Tag: credit risk modeling

For lenders, first payment default (FPD) is more than just financial jargon; it's a crucial metric in assessing credit risk. This blog post will walk you through the essentials of FPD,  from defining the term to exploring how you can prevent and mitigate its potential impact. Understanding first payment default FPD occurs when a consumer fails to make their initial payment on a loan or credit agreement, which is often perceived as an early signal of a potential cascade of risky behavior. Recognizing FPD is the starting point for lenders to address potential issues with new borrowers before they escalate. One important aspect to grasp is the timeline of FPD. It’s not just about missing the first payment; it's about "early" missing. The timing of defaults is often critical in assessing the overall risk profile of a borrower or group of borrowers. The earlier a borrower starts to miss payments, the riskier they tend to be. Examining the causes of FPD The roots of FPD are diverse and can be classified into two broad categories: External factors: These include sudden financial crises, changes in employment status, or unforeseen expenses. Such factors are often beyond the borrower's immediate control. Internal factors: This category covers more deliberate or chronic financial habits, such as overspending, lack of savings, or overleveraging on credit. It's often indicative of longer-term financial instability. Understanding the causes of early payment default is the first step in effective risk management and customer engagement strategies. Implications of FPD for lenders FPD doesn't just signal immediate financial loss for lenders in terms of the missed installment. It sets off a cascade of consequences that affect the bottom line and the reputation of the institution. Financial loss. Lenders incur direct financial losses when a payment is missed, but the implications go beyond the missed payment amount. There are immediate costs associated with servicing, collections, and customer support. In the longer term, repeated defaults can lead to write-offs, impacting the institution's profitability and regulatory standing. Regulatory scrutiny. Repeated instances of FPD can also draw the attention of regulators, leading to scrutiny and potentially increased compliance costs. Mitigating first payment default Mitigating FPD requires a multifaceted approach that blends data, advanced analytics, customer engagement, and agile risk management. Lenders need to adopt strategies that can detect early signs of potential FPD and intervene preemptively. Data-driven decision-making. Leveraging advanced analytics and credit risk modeling is crucial. By incorporating transactional and behavioral data, lenders can make more accurate assessments of a borrower's risk profile. Utilizing predictive models can help forecast which borrowers are likely to default on their first payment, allowing for early intervention. Proactive customer engagement. Initiatives that revolve around education, personalized financial planning advice, and flexible payment arrangements can help borrowers who might be at risk of FPD. Proactive outreach can engage customers before a default occurs, turning a potential negative event into a positive experience. Agile risk management. Risk management strategies should be dynamic and responsive to changing market and customer conditions. Regularly reviewing and updating underwriting criteria, credit policies, and risk assessment tools ensures that lenders are prepared to tackle FPD challenges as they arise. Using FPD as a customer management tool Lastly, and perhaps most importantly, lenders can use FPD as a tool to foster better customer management. Every FPD is a data point that can provide insights into customer behavior and financial trends. By studying the causes and outcomes of FPD, lenders can refine their risk mitigation tools and improve their customer service offerings. Building trust through handling defaults. How lenders handle defaults, specifically the first ones, can significantly impact customer trust. Transparent communication, fair and considerate policies, and supportive customer service can make a difference in retaining customers and improving the lender's brand image. Leveraging data for personalization. The increasing availability of data means lenders can offer more personalized services. By segmenting customers based on payment behavior and response to early interventions, lenders can tailor offerings that meet the specific financial needs and challenges of individual borrowers. How Experian® can help First payment default is a critical aspect of credit risk management that requires attention and proactive strategies. By understanding the causes, implications, and mitigation strategies associated with FPD, financial institutions can not only avoid potential losses but also build stronger, more enduring relationships with their customers. Learn more about Experian’s credit risk modeling solutions. Learn more This article includes content created by an AI language model and is intended to provide general information.

This article was updated on February 28, 2024. There's always a risk that a borrower will miss or completely stop making payments. And when lending is your business, quantifying that credit risk is imperative. However, your credit risk analysts need the right tools and resources to perform at the highest level — which is why understanding the latest developments in credit risk analytics and finding the right partner are important. What is credit risk analytics? Credit risk analytics help turn historical and forecast data into actionable analytical insights, enabling financial institutions to assess risk and make lending and account management decisions. One way organizations do this is by incorporating credit risk modeling into their decisions. Credit risk modeling Financial institutions can use credit risk modeling tools in different ways. They might use one credit risk model, also called a scorecard, to assess credit risk (the likelihood that you won't be repaid) at the time of application. Its output helps you determine whether to approve or deny an application and set the terms of approved accounts. Later in the customer lifecycle, a behavior scorecard might help you understand the risk in your portfolio, adjust credit lines and identify up- or cross-selling opportunities. Risk modeling can also go beyond individual account management to help drive high-level portfolio and strategic decisions. However, managing risk models is an ongoing task. As market conditions and business goals change, monitoring, testing and recalibrating your models is important for accurately assessing credit risk. Credit scoring models Application credit scoring models are one of the most popular applications for credit risk modeling. Designed to predict the probability of default (PD) when making lending decisions, conventional credit risk scoring models focus on the likelihood that a borrower will become 90 days past due (DPD) on a credit obligation in the following 24 months. These risk scores are traditionally logistic regression models built on historical credit bureau data. They often have a 300 to 850 scoring range, and they rank-order consumers so people with higher scores are less likely to go 90 DPD than those with lower scores. However, credit risk models can have different score ranges and be developed to predict different outcomes over varying horizons, such as 60 DPD in the next 12 months. In addition to the conventional credit risk scores, organizations can use in-house and custom credit risk models that incorporate additional data points to better predict PD for their target market. However, they need to have the resources to manage the entire development and deployment or find an experienced partner who can help. The latest trends in credit risk scoring Organizations have used statistical and mathematical tools to measure risk and predict outcomes for decades. But the future of credit underwriting is playing out as big data meets advanced data analytics and increased computing power. Some of the recent trends that we see are: Machine learning credit risk models: Machine learning (ML) is a type of artificial intelligence (AI) that's proven to be especially helpful in evaluating credit risk. ML models can outperform traditional models by 10 to 15 percent.1 Experian survey data from September 2021 found that about 80 percent of businesses are confident in AI and cloud-based credit risk decisioning, and 70 percent frequently discuss using advanced analytics and AI for determining credit risk and collection efforts.2 Expanding data sources: The ML models' performance lift is due, in part, to their ability to incorporate internal and alternative credit data* (or expanded FCRA-regulated data), such as credit data from alternative financial services, rental payments and Buy Now Pay Later loans. Cognitively countering bias: Lenders have a regulatory and moral imperative to remove biases from their lending decisions. They need to beware of how biased training data could influence their credit risk models (ML or otherwise) and monitor the outcomes for unintentionally discriminatory results. This is also why lenders need to be certain that their ML-driven models are fully explainable — there are no black boxes. A focus on agility: The pandemic highlighted the need to have credit risk models and systems that you can quickly adjust to account for unexpected world events and changes in consumer behavior. Real-time analytical insights can increase accuracy during these transitory periods. Financial institutions that can efficiently incorporate the latest developments in credit risk analytics have a lot to gain. For instance, a digital-first lending platform coupled with ML models allows lenders to increasingly automate loan underwriting, which can help them manage rising loan volumes, improve customer satisfaction and free up resources for other growth opportunities. READ: The getting AI-driven decisioning right in financial services white paper to learn more about the current AI decisioning landscape. Why does getting credit risk right matter? Getting credit risk right is at the heart of what lenders do and accurately predicting the likelihood that a borrower won't repay a loan is the starting point. From there, you can look for ways to more accurately score a wider population of consumers, and focus on how to automate and efficiently scale your system. Credit risk analysis also goes beyond simply using the output from a scoring model. Organizations must make lending decisions within the constraints of their internal resources, goals and policies, as well as the external regulatory requirements and market conditions. Analytics and modeling are essential tools, but as credit analysts will tell you, there's also an art to the practice. CASE STUDY: Atlas Credit, a small-dollar lender, worked with Experian's analytics experts to create a custom explainable ML-powered model using various data sources. After reworking the prequalification and credit decisioning processes and optimizing their score cutoffs and business rules, the company can now make instant decisions. It also doubled its approval rate while reducing risk by 15 to 20 percent. How Experian helps clients With decades of experience in credit risk analytics and data management, Experian offers a variety of products and services for financial services firms. Ascend Intelligence Services™ is an award-winning, end-to-end suite of analytics solutions. At a high level, the offering set can rapidly develop new credit risk models, seamlessly deploy them into production and optimize decisioning strategies. It also has the capability to continuously monitor and retrain models to improve performance over time. For organizations that have the experience and resources to develop new credit risk models on their own, Experian can give you access to data and expertise to help guide and improve the process. But there are also off-the-shelf options for organizations that want to quickly benefit from the latest developments in credit risk modeling. Learn more 1Experian (2020). Machine Learning Decisions in Milliseconds 2Experian (2021). Global Insights Report September/October 2021

Today's lenders use expanded data sources and advanced analytics to predict credit risk more accurately and optimize their lending and operations. The result may be a win-win for lenders and customers. What is credit risk? Credit risk is the possibility that a borrower will not repay a debt as agreed. Credit risk management encompasses the policies, tools and systems that lenders use to understand this risk. These can be important throughout the customer lifecycle, from marketing and sending preapproved offers to underwriting and portfolio management. Poor risk management can lead to unnecessary losses and missed opportunities, especially because risk departments need to manage risk with their organization's budgetary, technical and regulatory constraints in mind. How is it assessed?  Credit risk is often assessed with credit risk analytics — statistical modeling that predicts the risk involved with credit lending. Lenders may create and use credit risk models to help drive decisions. Additionally (or alternatively), they rely on generic or custom credit risk scores: Generic scores: Analytics companies create predictive models that rank order consumers based on the likelihood that a person will fall 90 or more days past due on any credit obligation in the next 24 months. Lenders can purchase these risk scores to help them evaluate risk. Custom scores: Custom credit risk modeling solutions help organizations tailor risk scores for particular products, markets, and customers. Custom scores can incorporate generic risk scores, traditional credit data, alternative credit data* (or expanded FCRA-regulated data), and a lender's proprietary data to increase their effectiveness. About 41 percent of consumer lending organizations use a model-first approach, and 55 percent use a score-first approach to credit decisioning.1 However, these aren't entirely exclusive groupings. For example, a credit score may be an input in a lender's credit risk model — almost every lender (99 percent) that uses credit risk models for decisioning also uses credit scores.2 Similarly, lenders that primarily rely on credit scores may also have business policies that affect their decisions. What are the current challenges? Risk departments and teams are facing several overarching challenges today: Staying flexible: Volatile market conditions and changing consumer preferences can lead to unexpected shifts in risk. Organizations need to actively monitor customer accounts and larger economic trends to understand when, if, and how they should adjust their risk policies. Digesting an overwhelming amount of data: More data can be beneficial, but only if it offers real insights and the organization has the resources to understand and use it efficiently. Artificial intelligence (AI) and machine learning (ML) are often important for turning raw data into actionable insights. Retaining IT talent: Many organizations are trying to figure out how to use vast amounts of data and AI/ML effectively. However, 82 percent of lenders have trouble hiring and retaining data scientists and analysts.3 Separating fraud and credit losses: Understanding a portfolio's credit losses can be important for improving credit risk models and performance. But some organizations struggle to properly distinguish between the two, particularly when synthetic identity fraud is involved. Best practices for credit risk management Leading financial institutions have moved on from legacy systems and outdated risk models or scores. And they're looking at the current challenges as an opportunity to pull away from the competition. Here's how they're doing it: Using additional data to gain a holistic picture: Lenders have an opportunity to access more data sources, including credit data from alternative financial services and consumer-permissioned data. When combined with traditional credit data, credit scores, and internal data, the outcome can be a more complete picture of a consumer's credit risk. Implementing AI/ML-driven models: Lenders can leverage AI/ML to analyze large amounts of data to improve organizational efficiency and credit risk assessments. 16 percent of consumer lending organizations expect to solely use ML algorithms for credit decisioning, while two-thirds expect to use both traditional and ML models going forward.4 Increasing model velocity: On average, it takes about 15 months to go from model development to deployment. But some organizations can do it in less than six.5 Increasing model velocity can help organizations quickly respond to changing consumer and economic conditions. Even if rapid model creation and deployment isn't an option, monitoring model health and recalibrating for drift is important. Nearly half (49 percent) of lenders check for model drift monthly or quarterly — one out of ten get automated alerts when their models start to drift.6 WATCH: Accelerating Model Velocity in Financial Institutions Improving automation and customer experience Lenders are using AI to automate their application, underwriting, and approval processes. Often, automation and ML-driven risk models go hand-in-hand. Lenders can use the models to measure the credit risk of consumers who don't qualify for traditional credit scores and automation to expedite the review process, leading to an improved customer experience. Learn more by exploring Experian's credit risk 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. 1-6. Experian (2023). Accelerating Model Velocity in Financial Institutions

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

Credit risk management best practices have been established and followed for years, but new technology and data sources offer lenders an opportunity to refine their credit risk management strategies.   What is credit risk management? Credit risk is the possibility that a borrower will not repay a debt as agreed. And credit risk management is the art and science of using risk mitigation tools to minimize losses while maximizing profits from lending activity.   Lenders can create credit underwriting criteria for each of their products and use risk-based pricing to alter the terms of a loan or line of credit based on the risk associated with the product and borrower. Credit portfolio management goes beyond originations and individual decisions to consider portfolios at large.   CASE STUDY: Atlas Credit worked with Experian to create a machine learning-powered model, optimize risk score cutoffs and automate their underwriting. The small-dollar lender nearly doubled its loan approval rates while reducing its losses by up to 20 percent. Why is credit risk management important? Continually managing credit risk matters because there's always a balancing act.   Tightening a credit box — using more restrictive underwriting criteria — might reduce credit losses. However, it can also decrease approval rates that would exclude borrowers who would have repaid as agreed. Expanding a credit box might increase approval rates but is only beneficial if the profit from good new loans exceeds credit losses.   Fraud is also on the rise and becoming more complex, making fraud management an important part of understanding risk. For instance, with synthetic identity fraud, fraudsters might “age an account" or make on-time payments before, “busting out” or maxing out a credit card and then abandoning the account.  If you look at payment activity alone, it might be hard to classify the loss as a fraud loss or credit loss.  Additionally, external economic forces and consumer behavior are constantly in flux. Financial institutions need effective consumer risk management and to adjust their strategies to limit losses. And they must dynamically adjust their underwriting criteria to account for these changes. You could be pushed off balance if you don't react in time. What does managing credit risk entail? Lenders have used the five C’s of credit to measure credit risk and make lending decisions for decades:  Character: The likelihood a borrower will repay the loan as agreed, often measured by analyzing their credit report and a credit risk score.   Capacity: The borrower's ability to pay, which lenders might measure by reviewing their outstanding debt, income, and debt-to-income ratio.   Capital: The borrower's commitment to the purchase, such as their down payment when buying a vehicle or home.   Collateral: The value of the collateral, such as a vehicle or home for an auto loan or mortgage.   Conditions: The external conditions that can impact a borrower's ability to afford payments, such as broader economic trends.  Credit risk management considers these within the context of a lender’s goals and its specific lending products. For example, capital and collateral aren't relevant for unsecured personal loans, which makes character and capacity the primary drivers of a decision.   Credit risk management best practices at origination Advances in analytics, computing power and real-time access to additional data sources are helping lenders better measure some of the C’s.   For example, credit risk scores can more precisely assess character for a lender's target market than generic risk scores. And open banking data allows lenders to more accurately understand a borrower's capacity by directly analyzing their cash flows.   With these advances in mind, leading lenders:  View underwriting as a dynamic process: Lenders have always had to respond to changing forces, and the pandemic highlighted the need to be nimble. Consider how you can use analytical insights to quickly adjust your strategies.   Test the latest credit risk modeling techniques: Artificial intelligence (AI) and machine learning (ML) techniques can improve credit risk model performance and drive automated credit risk decisioning. We've seen ML models consistently outperform traditional credit risk models by 10 to 15 percent.¹ Use multiple data sources: Alternative credit data* and consumer-permissioned data offer increased and real-time visibility into borrowers' creditworthiness. These additional data sources can also help fuel ML credit risk models.   Expand their lending universe: Alternative data can also help lenders more accurately assess the credit risk of the 49 million Americans who don't have a credit file or aren't scoreable by conventional models.² At the same time, they consciously remove biases from their decisions to increase financial inclusion.  READ: The Getting AI-driven decisioning right in financial services white paper explores trends, advantages, challenges and best practices for using AI in decisioning.   Experian helps lenders measure and manage credit risk Experian can trace its history of helping companies manage their credit risk back to 1803.³ Of course, a lot has changed since then, and today Experian is a leading provider of traditional credit data, alternative credit data and credit risk analytics.   For those who want to quickly benefit from the latest technological advancements, our Lift Premium™ credit risk model uses traditional and alternative data to score up to 96 percent of U.S. consumers — compared to the 81 percent that conventional models can score.4 Experian’s Ascend Platform and Ascend Intelligence Services™ can help lenders develop, deploy and monitor custom credit risk models to optimize their decisions.    With end-to-end platforms, our account and portfolio management services can help you limit risk, detect fraud, automate underwriting and identify opportunities to grow your business.   Learn more about Experian's approach to credit risk management ¹Experian (2020). Machine Learning Decisions in Milliseconds ²Oliver Wyman (2022). Financial Inclusion and Access to Credit ³Experian (2013). A Brief History of Experian 4Experian (2023). Lift Premium™ and Lift Plus™ *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 and can be used interchangeably.

In a changing economy, banks of all sizes are more budget conscious, leading many to pull back on their marketing spend for new customer acquisition. But by making strategic marketing moves now, banks can uncover new opportunities and drive profitable, long-term growth. So, how can you find, engage, and win over high-value customers? Know who’s in the market for credit To build an effective bank customer acquisition strategy, you’ll want to be proactive with your campaign planning. Let’s say you’ve already defined your customer profile and have insights into their interests, lifestyles, and demographics. With predictive metrics and advanced tools like trended data and propensity-to-open models, you can further refine your segmentation strategies by identifying individuals who are likely to be in the market for your product. This way, you can reach consumers at the right time and personalize offers to achieve higher open rates. Embrace the digital era With today’s consumers increasing their banking activities online, leveraging digital channels in your bank customer acquisition strategy is imperative. In addition to connecting with consumers through direct mail, consider reaching out to them through email, social media, or your mobile banking applications. This will not only help increase the visibility of the offer, but also allow consumers to receive and respond faster. Another way to enhance your banking strategies for growth while meeting consumer expectations for digital is by making it easier and more convenient for consumers to onboard. With an automated and data-driven credit decisioning solution, you can streamline steps that are traditionally manual and time-consuming, such as data collection and identity verification. By providing seamless customer acquisition in banking, you can accelerate your decision-making and increase the likelihood of conversion. Make the most of your marketing spend While customer acquisition in banking should remain a high priority, we understand that driving growth on a tight marketing budget can be challenging. That’s why we created a tip sheet outlining ways for banks and other lenders to enhance their customer acquisition processes while effectively managing costs. Some of the tips include: Going beyond conventional scoring methods. By leveraging an advanced customer acquisition solution, you can gain a holistic view of your prospective customers to enhance predictive performance and identify hidden growth opportunities. Focusing on high-potential customers. Pinpointing consumers who are actively seeking credit enables you to focus your offers and resources on those who are likely to respond, resulting in a greater return on marketing investment. Amplifying your credit offers. Re-presenting preapproved credit offers through the digital channels that consumers most frequent enables you to expand your campaign reach, increase response rates, and reduce direct mailing costs. View the tip sheet to learn how you can make the most of your marketing budget to acquire new customers and drive long-term growth. Access tip sheet