, warned that cybercriminals are stealing data from fintech platforms to create synthetic identities and commit fraud. These actions, in turn, are alleged to be responsible for exploiting fintech platforms’ integration with other financial institutions, putting banks and consumers at risk. According to Blanco, “by using stolen data to create fraudulent accounts on fintech platforms, cybercriminals can exploit the platforms’ integration with various financial services to initiate seemingly legitimate financial activity while creating a degree of separation from traditional fraud detection efforts.” Fintech executives were quick to respond, and while agreeing that synthetic IDs are a problem, they pushed back on the notion that cybercriminals specifically target fintech platforms. Innovation and technology have indeed opened new doors of possibility for financial institutions, however, the question remains as to whether it has also created an opportunity for criminals to implement more sophisticated fraud strategies. Currently, there appears to be little evidence pointing to an acute vulnerability of fintech firms, but one thing can be said for certain: synthetic ID fraud is the fastest-growing financial crime in the United States. Perhaps, in part, because it can be difficult to detect. Synthetic ID is a type of fraud carried out by criminals that have created fictitious identities. Truly savvy fraudsters can make these identities nearly indistinguishable from real ones. According to Kathleen Peters, Experian’s SVP, Head of Fraud and Identity, it typically takes fraudsters 12 to 18 months to create and nurture a synthetic identity before it’s ready to “bust out” – the act of building a credit history with the intent of maxing out all available credit and eventually disappearing. These types of fraud attacks are concerning to any company’s bottom line. Experian’s 2019 Global Fraud and Identity Report further details the financial impact of fraud, noting that 55% of businesses globally reported an increase in fraud-related losses over the past 12 months. Given the significant risk factor, organizations across the board need to make meaningful investments in fraud prevention strategies. In many circumstances, the pace of fraud is so fast that by the time organizations implement solutions, the shelf life may already be old. To stay ahead of fraudsters, companies must be proactive about future-proofing their fraud strategies and toolkits. And the advantage that many fintech companies have is their aptitude for being nimble and propensity for early adoption. Experian can help too. Our Synthetic Fraud Risk Level Indicator helps both fintechs and traditional financial institutions in identifying applicants likely to be associated with a synthetic identity based on a complex set of relationships and account conditions over time. This indicator is now available in our credit report, allowing organizations to reduce exposure to identity fraud through early detection. To learn more about Experian’s Synthetic Fraud Risk Level Indicator click here, or visit experian.com/fintech.

It’s Halloween time – time for trick or treating, costume parties and monsters lurking in the background. But this year, the monsters aren’t just in the background. They’re in your portfolio.  This year, “Frankenstein” has another meaning. Much more ominous than the neighbor kid in the costume.   “Frankenstein IDs” refer to synthetic identities — a type of fraud carried out by criminals that have created fictitious identities. Just as Dr. Frankenstein’s monster was stitched together from parts, synthetic IDs are stitched together pieces of mismatched identities — some fake, some real, some even deceased.   It typically takes fraudsters 12 to 18 months to create and nurture a synthetic identity before it’s ready to "bust out" – the act of building a credit history with the intent of maxing out all available credit and eventually disappearing. That means fraudsters are investing money and time to build numerous tradelines, ensure these "fake" identities are in good credit standing, and ultimately steal the largest amount of money possible.   “Wait Master, it might be dangerous . . . you go, first.” — Igor   Synthetic identities are a notable challenge for many financial institutions and retail organizations. According to the recently released Federal Reserve Board White Paper, synthetic identity fraud accounts for roughly 20% of all credit losses, and cost U.S. businesses roughly $6 billion in 2016 with an estimated 41% growth over 2 years. 85-95% of applicants identified as potential synthetic are not even flagged by traditional fraud models.   The Social Security Administration recently announced plans for the electronic Consent Based Social Security Number Verification service – pilot program scheduled for June 2020. This service is designed to bring efficiency to the process for verifying Social Security numbers directly with the government agency. Once available, this verification could be an important tool in the fight against the elusive “Frankenstein” identity monster.   But with the Social Security Administration's pilot program not scheduled for launch until the middle of next year, how can financial institutions and other organizations bridge the gap and adequately prepare for a potential uptick in synthetic identity fraud attacks? It comes down to a multilayered approach that relies on advanced data, analytics, and technology — and focuses on identity.   Any significant progress in making synthetic identities easier to detect could cost fraudsters significant time and money.   Far too many financial institutions and other organizations depend solely on basic demographic information and snapshots in time to confirm the legitimacy of an identity. These organizations need to think beyond those capabilities. The real value of data in many cases lies between the data points. We have seen this with synthetic identity — where a seemingly legitimate identity only shows risk when we can analyze its connections and relationships to other individuals and characteristics.   In addition to our High Risk Fraud Score, we now have a Synthetic Fraud Risk Level Indicator available on credit profiles. These advanced detection capabilities are delivered via the simplicity of a straightforward indicator returned on the credit profile which lenders can use to trigger additional identity verification processes.   While there are programs and initiatives in the works to help financial institutions and other organizations combat synthetic identity fraud, it's important to keep in mind there's no silver bullet, or stake to the heart, to completely keep these Frankenstein IDs out.   Oh, and don’t forget… “It’s pronounced ‘Fronkensteen.’ ” — Dr. Frankenstein

The future is, factually speaking, uncertain. We don't know if we'll find a cure for cancer, the economic outlook, if we'll be living in an algorithmic world or if our work cubical mate will soon be replaced by a robot. While futurists can dish out some exciting and downright scary visions for the future of technology and science, there are no future facts. However, the uncertainty presents opportunity. Technology in today's world From the moment you wake up, to the moment you go back to sleep, technology is everywhere. The highly digital life we live and the development of our technological world have become the new normal. According to The International Telecommunication Union (ITU), almost 50% of the world's population uses the internet, leading to over 3.5 billion daily searches on Google and more than 570 new websites being launched each minute. And even more mind-boggling? Over 90% of the world's data has been created in just the last couple of years. With data growing faster than ever before, the future of technology is even more interesting than what is happening now. We're just at the beginning of a revolution that will touch every business and every life on this planet. By 2020, at least a third of all data will pass through the cloud, and within five years, there will be over 50 billion smart connected devices in the world. Keeping pace with digital transformation At the rate at which data and our ability to analyze it are growing, businesses of all sizes will be forced to modify how they operate. Businesses that digitally transform, will be able to offer customers a seamless and frictionless experience, and as a result, claim a greater share of profit in their sectors. Take, for example, the financial services industry - specifically banking. Whereas most banking used to be done at a local branch, recent reports show that 40% of Americans have not stepped through the door of a bank or credit union within the last six months, largely due to the rise of online and mobile banking. According to Citi's 2018 Mobile Banking Study, mobile banking is one of the top three most-used apps by Americans. Similarly, the Federal Reserve reported that more than half of U.S. adults with bank accounts have used a mobile app to access their accounts in the last year, presenting forward-looking banks with an incredible opportunity to increase the number of relationship touchpoints they have with their customers by introducing a wider array of banking products via mobile. Be part of the movement Rather than viewing digital disruption as worrisome and challenging, embrace the uncertainty and potential that advances in new technologies, data analytics and artificial intelligence will bring. The pressure to innovate amid technological progress poses an opportunity for us all to rethink the work we do and the way we do it. Are you ready? Learn more about powering your digital transformation in our latest eBook. Download eBook Are you an innovation junkie? Join us at Vision 2020 for future-facing sessions like:  -  Cloud and beyond - transforming technologies - ML and AI - real-world expandability and compliance