Tag: credit risk analytics

Experian and Plaid are teaming up to power smarter, faster, and more inclusive lending — fueled by real-time cashflow insights. The financial landscape is becoming more dynamic and digitally connected. Consumers are increasingly turning to digital platforms not only to pay bills and track spending, but to better understand their financial health, monitor their credit standing, and plan confidently for the future. This evolution presents a timely opportunity for innovation in underwriting — one that empowers consumers to take control of their financial futures and enables lenders to make faster, smarter, and more inclusive decisions. What happens when the leading global data and technology company joins forces with the largest open banking network in the world? Experian and Plaid are coming together to solve some of the most pressing challenges lenders face, bringing cashflow insights into credit decisions, seamlessly. Smarter lending: Elevating the credit decision process For lenders seeking a holistic view of borrowers to make faster, more informed decisions, this new collaboration is a game-changer. Experian and Plaid are combining real-time, unmatched cashflow data and analytics to help lenders improve decisioning, pinpoint risk precisely, and drive financial inclusion. This marks a pivotal shift in how credit is assessed, moving us toward faster, and fundamentally smarter lending decisions. This strategic collaboration delivers real-time cashflow insights in a comprehensive solution, built on core principles designed to directly enhance your lending capabilities: Speed and simplicity: Driving efficiency with seamless integration In today’s fast-paced financial landscape, efficiency in underwriting isn’t just an advantage; it’s a necessity. Our combined solution prioritizes speed and simplicity by offering easy integration through APIs. This ensures fast access to meaningful risk insights, streamlining your workflows. Imagine easily leveraging real-time cashflow risk insights directly into your existing processes for faster and smarter lending decisions. This is about delivering modern infrastructure that allows you to move at the speed of today's market, empowering your business to expand with confidence. Broader visibility: Unveiling a holistic consumer view Traditional credit scores are a reliable, crucial tool for measuring a borrower’s creditworthiness. When coupled with real-time cashflow data and risk insights, lenders are empowered with broader visibility, bringing to light a more holistic view of a borrower’s current financial reality and opportunities that may have been missed. You gain a comprehensive consumer financial picture, allowing for more precise identification of both strong financial capacity and potential risks, ultimately helping you target and acquire customers who align with your growth objectives. Smarter decisions: Enhancing models with combined intelligence The power to make truly informed decisions hinges on the quality and depth of your data. Without robust insights, risk models can be limited, impacting precision and speed. With Experian's advanced cashflow analytic capabilities and Plaid's streamlined access to real-time cashflow data via Consumer Report, you can enhance your risk assessment for smarter decisions. This synergy empowers financial institutions to expand credit access and uncover hidden risks, leading to more precise underwriting. It’s about leveraging advanced analytics in real-time to drive improved decision-making and build stronger portfolios. More inclusive lending: Expanding access, responsibly A significant challenge in lending is ensuring access for all creditworthy individuals, including those with limited traditional credit histories who may be overlooked. This represents an untapped market and a vital opportunity for responsible growth. Our solution champions more inclusive lending, enabling you to reach underserved communities and empower consumers who demonstrate strong financial capacity. This not only fosters stronger portfolios but critically helps your business grow by efficiently acquiring customers across a broader spectrum. Proven trust: Lending with confidence In the financial industry, the bedrock of any solution is trust – in the data, security, and partners. Lenders require unwavering confidence in the tools they adopt. This collaboration is built on proven trust, leveraging the reach, reliability, and security of two of the most trusted names in financial services. Experian’s expertise in credit data and consumer protection, combined with Plaid’s modern infrastructure and trusted open banking network, offers unparalleled assurance. You can securely integrate these powerful insights, knowing you are backed by industry leaders committed to best-in-class security and compliance, enabling your business to grow with confidence without compromise. Smarter lending starts now The evolution of underwriting demands a more dynamic, inclusive, and precise approach. With Experian and Plaid, you're not just adapting to change; you're leading it. Empower your organization to approve more borrowers, reduce risk more effectively, and make smarter, faster decisions for sustainable success. Ready to transform your lending strategy? Learn more about how to bring cashflow insights into your credit decisions seamlessly. Learn more

Rising balances and delinquency rates are causing lenders to proactively minimize credit risk through pre-delinquency treatments. However, the success of these types of account management strategies depends on timely and predictive data. Credit attributes summarize credit data into specific characteristics or variables to provide a more granular view of a consumer’s behavior. Credit attributes give context about a consumer’s behavior at a specific point in time, such as their current revolving credit utilization ratio or their total available credit. Trended credit attributes analyze credit history data for consumer behavior patterns over time, including changes in utilization rates or how often a balance exceeded an account’s credit limit during the previous 12 months. In a recent analysis, we found that credit attributes related to utilization were highly predictive of future delinquencies in bankcard accounts, with many lenders better managing their credit risk when incorporating these attributes into their account management processes. READ: Find out how custom attributes and models can help you stay ahead of your competitors in the "Build a profitable portfolio with credit attributes" e-book. Using attributes to manage credit risk An enhanced understanding of credit attributes can be leveraged to manage risk throughout the customer lifecycle. They can be important when you want to: Improve credit strategies and efficiencies: Overlay attributes and incorporate them into credit policy rules, such as knockout criteria, to expand your lending population and increase automation without taking on more credit risk. Better understand customers' credit trends: Experian’s wide range of credit data, including trended credit attributes, can help you quickly understand how consumers are faring off-book for visibility into other lending relationships and if they’ll likely experience financial stress in the future. Credit attributes can also help precisely segment populations. For example, attributes can help you distinguish between two people who have similar credit risk scores — but very different trajectories — and will better determine who's the least risky customer. Predicting 60+ day delinquencies with credit attributes To evaluate the effectiveness of credit attributes during account review, we looked at 2.9 million open and active bankcard accounts to see which attributes best predicted the likelihood of an account reaching 60 days past due. For this analysis, we used snapshots of bankcard accounts that were reported in October 2022 and April 2023. Additionally, we analyzed the predictive power of over 4,000 attributes from Experian Premier AttributesSM and Trended 3DTM. Key findings Nine of the top 20 most predictive credit attributes were related to credit utilization rates. Delinquency-related attributes were predictive but weren’t part of the top 10. Three of the top 10 attributes were related to available credit. Turning insight into action While we analyzed credit attributes for account review, determining attribute effectiveness for other use cases will depend on your own portfolio and goals. However, you can use a similar approach to finding the predictive power of attributes. Once you identify the most predictive credit attributes for your population, you can also create an account review program to track these metrics, such as changes in utilization rates or available credit balances. Using Experian’s Risk and Retention Triggers℠ can immediately notify you of customers' daily credit activity to monitor those changes. Ongoing monitoring of attributes and triggers can help you identify customers who are facing financial stress and are headed toward delinquency. You can then proactively take steps to reduce your risk exposure, prioritize accounts, and modify pre-collections strategy based on triggering events. Experian offers credit attributes and the tools to use them Creating and managing credit attributes can be a complex and never-ending task. You need to regularly monitor attributes for performance drift and to address changing regulatory requirements. You may also want to develop new attributes based on expanding data sources and industry trends. Many organizations don’t have the resources to create, manage, and update credit attributes on their own. That’s where Experian’s 4,500+ attributes and tools can help to save time and money. Premier Attributes includes our core attributes and subsets for over 50 industries. Trended 3D attributes can help you better understand changes in consumer behavior and creditworthiness. Clear View AttributesTM offers insights from expanded FCRA data* that generally isn’t reported to consumer credit bureaus. You can easily review and manage your portfolios with Experian’s Ascend Quest™ platform. The always-on access allows you to request thousands of data elements, including credit attributes, risk scores, income models, segmentation data, and payment history, at any time. Use insights from the data and leverage Ascend Quest to quickly identify accounts that may be experiencing financial stress to limit your credit risk — and target others with retention and up-selling opportunities. Watch the Ascend Quest demo to see it in action, or contact us to learn more about Experian’s credit attributes and account review solutions. Watch demo Contact us

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

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.