Models & Scores

For businesses across all sectors solutions that improve productivity are more important than ever. As technology advances, organizations across industries are looking to capitalize by investing in artificial intelligence (AI) solutions. Studies have recently shown that productivity is a leading measure of how well these AI tools are performing. About 60% of organizations surveyed are using “improved productivity” as a metric to measure the success of implementing AI solutions.[1] Experian research shows it takes an average of 15 months to build a model and put it into production. This can hinder productivity and the ability to quickly go to market. Without a deep understanding of key data points, organizations may also have difficulty realizing time to value efficiently. To improve upon the modeling lifecycle, businesses must examine the challenges involved in the process. The challenges of model building One of the most significant challenges of the modeling lifecycle is speed. Slow modeling processes can cause delays and missed opportunities for businesses which they may have otherwise capitalized on. Another difficulty organizations face is having limited access to high-quality data to build more efficient models. Without the right data, businesses can miss out on actionable insights that could give them a competitive edge. In addition, when organizations have inefficient resources, expenses can skyrocket due to the need for experts to intervene and address ongoing issues. This can result in a steep learning curve as new tools and platforms are adopted, making it difficult for organizations to operate efficiently without outside help. Businesses can combat these challenges by implementing tools such as artificial intelligence (AI) to drive efficiency and productivity. The AI journey While generative AI and large language models are becoming more prevalent in everyday life, the path to incorporating a fully functional AI tool into an organization’s business operations involves multiple steps. Beginning with a proof of concept, many organizations start their AI journey with building ideas and use cases, experimentation, and identifying and mitigating potential pitfalls, such as inaccurate or irrelevant information. Once a proof of concept reaches an acceptable state of validity, organizations can move on to production and value at scale. During this phase, organizations will select specific use cases to move into production and measure their performance. Analyzing the results can help businesses glean valuable information about which techniques work most effectively, so they can apply those techniques to new use cases. Following successful iterations of an efficiently functioning AI, the organization can then implement that AI as a part of their business by working the technology into everyday operations. This can help organizations drive productivity at scale across various business processes. Experian’s AI journey has been ongoing, with years of expertise in implementing AI into various products and services. With a goal of providing greater insights to both businesses and consumers while adhering to proper consumer data privacy and compliance, Experian is committed to responsibly using AI to combat fraud and foster greater financial access and inclusion. Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights. Introducing Experian Assistant Experian Assistant, a new GenAI tool announced in October at Money20/20 in Las Vegas, is helping organizations take their productivity to the next level by drastically speeding up the modeling lifecycle. To drive automation and greater intelligence for Experian partners, Experian Assistant enables users to interact with a virtual assistant in real time and offers customized guidance and code generation for our suite of software solutions. Our experts – Senior Director of Product Management Ankit Sinha and Director of Analyst Relations Erin Haselkorn – recently revealed the details of how Experian Assistant can cut down model-development timelines from months to days, and in some cases even hours. The webinar, which took place on November 7th, covered a wide range of features and benefits of the new tool, including:  Spending less time writing code  Enhancing understanding of data and attributes Accelerating time to value Improving regulatory compliance A case study in building models faster Continental Finance Company, LLC’s Chief Data Scientist shared their experience using Experian Assistant and how it has improved their organization’s modeling capabilities: “With Experian Assistant, there is a lot of efficiency and improvement in productivity. We have reduced the time spent on data building by almost 75%, so we can build a model much quicker, and the code being generated by Experian Assistant is very high quality, enabling us to move forward much faster.” For businesses looking to accelerate their modeling lifecycle and move more quickly with less effort, Experian Assistant provides a unique opportunity to significantly improve productivity and efficiency. Experian Assistant tech showcase Did you miss the Experian Assistant Tech Showcase webinar? Watch it on demand here and visit our website to learn more. Visit our website [1] Forrester’s Q2 AI Pulse Survey, 2024

How can lenders ensure they’re making the most accurate and fair lending decisions? The answer lies in consistent model validations. What are model validations? Model validations are vital for effective lending and risk-based pricing programs. In addition to helping you determine which credit scoring model works best on your portfolio, the performance (odds) charts from validation results are often used to set score cutoffs and risk-based pricing tiers. Validations also provide the information you need to implement a new score into your decisioning process. Factors affecting model validations Understanding how well a score predicts behavior, such as payment delinquency or bankruptcy, enables you to make more confident lending decisions. Model performance and validation results can be impacted by several factors, including: Dynamic economic environment – Shifts in unemployment rates, interest rate hikes and other economic indicators can impact consumer behavior. Regulatory changes affecting consumers – For example, borrowers who benefited from a temporary student loan payment pause may face challenges as they resume payments. Scorecard degradation – A model that performed well several years ago may not perform as well under current conditions. When to perform model validations The Office of the Comptroller of the Currency’s Supervisory Guidance on Model Risk Management states model validations should be performed at least annually to help reduce risk. The validation process should be comprehensive and produce proper documentation. While some organizations perform their own validations, those with fewer resources and access to historical data may not be able to validate and meet the guidance recommendations. Regular validations support compliance and can also give you confidence that your lending strategies are built on solid, current data that drive better outcomes. Good model validation practices are critical if lenders are to continue to make data-driven decisions that promote fairness for consumers and financial soundness for the institution. Make better lending decisions If you’re a credit risk manager responsible for the models driving your lending policies, there are several things you can do to ensure that your organization continues to make fair and sound lending decisions: Assess your model inventory. Ensure you have comprehensive documentation showing when each model was developed and when it was last validated. Validate the scores you are using on your data, along with those you are considering, to compare how well each model performs and determine if you are using the most effective model for your needs. Produce validation documentation, including performance (odds) charts and key performance metrics, which can be shared with regulators. Utilize the performance charts produced from the validation to analyze bad rates/approval rates and adjust cutoff scores as needed. Explore alternative credit scoring models to potentially enhance your scoring process. As market conditions and regulations continue to evolve, model validations will remain an essential tool for staying competitive and making sound lending decisions. Ready to ensure your lending decisions are based on the latest data? Learn more about Experian’s flexible validation services and how we can support your ongoing success. Contact us today to schedule a consultation. Learn more

In today's data-driven business landscape, leveraging advanced targeting techniques is crucial for effective consumer engagement, particularly in the financial services sector. Prescreen targeting solutions have evolved significantly, offering a competitive edge through more precise and impactful outreach strategies. The power of data analytics and predictive modeling At the heart of modern prescreen targeting solutions lies the integration of extensive data analytics and predictive modeling. These systems combine detailed consumer information, including purchasing behaviors and credit scores, with sophisticated algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This approach not only streamlines campaign efforts but also enhances the tactical effectiveness of each interaction. Direct mail: a proven channel for financial services In the competitive North American financial services market, direct mail has demonstrated distinct advantages as a targeting channel. Its tangible nature helps cut through digital noise, capturing consumer attention effectively. For credit products, direct mail typically achieves engagement rates of 0.2-2% for prime consumers and 1-3% for near-prime and subprime consumers[1]. Key advantages of prescreen targeting solutions Enhanced response rates Custom response models can significantly boost prospect response rates by targeting a well-defined, high-propensity audience. These models have the potential to improve average response rates of prescreen direct mail campaigns by 10-25%. Risk mitigation By focusing on well-defined, high-propensity audiences, prescreen targeting via direct mail aims to attract the right prospects, minimizing fraud and delinquency risks. This targeted approach can lead to substantial savings on underwriting costs. Improved customer engagement and retention Personalized direct mail strengthens customer relationships by making recipients feel valued, leading to higher engagement and loyalty – crucial factors for long-term business success. Regulatory compliance and security Prescreen solutions come equipped with compliance safeguards, simplifying adherence to industry regulations and consumer privacy standards. This is particularly critical in the highly regulated financial sector. The future of targeting and enhancement As markets continue to evolve, the strategic importance of precise and efficient marketing techniques will only grow. Financial institutions leveraging optimized prescreen targeting and enhancement solutions can gain a significant competitive advantage, achieving higher immediate returns and fostering long-term customer loyalty and brand strength. Future advancements in AI and machine learning are expected to further refine prescreen targeting capabilities, offering even more sophisticated tools for marketers to engage effectively with their target audiences. Ascend Intelligence Services™ Target Ascend Intelligence Services Target is a sophisticated prescreening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Visit our website to learn more [1] Experian Research, Data Science Team, July 2024 

The open banking revolution is transforming the financial services landscape, offering banks and financial institutions unprecedented access to consumer-permissioned data. However, during our recent webinar, “Navigating Open Banking: Strategies for Banks and Financial Institutions,” over 78% of attendees stated that they do not currently have an open banking strategy in place. This highlights a significant gap in the industry. By tapping into consumer-permissioned data, you can develop more personalized products, streamline credit decisioning, and improve overall customer engagement. With the right strategies, open banking offers a pathway to growth, innovation, and enhanced customer experiences. Here’s a snippet from the webinar’s Q&A session with Ashley Knight, Senior Vice President of Product Management, who shared her perspective on open banking trends and opportunities. Q: What specific analytic skill is the most important when working on open banking data?A: The ability to parse and transform raw data, a deep understanding of data mining, experience in credit risk, and general modeling skills to improve underwriting. Q: What lessons did the U.S. learn from the experience of other countries that implemented open banking? A: The use cases are common globally; typical uses of open banking data include second-chance underwriting to help score more consumers and customer management, which involves assessing cashflow data to leverage on an existing portfolio (first-party data). This can be used in various ways, such as cross-sell, up-sell, credit line increase, and growing/retaining deposits. Q: Does Experian have access to all a consumer’s bank accounts in cases where the consumer has multiple accounts?A: Data access is always driven by consumer permission unless the organization owns this data (i.e., first-party data). Where first-party data is unavailable, we collect it through clients or lenders who send it to us directly, having gained the proper consent. Yes, we can intake data from multiple accounts and provide a categorization and attribute calculation. Q: Where does the cashflow data come from? Is it only credit card spending?A: It includes all spending data from bank accounts, checking accounts, credit cards, savings, debit cards, etc. All of this can be categorized, and we can calculate attributes and/or scores based on that data. Q: What is the coverage of Experian’s cashflow data, and how is it distributed across risk bands?A: Cashflow data moves through Experian directly from consumer permissioning for B2B use cases or from institutions with first-party data. We perform analytics and calculate attributes on that portfolio. Don’t miss the chance to learn from our industry leaders on how to navigate the complexities of open banking. Whether you are a seasoned professional or just starting to explore its potential, this webinar will equip you with the knowledge you need to stay ahead. Watch on-demand recording Learn more Meet our expert Ashley Knight, Senior VP of Product Management, Experian Ashley leads our product management team focusing on alternative data, scores, and open banking. She fosters innovation and drives financial inclusion by using new data, such as cash flow, analytics, and Experian’s deep expertise in credit.

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. 

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

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

Click here to watch our recent webinar on first-time homebuyers. The younger generations comprise nearly 70% of first-time homebuyers, according to recent Experian Mortgage research. Understanding the generational traits of first-time homebuyers, particularly motivated younger generations, is critical to building highly targeted marketing strategies. Gen Z and Gen Y are essential in the first-time homebuyer market and represent close to 40% of repeat buyers, indicating they consider homeownership important beyond just their first purchase. Generation Y borrowers lead the pack Generation Y borrowers see homeownership as part of the American Dream but have waited longer than previous generations to purchase their first home.1 Additionally, as digital natives, they have grown up in a world with online resources and digital tools, making the home buying process more convenient for them. They can effortlessly research homes, compare mortgage rates, and even complete paperwork without leaving their home – a time and cost-saving advantage. With their desire for stability and their technological proficiency, it comes as no surprise that Gen Y borrowers are at the forefront of the homebuying market, accounting for 52% of all first-time buyers. Keep your eye on the next wave: Generation Z borrowers Although Generation Z is the youngest group with both young adults and those entering adulthood, they should not be overlooked in the real estate market. Despite their age, Gen Z possesses characteristics and tendencies that make them legitimate potential first-time homebuyers. Having grown up in an era characterized by technical advancements and economic instability, Gen Z has observed various challenges, such as the impact of the 2008 financial crisis on their families. They have also witnessed their parents and older siblings navigating student loan debt and a volatile job market. As a result, Gen Z individuals tend to approach life decisions with a cautious mindset. However, it is important to note that Gen Z is a generation known for their ambition and determination. They have an entrepreneurial spirit. A strong desire for stability. According to a recent survey conducted by Chase2, homeownership holds an important place in the dreams of nearly 90% of Generation Z individuals. This unwavering aspiration for owning a home and increasing purchasing power establishes Generation Z as a significant influence in the real estate market. Market to each generation where they are most comfortable, for Y and Z it is online and on the go To get the attention of these younger generations, mortgage lenders must understand that for these groups, digital technology is the norm, integrated into all aspects of their lives. They rely heavily on social media, online reviews, and mobile apps for research and communication. Therefore, it is crucial for lenders to implement a marketing strategy that encompasses social media platforms and personalized email, and, increasingly, text communications, to resonate with the tech savvy nature of these generations. That said, there is nuance in every population, and we see this when observing communication preferences across generations. We know, for example, that first-time homebuyers are considerably more likely than the general public to respond to e-mail offers. Understanding communication preferences for each prospect is important for tailoring your omni-channel marketing approach. Growing up in a world where technology is constantly advancing, Generations Y and Z are accustomed to having immediate access to information and services at their fingertips. As a result, they expect an efficient mortgage lending process that uses online, smartphone-enabled tools and platforms. They count on the ability to complete applications and paperwork online, receive updates and notifications via email or text, and have access to resources and tools to track and manage their mortgage journey. Lenders embracing these realities about Gen Y and Gen Z and connecting with them where they are, will be better positioned to serve this demographic and grow their own business. For more information about the lending possibilities for first-time homebuyers, download our latest white paper. Download white paper 1 “Bank of America’s 2023 Homebuyer Insights Report Explores How Hopeful Buyers are Forging Ahead,” bankofamerica.com.  2 “Millennial and Gen Z Adults Still See American Dream Within Reach Despite Challenges,” chase.com.  

Current economic conditions present genuine challenges for mortgage lenders. In this environment, first-time homebuyers offer exciting, perhaps unexpected, business growth potential. Market uncertainties have kept potential borrowers anxious and on the sidelines. The Federal Reserve's recent announcement that interest rates will remain steady for now has added to borrower anxiety. First-time homebuyers are no exception. They are concerned about the “right” time to jump in, buy a home, and own a mortgage. Despite worries over high interest rates and low inventory, many first-time homebuyers are tired of waiting for rates to drop and inventory to blossom. First-time buyers are eager to explore all avenues necessary to achieve homeownership. They show a willingness to be flexible when it comes to finding a house, considering options like a fixer upper or expanding their search to more affordable locations. The desire to escape the uncertainty and financial burden of renting is a strong driving force for first-time buyers. They see homeownership as a way to establish stability and build equity for their future. Despite the obstacles renters face in the competitive housing market, these potential buyers are motivated. Lenders who take time to understand who these buyers are and what matters to them will be ahead of the game. Notwithstanding stubbornly high interest rates, first-time homebuyers historically have shown remarkable resilience amid market fluctuations. According to a recent deep dive by Experian Mortgage experts into the buying patterns of first-time homebuyers, this group made 35-48% of all new purchases and 8-12% of all refinances between July 2022 and September 2023. First-time buyers represent both immediate potential and long-term client opportunities. How can lenders attract first-time homebuyers and drive growth from this market? The first-time homebuyer market largely consists of individuals in their early 40s and younger, also known as Gen Y and Gen Z. Rising costs of renting a home frustrate these individuals who are trying to save money for a down payment on a house and ultimately, buy their dream home. They want to settle down and look ahead to the future. For mortgage lenders who focus on understanding this younger first-time buyer market and developing targeted business strategies to attract them, great growth potential exists. Often, younger people feel locked out of buying opportunities, which creates uncertainty and apprehension about entering the market. This presents mortgage industry professionals with an incredible opportunity to show their value and grow their client base. To attract this market segment, lenders must adapt. Lenders must develop a comprehensive picture of this younger generation. Who are they? How do they shop? Where do they want to live? What is their financial situation? What are their financial and personal goals?  Acknowledging difficulties in the housing market and showing them a well-conceived path forward to home ownership will win the day for the lender and the buyer. As interest rates are poised to decrease in 2024-2025, there is potential for a surge in demand from first-time homebuyers. Lenders should prepare for these potential buyers, now. It is crucial to reevaluate how to approach first-time buyers to identify new opportunities for expansion. Experian Mortgage examined first-time homebuyer trends to pinpoint prospects with good credit and provide analysis on potential areas of opportunity. For more information about the lending possibilities for first-time homebuyers, download our white paper. Download white paper

This article was updated on March 12, 2024. The number of decisions that a business must make in the marketing space is on the rise. Which audience to target, what is the best method of communication, which marketing campaign should they receive? To stay ahead, a growing number of businesses are embracing artificial intelligence (AI) analytics, machine learning, and mathematical optimization in their decisioning models and strategies. What is an optimization model? While machine learning models provide predictive insights, it’s the mathematical optimization models that provide actionable insights that drive decisioning. Optimization models factor in multiple constraints and goals to leave you with the next best steps. Each step in the optimization process can significantly improve the overall impact of your marketing outreach — for both you and your customers. Using a mathematical optimization software, you can enhance your targeting, increase response rates, lower cost per acquisition, and drive engagement. Better engagement can lead to stronger business performance and profitability. Here are a few key areas where machine learning and optimization modeling can help increase your return on investment (ROI): Prospecting: Advanced analytics and optimization can be used to better identify individuals who meet your credit criteria and are most likely to respond to your offers. Taking this customer-focused approach, you can provide the most relevant marketing messages to customers at the right time and place. Cross-sell and upsell: The same optimized targeting can be applied to increase profitability with your existing customer base in cross-sell and up-sell opportunities. Gain insights into the best offer to send to each customer, the best time to send it, and which channel the customer will respond best to. Additionally, implement logic that maintains your customer contact protocols. Retention: Employing optimization modeling in the retention stage helps you make quicker decisions in a competitive environment. Instantly identify triggers that warrant a retention offer and determine the likelihood of the customer responding to different offers. LEARN MORE: eBook: Debunking the top 5 myths about optimization Gaining insight and strengthening decisions with our solutions Experian’s suite of advanced analytics solutions, including our optimization software, can help improve your marketing strategies. Use our ROI calculator to get a personalized estimate of how optimization can lift your campaigns without additional marketing spend. Start by inputting your organization’s details below. initIframe('62e81cb25d4dbf17c7dfea55'); Learn more about how optimization modeling can help you achieve your marketing and growth goals. Learn more  

This article was updated on March 6, 2024. Advances in analytics and modeling are making credit risk decisioning more efficient and precise. And while businesses may face challenges in developing and deploying new credit risk models, machine learning (ML) — a type of artificial intelligence (AI) — is paving the way for shorter design cycles and greater performance lifts. LEARN MORE: Get personalized recommendations on optimizing your decisioning strategy Limitations of traditional lending models Traditional lending models have worked well for years, and many financial institutions continue to rely on legacy models and develop new challenger models the old-fashioned way. This approach has benefits, including the ability to rely on existing internal expertise and the explainability of the models. However, there are limitations as well. Slow reaction times:  Building and deploying a traditional credit risk model can take many months. That might be okay during relatively stable economic conditions, but these models may start to underperform if there's a sudden shift in consumer behavior or a world event that impacts people's finances. Fewer data sources:  Traditional scoring models may be able to analyze some types of FCRA-regulated data (also called alternative credit data*), such as utility or rent payments, that appear in credit reports. Custom credit risk scores and models could go a step further by incorporating data from additional sources, such as internal data, even if they're designed in a traditional way. But AI-driven models can analyze vast amounts of information and uncover data points that are more highly predictive of risk. Less effective performance:  Experian has found that applying machine learning models can increase accuracy and effectiveness, allowing lenders to make better decisions. When applied to credit decisioning, lenders see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.1 Leveraging machine learning-driven models to segment your universe From initial segmentation to sending right-sized offers, detecting fraud and managing collection efforts, organizations are already using machine learning throughout the customer life cycle. In fact, 79% are prioritizing the adoption of advanced analytics with AI and ML capabilities, while 65% believe that AI and ML provide their organization with a competitive advantage.2 While machine learning approaches to modeling aren't new, advances in computer science and computing power are unlocking new possibilities.3 Machine learning models can now quickly incorporate your internal data, alternative data, credit bureau data, credit attributes and other scores to give you a more accurate view of a consumer's creditworthiness. By more precisely scoring applicants, you can shrink the population in the middle of your score range, the segment of medium-risk applicants that are difficult to evaluate. You can then lower your high-end cutoff and raise your low-end cutoff, which may allow you to more confidently swap in  good accounts (the applicants you turned down with other models that would have been good) and swap out bad accounts (those you would have approved who turned bad). Machine learning models may also be able to use additional types of data to score applicants who don't qualify for a score  from traditional models. These applicants aren't necessarily riskier — there simply hasn't been a good way to understand the risk they present. Once you can make an accurate assessment, you can increase your lending universe by including this segment of previously "unscorable" consumers, which can drive revenue growth without additional risk. At the same time, you're helping expand financial inclusion to segments of the population that may otherwise struggle to access credit. READ MORE: Is Financial Inclusion Fueling Business Growth for Lenders? Connecting the model to a decision Even a machine learning model doesn't make decisions.4 The model estimates the creditworthiness of an applicant so lenders can make better-informed decisions. AI-driven credit decisioning software can take your parameters (such cutoff points) and the model's outputs to automatically approve or deny more applicants. Models that can more accurately segment and score populations will result in fewer applications going to manual review, which can save you money and improve your customers' experiences. CASE STUDY:  Atlas Credit, a small-dollar lender, nearly doubled its loan approval rates while decreasing risk losses by up to 20 percent using a machine learning-powered model and increased automation. Concerns around explainability One of the primary concerns lenders have about machine learning models come from so-called “black box" models.5 Although these models may offer large lifts, you can't verify how they work internally. As a result, lenders can't explain why decisions are made to regulators or consumers — effectively making them unusable. While it's a valid concern, there are machine learning models that don't use a black box approach. The machine learning model doesn't build itself and it's not really “learning" on its own — that's where the black box would come in. Instead, developers can use machine learning techniques to create more efficient models that are explainable, don't have a disparate impact on protected classes and can generate reason codes that help consumers understand the outcomes. LEARN MORE: Explainability: Machine learning and artificial intelligence in credit decisioning Building and using machine learning models Organizations may lack the expertise and IT infrastructure required to develop or deploy machine learning models. But similar to how digital transformations in other parts of the business are leading companies to use outside cloud-based solutions, there are options that don't require in-house data scientists and developers. Experian's expert-guided options can help you create, test and use machine learning models and AI-driven automated decisioning; Ascend Intelligence Services™ Acquire:  Our model development service allows you to prebuild and test the performance of a new model before Experian data scientists complete the model. It's collaborative, and you can upload internal data through the web portal and make comments or suggestions. The service periodically retrains your model to increase its effectiveness. Ascend Intelligence Services™ Pulse:  Monitor, validate and challenge your existing models to ensure you're not missing out on potential improvements. The service includes a model health index and alerts, performance summary, automatic validations and stress-testing results. It can also automatically build challenger models and share the estimated lift and financial benefit of deployment. PowerCurve® Originations Essentials:  Cloud-based decision engine software that you can use to make automated decisions that are tailored to your goals and needs. A machine learning approach to credit risk and AI-driven decisioning can help improve outcomes for borrowers and increase financial inclusion while reducing your overall costs. With a trusted and experienced partner, you'll also be able to back up your decisions with customizable and regulatorily-compliant reports. Learn more about our credit decisioning solutions. Learn more When we refer to "Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term "Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.1Experian (2024). Improving Your Credit Risk Machine Learning Model Deployment2Experian and Forrester Research (2023). Raising the AI Bar3Experian (2022). Driving Growth During Economic Uncertainty with AI/ML Strategies4Ibid5Experian (2020). Explainability ML and AI in Credit Decisioning

This 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

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

Developing machine learning (ML) credit risk models can be more challenging than traditional credit risk modeling approaches. But once deployed, ML models can increase automation and expand a lender’s credit universe.  For example, by using ML-driven credit risk models and combining traditional credit data with transactional bank data, a type of alternative credit data* , some lenders see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.1   New approaches to model operations are also helping lenders accelerate their machine learning model development processes and go from collecting data to deploying a new model in days instead of months.  READ MORE: Getting AI-driven decisioning right in financial services What is machine learning model development? Machine learning model development is what happens before the model gets deployed. It's often broken down into several steps. Define the problem: If you’re building an ML credit risk model, the problem you may be trying to solve is anticipating defaults, improving affordability for borrowers or expanding your lending universe by scoring more thin-file and previously unscorable consumers.  Gather, clean and stage data: Identify helpful data sources, such as internal, credit bureau and alternative credit data. The data will then need to be consolidated, structured, labeled and categorized. Machine learning can be useful here as well, as ML models can be trained to label and categorize raw data. Feature engineering: The data is then analyzed to identify the individual variables and clusters of variables that may offer the most lift. Features that may directly or unintentionally create bias should be removed or limited.  Create the model: Deciding which algorithms and techniques to use when developing a model can be part art and part science. Because lenders need to be able to explain the decisions they make to consumers and regulators, many lenders build model explainability into new ML-driven credit risk models. Validate and deploy: New models are validated and rigorously tested, often as challengers to the existing champion model. If the new model can consistently outperform, it may move on to production.  The work doesn’t stop once a model is live — it needs to be continuously monitored for drift, and potentially recalibrated or replaced with a new model. About 10 percent of lenders use tools to automatically alert them when their models start to drift. But around half make a point of checking deployed models for drift every month or quarter.3  READ MORE: Journey of an ML Model What is model deployment? Model deployment is one of the final steps in the model lifecycle — it’s when you move the model from development and validation to live production.  New models can be deployed in various ways, including via API integration and cloud service deployment using public, private or hybrid architecture. However, integrating a new model with existing systems can be challenging. About a third (33 percent) of consumer lending organizations surveyed in 2023 said it took them one to two months for model deployment-related activities. A little less (29 percent) said it took them three to six months.  Overall, it often takes up to 15 months for the entire development to deployment process — and 55 percent of lenders report building models that never get deployed.2  READ MORE: Accelerating the Model Development and Deployment Lifecycle Benefits of deploying machine learning credit risk models Developing, deploying, monitoring and recalibrating ML models can be difficult and costly. But financial institutions have a lot to gain from embracing the future of underwriting. Improve credit risk assessment: ML-driven models can incorporate more data sources and more precisely assess credit risk to help lenders price credit offers and decrease charge-offs.  Expand automation: More precise scoring can also increase automation by reducing how many applications need to go to manual review.  Increase financial inclusion: ML-models may be able to evaluate consumers who don’t have recent credit information or thick enough credit files to be scorable by traditional models. In short, ML models can help lenders make better loan offers to more people while taking on less risk and using fewer internal resources to review applications.  CASE STUDY: Atlas Credit, a small-dollar lender, partnered with Experian® to develop a fully explainable machine learning credit risk model that incorporated internal data, trended data, alternative financial services data and Experian’s attributes. Atlas Credit can use the new model to make instant decisions and is expected to double its approvals while decreasing losses by up to 20 percent.  How we can help Experian offers many machine learning solutions for different industries and use cases via the Experian Ascend Technology Platform™. For example, with Ascend ML Builder™, lenders can access an on-demand development environment that can increase model velocity — the time it takes to complete a new model’s lifecycle. You can configure Ascend ML Builder based on the compute you allocate and your use cases, and the included code templates (called Accelerators) can help with data wrangling, analysis and modeling.  There’s also Ascend Ops™, a cloud-based model operations solution. You can use Ascend Ops to register, test and deploy custom features and models. Automated model monitoring and management can also help you track feature and model data drift and model performance to improve models in production. Learn more about our machine learning and model deployment solutions *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. 1. Experian (2023). Raising the AI Bar 2. Experian (2023). Accelerating Model Velocity in Financial Institutions 3. Ibid.

As a community bank or credit union, your goal is to provide personalized care and attention to your customers and members while effectively managing regulatory requirements and operational efficiency. By incorporating tools such as income and employment verification, you can streamline the approval process for both account holders and prospects. With the ability to validate their information in seconds, you'll be able to make well-informed decisions faster and accelerate conversion. In this blog post, we will explore the empowering impact of income and employment verification on financial institutions. Better Data, Better Decisions Choosing a verification partner with an instant employer payroll network allows financial institutions to access reliable and up-to-date income and employment information for confident decision-making. With accurate and timely data at their fingertips, you can gain a deeper understanding of your account holders’ capacity to pay, a critical component to assessing overall financial health. This not only helps mitigate risk but also helps you serve your customers and members more effectively. There are additional benefits to partnering with a verification solution provider that is also a Credit Reporting Agency (CRA) offering FCRA-compliant technologies. These organizations are well versed in compliance matters and can help you more effectively mitigate risk. Streamline Approval Times and Remove Friction When developing your verification process, it is advantageous to adopt a waterfall or multi-step approach that encompasses instant verification, permissioned verification, and, as a last resort, manual verification. This tiered approach will significantly reduce approval times, manage costs effectively, and streamline the approval process. Instant verification relies on advanced technology to provide swift and efficient results. In cases where instant verification is unavailable, the process seamlessly transitions to permissioned verification, where explicit consent is obtained from individuals to access their payroll data directly from their respective providers. Lastly, manual verification involves collecting payroll and employment documents, which is a more time-consuming and costly process. By implementing this comprehensive approach, you can enhance the efficiency and effectiveness of your verification process while maintaining the integrity of the results. A Flexible Solution Community banks and credit unions are integral to the lending industry. It is crucial for them to select a versatile verification solution that can keep pace with the approval speed of both regional and large banks. Given that community banks and credit unions operate in smaller geographic regions compared to larger institutions, it is imperative for them to have a verification solution that is versatile and can be applied across their entire spectrum of loan offerings, including mortgage loans, automotive loans, credit cards, home equity loans, and consumer loans. This adaptability enables community banks and credit unions to consistently serve their account holders and enhances their ability to compete effectively with larger financial institutions. With a robust verification solution in place, community banks and credit unions can confidently navigate the complexities of the lending landscape and deliver exceptional results for their valued account holders. World-Class Service and Support To ensure a seamless verification journey, community banks and credit unions should choose a solution provider that delivers exceptional service and support. From the initial onboarding process and comprehensive training to ongoing troubleshooting and guidance, a dedicated and knowledgeable support team becomes indispensable in establishing a successful verification process. Having hands-on training and support not only instills peace of mind but also empowers community-focused financial institutions to consistently provide a high level of personalized service, fostering trust and loyalty among their customers and members. By investing in a robust support system, community banks and credit unions can confidently navigate the verification landscape and stay ahead in an ever-evolving financial industry, reinforcing their commitment to delivering an outstanding experience to their communities. As a longstanding leader in the financial industry, Experian understands the unique challenges faced by community banks and credit unions. Our verification solution, Experian VerifyTM, provides accurate, efficient, and compliant income and employment verification services. With Experian Verify, community focused financial institutions can navigate the complexities of income and employment verification with ease, achieving new levels of efficiency and success. To learn more about how Experian Verify can benefit your bank or credit union, we invite you to visit our website and schedule a personalized demo. Together, let's unlock the potential of income and employment verification and elevate your financial institution to new heights of success. Learn more