From consumers seeking versatility and additional cargo space to more models becoming available—a discernible trend the automotive industry has seen in recent years is the shift towards utility vehicles such as SUVs and crossover utility vehicles (CUVs). In fact, Experian’s Automotive Market Trends Report: Q4 2023 found that utility vehicles were a significant driver in new vehicle registrations, coming in at 57.3%, up from 56.2% through Q4 2022. Meanwhile, pickup trucks declined from 18.5% last year to 17.2% this quarter and sedans went from 17.1% to 16.5% in the same time frame. Optimizing vehicle maintenance post-manufacturer warranty Despite utility vehicles making up the majority of new vehicle registrations through Q4 2023, passenger vehicles (85.1%) and light trucks (82.7%) had the most vehicles that were outside of the general manufacturer warranty this quarter—mostly due to a high volume of registrations in previous years. By comparison, 67.1% of all utility vehicles were outside the general manufacturer warranty. Understanding the current status of these vehicles enables aftermarket professionals to tailor their service recommendations accordingly. Furthermore, it will be important to monitor this trend over the next few years as the vehicles that are currently under manufacturer warranty will likely need maintenance after it expires. !function(e,n,i,s){var d="InfogramEmbeds";var o=e.getElementsByTagName(n)[0];if(window[d]&&window[d].initialized)window[d].process&&window[d].process();else if(!e.getElementById(i)){var r=e.createElement(n);r.async=1,r.id=i,r.src=s,o.parentNode.insertBefore(r,o)}}(document,"script","infogram-async","https://e.infogram.com/js/dist/embed-loader-min.js"); Vehicle registrations and aftermarket sweet spot When looking at overall registration trends, new vehicles increased 12.5% from last year—reaching 15.3 million through Q4 2023 and used vehicles declined 1.5% year-over-year to 38.2 million this quarter. While monitoring vehicle registration trends helps aftermarket professionals properly assist consumers now and in the future, identifying and understanding the aftermarket “sweet spot” allows them to stay ahead of the curve and adapt to changes as the market continues to evolve. Vehicles in the sweet spot are generally between six- to 12-model-years-old and have aged out of general OEM manufacturer warranties for any repairs. Through Q4 2023, 35.5% of all vehicles in operation landed in the sweet spot, marking a 3.6% year-over-year increase. Though, the aftermarket sweet spot volume is expected to hit its peak in the next few months at nearly 116 million vehicles—considering the record high was 104 million through 2011 and the sweet spot volume reached 102.4 million through Q4 2023. As aftermarket professionals look for ways to reach the right audience, leveraging registration data and the types of vehicles entering the market enables them to adjust their marketing strategies accordingly and plan their services effectively. To learn more about vehicle market trends, view the full Automotive Market Trends Report: Q4 2023 presentation on demand.

Financial institutions have long relied on anti-money laundering (AML) and anti-fraud systems to protect themselves and their customers. These departments and systems have historically operated in siloes, but that’s no longer best practice.  Now, a new framework that integrates fraud and AML, or FRAML, is taking hold as financial institutions see the value of sharing resources to fight fraud and other financial crimes.  You don’t need to keep them separated For fraudsters, fraud and money laundering go hand-in-hand. By definition, someone opening an account and laundering money is committing a crime. The laundered funds are also often from illegal activity — otherwise, they wouldn’t need to be laundered.  For financial institutions, different departments have historically owned AML and anti-fraud programs. In part, because AML and fraud prevention have different goals: AML is about staying compliant: AML is often owned by an organization’s compliance department, which ensures the proper processes and reporting are in place to comply with relevant regulations.  Fraud is about avoiding losses: The fraud department identifies and stops fraudulent activity to help protect the organization from reputational harm and fraud losses. As fraudsters’ operations become more complex, the traditional separation of the two departments may be doing more harm than good.  Common areas of focus There has always been some overlap in AML and fraud prevention. After all, an AML program can stop criminals from opening or using accounts that could lead to fraud losses. And fraud departments might stop suspicious activity that’s a criminal placing or layering funds. While AML and fraud both involve ongoing account monitoring, let’s take a closer look at similarities during the account creation: Verifying identities: Financial institutions’ AML programs must include know your customer (KYC) procedures and a Customer Identification Program (CIP). Being able to verify the identity of a new customer can be important for tracing transactions back to an individual or entity later. Similarly, fraud departments want to be sure there aren’t any red flags when opening a new account, such as a connection between the person or entity and previous fraudulent activity.  Preventing synthetic identity fraud: Criminals may try to use synthetic identities to avoid triggering AML or fraud checks. Synthetic identity fraud has been a growing problem, but the latest solutions and tools can help financial institutions stop synthetic identity fraud across the customer lifecycle.  Detecting money mules: Some criminals recruit money mules rather than using their own identity or creating a synthetic identity. The mules are paid to use their legitimate bank account to accept and transfer funds on behalf of the criminal. In some cases, the mule is an unwitting victim of a scam and an accomplice in money laundering.  Although the exact requirements, tools, processes, and reports for AML and fraud differ, there’s certainly one commonality — identify and stop bad actors.    Interactive infographic: Building a multilayered fraud and identity strategy The win-win of the FRAML approach Aligning AML and fraud could lead to cost savings and benefits for the organization and its customers in many ways. Save on IT costs: Fraud and AML teams may benefit from similar types of advanced analytics for detecting suspicious activity. In 2023, around 60 percent of businesses were using or trying to use machine learning (ML) in their fraud strategies, but a quarter said cost was impeding implementation.1 If fraud and AML can share IT resources and assets, they might be able to better afford the latest ML and AI solutions.  Avoid duplicate work: Cost savings can also happen if you can avoid having separate AML and fraud investigations into the same case. The diverse backgrounds and approaches to investigations may also lead to more efficient and successful outcomes.  Get a holistic view of customers: Sharing information about customers and accounts also might help you more accurately assess risk and identify fraud groups.  Improve your customer experience: Shared data can also reduce customer outreach for identity or transaction verifications. Creating a single view of each account or customer can also improve customer onboarding and account monitoring, leading to fewer false positives and a better customer experience.  Some financial institutions have implemented collaboration with the creation of a new team, sometimes called the financial crimes unit (FCU). Others may keep the departments separate but develop systems for sharing data and resources.  Watch the webinar: Fraud and identity challenges for Fintechs How Experian can help  Creating new systems and changing company culture doesn’t happen overnight, but the shift toward collaboration may be one of the big trends in AML and fraud for 2024. As a leader in identity verification and fraud prevention, Experian can offer the tools and strategies that organizations need to update their AML and fraud processes across the entire customer lifecycle.  CrossCore® is our integrated digital identity and fraud risk platform which enables organizations to connect, access, and orchestrate decisions that leverage multiple data sources and services. CrossCore cloud platform combines risk-based authentication, identity proofing and fraud detection, which enables organizations to streamline processes and quickly respond to an ever-changing environment. In its 2023 Fraud Reduction Intelligence Platforms (FRIP), Kuppinger Cole wrote, “Once again, Experian is a Leader in Fraud Reduction Intelligence Platforms. Any organizations looking for a full-featured FRIP service with global support should consider Experian CrossCore.”  Learn more about Experian’s AML and fraud solutions. 1. Experian (2023). Experian's 2023 Identity and Fraud Report  

Know Your Customer (KYC) procedures are a requirement for banks and other financial institutions to collect and verify the identity of their customers. When a bank verifies the identity of another organization or its owners, the process may be called Know Your Business (KYB) instead.  As part of banks’ anti-money laundering (AML) programs, KYC can help stop corruption, money laundering and terrorist financing. Creating and maintaining KYC programs is also important for regulatory compliance, reputation management and fraud prevention.  READ: How to Build a Know Your Customer Checklist – Everything You Need to Know The three components of KYC programs Banks can largely determine how to set up their KYC and AML programs within the applicable regulatory guidelines. In the United States, KYC needs to happen when banks initially onboard a new customer. But it’s not a one-and-done event—ongoing customer and transaction monitoring is also important.  Customer Identification Program (CIP) Creating a robust Customer Identification Program (CIP) is an essential part of KYC. At a minimum, a bank’s CIP requires it to collect the following information from new customers: Name Date of birth Address Identification number, such as a Social Security number (SSN) or Employer Identification Number (EIN) Banks' CIPs also have to use risk-based procedures to verify customers’ identities and form a reasonable belief that they know the customer's true identity.1 This might involve comparing the information from the application to the customer’s government-issued ID, other identifying documents and authoritative data sources, such as credit bureau databases. Additionally, the bank's CIP will govern how the bank:  Retains the customer’s identifying information Compares customer to government lists  Provides customers with adequate notices Banks can create CIPs that meet all the requirements in various ways, and many use third-party solutions to quickly collect data, detect forged or falsified documents and verify the provided information.  INFOGRAPHIC: Streamlining the Digital Onboarding Process: Beating Fraud at its Game Customer due diligence (CDD)  CIP and CDD overlap, but the CIP primarily verifies a customer’s identity while customer due diligence (CDD) helps banks understand the risk that each customer poses. To do this, banks try to understand what various types of customers do, what those customers’ normal banking activity looks like, and in contrast, what could be unusual or suspicious activity.  Financial institutions can use risk ratings and scores to evaluate customers and then use simplified, standard or enhanced due diligence (EDD) processes based on the results. For example, customers who might pose a greater risk of laundering money or financing terrorism may need to undergo additional screenings and clarify the source of their funds. Ongoing monitoring Ongoing or continuous monitoring of customers’ identities and transactions is also important for staying compliant with AML regulations and stopping fraud.  The monitoring can help banks spot a significant change in the identity of the customer, beneficial owner or account, which may require a new KYC check. Unusual transactions can also be a sign of money laundering or fraud, and they may require the bank to file a suspicious activity report (SAR). Why is KYC important in banking? Understanding and implementing KYC processes can be important for several reasons:  Regulatory compliance: Although the specific laws and rules can vary by country or region, many banks are required to have AML procedures, including KYC. The fines for violating AML regulations can be in the hundreds of millions— a few banks have been fined over $1 billion for lax AML enforcement and sanctions breaching. Reputation management: In some cases, enforcement actions and fines were headline news. Banks that don’t have robust KYC procedures in place risk losing their customers' trust and respect.  Fraud prevention: In addition to the regulatory requirements, KYC policies and systems can also work alongside fraud management solutions for banks. Identity verification at onboarding can help banks identify synthetic identities attempting to open money mule accounts or take out loans. Ongoing monitoring can also be important for identifying long-term fraud schemes and large fraud rings.  ON-DEMAND WEBINAR: Fraud Strategies for a Positive Customer Experience KYC in a digital-first world Many financial institutions have been going through digital transformations. Part of that journey is updating the systems and tools in place to meet the expectations of customers and regulators.  An Experian survey found that about half of consumers (51 percent) consider abandoning the creation of a new account because of friction or a less-than-positive experience — that increased to 69 percent for high-income households.2 The survey wasn’t specific to financial services, but friction could be a problem for banks wanting to attract new account holders. Just as access to additional data sources and machine learning help automate underwriting, financial institutions can use technological advances to add an appropriate amount of friction based on various risk signals. Some of these can be run in the background, such as an electronic Consent Based Social Security Number Verification (eCBSV) check to verify the customer’s name, SSN and date of birth match the Social Security Administration’s records. Others may require more customer involvement, such as taking a selfie that’s then compared to the image on their photo ID — Experian CrossCore® Doc Capture enables this type of verification.  Experian is a leader in identity and data management  Experian's identity verification solutions use proprietary and third-party data to help banks manage their KYC procedures, including identity verification and Customer Identification Programs. By bundling identity verification with fraud assessment, banks can stop fraudsters while quickly resolving identity discrepancies. The automated processes also allow you to offer a low-friction identity verification experience and use step-up authentications as needed.  Learn more about Experian’s identity solutions.  1FDIC (2021). Customer Identification Program 2Experian (2023). Experian's 2023 Identity and Fraud Report

This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies. As we near the end of the first quarter, the U.S. economy has maintained its solid standing. We're also starting to see some easing in a few areas. This month saw a slight uptick in unemployment, slowed spending growth, and a slight increase in annual headline inflation. At the same time, job creation was robust, incomes continued to grow, and annual core inflation cooled. In light of the mixed economic landscape, this month’s upcoming Federal Reserve meeting and their refreshed Summary of Economic Projections should shine some light on what’s in store in the coming months. Data highlights from this month’s report include: Annual headline inflation increased from 3.1% to 3.2%, while annual core inflation cooled from 3.9% to 3.8%. Job creation remained solid, with 275,000 jobs added this month. Unemployment increased to 3.9% from 3.7% three months prior. Mortgage delinquencies rose for accounts (2.3%) and balances (1.8%) in February, contributing to overall delinquencies across product types. Check out our report for a deep dive into the rest of March’s data, including consumer spending, the housing market, and originations. To have a holistic view of our current environment, we must understand our economic past, present, and future. Check out our annual chartbook for a comprehensive view of the past year and download our latest forecasting report for a look at the year ahead. Download March's State of the Economy report  Download latest forecast For more economic trends and market insights, visit Experian Edge.

To say “yes” to consumers faster and more efficiently, financial institutions need flexible access to instant income and employment verification data. In an episode of “The Chrisman Commentary” podcast, Joy Mina, Director of Product Commercialization at Experian, talks about how income and employment verification has changed since Experian entered the market, the benefits of a waterfall strategy, and what’s next in our verifications journey. “Back then, we were hearing lenders primarily asking for more innovative solutions,” said Joy. “They wanted more flexibility without sacrificing quality of service.” Listen to the full episode to learn more about what lenders look for in an income and employment verification solution and how Experian VerifyTM is meeting these needs. Listen to podcast  Learn more

Ensuring the reliability of tenant applications is paramount to running a successful property management business. But with an exponential rise in prospective residents using fake financial documents to inflate income and employment status, how do property managers navigate and detect fake paystubs without stepping on a landmine of liability? The marketplace of deception Paystub generator websites As you embrace the commitment to diligence, be aware that some legitimate websites can be unknowingly used by fraudsters to create counterfeit financial documents. Knowledge is your ally here. At the touch of a button, even the minimally tech inclined can produce pay stubs that appear convincing. There are dozens of sites that offer paystub generator software, including: Design and editing software websites that are accessible to people beyond just creative professionals. Popular e-commerce platform stores that host apps capable of creating paystubs. Mobile app stores that allow users to download apps for use on all major mobile devices. Key indicators of a fake paystub Remember, as a property manager or owner, you are responsible for scrutinizing these documents to protect your business interests. Use your awareness to be vigilant, verifying every piece of information to ensure the credibility of prospective tenants. While some of these falsified paystubs may appear to be legitimate, they are usually not perfect. Here are some quick checks which may help you spot a fake or trigger a deeper review quickly. Watch out for elusive typos Erroneous spelling, particularly in company names and financial terms, is a big red flag. Keep your eyes peeled for these unruly characters. Distorted watermarks A legitimate paystub should carry official watermarks or specific symbols that indicate its authenticity. However, be on the lookout for watermarks that seem off — sometimes, they're too conspicuous or amateurish, which can be a tell-tale sign of forgery. Authentic watermarks should be subtle and consistent with the company's brand. Crunching the numbers Inaccurate calculations can unravel a fake paystub. If the numbers just don't add up or pay dates vary inexplicably, you should investigate further. Inconsistent font Professional payroll systems stick to a consistent font. If you notice various font styles and sizes, it's worth investigating further. Authenticity lies in uniformity. Going logo-less? A missing company logo, or one that looks like it was copied from a low-resolution image on the internet, should trigger suspicion. Unusual tax deductions Abnormal tax deductions could indicate someone's fiddling with the figures. Brush up on your tax knowledge or consult with an expert if something seems off-the-wall. Final food for thought Remember, having the right knowledge and tools empowers you to make informed decisions, safeguarding your property from potential fraudsters. Be diligent, stay informed, and leverage technology to support your processes. Action steps to take today Educate your team: Make sure everyone involved in the application review process knows what to look for. Develop a standard operating procedure: Update your existing (or develop) Standard Operating Procedures: As new ways of gaming the system arise, make sure your particular procedures are keeping up with the times. For example, include steps for the following: Understand tenant screening laws in your area. Create consistent resident screening criteria. Check credit report and background. Verify employment and income. Review rental history and evictions (if any). Check criminal record with multi-state search. Interview residents before signing a lease. Follow a consistent policy when accepting or rejecting applicants. Embrace technology: Income and employment verification solutions can verify income directly from a trusted data source and avoid the paystub predicament altogether. Consider implementing a verification system that leaves no room for guesswork. Our verification solution, Experian VerifyTM, provides accurate, efficient, and compliant income and employment verification services. With Experian Verify, property managers can navigate the complexities of tenant-related income and employment verification with ease, ensuring they are adhering to Fair Housing laws and detecting fraudulent behavior. To learn more about how Experian Verify can benefit your property business, please contact us and visit us online. Learn more

According to Experian’s State of the Automotive Finance Market Report: Q4 2023, EVs comprised 8.6% of total new retail transactions, an increase from 7.1% in Q4 2022.

This article was updated on March 12, 2024. The number of decisions that a business must make in the marketing space is on the rise. Which audience to target, what is the best method of communication, which marketing campaign should they receive? To stay ahead, a growing number of businesses are embracing artificial intelligence (AI) analytics, machine learning, and mathematical optimization in their decisioning models and strategies. What is an optimization model? While machine learning models provide predictive insights, it’s the mathematical optimization models that provide actionable insights that drive decisioning. Optimization models factor in multiple constraints and goals to leave you with the next best steps. Each step in the optimization process can significantly improve the overall impact of your marketing outreach — for both you and your customers. Using a mathematical optimization software, you can enhance your targeting, increase response rates, lower cost per acquisition, and drive engagement. Better engagement can lead to stronger business performance and profitability. Here are a few key areas where machine learning and optimization modeling can help increase your return on investment (ROI): Prospecting: Advanced analytics and optimization can be used to better identify individuals who meet your credit criteria and are most likely to respond to your offers. Taking this customer-focused approach, you can provide the most relevant marketing messages to customers at the right time and place. Cross-sell and upsell: The same optimized targeting can be applied to increase profitability with your existing customer base in cross-sell and up-sell opportunities. Gain insights into the best offer to send to each customer, the best time to send it, and which channel the customer will respond best to. Additionally, implement logic that maintains your customer contact protocols. Retention: Employing optimization modeling in the retention stage helps you make quicker decisions in a competitive environment. Instantly identify triggers that warrant a retention offer and determine the likelihood of the customer responding to different offers. LEARN MORE: eBook: Debunking the top 5 myths about optimization Gaining insight and strengthening decisions with our solutions Experian’s suite of advanced analytics solutions, including our optimization software, can help improve your marketing strategies. Use our ROI calculator to get a personalized estimate of how optimization can lift your campaigns without additional marketing spend. Start by inputting your organization’s details below. initIframe('62e81cb25d4dbf17c7dfea55'); Learn more about how optimization modeling can help you achieve your marketing and growth goals. Learn more  

This article was updated on March 11, 2024. As a lender, it’s important to understand a consumer’s credit behavior and whether it's improving or deteriorating over time. Sure, you can pull a credit score at any moment, but it's merely a snapshot. Knowing a consumer’s credit information at a single point in time only tells part of the story. Two consumers can have the same credit score, but one consumer’s score could be moving up while another’s score could be moving down. To understand the whole story, lenders need the ability to leverage trended data to assess a consumer’s credit behavior over time. What to know about trended data Trended data provides key balance and payment data for the previous 24 months. By analyzing historical payment information, lenders can determine if a consumer is consistently paying more than the minimum payment, has a demonstrated ability to pay, and shows no signs of payment stress. It can conversely identify if a consumer is making only minimum payments and has increasing payment stress. Experian’s Trended Data is comprised of five fields of historical payment information over a 24-month period. It includes: Balance Amount Original Loan / Limit Amount Scheduled Payment Amount Actual Payment Amount Last Payment Date Knowing how a consumer uses credit, or pays back debt over time, can help lenders offer the right products and terms to increase response rates, determine up-sell and cross-sell opportunities, and limit loss exposure. Using a consumer’s historical payment information also provides a more accurate assessment of future behavior, helping lenders effectively manage changes in risk, predict balance transfer activity, and prevent attrition. The challenge For lenders to extract the benefits of trended data, they need to analyze an enormous amount of data. Five fields of data across 24 months on every trade is huge and can be difficult for lenders with limited analytical resources to manage. For example, a single consumer with 10 trades on file would have upwards of 1,200 data points to analyze. Multiply that by a file of 100,000 consumers and you are now dealing with over 120,000,000 data points. Additionally, if lenders utilize the trended data in their underwriting processing and intend to use it to decline consumers, they need to create their own adverse action reason codes to communicate to the consumer. Not all lenders are equipped to take on this level of effort. Still, there are trended data solutions to assist lenders with managing and unlocking the power of trended data. How Experian can help Experian’s pre-calculated solutions allow even the smallest lenders to quickly and effectively action on the benefits of trended data, minus the hassles of analyzing it. Trended data, and the solutions built from it, allow lenders to effectively predict where a consumer is going based on where they’ve been. And really, that can make all the difference when it comes to smart lending decisions. Get started today

In the ever-expanding financial crime landscape, envision the most recent perpetrator targeting your organization. Did you catch them? Could you recover the stolen funds? Now, picture that same individual attempting to replicate their scheme at another establishment, only to be thwarted by an advanced system flagging their activity. The reason? Both companies are part of an anti-fraud data consortium, safeguarding financial institutions (FIs) from recurring fraud. In the relentless battle against fraud and financial crime, FIs find themselves at a significant disadvantage due to stringent regulations governing their operations. Criminals, however, operate without boundaries, collaborating across jurisdictions and international borders. Recognizing the need to level the playing field, FIs are increasingly turning to collaborative solutions, such as participation in fraud consortiums, to enhance their anti-fraud and Anti-Money Laundering (AML) efforts. Understanding consortium data for fraud prevention A fraud consortium is a strategic alliance of financial institutions and service providers united in the common goal of comprehensively understanding and combatting fraud. As online transactions surge, so does the risk of fraudulent activities. However, according to Experian’s 2023 U.S. Identity and Fraud Report, 55% of U.S. consumers reported setting up a new account in the last six months despite concerns around fraud and online security. The highest account openings were reported for streaming services (43%), social media sites and applications (40%), and payment system providers (39%). Organizations grappling with fraud turn to consortium data as a robust defense mechanism against evolving fraud strategies. Consortium data for fraud prevention involves sharing transaction data and information among a coalition of similar businesses. This collaborative approach empowers companies with enhanced data analytics and insights, bolstering their ability to combat fraudulent activities effectively. The logic is simple: the more transaction data available for analysis by artificial-intelligence-powered systems, the more adept they become at detecting and preventing fraud by identifying patterns and anomalies. Advantages of data consortiums for fraud and AML teams Participation in an anti-fraud data consortium provides numerous advantages for a financial institution's risk management team. Key benefits include: Case management resolution: Members can exchange detailed case studies, sharing insights on how they responded to specific suspicious activities and financial crime incidents. This collaborative approach facilitates the development of best practices for incident handling. Perpetrator IDs: Identifying repeat offenders becomes more efficient as consortium members share data on suspicious activities. Recognizing patterns in names, addresses, device fingerprints, and other identifiers enables proactive prevention of financial crimes. Fraud trends: Consortium members can collectively analyze and share data on the frequency of various fraud attempts, allowing for the calibration of anti-fraud systems to effectively combat prevalent types of fraud. Regulatory changes: Staying ahead of evolving financial regulations is critical. Consortiums enable FIs to promptly share updates on regulatory changes, ensuring quick modifications to anti-fraud/AML systems for ongoing compliance. Who should join a fraud consortium? A fraud consortium can benefit any organization that faces fraud risks and challenges, especially in the financial industry. However, some organizations may benefit more, depending on their size, type, and fraud exposure. Some of the organizations that should consider joining a fraud consortium are: Financial institutions: Banks, credit unions, and other financial institutions are prime targets for fraudsters, who use various methods such as identity theft, account takeover, card fraud, wire fraud, and loan fraud to steal money and information from them. Fintech companies: Fintech companies are innovative and disruptive players in the financial industry, who offer new and alternative products and services such as digital payments, peer-to-peer lending, crowdfunding, and robot-advisors. Online merchants: Online merchants are vulnerable to fraudsters, who use various methods such as card-not-present fraud, friendly fraud, and chargeback fraud to exploit their online transactions and payment systems. Why partner with Experian? What companies need is a consortium that allows FIs to collaboratively research anti-fraud and AML information, eliminating the need for redundant individual efforts. This approach promotes tighter standardization of anti-crime procedures, expedited deployment of effective anti-fraud/AML solutions, and a proactive focus on preventing financial crime rather than reacting to its aftermath. Experian Hunter is a sophisticated global application fraud and risk management solution. It leverages detection rules to screen incoming application data for identifying and preventing fraudulent activities. It matches incoming application data against multiple internal and external data sources, shared fraud databases and dedicated watch lists. It uses client-flexible matching rules to crossmatch data sources for highlighting data anomalies and velocity attempts. In addition, it looks for connections to previous suspected and known fraudulent applications. Hunter generates a fraud score to indicate a fraud risk level used to prioritize referrals. Suspicious applications are moved into the case management tool for further investigation. Overall, Hunter prevents application fraud by highlighting suspicious applications, allowing you to investigate and prevent fraud without inconveniencing genuine customers. To learn more about our fraud management solutions, visit us online or request a call. Learn more This article includes content created by an AI language model and is intended to provide general information.

This article was updated on March 7, 2024. Like so many government agencies, the U.S. military is a source of many acronyms. Okay, maybe a few less, but there really is a host of abbreviations and acronyms attached to the military – and in the regulatory and compliance space, that includes SCRA and MLA. So, what is the difference between the two? And what do financial institutions need to know about them? Let’s break it down in this basic Q&A. SCRA and MLA: Who is covered and when are they covered? The Servicemember Civil Relief Act (SCRA) protects service members and their dependents (indirectly) on existing debts when the service member becomes active duty. In contrast, the Military Lending Act (MLA) protects service members, their spouses and/or covered dependents at point of origination if they are on active duty at that time. For example, if a service member opens an account with a financial institution and then becomes active military, SCRA protections will apply. On the other hand, if the service member is of active duty status when the service member or dependent is extended credit, then MLA protections will apply. Both SCRA and MLA protections cease to apply to a credit transaction when the service member ceases to be on active duty status. What is covered? MLA protections apply to all forms of payday loans, vehicle title loans, refund anticipation loans, deposit advance loans, installment loans, unsecured open-end lines of credit, and credit cards. However, MLA protections exclude loans secured by real estate and purchase-money loans, including a loan to finance the purchase of a vehicle. What are the interest rate limitations for SCRA and MLA? The SCRA caps interest rate charges, including late fees and other transaction fees, at 6 percent. The MLA limits interest rates and fees to 36 percent Military Annual Percentage Rate (MAPR). The MAPR is not just the interest rate on the loan, but also includes additional fees and charges including: Credit insurance premiums/fees Debt cancellation contract fees Debt suspension agreement fees and Fees associated with ancillary products. Although closed-end credit MAPR will be a one-time calculation, open-end credit transactions will need to be calculated for each covered billing cycle to affirm lender compliance with interest rate limitations. Are there any lender disclosure requirements? There is only one set of circumstances that triggers SCRA disclosures. The Department of Housing and Urban Development (HUD) requires that SCRA disclosures be provided by mortgage servicers on mortgages at 45 days of delinquency. This disclosure must be provided in written format only. For MLA compliance, financial institutions must provide the following disclosures: MAPR statement Payment obligation descriptions Other applicable Regulation Z disclosures. For MLA, it is also important to note that disclosures are required both orally and in a written format the borrower can keep. How Experian can help Experian's solutions help you comply with the Department of Defense's (DOD's) final amendment rule. We can access the DOD's database on your behalf to identify MLA-covered borrowers and provide a safe harbor for creditors ascertaining whether a consumer is covered by the final rule's protection. Visit us online to learn more about our SCRA and military lending act compliance solutions. Learn more

Finding a reliable, customer-friendly way to protect your business against new account fraud is vital to surviving in today's digital-driven economy. Not only can ignoring the problem cause you to lose valuable money and client goodwill, but implementing the wrong solutions can lead to onboarding issues that drive away potential customers. The Experian® 2023 Identity and Fraud Report revealed that nearly 70 percent of businesses reported fraud loss in recent years, with many of these involving new account fraud. At the same time, problems with onboarding caused 37 percent of consumers to drop off and take their business elsewhere. In other words, your customers want protection, but they aren't willing to compromise their digital experience to get it. You need to find a way to meet both these needs when combating new account fraud. What is new account fraud? New account fraud occurs any time a bad actor creates an account in your system utilizing a fake or stolen identity. This process is referred to by different names, such as account takeover fraud, account creation fraud, or account opening fraud. Examples of some of the more common types of new account fraud include: Synthetic identity (ID) fraud: This type of fraud occurs when the scammer uses a real, stolen credential combined with fake credentials. For example, they might use someone's real Social Security number combined with a fake email. Identity theft: In this case, the fraudster uses personal information they stole to create a new scam account. Fake identity: With this type of fraud, scammers create an account with wholly fake credentials that haven't been stolen from any particular person. New account fraud may target individuals, but the repercussions spill over to impact entire organizations. In fact, many scammers utilize bots to attempt to steal information or create fake accounts en masse, upping the stakes even more. How does new account fraud work? New account fraud begins at a single weak security point, such as: Data breaches: The Bureau of Justice reported that in 2021 alone, 12 percent of people ages 16 or older received notifications that their personal information was involved in a data breach.1 Phishing scams: The fraudster creates an email or social media account that pretends to be from a legitimate organization or person to gain confidential information.2 Skimmers: These are put on ATMs or fuel pumps to steal credit or debit card information.2 Bot scrapers: These tools scrape information posted publicly on social media or on websites.2 Synthetic ID fraud: 80 percent of new account fraud is linked to synthetic ID fraud.3 The scammer just needs one piece of legitimate information. If they have a real Social Security number, they might combine it with a fake name and birth date (or vice versa.) After the information is stolen, the rest of the fraud takes place in steps. The fake or stolen identity might first be used to open a new account, like a credit card or a demand deposit account. Over time, the account establishes a credit history until it can be used for higher-value targets, like loans and bank withdrawals. How can organizations prevent new account fraud? Some traditional methods used to combat new account fraud include: Completely Automated Public Turing Tests (CAPTCHAs): These tests help reduce bot attacks that lead to data breaches and ensure that individuals logging into your system are actual people. Multifactor authentication (MFA): MFA bolsters users' password protection and helps guard against account takeover. If a scammer tries to take over an account, they won't be able to complete the process. Password protection: Robust password managers can help ensure that one stolen password doesn't lead to multiple breaches. Knowledge-based authentication: Knowledge-based authentication can be combined with MFA solutions, providing an additional layer of identity verification. Know-your-customer (KYC) solutions: Businesses may utilize KYC to verify customers via government IDs, background checks, ongoing monitoring, and the like. Additional protective measures may involve more robust identity verification behind the scenes. Examples include biometric verification, government ID authentication, public records analysis, and more. Unfortunately, these traditional protective measures may not be enough, for many reasons: New account fraud is frequently being perpetrated by bots, which can be tougher to keep up with and might overwhelm systems. Institutions might use multiple security solutions that aren't built to work together, leading to overlap and inefficiency. Security measures may create so much friction in the account creation process that potential new customers are turned away. How we can help Experian's fraud management services provide a multi-layered approach that lets businesses customize solutions to their particular needs. Advanced machine learning analytics utilizes extensive, proprietary data to provide a unique experience that not only protects your company, but it also protects your customers' experience. Customer identification program (CIP) Experian's KYC solutions allow you to confidently identify your customers via a low-friction experience. The tools start with onboarding, but continue throughout the customer journey, including portfolio management. The tools also help your company comply with relevant KYC regulations. Cross-industry analysis of identity behavior Experian has created an identity graph that aggregates consumer information in a way that gives companies access to a cross-industry view of identity behavior as it changes over time. This means that when a new account is opened, your company can determine behind the scenes if any part of the identity is connected to instances of fraud or presents actions not normally associated with the customer's identity. It's essentially a new paradigm that works faster behind the scenes and is part of Experian's Ascend Fraud Platform™. Multifactor authentication solutions Experian's MFA solutions utilize low-friction techniques like two-factor authentication, knowledge-based authentication, and unique one-time password authentication during remote transactions to guard against hacking. Synthetic ID fraud protection Experian's fraud management solutions include robust protection against synthetic ID fraud. Our groundbreaking technology detects and predicts synthetic identities throughout the customer lifecycle, utilizing advanced analytics capabilities. CrossCore® CrossCore combines risk-based authentication, identity proofing, and fraud detection into one cloud platform, allowing for real-time decisions to be made with flexible decisioning workflows and advanced analytics. Interactive infographic: Building a multilayered fraud and identity strategy Precise ID® The Precise ID platform lets customers choose the combination of fraud analytics, identification verification, and workflows that best meet their business needs. This includes machine-learned fraud risk models, robust consumer data assets, one-time passwords (OTPs), knowledge-based authentication (KBAs), and powerful insights via the Identity Element Network®. Account takeover fraud represents a significant threat to your business that you can't ignore. But with Experian's broad range of solutions, you can keep your systems secure while not sacrificing customer experience. Experian can keep your business secure from new account fraud Experian's innovative approach can streamline your new account fraud protection. Learn more about how our fraud management solutions can help you. Learn more References 1. Harrell, Erika. "Just the Stats: Data Breach Notifications and Identity Theft, 2021." Bureau of Justice Statistics, January 2024. https://bjs.ojp.gov/data-breach-notifications-and-identity-theft-2021 2. "Identity Theft." USA.gov, December 6, 2023. https://www.usa.gov/identity-theft 3. Purcell, Michael. "Synthetic Identity Fraud: What is It and How to Combat It." Thomson Reuters, April 28, 2023. https://legal.thomsonreuters.com/blog/synthetic-identity-fraud-what-is-it-and-how-to-combat-it/

This article was updated on March 6, 2024. Advances in analytics and modeling are making credit risk decisioning more efficient and precise. And while businesses may face challenges in developing and deploying new credit risk models, machine learning (ML) — a type of artificial intelligence (AI) — is paving the way for shorter design cycles and greater performance lifts. LEARN MORE: Get personalized recommendations on optimizing your decisioning strategy Limitations of traditional lending models Traditional lending models have worked well for years, and many financial institutions continue to rely on legacy models and develop new challenger models the old-fashioned way. This approach has benefits, including the ability to rely on existing internal expertise and the explainability of the models. However, there are limitations as well. Slow reaction times:  Building and deploying a traditional credit risk model can take many months. That might be okay during relatively stable economic conditions, but these models may start to underperform if there's a sudden shift in consumer behavior or a world event that impacts people's finances. Fewer data sources:  Traditional scoring models may be able to analyze some types of FCRA-regulated data (also called alternative credit data*), such as utility or rent payments, that appear in credit reports. Custom credit risk scores and models could go a step further by incorporating data from additional sources, such as internal data, even if they're designed in a traditional way. But AI-driven models can analyze vast amounts of information and uncover data points that are more highly predictive of risk. Less effective performance:  Experian has found that applying machine learning models can increase accuracy and effectiveness, allowing lenders to make better decisions. When applied to credit decisioning, lenders see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.1 Leveraging machine learning-driven models to segment your universe From initial segmentation to sending right-sized offers, detecting fraud and managing collection efforts, organizations are already using machine learning throughout the customer life cycle. In fact, 79% are prioritizing the adoption of advanced analytics with AI and ML capabilities, while 65% believe that AI and ML provide their organization with a competitive advantage.2 While machine learning approaches to modeling aren't new, advances in computer science and computing power are unlocking new possibilities.3 Machine learning models can now quickly incorporate your internal data, alternative data, credit bureau data, credit attributes and other scores to give you a more accurate view of a consumer's creditworthiness. By more precisely scoring applicants, you can shrink the population in the middle of your score range, the segment of medium-risk applicants that are difficult to evaluate. You can then lower your high-end cutoff and raise your low-end cutoff, which may allow you to more confidently swap in  good accounts (the applicants you turned down with other models that would have been good) and swap out bad accounts (those you would have approved who turned bad). Machine learning models may also be able to use additional types of data to score applicants who don't qualify for a score  from traditional models. These applicants aren't necessarily riskier — there simply hasn't been a good way to understand the risk they present. Once you can make an accurate assessment, you can increase your lending universe by including this segment of previously "unscorable" consumers, which can drive revenue growth without additional risk. At the same time, you're helping expand financial inclusion to segments of the population that may otherwise struggle to access credit. READ MORE: Is Financial Inclusion Fueling Business Growth for Lenders? Connecting the model to a decision Even a machine learning model doesn't make decisions.4 The model estimates the creditworthiness of an applicant so lenders can make better-informed decisions. AI-driven credit decisioning software can take your parameters (such cutoff points) and the model's outputs to automatically approve or deny more applicants. Models that can more accurately segment and score populations will result in fewer applications going to manual review, which can save you money and improve your customers' experiences. CASE STUDY:  Atlas Credit, a small-dollar lender, nearly doubled its loan approval rates while decreasing risk losses by up to 20 percent using a machine learning-powered model and increased automation. Concerns around explainability One of the primary concerns lenders have about machine learning models come from so-called “black box" models.5 Although these models may offer large lifts, you can't verify how they work internally. As a result, lenders can't explain why decisions are made to regulators or consumers — effectively making them unusable. While it's a valid concern, there are machine learning models that don't use a black box approach. The machine learning model doesn't build itself and it's not really “learning" on its own — that's where the black box would come in. Instead, developers can use machine learning techniques to create more efficient models that are explainable, don't have a disparate impact on protected classes and can generate reason codes that help consumers understand the outcomes. LEARN MORE: Explainability: Machine learning and artificial intelligence in credit decisioning Building and using machine learning models Organizations may lack the expertise and IT infrastructure required to develop or deploy machine learning models. But similar to how digital transformations in other parts of the business are leading companies to use outside cloud-based solutions, there are options that don't require in-house data scientists and developers. Experian's expert-guided options can help you create, test and use machine learning models and AI-driven automated decisioning; Ascend Intelligence Services™ Acquire:  Our model development service allows you to prebuild and test the performance of a new model before Experian data scientists complete the model. It's collaborative, and you can upload internal data through the web portal and make comments or suggestions. The service periodically retrains your model to increase its effectiveness. Ascend Intelligence Services™ Pulse:  Monitor, validate and challenge your existing models to ensure you're not missing out on potential improvements. The service includes a model health index and alerts, performance summary, automatic validations and stress-testing results. It can also automatically build challenger models and share the estimated lift and financial benefit of deployment. PowerCurve® Originations Essentials:  Cloud-based decision engine software that you can use to make automated decisions that are tailored to your goals and needs. A machine learning approach to credit risk and AI-driven decisioning can help improve outcomes for borrowers and increase financial inclusion while reducing your overall costs. With a trusted and experienced partner, you'll also be able to back up your decisions with customizable and regulatorily-compliant reports. Learn more about our credit decisioning solutions. Learn more When 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 (FCRA). Hence, the term "Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.1Experian (2024). Improving Your Credit Risk Machine Learning Model Deployment2Experian and Forrester Research (2023). Raising the AI Bar3Experian (2022). Driving Growth During Economic Uncertainty with AI/ML Strategies4Ibid5Experian (2020). Explainability ML and AI in Credit Decisioning

This article was updated on March 4, 2024. If you steal an identity to commit fraud, your success is determined by how long it takes the victim to find out. That window gets shorter as businesses get better at knowing when and how to reach an identity owner when fraud is suspected. In response, frustrated fraudsters have been developing techniques to commit fraud that does not involve a real identity, giving them a longer run-time and a bigger payday.  That's the idea behind  synthetic identity (SID) fraud — one of the fastest-growing types of fraud.  Defining synthetic identity fraud Organizations tend to have different  definitions of synthetic identity fraud, as a synthetic identity will look different to the businesses it attacks. Some may see a new account that goes bad immediately, while others might see a longer tenured account fall delinquent and default. The qualifications of the synthetic identity also change over time, as the fraudster works to increase the identity’s appearance of legitimacy. In the end, there is no person to confirm that fraud has occurred, in the very best case, identifying a synthetic identity is inferred and verified. As a result, inconsistent reporting and categorization can make tracking and fighting SID fraud more difficult.  To help create a more unified understanding and response to the issue, the Federal Reserve and 12 fraud experts worked together to develop a definition. In 2021, the  Boston Federal Reserve  published the result, “Synthetic identity fraud is the use of a combination of personally identifiable information to fabricate a person or entity to commit a dishonest act for personal or financial gain."1 To break down the definition, personally identifiable information (PII) can include:  Primary PII:  Such as a name, date of birth (DOB), Social Security number (SSN) or another government-issued identifier. When combined, these are generally unique to a person or entity. Secondary PII:  Such as an address, email, phone number or device ID. These elements can help verify a person or entity's identity.   Synthetic identities are created when fraudsters establish an identity from scratch using fake PII. Or they may combine real and fake PII (I.e., a stolen SSN with a fake name and DOB) to create a new identity. Additionally, fraudsters might steal and use someone's SSN to create an identity - children, the  elderly  and incarcerated people are popular targets because they don't commonly use credit.4 But any losses would still be tied to the SID rather than the victim. Exploring the Impact of SID fraud The most immediate and obvious impact of SID fraud is the fraud losses. Criminals may create a synthetic identity and spend months  building up its credit profile, opening accounts and increasing credit limits. The identities and behaviors are constructed to look like legitimate borrowers, with some having a record of on-time payments. But once the fraudster decides to monetize the identity, they can apply for loans and max out credit cards before ‘busting out’ and disappearing with the money.  Aite-Novaric Group estimates that SID fraud losses totaled $1.8 billion in 2020 and will increase to $2.94 billion in 2024.2 However, organizations that do not identify SIDs may classify a default as a credit loss rather than a fraud loss.  By some estimates, synthetic identity fraud could account for up to 20 percent of loan and credit card charge-offs, meaning the annual charge-off losses in the U.S. could be closer to $11 billion.3 Additionally, organizations lose time and resources on collection efforts if they do not identify the SID fraud.  Those estimates are only for unsecured U.S. credit products. But fraudsters use synthetic identities to take out secured loans, including auto loans.   As part of schemes used to steal relief funds during the pandemic, criminals used synthetic identities to open demand deposit accounts to receive funds. These accounts can be used to launder money from other sources and commit peer-to-peer payment fraud. Deposit account holders are also a primary source of cross-marketing for some financial institutions. Criminals can take advantage of vulnerable onboarding processes for deposit accounts where there’s low risk to the institution and receive offers for lending products. Building a successful SID prevention strategy Having an effective SID prevention strategy is more crucial than ever for organizations. Aside from fraud losses, consumers listed identity theft as their top concern when conducting activities online. And while 92% of businesses have an identity verification strategy in place, 63% of consumers are "somewhat confident" or "not very confident" in businesses' ability to accurately identify them online. Read: Experian's 2023 Identity and Fraud Report Many traditional fraud models and identity verification methods are not designed to detect fake people. And even a step up to a phone call for verification isn't enough when the fraudster will be the one answering the phone. Criminals also quickly respond when organizations update their fraud detection methods by looking for less-protected targets. Fraudsters have even signed their SIDs up for social media accounts and apps with low verification hurdles to help their SIDs pass identity checks.5  Understand synthetic identity risks across the lifecycle  Synthetic Identities are dynamic. When lending criteria is tightened to synthetics from opening new accounts, they simply come back when they can qualify. If waiting brings a higher credit line, they’ll wait. It’s important to recognize that synthetic identity isn’t a new account or a portfolio management problem - it’s both.    Use analytics that are tailored to synthetic identity  Many of our customers in the financial services space have been trying to solve synthetic identity fraud with credit data. There’s a false sense of security when criteria is tightened and losses go down—but the losses that are being impacted tend to not be related to credit. A better approach to synthetic ID fraud leverages a larger pool of data to assess behaviors and data linkages that are not contained in traditional credit data.  You can then escalate suspicious accounts to require additional reviews, such as screening through the Social Security Administration's Electronic Consent Based SSN Verification (eCBSV) system or more stringent document verification.  Find a trusted partner  Experian's interconnected data and analytics platforms offer lenders turnkey identity and synthetic identity fraud solutions. In addition, lenders can take advantage of the risk management system and continuous monitoring to look for signs of SIDs and fraudulent activity, which is important for flagging accounts after opening. These tools can also help lenders identify and prevent other common forms of fraud, including account takeovers, e-commerce fraud, child identity theft fraud and elderly fraud. Learn more about our synthetic identity fraud solutions. Learn more 1Federal Reserve Bank (2021). Defining Synthetic Identity Fraud 2Aite Novarica (2022). Synthetic Identity Fraud: Solution Providers Shining Light into the Darkness 3Experian (2022). Preventing synthetic identity fraud 4The Federal Reserve (2022). Synthetic Identity Fraud: What Is it and Why You Should Care? 5Experian (2022). Preventing synthetic identity fraud 

This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies. In February, economic growth and job creation outperformed economists’ expectations, likely giving confirmation to the Federal Reserve that it remains too early to begin cutting rates. Data highlights from this month’s report include: U.S. real GDP rose 3.3% in Q4 2023, driven by consumer spending and bringing the average annual 2023 growth to 2.5%, the same as the five-year average growth prior to the pandemic. The labor market maintained its strength, with 353,000 jobs added this month and unemployment holding at 3.7% for the third month in a row. Consumer sentiment rose 13% in January, following a 14% increase in December, as consumers are feeling some relief from cooling inflation. Check out our report for a deep dive into the rest of February’s data, including inflation, the latest Federal Reserve announcement, the housing market, and credit card balances. To have a holistic view of our current environment, we must understand our economic past, present, and future. Check out our annual chartbook for a comprehensive view of the past year and register for our upcoming Macroeconomic Forecasting webinar for a look at the year ahead. Download report Register for webinar For more economic trends and market insights, visit Experian Edge.