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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