Tag: underwriting

In this article...What is reject inference? How can reject inference enhance underwriting? Techniques in reject inference Enhancing reject inference design for better classification How Experian can assist with reject inference  In the lending world, making precise underwriting decisions is key to minimizing risks and optimizing returns. One valuable yet often overlooked technique that can significantly enhance your credit underwriting process is reject inferencing. This blog post offers insights into what reject inference is, how it can improve underwriting, and various reject inference methods.  What is reject inference?  Reject inference is a statistical method used to predict the potential performance of applicants who were rejected for a loan or credit — or approved but did not book. In essence, it helps lenders and financial institutions gauge how rejected or non-booked applicants might have performed had they been accepted or booked. By incorporating reject inference, you gain a more comprehensive view of the applicant pool, which leads to more informed underwriting decisions.  Utilizing reject inference helps reduce biases in your models, as decisions are based on a complete set of data, including those who were initially rejected. This technique is crucial for refining credit risk models, leading to more accurate predictions and improved financial outcomes.  How can reject inference enhance underwriting?  Incorporating reject inference into your underwriting process offers several advantages:  Identifying high-potential customers: By understanding the potential behavior of rejected applicants, you can uncover high-potential customers who might have been overlooked before.  Improved risk assessment: Considering the full spectrum of applicants provides a clearer picture of the overall risk landscape, allowing for more informed lending decisions. This can help reduce default rates and enhance portfolio performance.  Optimizing credit decisioning models: Including inferred data from rejected and non-booked applicants makes your credit scoring models more representative of the entire applicant population. This results in more robust and reliable predictions.  Techniques in reject inference  Several techniques are employed in reject inference, each with unique strengths and applications. Understanding these techniques is crucial for effectively implementing reject inference in your underwriting process. Let's discuss three commonly used techniques:  Parceling: This technique involves segmenting rejected applicants based on their characteristics and behaviors, creating a more detailed view of the applicant pool for more precise predictions.  Augmentation: This method adds inferred data to the dataset of approved applicants, producing a more comprehensive model that includes both approved and inferred rejected applicants, leading to better predictions.  Reweighting: This technique adjusts the weights of approved applicants to reflect the characteristics of rejected applicants, minimizing bias towards the approved applicants and improving prediction accuracy.  Pre-diction method  The pre-diction method is a common approach in reject inference that uses data collected at the time of application to predict the performance of rejected applicants. The advantage of this method is its reliance on real-time data, making it highly relevant and current.  For example, pre-diction data can include credit bureau attributes from the time of application. This method helps develop a model that predicts the outcomes of rejected applicants based on performance data from approved applicants. However, it may not capture long-term trends and could be less effective for applicants with unique characteristics.  Post-diction method  The post-diction method uses data collected after the performance window to predict the performance of rejected applicants. Leveraging historical data, this method is ideal for capturing long-term trends and behaviors.  Post-diction data may include credit bureau attributes from the end of the performance window. This method helps develop a model based on historical performance data, which is beneficial for applicants with unique characteristics and can lead to higher performance metrics. However, it may be less timely and require more complex data processing compared to pre-diction.  Enhancing reject inference design for better classification  To optimize your reject inference design, focus on creating a model that accurately classifies the performance of rejected and non-booked applicants. Utilize a combination of pre-diction and post-diction data to capture both real-time and historical trends.  Start by developing a parceling model using pre-diction data, such as credit bureau attributes from the time of application, to predict rejected applicants' outcomes. Regularly update your model with the latest data to maintain its relevance.  Next, incorporate post-diction data, including attributes from the end of the performance window, to capture long-term trends. Combining both data types will result in a more comprehensive model.  Consider leveraging advanced analytics techniques like machine learning and artificial intelligence to refine your model further, identifying hidden patterns and relationships for more accurate predictions. How Experian can assist with reject inference  Reject inference is a powerful tool for enhancing your underwriting process. By predicting the potential performance of rejected and non-booked applicants, you can make more inclusive and accurate decisions, leading to improved risk assessment and optimized credit scoring models.  Experian offers various services and solutions to help financial institutions and lenders effectively implement reject inference into their decisioning strategy. Our solutions include comprehensive and high-quality datasets, which empower you to build models that are more representative of the entire applicant population. Additionally, our advanced analytics tools simplify data analysis and model development, enabling you to implement reject inference efficiently without extensive technical expertise.  Ready to elevate your underwriting process? Contact us today to learn more about our suite of advanced analytics solutions or hear what our experts have to say in this webinar.  Watch Webinar Learn More This article includes content created by an AI language model and is intended to provide general information. 

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

Using data to understand risk and make lending decisions has long been a forte of leading financial institutions. Now, with artificial intelligence (AI) taking the world by storm, lenders are finding innovative ways to improve their analytical capabilities. How AI analytics differs from traditional analytics Data analytics is analyzing data to find patterns, relationships and other insights. There are four main types of data analytics: descriptive, diagnostic, predictive and prescriptive. In short, understanding the past and why something happened, predicting future outcomes and offering suggestions based on likely outcomes. Traditionally, data analysts and scientists build models and help create decisioning strategies to align with business needs. They may form a hypothesis, find and organize relevant data and then run analytics models to test their hypothesis. However, time and resource constraints can limit the traditional analytics approach. As a result, there might be a focus on answering a few specific questions: Will this customer pay their bills on time? How did [X] perform last quarter? What are the chances of [Y] happening next year? AI analytics isn't completely different — think of it as a complementary improvement rather than a replacement. It relies on advances in computing power, analytics techniques and different types of training to create models more efficient than traditional analytics. By leveraging AI, companies can automate much of the data gathering, cleaning and analysis, saving them time and money. The AI models can also answer more complex questions and work at a scale that traditional analytics can't keep up with. Advances in AI are additionally offering new ways to use and interact with data. Organizations are already experimenting with using natural language processing and generative AI models. These can help even the most non-technical employees and customers to interact with vast amounts of data using intuitive and conversational interfaces. Benefits of AI analytics The primary benefits of AI-driven analytics solutions are speed, scale and the ability to identify more complex relationships in data. Speed: Where traditional analytics might involve downloading and analyzing spreadsheets to answer a single question, AI analytics automates these processes – and many others.Scale: AI analytics can ingest large amounts of data from multiple data sources to find analytical insights that traditional approaches may miss. When combined with automation and faster processing times, organizations can scale AI analytics more efficiently than traditional analytics.Complexity: AI analytics can answer ambiguous questions. For example, a marketing team may use traditional analytics to segment customers by known characteristics, such as age or location. But they can use AI analytics to find segments based on undefined shared traits or interests, and the results could include segments that they wouldn't have thought to create on their own. The insights from data analytics might be incorporated into a business intelligence platform. Traditionally, data analysts would upload reports or update a dashboard that business leaders could use to see the results and make educated decisions. Modern business intelligence and analytics solutions allow non-technical business leaders to analyze data on their own. With AI analytics running in the background, business leaders can quickly and easily create their own reports and test hypotheses. The AI-powered tools may even be able to learn from users' interactions to make the results more relevant and helpful over time. WATCH: See how organizations are using business intelligence to unlock better lending decisions with expert insights and a live demo. Using AI analytics to improve underwriting From global retailers managing supply chains to doctors making life-changing diagnoses, many industries are turning to AI analytics to make better data-driven decisions. Within financial services, there are significant opportunities throughout customer lifecycles. For example, some lenders use machine learning (ML), a subset of AI, to help create credit risk models that estimate the likelihood that a borrower will miss a payment in the future. Credit risk models aren't new — lenders have used models and credit scores for decades. However, ML-driven models have been able to outperform traditional credit risk models by up to 15 percent.1 In part, this is because the machine learning models might use traditional credit data and alternative credit data* (or expanded FCRA-regulated data), including information from alternative financial services and buy now pay later loans. They can also analyze the vast amounts of data to uncover predictive attributes that logistic regression (a more traditional approach) models might miss. The resulting ML models can score more consumers than traditional models and do so more accurately. Lenders that use these AI-driven models may be able to expand their lending universe and increase automation in their underwriting process without taking on additional risk. However, lenders may need to use a supervised learning approach to create explainable models for credit underwriting to comply with regulations and ensure fair lending practices. Read: The Explainability: ML and AI in credit decisioning report explores why ML models will become the norm, why explainability is important and how to use machine learning. Experian helps clients use AI analytics Although AI analytics can lead to more productive and efficient analytics operations over time, the required upfront cost or expertise may be prohibitive for some organizations. But there are simple solutions. Built with advanced analytics, our Lift Premium™ scoring model uses traditional and alternative credit data to score more consumers than conventional scoring models. It can help organizations increase approvals among thin-file and credit-invisible consumers, and more accurately score thick-file consumers.2 Experian can also help you create, test, deploy and monitor AI models and decisioning strategies in a collaborative environment. The models can be trained on Experian's vast data sources and your internal data to create a custom solution that improves your underwriting accuracy and capabilities. Learn more about machine learning and AI analytics. * 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. Experian (2020). Machine Learning Decisions in Milliseconds 2. Experian (2022). Lift PremiumTM product sheet

Rapid improvements in technology and the rise in online activity are driving higher consumer expectations for fast and frictionless digital experiences. And yet, only 50% of credit unions are executing on a digital strategy compared to 79% of banks.1 What can credit unions do to stand out from the competition and keep up with increasing consumer demands? 23% of consumers say their expectations for the digital experience have only somewhat or not at all been met.2 The answer lies in digital prequalification. With a frictionless digital prequalification solution, members can prequalify themselves online in real time before starting the formal application process. This puts members in the driver’s seat, allowing them to see their eligibility for credit offers and choose whether they’d like to proceed with the application. By delivering immediate feedback and offers to members online, credit unions can increase response rates, improve digital engagement and enhance the prequalification experience. Case Study: Achieving growth through a seamless digital prequalification experience Gather Federal Credit Union is the largest neighbor-island credit union in Hawaii, providing financial products and services to more than 35,000 members. Wanting to grow more loans while providing members with a seamless and efficient online experience, the credit union looked for a comprehensive solution that could improve their decisioning and enhance their prequalification strategy. They partnered with Experian and Rate Reset to implement a frictionless digital experience that enables members to opt-in for prequalified offers. Leveraging the power of Experian’s PowerCurve® and Rate Reset’s The ButtonTM, Gather had flexible access to consumer data, attributes and scores, allowing them to verify user identities and match members with loan products before their application formally went through the credit underwriting process. By gaining a better understanding of which credit options they prequalified for, members were able to opt-in instantly, creating a faster, more personalized digital prequalification experience. Within three weeks of implementation, Gather booked over $600,000 in new personal loans and credit cards. Additionally, of all the applicants that passed the credit union’s credit prequalification criteria, 54% accepted their offer and received a loan. “With a few clicks, members and non-members alike can instantly prequalify themselves for a loan. We’re extremely pleased with this offering, which has enabled us to extend our reach and grow the Gather community,” said Justin Ganaden, Executive Vice President, Gather Federal Credit Union. Read the full case study to learn more about how Experian can help grow your business with a frictionless digital prequalification experience. Download the full case study 1 https://www.big-fintech.com/Media/BIG-News/ArticleID/779/New-Digital-Banking-Platform Digital Transformation Revolution – Is it Leaving Credit Unions Behind? 2 2022 Global Insights Report, Experian, 2022.

  In today’s evolving and competitive market, the stakes are high to deliver both quantity and quality. That is, to deliver growth goals while increasing customer satisfaction. OneAZ Credit Union is the second largest credit union in Arizona, serving over 157,000 members across 21 branches. Wanting to fund more loans faster and offer a better member experience through their existing loan origination system (LOS), OneAZ looked to improve their decisioning system and long-standing underwriting criteria. They partnered with Experian to create an automated underwriting strategy to meet their aggressive approval rate and loss rate goals. By implementing an integrated decisioning system, OneAZ had flexible access to data credit attributes and scores, resulting in increased automation through their existing LOS – meaning they didn’t have to completely overhaul their decisioning systems. Additionally, they leveraged software that enabled champion/challenger strategies and the flexibility to manage their decision criteria. Within one month of implementation, OneAZ saw a 26% increase in loan funding rates and a 25% decrease in manual reviews. They can now pivot quickly to respond to continuously evolving conditions. “The speed at which we can return a decision and our better understanding of future performance has really propelled us in being able to better serve our members,” said John Schooner, VP Credit Risk Management at OneAZ. Read our case study for more insight on how automation and PowerCurve Originations Essentials can move the needle for your organization, including: Streamlined strategy development and execution to minimize costly customizations and coding Comprehensive data assets across multiple sources to ensure ID verification and a holistic view of your prospect Proactive monitoring and real-time visibility to challenge and rapidly adjust strategies as needed Download the full case study

Shri Santhanam, Executive Vice President and General Manager of Global Analytics and Artificial Intelligence (AI) was recently featured on Lendit’s ‘Fintech One-on-One’ podcast. Shri and podcast creator, Peter Renton, discussed advanced analytics and AI’s role in lending and how Experian is helping lenders during what he calls the ‘digital lending revolution.’ Digital lending revolution “Over the last decade and a half, the notion of digital tools, decisioning, analytics and underwriting has come into play. The COVID-19 pandemic has dramatically accelerated that, and we’re seeing three big trends shake up the financial services industry,” said Shri. A shift in consumer expectations More than ever before, there is a deep focus on the customer experience. Five or six years ago, consumers and businesses were more accepting of waiting several days, sometimes even weeks, for loan approvals and decisions. However, the expectation has dramatically changed. In today’s digital world, consumers expect lending institutions to make quick approvals and real-time decisions. Fintechs being quick to act Fintech lenders have been disrupting the traditional financial services space in ways that positively impacts consumers. They’ve made it easier for borrowers to access credit – particularly those who have been traditional excluded or denied – and are quick to identify, develop and distribute market solutions. An increased adoption of machine learning, advanced analytics and AI Fintechs and financial institutions of all sizes are further exploring using AI-powered solutions to unlock growth and improve operational efficiencies. AI-driven strategies, which were once a ‘nice-to-have,’ have become a necessity. To help organizations reduce the resources and costs associated with building in-house models, Experian has launched Ascend Intelligence Services™, an analytics solution delivered on a modern tech AI platform. Ascend Intelligence Services helps streamline model builds and increases decision automation and approval rates. The future of lending: will all lending be done via AI, and what will it take to get there? According to Shri, lending in AI is inevitable. The biggest challenge the lending industry may face is trust in advanced analytics and AI decisioning to ensure lending is fair and transparent. Can AI-based lending help solve for biases in credit decisioning? We believe so, with the right frameworks and rules in place. Want to learn more? Explore our fintech solutions or click below. Listen to Podcast Learn more about Ascend Intelligence Services

Key drivers to auto financial services are speed and precision. What model year is your decisioning system? In the auto world the twin engineering goals are performance and durability. Some memorable quotes have been offered about the results of all that complex engineering. And some not so complex observations. The world of racing has offered some best examples of the latter. Here’s a memorable one: “There’s no secret. You just press the accelerator to the floor and steer left. – Bill Vukovich When considering an effective auto financial services relationship one quickly comes to the conclusion that the 2 key drivers of an improved booking rate is the speed of the decision to the consumer/dealer and the precision of that decision – both the ‘yes/no’ and the ‘at what rate’. In the ‘good old days’ a lender relied upon his dealer relationship and a crew of experienced underwriters to quickly respond to a sales opportunities. Well, these days dealers will jump to the service provider that delivers the most happy customers. But, for all too many lenders some automated decisioning is leveraged but it is not uncommon to still see a significantly large ‘grey area’ of decisions that falls to the experienced underwriter. And that service model is a failure of speed and precision. You may make the decision to approve but your competition came in with a better price at the same time. His application got booked. Your decision and the cost incurred was left in the dust – bin. High on the list of solutions to this business issue is an improved use of available data and decisioning solutions. Too many lenders still underutilize available analytics and automated decisions to deliver an improved booking rate. Is your system last year’s model? Does your current underwriting system fully leverage available third party data to reduce delays due to fraud flags. Is your ability to pay component reliant upon a complex application or follow-up requests for additional information to the consumer? Does your management information reporting provide details to the incidence and disposition of all exception processes? Are you able to implement newer analytics and/or policy modifications in hours or days versus sitting in the IT queue for weeks or months? Can you modify policies to align with new dealer demographics and risk factors? The new model is in and Experian® is ready to help you give it a ride.  Purchase auto credit data now.