Data Quality

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.

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 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.

This article was updated on February 21, 2024. With the rise of technology and data analytics in the financial industry today, it's no longer enough for companies to rely solely on traditional marketing methods. Data-driven marketing insights provide a more sophisticated and comprehensive view of shifting customer preferences and behaviors. With this in mind, this blog post will highlight the importance of data-driven marketing insights, particularly for financial institutions. The importance of data-driven marketing insights 30% of companies say poor data quality is a key challenge to delivering excellent customer experiences. Today’s consumers want personalized experiences built around their individual needs and preferences. Data-driven marketing insights can help marketers meet this demand, but only if it is fresh and accurate. When extending firm credit offers to consumers, lenders must ensure they reach individuals who are both creditworthy and likely to respond. Additionally, their message must be relevant and delivered at the right time and place. Without comprehensive data insights, it can be difficult to gauge whether a consumer is in the market for credit or determine how to best approach them. READ: Case study: Deliver timely and personalized credit offers The benefits of data-driven marketing insights By drawing data-driven marketing insights, you can reach and engage the best customers for your business. This means: Better understanding current and potential customers To increase response and conversion rates, organizations must identify high-propensity consumers and create personalized messaging that resonates. By leveraging customer data that is valid, fresh, and regularly updated, you’ll gain deeper insights into who your customers are, what they’re looking for and how to effectively communicate with them. Additionally, you can analyze the performance of your campaigns and better predict future behaviors. Utilizing technology to manage your customer data With different sources of information, it’s imperative to consolidate and optimize your data to create a single customer view. Using a data-driven technology platform, you can break down data silos by collecting and connecting consumer information across multiple sources and platforms. This way, you can make data available and accessible when and where needed while providing consumers with a cohesive experience across channels and devices. Monitoring the accuracy of your data over time Data is constantly changing, so implementing processes to effectively monitor and control quality over time is crucial. This means leveraging data quality tools that perform regular data cleanses, spot incomplete or duplicated data, and address common data errors. By monitoring the accuracy of your data over time, you can make confident decisions and improve the customer experience. Turning insights into action With data-driven marketing insights, you can level up your campaigns to find the best customers while decreasing time and dollars wasted on unqualified prospects. Visit us to learn more about how data-driven insights can power your marketing initiatives. Learn more Enhance your marketing strategies today This article includes content created by an AI language model and is intended to provide general information.

This article was updated on February 12, 2024. The Buy Now, Pay Later (BNPL) space has grown massively over the last few years. But with rapid growth comes an increased risk of fraud, making "Buy Now, Pay Never" a crucial fraud threat to watch out for in 2024 and beyond. What is BNPL? BNPL, a type of short-term financing, has been around for decades in different forms. It's attractive to consumers because it offers the option to split up a specific purchase into installments rather than paying the full total upfront. The modern form of BNPL typically offers four installments, with the first payment at the time of purchase, as well as 0% APR and no hidden fees. According to an Experian survey, consumers cited managing spending (34%), convenience (31%), and avoiding interest payments (23%) as main reasons for choosing BNPL. Participating retailers generally offer BNPL at point-of-sale, making it easy for customers to opt-in and get instantly approved. The customer then makes a down payment and pays off the installments from their preferred account. BNPL is on the rise The fintech and online-payment-driven world is seeing a rise in the popularity of BNPL. According to Experian research, 3 in 4 consumers have used BNPL in 2023, with 11% using BNPL weekly to make purchases. The interest in BNPL also spans generations — 36% of Gen Z, 43% of Millennials, 32% of Gen X, and 12% of Baby Boomers have used this payment method. The risks of BNPL While BNPL is a convenient, easy way for consumers to plan for their purchases, experts warn that with lax checkout and identity verification processes it is a target for digital fraud. Experian predicts an uptick in three primary risks for BNPL providers and their customers: identity theft, first-party fraud, and synthetic identity fraud. WATCH: Fraud and Identity Challenges for Fintechs Victims of identity theft can be hit with charges from BNPL providers for products they have never purchased. First-party and synthetic identity risks will emerge as a shopper's buying power grows and the temptation to abandon repayment increases. Fraudsters may use their own or fabricated identities to make purchases with no intent to repay. This leaves the BNPL provider at the risk of unrecoverable monetary losses and can impact the business' risk tolerance, causing them to narrow their lending band and miss out on properly verified consumers. An additional risk lies with fraudsters who may leverage account takeover to gain access to a legitimate user's account and payment information to make unauthorized purchases. READ: Payment Fraud Detection and Prevention: What You Need to Know Mitigating BNPL risks Luckily, there are predictive credit, identity verification, and fraud prevention tools available to help businesses minimize the risks associated with BNPL. Paired with the right data, these tools can give businesses a comprehensive view of consumer payments, including the number of outstanding BNPL loans, total BNPL loan amounts, and BNPL payment status, as well as helping to detect and apply the relevant treatment to different types of fraud. By accurately identifying customers and assessing risk in real-time, businesses can make confident lending and fraud prevention decisions. To learn more about how Experian is enabling the protection of consumer credit scores, better risk assessments, and more inclusive lending, visit us or request a call. And keep an eye out for additional in-depth explorations of our Future of Fraud Forecast. Learn more Future of Fraud Forecast

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 2024 unfolds, the economy is beginning to shift from last year’s trends. Instead of focusing on rate hikes, we’re looking at the potential for rate cuts. Our labor market is beginning to ease, and inflation is moving closer to the Federal Reserve’s 2% mark. Each month’s data gives us a clearer picture of our economic trajectory and the Federal Reserve’s (Fed) policy moving forward, as well as new and direct implications on credit metrics. Data highlights from this month’s report include: The U.S. economy added 216,000 jobs in December, but after November and October levels were revised, three-month average job creation now sits below the pre-pandemic level. While there was no change in November, annual core inflation, which excludes the volatile food and energy components, cooled in December from 4.0% to 3.9%. Consumer sentiment rose 14% in December, reversing the past four monthly declines, driven by increased optimism toward the trajectory of inflation. Check out our report for a deep dive into the rest of this month’s data, including student loans, consumer spending, the housing market, and delinquencies. To have a holistic view of our current environment, we must understand our economic past, present, and future. Keep an eye out for this year’s chartbook for a comprehensive view of the past year and download our latest forecast for a view of what’s to come. Download report View forecast For more economic trends and market insights, visit Experian Edge.

Well-designed underwriting strategies are critical to creating more value out of your member relationships and driving growth for your business. But what makes an advanced underwriting strategy? It’s all about the data, analytics, and the people behind it. How a credit union achieved record loan growth Educational Federal Credit Union (EdFed) is a member-owned cooperative dedicated to serving the financial needs of school employees, students, and parents within the education community. After migrating to a new loan origination system, the credit union wanted to design a more profitable underwriting strategy to increase efficiency and grow their business. EdFed partnered with Experian to design an advanced underwriting strategy using our vast data sources, advanced analytics, and recommendations for greater automation. After 30 months of implementing the new loan origination system and underwriting strategies, the credit union increased their loans by 32% and automated approvals by 21%. “The partnership provided by Experian, backed by analytics, makes them the dream resource for our growth as a credit union. It isn’t just the data… it’s the people.” – Michael Aubrey, SVP Lending at Educational Federal Credit Union Learn more about how Experian can help you enhance your underwriting strategy. Learn more

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.

Changes in your portfolio are a constant. To accelerate growth while proactively identifying risk, you’ll need a well-informed portfolio risk management strategy. What is portfolio risk management? Portfolio risk management is the process of identifying, assessing, and mitigating risks within a portfolio. It involves implementing strategies that allow lenders to make more informed decisions, such as whether to offer additional credit products to customers or identify credit problems before they impact their bottom line. Leveraging the right portfolio risk management solution Traditional approaches to portfolio risk management may lack a comprehensive view of customers. To effectively mitigate risk and maximize revenue within your portfolio, you’ll need a portfolio risk management tool that uses expanded customer data, advanced analytics, and modeling. Expanded data. Differentiated data sources include marketing data, traditional credit and trended data, alternative financial services data, and more. With robust consumer data fueling your portfolio risk management solution, you can gain valuable insights into your customers and make smarter decisions. Advanced analytics. Advanced analytics can analyze large volumes of data to unlock greater insights, resulting in increased predictiveness and operational efficiency. Model development. Portfolio risk modeling methodologies forecast future customer behavior, enabling you to better predict risk and gain greater precision in your decisions. Benefits of portfolio risk management Managing portfolio risk is crucial for any organization. With an advanced portfolio risk management solution, you can: Minimize losses. By monitoring accounts for negative performance, you can identify risks before they occur, resulting in minimized losses. Identify growth opportunities. With comprehensive consumer data, you can connect with customers who have untapped potential to drive cross-sell and upsell opportunities. Enhance collection efforts. For debt portfolios, having the right portfolio risk management tool can help you quickly and accurately evaluate collections recovery. Maximize your portfolio potential Experian offers portfolio risk analytics and portfolio risk management tools that can help you mitigate risk and maximize revenue with your portfolio. Get started today. Learn more

From science fiction-worthy image generators to automated underwriting, artificial intelligence (AI), big data sets and advances in computing power are transforming how we play and work. While the focus in the lending space has often been on improving the AI models that analyze data, the data that feeds into the models is just as important. Enter: data-centric AI. What is a data-centric AI? Dr. Andrew Ng, a leader in the AI field, advocates for data-centric AI and is often credited with coining the term. According to Dr. Ng, data-centric AI is, ‘the discipline of systematically engineering the data used to build an AI system.’1 To break down the definition, think of AI systems as a combination of code and data. The code is the model or algorithm that analyzes data to produce a result. The data is the information you use to train the model or later feed into the model to request a result. Traditional approaches to AI focus on the code — the models. Multiple organizations download and use the same data sets to create and improve models. But today, continued focus on model development may offer a limited return in certain industries and use cases. A data-centric AI approach focuses on developing tools and practices that improve the data. You may still need to pay attention to model development but no longer treat the data as constant. Instead, you try to improve a model's performance by increasing data quality. This can be achieved in different ways, such as using more consistent labeling, removing noisy data and collecting additional data.2 Data-centric AI isn't just about improving data quality when you build a model — it's also part of the ongoing iterative process. The data-focused approach should continue during post-deployment model monitoring and maintenance. Data-centric AI in lending Organizations in multiple industries are exploring how a data-centric approach can help them improve model performance, fairness and business outcomes. For example, lenders that take a data-centric approach to underwriting may be able to expand their lending universe, drive growth and fulfill financial inclusion goals without taking on additional risk. Conventional credit scoring models have been trained on consumer credit bureau data for decades. New versions of these models might offer increased performance because they incorporate changes in the economic landscape, consumer behavior and advances in analytics. And some new models are built with a more data-centric approach that considers additional data points from the existing data sets — such as trended data — to score consumers more accurately. However, they still solely rely on credit bureau data. Explainability and transparency are essential components of responsible AI and machine learning (a type of AI) in underwriting. Organizations need to be able to explain how their models come to decisions and ensure they are behaving as expected. Model developers and lenders that use AI to build credit risk models can incorporate new high-quality data to supplement existing data sets. Alternative credit data can include information from alternative financial services, public records, consumer-permissioned data, and buy now, pay later (BNPL) data that lenders can use in compliance with the Fair Credit Reporting Act (FCRA).* The resulting AI-driven models may more accurately predict credit risk — decreasing lenders' losses. The models can also use alternative credit data to score consumers that conventional models can't score. Infographic: From initial strategy to results — with stops at verification, decisioning and approval — see how customers travel across an Automated Loan Underwriting Journey. Business benefit of using data-centric AI models Financial services organizations can benefit from using a data-centric AI approach to create models across the customer lifecycle. That may be why about 70 percent of businesses frequently discuss using advanced analytics and AI within underwriting and collections.3 Many have gone a step further and implemented AI. Underwriting is one of the main applications for machine learning models today, and lenders are using machine learning to:4 More accurately assess credit risk models. Decrease model development, deployment and recalibration timelines. Incorporate more alternative credit data into credit decisioning. AI analytics solutions may also increase customer lifetime value by helping lenders manage credit lines, increase retention, cross-sell products and improve collection efforts. Additionally, data-centric AI can assist with fraud detection and prevention. Case study: Learn how Atlas Credit, a small-dollar lender, used a machine learning model and loan automation to nearly doubled its loan approval rates while decreasing its credit risk losses. How Experian helps clients leverage data-centric AI for better business outcomes During a presentation in 2021, Dr. Ng used the 80-20 rule and cooking as an analogy to explain why the shift to data-centric AI makes sense.5 You might be able to make an okay meal with old or low-quality ingredients. However, if you source and prepare high-quality ingredients, you're already 80% of the way toward making a great meal. Your data is the primary ingredient for your model — do you want to use old and low-quality data? Experian has provided organizations with high-quality consumer and business credit solutions for decades, and our industry-leading data sources, models and analytics allow you to build models and make confident decisions. If you need a sous-chef, Experian offers services and has data professionals who can help you create AI-powered predictive analytics models using bureau data, alternative data and your in-house data. Learn more about our AI analytics solutions and how you can get started today. 1DataCentricAI. (2023). Data-Centric AI.2Exchange.scale (2021). The Data-Centric AI Approach With Andrew Ng.3Experian (2021). Global Insights Report September/October 2021.4FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context. 5YouTube (2021). A Chat with Andrew on MLOps: From Model-Centric to Data-Centric AI *Disclaimer: 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. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

More than half of U.S. businesses say they discuss fraud management often, making fraud detection in banking top-of-mind. Banking fraud prevention can seem daunting, but with the proper tools, banks, credit unions, fintechs, and other financial institutions can frustrate and root out fraudsters while maintaining a positive experience for good customers. What is banking fraud? Banking fraud is a type of financial crime that uses illegal means to obtain money, assets, or other property owned or held by a bank, other financial institution, or customers of the bank. This type of fraud can be difficult to detect when misclassified as credit risk or written off as a loss rather than investigated and prevented in the future. Fraud that impacts financial institutions consists of small-scale one-off events or larger efforts perpetrated by fraud rings. Not long ago, many of the techniques utilized by fraudsters required in-person or phone-based activities. Now, many of these activities are online, making it easier for fraudsters to disguise their intent and perpetrate multiple attacks at once or in sequence. Banking fraud can include: Identity theft: When a bad actor steals a consumer’s personal information and uses it to take money, open credit accounts, make purchases, and more. Check fraud: This type of fraud occurs when a fraudster writes a bad check, forges information, or steals and alters someone else’s check. Credit card fraud: A form of identity theft where a bad actor makes purchases or gets a cash advance in the name of an unsuspecting consumer. The fraudster may takeover an existing account by gaining access to account numbers online, steal a physical card, or open a new account in someone else’s name.  Phishing: These malicious efforts allow scammers to steal personal and account information through use of email, or in the case of smishing, through text messages. The fraudster often sends a link to the consumer that looks legitimate but is designed to steal login information, personally identifiable information, and more. Direct deposit account fraud: Also known as DDA fraud, criminals monetize stolen information to open new accounts and divert funds from payroll, assistance programs, and more. Unfortunately, this type of fraud doesn’t just lead to lost funds – it also exposes consumer data, impacts banks’ reputations, and has larger implications for the financial system. Today, top concerns for banks include generative AI (GenAI) fraud, peer-to-peer (P2P) payment scams, identity theft and transaction fraud. Without the proper detection and prevention techniques, it’s difficult for banks to keep fraudsters perpetrating these schemes out. What is banking fraud prevention? Detecting and preventing banking fraud consists of a set of techniques and tasks that help protect customers, assets and systems from those with malicious intent. Risk management solutions for banks identify fraudulent access attempts, suspicious transfer requests, signs of false identities, and more. The financial industry is constantly evolving, and so are fraudsters. As a result, it’s important for organizations to stay ahead of the curve by investing in new fraud prevention technologies. Depending on the size and sophistication of your institution, the tools and techniques that comprise your banking fraud prevention solutions may look different. However, every strategy should include multiple layers of friction designed to trip up fraudsters enough to abandon their efforts, and include flags for suspicious activity and other indicators that a user or transaction requires further scrutiny.   Some of the emerging trends in banking fraud prevention include: Use of artificial intelligence (AI) and machine learning (ML). While these technologies aren’t new, they are finding footing across industries as they can be used to identify patterns consistent with fraudulent activity – some of which are difficult or time-consuming to detect with traditional methods. Behavioral analytics and biometrics. By noting standard customer behaviors — e.g., which devices they use and when — and how they use those devices — looking for markers of human behavior vs. bot or fraud ring activity — organizations can flag riskier users for additional authentication and verification. Leveraging additional data sources. By looking beyond standard credit reports when opening credit accounts, organizations can better detect signs of identity theft, synthetic identities, and even potential first-party fraud.     With real-time fraud detection tools in place, financial institutions can more easily identify good consumers and allow them to complete their requests while applying the right amount and type of friction to detect and prevent fraud.   How to prevent and detect banking fraud In order to be successful in the fight against fraud and keep yourself and your customers safe, financial institutions of all sizes and types must: Balance risk mitigation with the customer experience Ensure seamless interactions across platforms for known consumers who present little to no risk Leverage proper identity resolution and verification tools Recognize good consumers and apply the proper fraud mitigation techniques to riskier scenarios With Experian’s interconnected approach to fraud detection in banking, incorporating data, analytics, fraud risk scores, device intelligence, and more, you can track and assess various activities and determine where additional authentication, friction, or human intervention is required. 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‘Big data’ might not be the buzzword du jour, but it's here to stay. Whether trying to improve your customer experience, portfolio performance, automation, or new AI capabilities, access to quality data from varying data sources can create growth opportunities. 85 percent of organizations believe that poor-quality customer contact data negatively affects their operations and efficiencies, which leads to wasted resources and damages their brand. And 77 percent said that inaccurate data hurt their response to market changes during the pandemic.1 If you want to use data to drive your business forward, consider where the data comes from and how you can glean useful insights. What is a data source? A data source is a location where you can access information. It's a broad description because data sources can come in different formats — the definition depends on how the data is being used rather than a specific storage type. For example, you can get data from a spreadsheet, sensors on an internet of things device or scrape it from websites. You might store the data you gather using different types of databases. And in turn, those databases can be data sources for other programs or organizations. Types of data sources Many organizations have chief data officers, along with data engineers, scientists and analysts who gather, clean, organize and manage data. This important work relies on understanding the technical aspects of varying data sources and connections. And it can turn a disorganized pool of data into structured databases that business leaders can easily access and analyze. From a non-technical point of view, it’s important to consider where the data comes from and the pros and cons of these data sources. For instance, marketers might define data sources as: First-party data: The data collected about customers and prospects, such as account details, transaction history and interactions with your website or app. The data can be especially valuable and insightful when you can connect the dots between previously siloed data sources within your organization.Zero-party data: Some organizations have a separate classification for information that customers voluntarily share, such as their communication preferences and survey results. It can be helpful to view this data separately because it reflects customers' desires and interests, which can be used to further customize your messaging and recommendations.Second-party data: Another organization's first-party data can be your second-party data if you purchase it or have a partnership that involves data sharing or data collaboration. Second-party data can be helpful because you know exactly where the information comes from and it can complement information you already have about customers or prospects.Third-party data: Third-party data comes from aggregators that collect and organize information from multiple sources. It can further enrich your customer view to improve marketing, underwriting, customer service and collection efforts. READ: The Realizing a Single Customer View white paper explores how organizations can use high-quality data to better understand their customers. How can a data-driven approach benefit your business? Organizations use data science to make sense of the increasingly large flow of information from varying data sources. A clear view can be important for driving growth and responding to changing consumer preferences and economic uncertainty. A 2022 survey of U.S. organizations found high-quality data can help:2 Grow your business: 91 percent said investing in data quality helped business growth.Improve customer experience: 90 percent said better data quality led to better customer experiences.Increase agility: 89 percent said best practices for data quality improved business agility. You can see these benefits play out in different areas. For example, you can more precisely segment customers based on reliable geographic, demographic, behavioral and psychographic data. Or combine data sources to get a more accurate view of consumer risk and increase your AI-powered credit risk decisioning capabilities. But building and scaling data systems while maintaining good quality isn't easy. Many organizations have to manage multiple internal and external data sources, and these can feed into databases that don't always communicate with one another. Most organizations (85 percent) are looking toward automation to improve efficiency and make up for skill shortages. Most are also investing in technology to help them monitor, report and visualize data — making it easier to understand and use.3 WATCH: See how you can go from data to information to insight and foresight in the Using Business Intelligence to Unlock Better Lending Decisions webinar. Access high-quality data from Experian Digital acceleration has made accessing quality data more important than ever. This includes learning how to collect and manage your zero- and first-party data. Experian's data quality management solutions can help you aggregate, cleanse and monitor your data. And the business intelligence tools and platform democratize access, allowing non-technical business leaders to find meaningful insights. You can also enhance your data sets with second- and third-party data. Our industry-leading data sources have information on over 245 million consumers and 32 million businesses, including proprietary data assets. These include traditional credit bureau data, alternative credit data, automotive data, commercial credit data, buy now pay later data, fraud data and residential property data. And you can use our API developer portal to access additional third-party data sources within the same interface. Learn more about Experian's data sources. 1. Experian (2022). 2022 Global Data Management Research Report2. Experian (2022). The Data Quality Imperative3. Ibid.

Every data-driven organization needs to turn raw data into insights and, potentially, foresight. There was a time when lack of data was a hindrance, but that's often no longer the case. Many organizations are overwhelmed with too much data and lack clarity on how to best organize or use it. Modern business intelligence platforms can help. And financial institutions can use business intelligence analytics to optimize their decisioning and uncover safe growth opportunities. What is business intelligence? Business intelligence is an overarching term for the platforms and processes that organizations use to collect, store, analyze and display data and information. The ability to go from raw data to useful insights and foresight presents organizations with a powerful advantage, and can help them greatly improve their operations and efficiencies. Let’s pause and break down the below terms before expanding on business intelligence. Data: The raw information, such as customers' credit scores. Many organizations collect so much data that keeping it all can be an expensive challenge. Access to new types of data, such as alternative credit data, can assist with decisioning — but additional data points are only helpful if you have the resources or expertise to process and analyze them.Information: Once you process and organize data points, you can display the resulting information in reports, dashboards, and other visualizations that are easier to understand. Therefore, turning raw data into information. For example, the information you acquire might dictate that customers with credit scores over 720 prefer one of your products twice as much as your other products.Insight: The information tells you what happened, but you must analyze it to find useful and actionable insights. There could be several reasons customers within a specific score band prefer one product over another, and insights offer context and help you decide what to do next. In addition, you could also see what happened to the customers who were not approved.Foresight: You can also use information and insights to make predictions about what can happen or how to act in the future given different scenarios. For example, how your customers' preferences will likely change if you offer new terms, introduce a new product or there's a large economic shift. Business intelligence isn't new — but it is changing. Traditionally, business intelligence heavily relied on IT teams to sift through the data and generate reports, dashboards and other visualizations. Business leaders could ask questions and wait for the IT team to answer the queries and present the results. Modern business intelligence platforms make that process much easier and offer analytical insights. Now even non-technical business leaders can quickly answer questions with cloud-based and self-service tools. Business intelligence vs. business intelligence analytics Business intelligence can refer to the overall systems in place that collect, store, organize and visualize your data. Business intelligence tends to focus on turning data into presentable information and descriptive analytics — telling you what happened and how it happened. Business intelligence analytics is a subset of business intelligence that focuses on diagnostics, predictive and prescriptive analytics. In other words, why something happened, what could happen in the future, and what you should do. Essentially, the insights and foresight that are listed above. How can modern business intelligence benefit lenders? A business intelligence strategy and advanced analytics and modeling can help lenders precisely target customers, improve product offerings, streamline originations, manage portfolios and increase recovery rates. More specifically, business intelligence can help lenders uncover various trends and insights, such as: Changes in consumers' financial health and credit behavior.How customers' credit scores migrate over time.The risk performance of various portfolios.How product offerings and terms compare to competitors.Which loans are they losing to peers?Which credit attributes are most predictive for their target market? Understanding what's working well today is imperative. But your competitors aren't standing still. You also need to monitor trends and forecast the impact — good or bad — of various changes. WATCH: Webinar: Using Business Intelligence to Unlock Better Lending Decisions Using business intelligence to safely grow your portfolio Let's take a deeper dive into how business intelligence could help you grow your portfolio without taking on additional risk. It's an appealing goal that could be addressed in different ways depending on the underlying issue and business objective. For example, you might be losing loans to peers because of an acquisition strategy that's resulting in declining good customers. Or, perhaps your competitors' products are more appealing to your target customers. Business intelligence can show you how many applications you received, approved, and booked — and how many approved or declined applicants accepted a competitor's offer. You can segment and analyze the results based on the applicant’s credit scores, income, debt-to-income, loan amounts, loan terms, loan performance and other metrics. An in-depth analysis might highlight meaningful insights. For example, you might find that you disproportionately lost longer-term loans to competitors. Perhaps matching your competitors' long-term loan offerings could help you book more loans. READ: White paper: Getting AI-driven decisioning right in financial services Experian's business intelligence analytics solutions Lenders can use modern business intelligence platforms to better understand their customers, products, competitors, trends, and the potential impact of shifting economic circumstances or consumer behavior. Experian's Ascend Intelligence Services™ suite of solutions can help you turn data points into actionable insights. Ascend Intelligence Services™ Acquire Model: Create custom machine learning models that can incorporate internal, bureau and alternative credit data to more accurately assess risk and increase your lending universe.Ascend Intelligence Services™ Acquire Strategy: Get a more granular view of applicants that can help you improve segmentation and increase automation.Ascend Intelligence Services™ Pulse: A model and strategy health dashboard that can help you proactively identify and remediate issues and nimbly react to market changes.Ascend Intelligence Services™ Limit: Set and manage credit limits during account opening and when managing accounts to increase revenue and mitigate risk.Ascend Intelligence Services™ Foresight: A modern business intelligence platform that offers easy-to-use tools that help business leaders make better-informed decisions. Businesses can also leverage Experian's industry-leading data assets and expertise with various types of project-based and ongoing engagements. Learn more about how you can implement or benefit from business intelligence analytics.

On average, the typical global consumer owns three or more connected devices.1 80% of consumers bounce between devices, while 31% who turned to digital channels for their last purchase used multiple devices along the way.2 Considering these trends, many lenders are leveraging multiple channels in addition to direct mail, including email and mobile applications, to maximize their credit marketing efforts. The challenge, however, is effectively engaging consumers without becoming overbearing or inconsistent. In this article, we explore what identity resolution for credit marketing is and how the right identity tools can enable financial institutions to create more cohesive and personalized customer interactions. What is identity resolution? Identity resolution connects unique identifiers across touchpoints to build a unified identity for an individual, household, or business. This requires an identity graph, a proprietary database that collects, stitches, and stores identifiers from digital and offline sources. As a result, organizations can create a persistent, high-definition customer view, allowing for more consistent and meaningful brand experiences. What are the types of identity resolution? There are two common approaches to identity resolution: probabilistic ID matching and deterministic ID matching. Probabilistic ID matching uses multiple algorithms and data sets to match identity profiles that are most likely the same customer. Data points used in probabilistic models include IP addresses and device types. Deterministic ID matching uses first-party data that customers have produced, enabling you to merge new data with customer records and identify matches among existing identifiers. Examples of this type of data include phone numbers and email addresses. What role does identity resolution play in credit marketing? Maintaining a comprehensive customer view is crucial to credit marketing — the insights gained allow lenders to determine who they should engage and the type of offer or messaging that would resonate most. But there are many factors that can prevent financial institutions from doing this effectively: poor data quality, consumers bouncing between multiple devices, and so on. Seven out of 10 consumers find it important that companies they interact with online identify them across visits. Identity resolution for credit marketing solves these issues by matching and linking customer data from disparate sources back to a single profile. This enables lenders to: Create highly targeted campaigns. If your data is incomplete or inaccurate, you may waste your marketing spend by engaging the wrong audience or sending out irrelevant credit offers. An identity resolution solution that leverages expansive, regularly updated data gives you access to high-definition views of individuals, resulting in more personalization and greater campaign engagement. Deliver seamless, omnichannel experiences. To further improve your credit marketing efforts, you’ll need to keep up with consumers not only as their needs or preferences change, but also as they move across channels and devices. Instead of creating multiple identity profiles for the same person, identity resolution can recognize an individual across touchpoints, allowing you to create consistent offers and cohesive experiences. Picking the right marketing identity resolution solution While the type of identity resolution for marketing solution can vary depending on your business’s goals and challenges, Experian can help you get started. To learn more, visit us today. 1 Global number of devices and connections per capita 2018-2023, Statista. 2 Cross Device Marketing - Statistics and Trends, Go-Globe.

To reach customers in our modern, diverse communications landscape, it's not enough to send out one-size-fits-all marketing messages. Today's consumers value and continue to do business with organizations that put them first. For financial institutions, this means providing personalized experiences that enable your customers to feel seen and your marketing dollars to go further. How can you achieve this? The answer is simple: a customer-driven credit marketing strategy. What is customer-driven marketing? Customer-driven marketing is a strategy that focuses on putting consumers first, rather than products. It means thinking about the needs, wants and motivations of the prospects you're trying to reach and centering your marketing campaigns and messages around that audience. When done well, this comprehensive approach extends beyond the marketing team to all members of a company. The benefits of customer-driven credit marketing One benefit of this type of personalized credit marketing is that you can target customers with a potentially higher lifetime value. By focusing your marketing efforts on the right prospects, you'll ensure that budgets are being spent wisely and that you're not wasting valuable marketing dollars communicating with consumers who either won't respond or aren't a fit for your business. Customer-driven marketing enables you to identify and reach the most profitable, highly responsive prospects in the most efficient way, while also engaging with current customers to optimize retention rates. When you create marketing programs that are customer-driven, you're not just selling; you're building relationships. Rather than being simply a service provider, you become a trusted financial partner and advisor. This kind of data-driven customer experience can help you onboard more customers and retain them for longer, translating to better results when it comes to your bottom line. Customer-driven marketing: How to get started Customer-driven marketing is less funnel, more spiral. You research, test, refine and repeat, all while taking into account customer feedback and campaign results. It starts with defining your target audience and creating customer personas. As you do this, think about all the factors that are involved in your target customers’ path to purchase, from general awareness and growing need to the final motivation that pushes them to commit. You'll also want to consider what their pain points may be and the barriers that may prevent them from buying. Next, develop a marketing strategy that aligns with your target customers' needs and outlines how and where you'll reach them. It may also be helpful to gather and respond to customer feedback to ensure the value propositions in your campaigns are aligned with customer expectations. These insights can help you refine your messaging, resulting in increased response and retention rates. Use the right data to extend relevant credit offers When you send credit offers, you want to ensure they're reaching the right prospects at the right time. You also want to make sure these credit offers are relevant to the consumers that receive them. That's where quality data comes in. By optimizing your data-driven customer segmentation, you can develop timely and personalized credit offers to boost response rates. For example, you might have a target audience of consumers who are both creditworthy and looking for a new vehicle. Segmenting this audience into smaller groups by demographic, life stage, financial and other factors helps you create credit marketing campaigns that speak to each type of customer as an individual, not just a number. Meet consumers on their preferred channels Nowadays, consumer behavior is more fragmented than ever. This is relevant not just from a demographic point of view, but from the perspective of purchasing behavior. Customer-driven marketing helps you interact with prospects as individuals so that the value propositions they encounter are a true fit for their life situation. For instance, different age groups tend to spend time on different platforms. But why they're on those channels at any particular time matters too. Messaging aimed at prospects in their leisure time should be different from messaging they'll encounter when actively researching potential purchases. Keep up with your customers This is one answer to the question of how to improve customer retention as well. Research demonstrates that it's more cost-effective to keep a customer than to acquire a new one. When you tailor retention efforts with a well-thought-out customer-driven marketing strategy, you're likely to boost retention rates, which in many cases lead to better profits over time. Importance of a customer-driven marketing strategy Putting consumers at the center of credit marketing strategies — and at the center of your business as a whole — is the foundation for personalized experiences that can ultimately increase response rates and customer satisfaction. For more on how your organization can develop an effective customer-driven marketing strategy, learn about our credit marketing solutions.