Regulatory Compliance

Market volatility, evolving regulations, and shifting consumer expectations are a catalyst to make energy providers to rethink how they operate. Rising energy costs, grid reliability concerns, and the push for sustainable energy sources add layers of complexity to an already challenging landscape. In this environment, data analytics in utilities has become a strategic imperative, enabling companies to optimize operations, mitigate risks, and enhance customer experiences. With a wealth of data at their disposal, utilities must harness the power of utility analytics to transform raw information into actionable intelligence. This is where Experian’s energy and utilities solutions come into play. With an unmatched data reach of more than 1.5 billion consumers and 201 million businesses, we are uniquely positioned to help energy and utility providers unlock greater potential within their organizations, whether that’s by boosting customer engagement, preventing fraud and verifying identities, or optimizing collections. Market Challenges Facing the Utilities Sector Utilities today face a series of economic, regulatory, and operational hurdles that demand innovative solutions. Regulatory and Compliance Pressures: Governments and regulatory bodies are tightening rules around emissions, sustainability, and grid reliability. Utilities must balance compliance with the need for cost efficiency. New carbon reduction mandates and reporting requirements force energy providers to adopt predictive modeling solutions that assess future demand and optimize energy distribution. Economic Uncertainty and Rising Costs: Inflation, fuel price fluctuations, and supply chain disruptions are impacting the cost of delivering energy. Utilities must find ways to improve financial forecasting and reduce inefficiencies—tasks well suited for advanced analytics solutions that optimize asset management and detect cost-saving opportunities. Grid Modernization and Infrastructure Investments: Aging infrastructure and increased energy demand require significant investments in modernization. Data-driven insights help utilities prioritize infrastructure upgrades, preventing costly failures and ensuring reliability. Predictive analytics models play a crucial role in identifying patterns that signal potential grid failures before they occur. Customer Expectations and Energy Transition: Consumers are more engaged than ever, demanding personalized service, real-time billing insights, and renewable energy options. Utilities must leverage advanced analytics to segment customer data, predict energy usage, and offer tailored solutions that align with shifting consumer preferences. Rising Fraud: Account takeover fraud, a form of identity theft where cybercriminals obtain credentials to online accounts, is on the rise in the utility sector. Pacific Gas and Electric Company reported over 26,000 reports of scam attempts in 2024 and has received over 1,700 reports of attempted scams in January 2025 alone. Utility and energy providers must leverage advanced fraud detection and identity verification tools to protect their customers and also their business. How Data Analytics Is Transforming the Utilities Industry Optimizing Revenue and Reducing Fraud Fraud and revenue leakage remain significant challenges. Utilities can use data and modeling to detect anomalies in energy usage, identify fraudulent accounts, and minimize losses. Experian’s predictive modeling solutions enable proactive fraud detection, ensuring financial stability for providers. Enhancing Demand Forecasting and Load Balancing With renewable energy sources fluctuating daily, accurate demand forecasting is critical. By leveraging utility analytics, providers can predict peak demand periods, optimize energy distribution, and reduce waste. Improving Credit Risk and Payment Management Economic uncertainty increases the risk of late or unpaid bills. Experian’s energy and utilities solutions help providers assess creditworthiness and develop more flexible payment plans, reducing bad debt while improving customer satisfaction. Why Experian? The Power of Data-Driven Decision Making Only Experian delivers a comprehensive suite of advanced analytics solutions that help utilities make smarter, faster, and more informed decisions. With more than 25 years of experience in the energy and utility industry, we are your partner of choice. Our predictive analytics models provide real-time risk assessment, fraud detection, and customer insights, ensuring utilities can confidently navigate today’s economic and regulatory challenges. In an industry defined by complexity and change, utilities that fail to leverage data analytics in utilities risk falling behind. From optimizing operations to enhancing customer engagement, the power of utility analytics is undeniable. Now is the time to act. Explore how Experian’s energy and utilities solutions can help your organization harness the power of advanced analytics to navigate market challenges and drive long-term success. Learn more Partner with our team

In this article...What is fair lending?Understanding machine learning modelsThe pitfalls: bias and fairness in ML modelsFairness metricsRegulatory frameworks and complianceHow Experian® can help As the financial sector continues to embrace technological innovations, machine learning models are becoming indispensable tools for credit decisioning. These models offer enhanced efficiency and predictive power, but they also introduce new challenges. These challenges particularly concern fairness and bias, as complex machine learning models can be difficult to explain. Understanding how to ensure fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations. What is fair lending? Fair lending is a cornerstone of ethical financial practices, prohibiting discrimination based on race, color, national origin, religion, sex, familial status, age, disability, or public assistance status during the lending process. This principle is enshrined in regulations such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA). Overall, fair lending is essential for promoting economic opportunity, preventing discrimination, and fostering financial inclusion. Key components of fair lending include: Equal treatment: Lenders must treat all applicants fairly and consistently throughout the lending process, regardless of their personal characteristics. This means evaluating applicants based on their creditworthiness and financial qualifications rather than discriminatory factors. Non-discrimination: Lenders are prohibited from discriminating against individuals or businesses on the basis of race, color, religion, national origin, sex, marital status, age, or other protected characteristics. Discriminatory practices include redlining (denying credit to applicants based on their location) and steering (channeling applicants into less favorable loan products based on discriminatory factors). Fair credit practices: Lenders must adhere to fair and transparent credit practices, such as providing clear information about loan terms and conditions, offering reasonable interest rates, and ensuring that borrowers have the ability to repay their loans. Compliance: Financial institutions are required to comply with fair lending laws and regulations, which are enforced by government agencies such as the Consumer Financial Protection Bureau (CFPB) in the United States. Compliance efforts include conducting fair lending risk assessments, monitoring lending practices for potential discrimination, and implementing policies and procedures to prevent unfair treatment. Model governance: Financial institutions should establish robust governance frameworks to oversee the development, implementation and monitoring of lending models and algorithms. This includes ensuring that models are fair, transparent, and free from biases that could lead to discriminatory outcomes. Data integrity and privacy: Lenders must ensure the accuracy, completeness, and integrity of the data used in lending decisions, including traditional credit and alternative credit data. They should also uphold borrowers’ privacy rights and adhere to data protection regulations when collecting, storing, and using personal information. Understanding machine learning models and their application in lending Machine learning in lending has revolutionized how financial institutions assess creditworthiness and manage risk. By analyzing vast amounts of data, machine learning models can identify patterns and trends that traditional methods might overlook, thereby enabling more accurate and efficient lending decisions. However, with these advancements come new challenges, particularly in the realms of model risk management and financial regulatory compliance. The complexity of machine learning models requires rigorous evaluation to ensure fair lending. Let’s explore why. The pitfalls: bias and fairness in machine learning lending models Despite their advantages, machine learning models can inadvertently introduce or perpetuate biases, especially when trained on historical data that reflects past prejudices. One of the primary concerns with machine learning models is their potential lack of transparency, often referred to as the "black box" problem. Model explainability aims to address this by providing clear and understandable explanations of how models make decisions. This transparency is crucial for building trust with consumers and regulators and for ensuring that lending practices are fair and non-discriminatory. Fairness metrics Key metrics used to evaluate fairness in models can include standardized mean difference (SMD), information value (IV), and disparate impact (DI). Each of these metrics offers insights into potential biases but also has limitations. Standardized mean difference (SMD). SMD quantifies the difference between two groups' score averages, divided by the pooled standard deviation. However, this metric may not fully capture the nuances of fairness when used in isolation. Information value (IV). IV compares distributions between control and protected groups across score bins. While useful, IV can sometimes mask deeper biases present in the data. Disparate impact (DI). DI, or the adverse impact ratio (AIR), measures the ratio of approval rates between protected and control classes. Although DI is widely used, it can oversimplify the complex interplay of factors influencing credit decisions. Regulatory frameworks and compliance in fair lending Ensuring compliance with fair lending regulations involves more than just implementing fairness metrics. It requires a comprehensive end-to-end approach, including regular audits, transparent reporting, and continuous monitoring and governance of machine learning models. Financial institutions must be vigilant in aligning their practices with regulatory standards to avoid legal repercussions and maintain ethical standards. Read more: Journey of a machine learning model How Experian® can help By remaining committed to regulatory compliance and fair lending practices, organizations can balance technological advancements with ethical responsibility. Partnering with Experian gives organizations a unique advantage in the rapidly evolving landscape of AI and machine learning in lending. As an industry leader, Experian offers state-of-the-art analytics and machine learning solutions that are designed to drive efficiency and accuracy in lending decisions while ensuring compliance with regulatory standards. Our expertise in model risk management and machine learning model governance empowers lenders to deploy robust and transparent models, mitigating potential biases and aligning with fair lending practices. When it comes to machine learning model explainability, Experian’s clear and proven methodology assesses the relative contribution and level of influence of each variable to the overall score — enabling organizations to demonstrate transparency and fair treatment to auditors, regulators, and customers. Interested in learning more about ensuring fair lending practices in your machine learning models?    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

In today’s complex business landscape, data-based decision-making has become the norm, with advanced technologies and analytics tools facilitating faster and more accurate modeling and predictions. However, with the increased reliance on models, the risk of errors has also increased, making it crucial for organizations to have a comprehensive model risk management framework. In this blog post, we will dive deeper into model risk management, its importance for organizations, and the key elements of a model risk management framework. What is model risk? First, let's define what we mean by model risk. Many institutions use models to forecast and predict the future performance of investments, portfolios or consumers' creditworthiness. Model risk can happen when the results produced by these models are inaccurate or not fit for the intended purpose. This risk arises due to several factors, like data limitations, model assumptions and inherent complexities in the underlying modeled processes. For example, in the credit industry, an inaccurately calibrated credit risk model may incorrectly assess a borrower's default risk, resulting in erroneous credit decisions and impacting overall portfolio performance. What is a risk management model and why is it important? A risk management model, or model risk management, refers to a systematic approach to manage the potential risks associated with the use of models and, more specifically, quantitative models built on data. Since models are based on a wide range of assumptions and predictions, it's essential to recognize the possibility of errors and acknowledge its impact on business decisions. The goal of model risk management is to provide a well-defined and structured approach to identifying, assessing, and mitigating risks associated with model use. The importance of model risk management for institutions that leverage quantitative risk models in their decisioning strategies cannot be overstated. Without proper risk management models, businesses are vulnerable to significant consequences, such as financial losses, regulatory enforcement actions and reputational damage. Model risk management: essential elements The foundation of model risk management includes standards and processes for model development, validation, implementation and ongoing monitoring. This includes: Policies and procedures that provide a clear framework for model use and the associated risks. Model inventory and management that captures all models used in an organization. Model development and implementation that documents the policies for developing and implementing models, defining critical steps and role descriptions. Validation and ongoing monitoring to ensure the models meet their stated objectives and to detect drift. In addition to these essential elements, a model risk management framework must integrate an ongoing system of transparency and communication to ensure that each stakeholder in model risk governance is aware of the policies, processes and decisions that support model use. Active engagement with modelers, validators, business stakeholders, and audit functions, among other stakeholders, is essential and should be included in the process. How we can help Experian® provides solutions and risk mitigation tools to help organizations of all sizes establish a solid model risk management framework to meet regulatory and model risk governance requirements, improve overall model performance and identify and mitigate potential risk. We provide services for back testing, benchmarking, sensitivity analysis and stress testing. In addition, our experts can review your organization’s current model risk management practices, conduct a gap analysis and assist with audit preparations. Learn more *This article includes content created by an AI language model and is intended to provide general information.

Meeting Know Your Customer (KYC) regulations and staying compliant is paramount to running your business with ensured confidence in who your customers are, the level of risk they pose, and maintained customer trust. What is KYC?KYC is the mandatory process to identify and verify the identity of clients of financial institutions, as required by the Financial Conduct Authority (FCA). KYC services go beyond simply standing up a customer identification program (CIP), though that is a key component. It involves fraud risk assessments in new and existing customer accounts. Financial institutions are required to incorporate risk-based procedures to monitor customer transactions and detect potential financial crimes or fraud risk. KYC policies help determine when suspicious activity reports (SAR) must be filed with the Department of Treasury’s FinCEN organization. According to the Federal Financial Institutions Examinations Council (FFIEC), a comprehensive KYC program should include:• Customer Identification Program (CIP): Identifies processes for verifying identities and establishing a reasonable belief that the identity is valid.• Customer due diligence: Verifying customer identities and assessing the associated risk of doing business.• Enhanced customer due diligence: Significant and comprehensive review of high-risk or high transactions and implementation of a suspicious activity-monitoring system to reduce risk to the institution. The following organizations have KYC oversight: Federal Financial Institutions Examinations Council (FFIEC), Federal Reserve Board, Federal Deposit Insurance Corporation (FDIC), national Credit Union Administration (NCUA), Office of the Comptroller of the Currency (OCC) and the Consumer Financial Protection Bureau (CFPB). How to get started on building your Know Your Customer checklist 1. Define your Customer Identification Program (CIP) The CIP outlines the process for gathering necessary information about your customers. To start building your KYC checklist, you need to define your CIP procedure. This may include the documentation you require from customers, the sources of information you may use for verification and the procedures for customer due diligence. Your CIP procedure should align with your organization’s risk appetite and be comply with regulations such as the Patriot Act or Anti-money laundering laws. 2. Identify the customer's information Identifying the information you need to gather on your customer is key in building an effective KYC checklist. Typically, this can include their first and last name, date of birth, address, phone number, email address, Social Security Number or any government-issued identification number. When gathering sensitive information, ensure that you have privacy and security controls such as encryption, and that customer data is not shared with unauthorized personnel. 3. Determine the verification method There are various methods to verify a customer's identity. Some common identity verification methods include document verification, facial recognition, voice recognition, knowledge-based authentication, biometrics or database checks. When selecting an identity verification method, consider the accuracy, speed, cost and reliability. Choose a provider that is highly secure and offers compliance with current regulations. 4. Review your checklist regularly Your KYC checklist is not a one and done process. Instead, it’s an ongoing process that requires periodic review, updates and testing. You need to periodically review your checklist to ensure your processes are up to date with the latest regulations and your business needs. Reviewing your checklist will help your business to identify gaps or outdated practices in your KYC process. Make changes as needed and keep management informed of any changes. 5. Final stage: quality control As a final step, you should perform a quality control assessment of the processes you’ve incorporated to ensure they’ve been carried out effectively. This includes checking if all necessary customer information has been collected, whether the right identity verification method was implemented, if your checklist matches your CIP and whether the results were recorded correctly. KYC is a vital process for your organization in today's digital age. Building an effective KYC checklist is essential to ensure compliance with regulations and mitigate risk factors associated with fraudulent activities. Building a solid checklist requires a clear understanding of your business needs, a comprehensive definition of your CIP, selection of the right verification method, and periodic reviews to ensure that the process is up to date. Remember, your customers' trust and privacy are at stake, so iensuring that your security processes and your KYC checklist are in place is essential. By following these guidelines, you can create a well-designed KYC checklist that reduces risk and satisfies your regulatory needs. Taking the next step Experian offers identity verification solutions as well as fully integrated, digital identity and fraud platforms. Experian’s CrossCore & Precise ID offering enables financial institutions to connect, access and orchestrate decisions that leverage multiple data sources and services. By combining risk-based authentication, identity proofing and fraud detection into a single, cloud-based platform with flexible orchestration and advanced analytics, Precise ID provides flexibility and solves for some of financial institutions’ biggest business challenges, including identity and fraud as it relates to digital onboarding and account take over; transaction monitoring and KYC/AML compliance and more, without adding undue friction. Learn more *This article includes content created by an AI language model and is intended to provide general information.

With great risk comes great reward, as the saying goes. But when it comes to business, there's huge value in reducing and managing that risk as much as possible to maximize benefits — and profits. In today's high-tech strategic landscape, financial institutions and other organizations are increasingly using risk modeling to map out potential scenarios and gain a clearer understanding of where various paths may lead. But what are risk models really, and how can you ensure you're creating and using them correctly in a way that actually helps you optimize decision-making? Here, we explore the details. What is a risk model? A risk model is a representation of a particular situation that's created specifically for the purpose of assessing risk. That risk model is then used to evaluate the potential impacts of different decisions, paths and events. From assigning interest rates and amortization terms to deciding whether to begin operating in a new market, risk models are a safe way to analyze data, test assumptions and visualize potential scenarios. Risk models are particularly valuable in the credit industry. Credit risk models and credit risk analytics allow lenders to evaluate the pluses and minuses of lending to clients in specific ways. They are able to consider the larger economic environment, as well as relevant factors on a micro level. By integrating risk models into their decision-making process, lenders can refine credit offerings to fit the assessed risk of a particular situation. It goes like this: a team of risk management experts builds a model that brings together comprehensive datasets and risk modeling tools that incorporate mathematics, statistics and machine learning. This predictive modeling tool uses advanced algorithmic techniques to analyze data, identify patterns and make forecasts about future outcomes. Think of it as a crystal ball — but with science behind it. Your team can then use this risk model for a wide range of applications: refining marketing targets, reworking product offerings or reshaping business strategies. How can risk models be implemented? Risk models consolidate and utilize a wide variety of data sets, historical benchmarks and qualitative inputs to model risk and allow business leaders to test assumptions and visualize the potential results of various decisions and events. Implementing risk modeling means creating models of systems that allow you to adjust variables to imitate real-world situations and see what the results might be. A mortgage lender, for example, needs to be able to predict the effects of external and internal policies and decisions. By creating a risk model, they can test how scenarios such as falling interest rates, rising unemployment or a shift in loan acceptance rates might affect their business — and make moves to adjust their strategies accordingly. One aspect of risk modeling that can't be underestimated is the importance of good data, both quantitative and qualitative. Efforts to implement or expand risk modeling should begin with refining your data governance strategy. Maximizing the full potential of your data also requires integrating data quality solutions into your operations in order to ensure that the building blocks of your risk model are as accurate and thorough as possible. It's also important to ensure your organization has sufficient model risk governance in place. No model is perfect, and each comes with its own risks. But these risks can be mitigated with the right set of policies and procedures, some of which are part of regulatory compliance. With a comprehensive model risk management strategy, including processes like back testing, benchmarking, sensitivity analysis and stress testing, you can ensure your risk models are working for your organization — not opening you up to more risk. How can risk modeling be used in the credit industry? Risk modeling isn't just for making credit decisions. For instance, you might model the risk of opening or expanding operations in an underserved country or the costs and benefits of existing one that is underperforming. In information technology, a critical branch of virtually every modern organization, risk modeling helps security teams evaluate the risk of malicious attacks. Banking and financial services is one industry for which understanding and planning for risk is key — not only for business reasons but to align with relevant regulations. The mortgage lender mentioned above, for example, might use credit risk models to better predict risk, enhance the customer journey and ensure transparency and compliance. It's important to highlight that risk modeling is a guide, not a prophecy. Datasets can contain flaws or gaps, and human error can happen at any stage.. It's also possible to rely too heavily on historical information — and while they do say that history repeats itself, they don't mean it repeats itself exactly. That's especially true in the presence of novel challenges, like the rise of artificial intelligence. Making the best use of risk modeling tools involves not just optimizing software and data but using expert insight to interpret predictions and recommendations so that decision-making comes from a place of breadth and depth. Why are risk models important for banks and financial institutions? In the world of credit, optimizing risk assessment has clear ramifications when meeting overall business objectives. By using risk modeling to better understand your current and potential clients, you are positioned to offer the right credit products to the right audience and take action to mitigate risk. When it comes to portfolio risk management, having adequate risk models in place is paramount to meet targets. And not only does implementing quality portfolio risk analytics help maximize sales opportunities, but it can also help you identify risk proactively to avoid costly mistakes down the road. Risk mitigation tools are a key component of any risk modeling strategy and can help you maintain compliance, expose potential fraud, maximize the value of your portfolio and create a better overall customer experience. Advanced risk modeling techniques In the realm of risk modeling, the integration of advanced techniques like machine learning (ML) and artificial intelligence (AI) is revolutionizing how financial institutions assess and manage risk. These technologies enhance the predictive power of risk models by allowing for more complex data processing and pattern recognition than traditional statistical methods. Machine learning in risk modeling: ML algorithms can process vast amounts of unstructured data — such as market trends, consumer behavior and economic indicators — to identify patterns that may not be visible to human analysts. For instance, ML can be used to model credit risk by analyzing a borrower’s transaction history, social media activities and other digital footprints to predict their likelihood of default beyond traditional credit scoring methods. Artificial intelligence in decisioning: AI can automate the decisioning process in risk management by providing real-time predictions and risk assessments. AI systems can be trained to make decisions based on historical data and can adjust those decisions as they learn from new data. This capability is particularly useful in credit underwriting where AI algorithms can make rapid decisions based on market conditions. Financial institutions looking to leverage these advanced techniques must invest in robust data infrastructure, skilled personnel who can bridge the gap between data science and financial expertise, and continuous monitoring systems to ensure the models perform as expected while adhering to regulatory standards. Challenges in risk model validation Validating risk models is crucial for ensuring they function appropriately and comply with regulatory standards. Validation involves verifying both the theoretical foundations of a model and its practical implementation. Key challenges in model validation: Model complexity: As risk models become more complex, incorporating elements like ML and AI, they become harder to validate. Complex models can behave in unpredictable ways, making it difficult to understand why they are making certain decisions (the so-called "black box" issue). Data quality and availability: Effective validation requires high-quality, relevant data. Issues with data completeness, accuracy or relevance can lead to incorrect model validations. Regulatory compliance: With regulations continually evolving, keeping risk models compliant can be challenging. Different jurisdictions may have varying requirements, adding to the complexity of validation processes. Best practices: Regular reviews: Continuous monitoring and periodic reviews help ensure that models remain accurate over time and adapt to changing market conditions. Third-party audits: Independent reviews by external experts can provide an unbiased assessment of the risk model’s performance and compliance. These practices help institutions maintain the reliability and integrity of their risk models, ensuring that they continue to function as intended and comply with regulatory requirements. Read more: Blog post: What is model governance? How Experian can help Risk is inherent to business, and there's no avoiding it entirely. But integrating credit risk modeling into your operations can ensure stability and profitability in a rapidly evolving business landscape. Start with Experian's credit modeling services, which use expansive data, analytical expertise and the latest credit risk modeling methodologies to better predict risk and accelerate growth. Learn more *This article includes content created by an AI language model and is intended to provide general information.

For companies that regularly engage in financial transactions, having a customer identification program (CIP) is mandatory to comply with the regulations around identity verification requirements across the customer lifecycle. In this blog post, we will delve into the essentials of a customer identification program, what it entails, and why it is important for businesses to implement one. What is a customer identification program? A CIP is a set of procedures implemented by financial institutions to verify the identity of their customers. The purpose of a CIP is to be a part of a financial institution’s fraud management solutions, with similar goals as to detect and prevent fraud like money laundering, identity theft, and other fraudulent activities. The program enables financial institutions to assess the risk level associated with a particular customer and determine whether their business dealings are legitimate. An effective CIP program should check the following boxes: Confidently verify customer identities Seamless authentication Understand and anticipate customer activities Where does Know Your Customer (KYC) fit in? KYC policies must include a robust CIP across the customer lifecycle from initial onboarding through portfolio management. KYC solutions encompass the financial institution’s customer identification program, customer due diligence and ongoing monitoring. What are the requirements for a CIP? Customer identification program requirements vary depending on the type of financial institution, the type of account opened, and other factors. However, the essential components of a CIP include verifying the customer's identity using government-issued identification, obtaining and verifying the customer's address, and checking the customer against a list of known criminals, terrorists, or suspicious individuals. These measures  help detect and prevent financial crimes. Why is a CIP important for businesses? CIP helps businesses mitigate risk by ensuring they have accurate and up-to-date information about their customers. This also helps financial institutions comply with laws and regulations that require them to monitor financial transactions for any suspicious activities. By having a robust CIP in place, businesses can establish trust and rapport with their customers. According to Experian’s 2024 U.S. Identity and Fraud Report, 63% of consumers say it's extremely or very important for businesses to recognize them online. Having an effective CIP in place is part of financial institutions showing their consumers that they have their best interests top of mind. Finding the right partner It’s important to find a partner you trust when working to establish processes and procedures for verifying customer identity, address, and other relevant information. Companies can also utilize specialized software that can help streamline the CIP process and ensure that it is being carried out accurately and consistently. Experian’s proprietary and partner data sources and flexible monitoring and segmentation tools allow you to resolve CIP discrepancies and fraud risk in a single step, all while keeping pace with emerging fraud threats with effective customer identification software. Putting consumers first is paramount. The security of their identity is priority one, but financial institutions must pay equal attention to their consumers’ preferences and experiences. It is not just enough to verify customer identities. Leading financial institutions will automate customer identification to reduce manual intervention and verify with a reasonable belief that the identity is valid and eligible to use the services you provide. Seamless experiences with the right amount of friction (I.e., multi-factor authentication) should also be pursued to preserve the quality of the customer experience. Putting it all together As cybersecurity threats are becoming more sophisticated, it is essential for financial institutions to protect their customerinformation and level up their fraud prevention solutions. Implementing a customer identification program is an essential component in achieving that objective. A robust CIP helps organizations detect, prevent, and deter fraudulent activities while ensuring compliance with regulatory requirements. While implementing a CIP can be complex, having a solid plan and establishing clear guidelines is the best way for companies to safeguard customer information and maintain their reputation. CIPs are an integral part of financial institutions security infrastructures and must be a business priority. By ensuring that they have accurate and up-to-date data on their customers, they can mitigate risk, establish trust, and comply with regulatory requirements. A sound CIP program can help financial institutions detect and prevent financial crimes and cyber threats while ensuring that legitimate business transactions are not disrupted, therefore safeguarding their customers' information and protecting their own reputation. Learn more

In previous posts, I’ve explored the potential ramifications of the end of the Public Health Emergency (PHE) and how it will impact agency plans such as Medicaid eligibility redeterminations. Many states may have already prepared a risk-based approach to address the unwinding process. States need to balance these plans with onboarding new applicants and maintaining the service levels required by the Centers for Medicare & Medicaid Services (CMS). Regardless of the approach, states should look for efficiency in all aspects of the redeterminations process, including aligning pending work with other program recertifications and maximizing the use of available information and tools. What does the end of the PHE mean for state agencies? From the end of the PHE, state agencies will have 12 months to initiate all citizen eligibility renewals and a total of 14 months to complete them. States may begin the unwinding process 60 days prior to the month in which the PHE ends. Many states have already begun Medicaid eligibility redeterminations in an effort to meet this deadline. CMS has provided extensive guidance in their Planning for Resumption document, which state agencies can refer to for full details. Building a proper redeterminations plan Redeterminations plans should verify citizen information with all available information, including residency, age, income, and deceased status. These plans should also support the assessment of identity risk and have the ability to ensure continuous outreach with accurate mailing addresses, phone numbers for calls and texts, email addresses, and assessments of returned mail. CMS guidance encourages states to verify eligibility requirements by mail, email, and other communications channels while minimizing the amount of time and documentation required of beneficiaries. The benefit of standing up this structure? More effective day-forward solutions that can help agencies assess any new and ongoing benefits requests and maintain accurate eligibility lists.   How can Experian help? Experian® has a range of products designed to help organizations verify contact information, such as phone numbers and mailing addresses, as well as income and employment. Our exclusive income and employment data can be leveraged incrementally in non-automated verification methods so that individuals not found by other services can be processed quickly via batch processing — minimizing any impact to beneficiaries while improving overall program performance. Our address verification tools provide improved outreach to beneficiaries with the best and most accurate mailing addresses, leveraging the National Change of Address (NCOA) database, as well as phone number information. The phone number information includes a mobile phone indicator, enabling text message outreach. Additionally, Experian can provide email address provisioning to verify or provide email addresses, which creates another path for contact. All of this helps agencies develop better redeterminations plans to manage the end of the PHE, and better process future benefits requests. To learn more about how we can help, visit us or request a call.  

Lenders are under pressure to improve access to financial services, but can it also be a vehicle for driving growth? With the global pandemic and social justice movements exposing societal issues of equity, financial institutions are being called upon to do their part to address these problems, too. Lenders are increasingly under pressure to improve access to the financial system and help close the wealth gap in America.  Specifically, there are calls to improve financial inclusion – the process of ensuring financial products and services are accessible and affordable to everyone. Financial inclusion seeks to remove barriers to accessing credit, which can ultimately help individuals and businesses create wealth and elevate communities. Activists and regulators have singled out the current credit scoring system as a significant obstacle for a large portion of U.S. consumers.  From an equity standpoint, tackling financial inclusion is a no-brainer: better access to credit allows more consumers to secure safer housing and better schools, which could lead to higher-paying jobs, as well as the ability to start businesses and get insurance. Being able to access credit in a regulated and transparent way underpins financial stability and prosperity for communities and is key to creating a stronger economic system. Beyond “doing the right thing," research shows that financial inclusion can also fuel business growth for lenders.  Get ahead of the game  There is mounting regulatory pressure to embrace financial inclusion, and financial institutions may soon need to comply with new mandates. Current lending practices overlook many marginalized communities and low-income consumers, and government agencies are seeking to change that.  Government agencies and organizations, such as the Consumer Financial Protection Bureau (CFPB) and Office of the Comptroller of the Currency (OCC), are requiring greater scrutiny and accountability of financial institutions, working to overhaul the credit reporting system to ensure fairness and equality.  As a lender, it makes good business sense to tackle this problem now. For starters, as more institutions embrace Corporate Social Responsibility (CSR) mandates—something that's increasingly demanded by shareholders and customers alike—financial inclusion is a natural place to start. It demonstrates a commitment to CSR principles and creates a positive brand built on equity.  Further, financial institutions that embrace these changes gain an early adopter advantage and can build a loyal customer base. As these consumers begin to build wealth and expand their use of financial products, lenders will be able to forge lifelong relationships with these customers.  Why not get a head start on making positive organizational change before the law compels it?  Grow your business (and profits)  To be sure, financial inclusion is a pressing moral imperative that financial institutions must address. But financial inclusion doesn't come at the expense of profit. It represents an enormous opportunity to do business with a large, untapped market without taking on additional risk. In many instances, unscorable and credit invisible consumers exhibit promising credit characteristics, which the conventional credit scoring system does not yet recognize.  Consider consumers coming to the U.S. from other countries. They may have good credit histories in their home countries but have not yet established a credit history here. Likewise, many young, emerging consumers haven't generated enough history to be categorized as creditworthy. And some consumers may simply not utilize traditional credit instruments, like credit cards or loans. Instead, they may be using non-bank credit instruments (like payday loans or buy-now-pay-later arrangements) but regularly make payments.  Ultimately, because of the way the credit system works, research shows that lenders are ignoring almost 20 percent of the U.S. population that don't have conventional credit scores as potential customers. These consumers may not be inherently riskier than scored consumers, but they often get labelled as such by the current credit scoring system. That's a major, missed opportunity!  Modern credit scoring tools can help fill the information gap and rectify this. They draw on wider data sources that include consumer activities (like rent, utility and non-bank loan payments) and provide holistic information to assist with more accurate decisioning. For example, Lift Premium™ can score 96 percent of Americans with this additional information—a vast improvement over the 81 percent who are currently scored with conventional credit data.1 By tapping into these tools, financial institutions can extend credit to underserved populations, foster consumer loyalty and grow their portfolio of profitable customers.  Do good for the economy  Research suggests that financial inclusion can provide better outcomes for both individuals and economies. Specifically, it can lead to greater investment in education and businesses, better health, lower inequality, and greater entrepreneurship.  For example, an entrepreneur who can access a small business loan due to an expanded credit scoring model is subsequently able to create jobs and generate taxable revenue. Small business owners spend money in their communities and add to the tax base – money that can be used to improve services and attract even more investment.  Of course, not every start-up is a success. But if even a portion of new businesses thrive, a system that allows more consumers to access opportunities to launch businesses will increase that possibility.  The last word  Financial inclusion promotes a stronger economy and thriving communities by opening the world of financial services to more people, which benefits everyone. It enables underserved populations to leverage credit to become homeowners, start businesses and use credit responsibly—all markers of financial health. That in turn creates generational wealth that goes a long way toward closing the wealth gap.  And widening the credit net also enables lenders to uncover new revenue sources by tapping new creditworthy consumers. Expanded data and advanced analytics allow lenders to get a fuller picture of credit invisible and unscorable consumers. Opening the door of credit will go a long way to establishing customer loyalty and creating opportunities for both consumers and lenders.  Learn more

The collections landscape is changing as a result of new and upcoming legislation and increased expectations from consumers. Because of this, businesses are looking to create more effective, consumer-focused collections processes while remaining within regulatory guidelines. Our latest tip sheet has insights that can help businesses and agencies optimize their collections efforts and remain compliant, including:   Start with the best data Keep pace with changing regulations Focus on agility Pick the right partner Download the tip sheet to learn how to maximize your collections efforts while reducing costs, avoiding reputational damage and fines, and improving overall engagement. Download tip sheet

The Telephone Consumer Protection Act (TCPA), which regulates telemarketing calls, autodialed calls, prerecorded calls, text messages and unsolicited faxes, was originally passed in 1991. Since that time, there have been many rulings and updates that impact businesses’ ability to maintain TCPA compliance.   Recent TCPA Changes   On December 30, 2020, the Federal Communications Commission (FCC) updated a number of TCPA exemptions, adding call limits and opt-out requirements, and codifying exemptions for calls to residential lines.   These changes, along with other industry changes, have added additional layers of complication to keeping compliant while still optimizing operations and the consumer experience.   Maintaining TCPA Compliance   Businesses who do not maintain TCPA compliance could be subject to a lawsuit and paying out damages, and potential hits to their reputation.   With the right partner in place, businesses can maintain data hygiene and accuracy to increase right-party contact (and reduce wrong-party contact) to keep collections streamlined and improve the customer experience.   Using the right technology in place, it’s easier to:   Monitor and verify consumer contact information for a better customer experience while remaining compliant. Receive and monitor daily notifications about changes in phone ownership information. Maintain compliance with Regulation F by leveraging a complete and accurate database of consumer information.   When searching for a partner, be sure to look for one who offers data scrubbing, phone type indicators, phone number scoring, phone number identity verification, ownership change monitoring, and who has direct access to phone carriers.   To learn more about how the right technology can help your business maintain TCPA compliance, visit us or request a call. Learn more

If it looks like a bank and acts like a bank, there’s a good chance the company behind that financial services transaction may not actually be a bank – but a fintech. Born out of Silicon Valley, New York and tech hubs in between, fintechs have been categorically unfettered from regulation and driven by a focus on customer acquisition and revenue growth. Today, the fintech market represents hundreds of billions of dollars globally and has been disrupting financial services with the goal of delightful customer experiences and democratizing access to credit and banking. Their success has led many fintechs to update their strategy and growth targets and set their sites outside of core banking to other sectors including payments, alternative lending, insurance, capital markets, personal wealth management, alternative lending and others. Depending on the strategy, many are seeking a bank charter, or a partnership with a chartered financial institution to accomplish their new growth goals. Meanwhile, all this disruption has caught the attention of banks and credit unions who are keen to work with these marketplace lenders to grow deposits and increase fee-revenue streams. Historically, obtaining a bank charter was an onerous process, which led many fintechs to actively seek out partnerships with financial institutions in order to leverage their chartered status without the regulatory hurdles of becoming a bank. In fact, fintech and FI partnerships have boomed in the last few years, growing more than five times over the past decade. Gone are the days of the zero-sum game that benefits solely the bank or the fintech. Today, there are more than 30 partner banks representing hundreds of fintech relationships and financial services. These partnerships vary in size and scope from household names like Goldman Sachs, which powers the Apple credit card, to Hatch Bank, which has $68 million in assets and started with a single fintech partner, HM Bradley.[1] But which scenario is right for your fintech? Much of that depends on which markets and lines of business round out your growth strategy and revenue goals. Regardless of what framework you determine is right for your fintech, you need to work with partners who have access to the freshest data and models and a firm handle on the regulatory and compliance landscape. Experian can help you navigate the fintech regulatory environment and think through if partnering with a bank or seeking your own fintech charter is the best match for your growth plan. In the meantime, check out this new eBook for more information on the bank charter process and benefits, fintech-FI partnerships and the implications of the Office of the Comptroller of the Currency (OCC) new fintech charter. Read now Explore Fintech solutions   [1] https://a16z.com/2020/06/11/the-partner-bank-boom/

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

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

2020 is finally over – been there, done that. And while it seems safe to say most everyone is all too eager to kick off a new calendar year, the reality is we’re still reeling – and will continue to reel – through the economic impacts of the COVID-19 global pandemic. As we inch closer to the one year marker of when many businesses were sent home – across all industries, including those tech-inclined and those less so – the understatement of the year is that the world has since changed as have consumer communication preferences, how businesses and customers interact, tweaked definitions of privacy, and new (heightened) expectations of evolving a positive customer experience with minimal friction and maximum security. While last year’s predictions of entering a new set of Roaring 20’s may not have panned out the way we had initially imagined, many of the trends thought to evolve over the last 365 days did. As we all look toward a post-pandemic world, here are six top trends to keep tabs on throughout 2021. 1. Data Data as a commodity and as a business differentiating factor has reached an all-time high. It’s doing more across the entire customer lifecycle and can elevate businesses to best prep for growth, especially as consumers begin to look for more financial products (whether looking for financial assistance as the CARES Act accommodation period ends, or to take advantage of the booming mortgage industry, etc.). Data can also give more insights into consumers than ever before. Far beyond just credit scores and financial data, today’s data sets can reveal consumers’ lifestyle preferences, their preferred communication channels, their rental histories, and so much more. With alternative credit data and non-traditional data (including consumer-permissioned data), businesses can get a holistic picture of their customers’ payment behaviors. That streaming media service monthly payment may seem minimal, but now could increase your credit score through Experian Boost. Experian is still making big strides in all efforts to use data for good. As of December 31, 2020, Experian Boost has “boosted” Americans’ credit scores nearly 47 million points. Additionally, throughout 2020, Experian worked with financial institutions and credit furnishers to continue to put consumers first and serve as the consumer’s bureau. Coming up in 2021? Using data for differentiation, which can ultimately drive business growth. From instant prescreens to identifying your best customers (and offering them cross-sell and upsell opportunities to increase retention and customer loyalty) to helping customers that may be on the brink of financial distress and connecting them with management solutions to help them get back on their feet, data can help businesses – and their customers – get there. 2. Fraud and Friction (And the Reduction of Both) With the pandemic, fraud saw increases across the board. Here are just some quick stats: 200% increase in first-time online banking usage immediately following shelter-in-place orders (Aite Group, “Workplace Distancing: Adapting Fraud and AML Operations to COVID-19,” April 2020) 652% year-over-year increase in records found on the dark web (Experian CyberAgent technology) 50% increase in human farming – real people being hired for purposes of fraud – month-over-month in March 2020 (Arkose Labs) And, unsurprisingly, consumer and business sentiments toward fraud are also evolving with these increasing trends. For example, according to Experian’s North America Trends Report, half of consumers continue to site security as the most important factor of their online experience. Additionally, there’s been an increase in the percentage of businesses who have recently increased or are planning to increase fraud budget from 76% in 2019 to 89% as of Sept. 2020. More complex phishing schemes and increased fraudster activity is due in part to numerous industries having to shift to online processes and business transactions overnight. Adoption for mobile wallets has jumped 11% since July 2020, according to the 2020 Global Insights Report. Systems and technology that were not ready or not armed with the necessary infrastructure left critical access points open that could be exploited by fraudsters. Fraud exists across the customer lifecycle, at every access point. And while fraud is complex, with Experian as your partner, solving it isn’t. Innovative technology enables businesses to prevent fraud by identifying credible customers and applying the correct treatment to the riskiest consumer and business accounts. We can help you develop a layered risk management strategy so you can focus resources on growing and protecting your customer relationships. 3. A New Administration – Changing of the Guards on the Regulatory Front With the new year enters the inauguration of a new president and administration. Though there is still much to be determined, certain areas are drawing a lot of attention with this changing of the guards. The highlights? The CFPB. Priorities and leadership could change. With COVID-19 top of mind, it is likely there will be aggressive agendas put forth to help protect the millions of consumers who have suffered economic distress and harm as a result of the pandemic. Data Portability. With an increased consumer appetite to port their data, questions and concerns around data security – and how to verify for a third party asking for the data – are also on the rise. There are a number of issues facing financial institutions around data portability, one of the largest being defining the line between consumer account information and proprietary data. All things privacy – state vs. national bills. The debate continues on how to move forward (whether privacy legislation will be handled by the states or at the national level), but for now it seems there is more progress at the state level. California was the first state to push through state-level privacy legislation in the form of the California Consumer Privacy Act of 2018. Twenty-four states are considering legislation that would require consent before collecting or disclosing personal information with third parties. 4. Analytics + Digitalization – Smarter, Better, Faster COVID-19 accelerated digital transformation for many. Some companies were ready, having already started making the headway in years prior, while others struggled – and some continue to struggle. The pandemic – and its corresponding recovery – is reason now, more than ever, to get some of your digital transformation priorities checked off of your list. Your customers demand it and your business needs it. Tackling analytics and digitalization not only brings your business up to speed, but improves your decisioning, enhances your offerings, and enables better platforms and data usage. In addition to digitalization, artificial intelligence for credit decisioning and personalized banking can also be expected to be a top trend, especially AI that is ethical and explainable, as will the increasing adoption and implementation of cloud computing. As consumer experience continues to reign supreme, any and all technology to enhance and improve that experience – think chatbots and virtual assistants – will also likely increase in presence. 5. Verification & Identity Identity has been a trending topic over the last few years, brought on by increasingly digital lifestyles and the intersection of personalization, frictionless transactions and adequate security. Identity verification and verification of other information such as income, employment and the like are increasingly needed in a today’s pandemic and tomorrow’s post-pandemic world. Leveraged across the lifecycle and during critical customer interactions, the need is especially heightened for insights, data accuracy, and diversification of data sets – to name a few. And while it was already established that identity verification is not just for marketing services, there are now even greater needs for financial institutions to be able to confidently know that their customers are who they say they are. Some areas to keep your eye on in 2021? Identity, income, assets and employment. 6. Redefining the Modern Mortgage As has been a common trend, spurred by the disruption caused by COVID-19, the mortgage industry is one of the many to have a magnifying glass brought to its areas for improvement. Some of those areas include operational efficiency, digital adoption and transparency. In line with the better and faster needs that lenders are continually trying to pace with, the need for speed is hitting mortgage originations, with an ideal situation outlined as closing in 30 days or less. Creating operational efficiencies through faster, fresher data can be the key for lenders to more accurately assess a borrower’s ability to pay upfront. Additionally, now, as most mortgage lenders are breaking previous origination records by a landslide (thanks pandemic), there’s new focus on other performance indicators. With such impetus, the modern mortgage is constantly evolving, incorporating customer-centric facets including a seamless digital process, providing meaningful customer experiences and leveraging the latest and greatest technology to better future-proof the industry through scalable technology, while aiming to reduce costs. For all your needs in 2021 and beyond, Experian has you covered. Learn More