Models & Scores

The science of turning historical data into actionable insights is far from magic. And while organizations have successfully used predictive analytics for years, we're in the midst of a transformation. New tools, vast amounts of data, enhanced computing power and decreasing implementation costs are making predictive analytics increasingly accessible. And business leaders from varying industries and functions can now use the outcomes to make strategic decisions and manage risk. What is predictive analytics? Predictive analytics is a type of data analytics that uses statistical modeling and machine learning techniques to make predictions based on historical data. Organizations can use predictive analytics to predict risks, needs and outcomes. You might use predictive analytics to make an immediate decision. For example, whether or not to approve a new credit application based on a credit score — the output from a predictive credit risk model. But organizations can also use predictive analytics to make long-term decisions, such as how much inventory to order or staff to hire based on expected demand. How can predictive business analytics help a business succeed? Businesses can use predictive analytics in different parts of their organizations to answer common and critical questions. These include forecasting market trends, inventory and staffing needs, sales and risk. With a wide range of potential applications, it’s no surprise that organizations across industries and functions are using predictive analytics to inform their decisions. Here are a few examples of how predictive analytics can be helpful: Financial services: Financial institutions can use predictive analytics to assess credit risk, detect fraudulent applicants or transactions, cross-sell customers and limit losses during recovery. Healthcare: Using data from health records and medical devices, predictive models can predict patient outcomes or identify patients who need critical care. Manufacturing: An organization can use models to predict when machines need to be turned off or repaired to improve their longevity and avoid accidents. Retail: Brick-and-mortar retailers might use predictive analytics when deciding where to expand, what to cross-sell loyalty program members and how to improve pricing. Hospitality: A large hospitality group might predict future reservations to help determine how much staff they need to hire or schedule. Advanced techniques in predictive modeling for financial services Emerging technologies, particularly AI and machine learning (ML), are revolutionizing predictive modeling in the financial sector by providing more accurate, faster and more nuanced insights. Taking a closer look at financial services, consider how an organization might use predictive credit analytics and credit risk scores across the customer lifecycle. Marketing: Segment consumers to run targeted marketing campaigns and send prescreened credit offers to the people who are most likely to respond. AI models can analyze customer data to offer personalized offers and product recommendations. Underwriting: AI technologies enable real-time data analysis, which is critical for underwriting. The outputs from credit risk models can help you to quickly approve, deny or send applications for manual review. Explainable machine learning models may be able to expand automation and outperform predictive models built with older techniques by 10 to 15 percent.1 Fraud detection models can also raise red flags based on suspicious information or behaviors. Account management: Manage portfolios and improve customer retention, experience and lifetime value. The outputs can help you determine when you should adjust credit lines and interest rates or extend offers to existing customers. AI can automate complex decision-making processes by learning from historical data, reducing the need for human intervention and minimizing human error. Collections: Optimize and automate collections based on models' predictions about consumers' propensity to pay and expected recovery amounts. ML models, which are capable of processing vast amounts of unstructured data, can uncover complex patterns that traditional models might miss. Although some businesses can use unsupervised or “black box" models, regulations may limit how financial institutions can use predictive analytics to make lending decisions. Fortunately, there are ways to use advanced analytics, including AI and ML, to improve performance with fully compliant and explainable credit risk models and scores. WHITE PAPER: Getting AI-driven decisioning right in financial services Developing predictive analytics models Going from historical data to actionable analytics insights can be a long journey. And if you're making major decisions based on a model's predictions, you need to be confident that there aren’t any missteps along the way. Internal and external data scientists can oversee the process of developing, testing and implementing predictive analytics models: Define your goal: Determine the predictions you want to make or problems you want to solve given the constraints you must act within. Collect data: Identify internal and external data sources that house information that could be potentially relevant to your goal. Prepare the data: Clean the data to prepare it for analysis by removing errors or outliers and determining if more data will be helpful. Develop and validate models: Create predictive models based on your data, desired outcomes and regulatory requirements. Deciding which tools and techniques to use during model development is part of the art that goes into the science of predictive analytics. You can then validate models to confirm that they accurately predict outcomes. Deploy the models: Once a model is validated, deploy it into a live environment to start making predictions. Depending on your IT environment, business leaders may be able to easily access the outputs using a dashboard, app or website. Monitor results: Test and monitor the model to ensure it's continually meeting performance expectations. You may need to regularly retrain or redevelop models using training data that better reflects current conditions. Depending on your goals and resources, you may want to start with off-the-shelf predictive models that can offer immediate insights. But if your resources and experience allow, custom models may offer more insights. CASE STUDY: Experian worked with one of the largest retail credit card issuers to develop a custom acquisition model. The client's goal was to quickly replace their outdated custom model while complying with their model governance requirements. By using proprietary attribute sets and a patented advanced model development process, Experian built a model that offered 10 percent performance improvements across segments. Predictive modeling techniques Data scientists can use different modeling techniques when building predictive models, including: Regression analysis: A traditional approach that identifies the most important relationships between two or more variables. Decision trees: Tree-like diagrams  show potential choices and their outcomes. Gradient-boosted trees: Builds on the output from individual decision trees to train more predictive trees by identifying and correcting errors. Random forest: Uses multiple decision trees that are built in parallel on slightly different subsets of the training data. Each tree will give an output, and the forest can analyze all of these outputs to determine the most likely result. Neural networks: Designed to mimic how the brain works to find underlying relationships between data points through repeated tests and pattern recognition. Support vector machines: A type of machine learning algorithm that can classify data into different groups and make predictions based on shared characteristics. Experienced data scientists may know which techniques will work well for specific business needs. However, developing and comparing several models using different techniques can help determine the best fit. Implementation challenges and solutions in predictive analytics Integrating predictive analytics into existing systems presents several challenges that range from technical hurdles to external scrutiny. Here are some common obstacles and practical solutions: Data integration and quality: Existing systems often comprise disparate data sources, including legacy systems that do not easily interact. Extracting high-quality data from these varied sources is a challenge due to inconsistent data formats and quality. Implementing robust data management practices, such as data warehousing and data governance frameworks, ensure data quality and consistency. The use  of APIs can facilitate seamless data integration. Scalability: Predictive business analytics models that perform well in a controlled test environment may not scale effectively across the entire organization. They can suffer from performance issues when deployed on a larger scale due to increased data volumes and transaction rates. Invest in scalable infrastructure, such as cloud-based platforms that can dynamically adjust resources based on demand. Regulatory compliance: Financial institutions are heavily regulated, and any analytics tool must comply with existing laws — such as the Fair Credit Reporting Act in the U.S. — which govern data privacy and model transparency. Including explainable AI capabilities helps to ensure transparency and compliance in your predictive models. Compliance protocols should be regularly reviewed to align with both internal audits and external regulations. Expertise: Predictive analytics requires specialized knowledge in data science, machine learning and analytics. Develop in-house expertise through training and development programs or consider partnerships with analytics firms to bridge the gap. By addressing these challenges with thoughtful strategies, organizations can effectively integrate predictive analytics into their systems to enhance decision-making and gain a competitive advantage. From prediction to prescription While prediction analytics focuses on predicting what may happen, prescription analytics focuses on what you should do next. When combined, you can use the results to optimize decisions throughout your organization. But it all starts with good data and prediction models. Learn more about Experian's predictive modeling solutions. 1Experian (2020). Machine Learning Decisions in Milliseconds *This article includes content created by an AI language model and is intended to provide general information.

Many organizations commit to diversity, equity, and inclusion (DEI) policies and practices to build a more diverse and just workplace. Organizations that  live by these values ensure they're reflected in the products and services they offer, and in how they attract and interact with customers. For financial institutions, there could be a direct link between their DEI efforts and financial inclusion, which can open up growth opportunities. Defining DEI and financial inclusion DEI and financial inclusion aren't new concepts, but it's still important to understand how organizations are using these terms and how you might define a successful outcome. What is DEI? DEI policies help promote and support individuals and groups regardless of their backgrounds or differences. In the Experian 2022 Diversity, Equity and Inclusion Report, we define these terms more specifically as: Diversity: The presence of differences that may include thought, style, sexual orientation, gender identity/expression, race, ethnicity, dis(ability), culture, and experience. Equity: Promoting justice, impartiality, and fairness within the procedures, processes, and distribution of resources by institutions or systems. Inclusion: An outcome to ensure those who self-identify as diverse feel and are welcomed. You meet your inclusion outcomes when you, your institution, and your programs are inviting to all. We also recognize the importance of belonging, or “a sense of fitting in or feeling you are an important member of a group." A company's DEI strategy might include internal efforts, such as implementing hiring and promotion practices to create a more diverse workforce, and supporting employee resource groups to foster a more inclusive culture. Companies can also set specific and trackable goals, such as Experian's commitment to increase its representation of women in senior leadership roles to 40 percent by 2024.1 But DEI efforts can expand beyond internal workforce metrics. For example, you might review how the products or services you sell — and the messaging around those offerings — affect different groups. Or consider whether the vendors, suppliers, nonprofits, communities, and customers you work with reflect your DEI strategy. What is financial inclusion? Financial inclusion is less specific to a company or organization. Instead, it describes the strategic approach and efforts that allow people to affordably and readily access financial products, services, and systems. Financial institutions can promote financial inclusion in different ways. A bank can change the requirements or fees for one of its accounts to better align with the needs of people who are currently unbanked. Or it can offer a solution to help people who are credit invisible or unscoreable by conventional scoring models establish their credit files for the first time. For example, Mission Asset Fund, a San Francisco-based nonprofit, organizes credit-building lending circles that have historical roots in savings programs from around the world. Participants can use them to build credit without paying any interest or fees. In particular, the organization focuses on helping immigrants establish and improve their credit in the U.S. Financial institutions are also using non-traditional data scoring to lend to applicants that conventional scoring models can't score. By incorporating alternative credit data1 (also known as expanded FCRA-regulated data) into their marketing and underwriting, lenders can expand their lending universe without taking on additional risk. READ MORE: Experian's Improving Financial Health Report 2022 has many examples of internal products and external partnerships that help promote financial literacy and inclusion. DEI and financial inclusion can complement each other Although DEI and financial inclusion involve different strategies, there's an undeniable connection that should ultimately be tied to a business's overall goal and mission. The groups who are historically underrepresented and underpaid in the workforce also tend to be marginalized by the established financial system. For example, on average, Black and Hispanic/Latino workers earn 76 percent and 73 percent, respectively, as much as white workers.2 And 27 percent of Black and 26 percent of Hispanic/Latino consumers are either credit invisible or unscoreable, compared to only 16 percent of white consumers.3 Financial institutions that work to address the inequities within their organizations and promote financial inclusion may find that these efforts complement each other. During a webinar in 2022 discussing how financial growth opportunities can also benefit underserved communities, Experian asked participants what they thought was the greatest business advantage of executing financial inclusion in their financial institution or business. The majority of respondents (78 percent) chose building trust and retention with customers and communities — undoubtedly an important outcome. But the second most popular choice (14 percent) was enhancing their brand and commitment to DEI, highlighting how these efforts can be interconnected.4 By building a more diverse workforce, organizations can also bring on talent that better relate to and understand consumers who weren't previously part of the company's target market. If the company culture supports a range of ideas, this can unlock new ways to propel the business forward. In turn, employees can be more engaged and excited about their work. Find partners that can help you succeed Setting measurable outcomes for your DEI and financial inclusion efforts and tracking your progress can be an important part of implementing successful programs. But you can also leverage partnerships to further define and achieve your goals. Experian launched Inclusion ForwardTM with these partnerships in mind. Building on our commitment to DEI and financial inclusion, we offer various tools to help consumers build and understand their credit and to help financial institutions reach underserved communities. Products like Experian GoTM and Experian BoostTM help consumers establish their credit file and add positive utility, rent, and streaming service payments to their Experian credit report. Lenders can benefit from access to various non-traditional credit data and expanded FCRA-regulated scoring models, including Experian's Lift PremiumTM, which can score 96 percent of U.S. adults. Whether you've established your strategy and need help with implementation or are at the starting stages, Experian can help you promote DEI and enhance your financial inclusion efforts. Learn more about driving financial inclusion to bring change  1Experian (2022). 2022 Diversity, Equity and Inclusion Report 2U.S. Department of Labor (N/A). Earnings Disparities by Race and Ethnicity 3Oliver Wyman (2022). Financial Inclusion and Access to Credit 4Experian (2022). Three Ways to Uncover Financial Growth Opportunities that Benefit Underserved Communities.

Believe it or not, 2023 is underway, and the new year could prove to be a challenging one for apartment operators in certain ways. In 2021 and into the beginning of 2022, demand for apartment rentals approached record levels, which shrunk vacancy rates and increased monthly rents. The rest of the year remained stagnant while other regions saw some decline, but inflation and other economic factors have many apartment communities confronted with labor shortages, and other challenges which can certainly make leasing and operating properties difficult. Against that backdrop, here are some of the technologies and solutions operators should consider for optimizing their success and efficiencies in 2023 and beyond. Tools that allow prospective residents to have a fully digital and contactless leasing experience — During the pandemic, many operators rushed to implement virtual tours, onsite self-guided tours and other solutions that allowed prospects to apply for and finalize their leases remotely. Prospective renters have undoubtedly grown fond of navigating the leasing process from their homes and taking self-guided tours when onsite, and the demand for digital solutions will surely continue even after COVID distancing is no longer a factor. Therefore, apartment owners and operators should think of these capabilities as long-term investments and always seek ways to optimize the digital leasing experience they provide. Along those lines, forward-thinking operators are employing solutions that allow them to embed credit functionality into their websites and mobile apps using modern, RESTful APIs like the Experian ConnectSM API. Not only does it enhance the information included in a lease application with credit report data, but it also allows prospective renters to easily apply for more than one property at once, enhancing their experience at the same time. Automated lease application form fill — By using information entered by a lease applicant (such as first name, last name, postal code and the last four digits of a Social Security number), this technology uses information from credit files to automatically fill other data fields in a lease application. This tool reduces the effort required by prospective renters to complete the application process, resulting in a better user experience, faster completions, greater accuracy and reduced application abandonment. Automated verification of income, assets, and employment — These solutions eliminate the need for associates to manually verify these components of a lease application. Manual verification is both time-consuming and prone to human error. In addition, automated tools eliminate the opportunity for applicants to supply falsified supporting documentation. The best part about verification is the variety of options available; leasing managers can pick and choose verification options that meet their needs. Renter Risk Score™ and custom-built scores and models applying RentBureau data — These options offer a score designed expressly to predict the likelihood that an applicant will pay rent. Renter risk score can be purchased with preset score logic, or for high-volume decisions, a model can be built calibrated for your specific leasing decisioning needs. A rental payment history report — The RentBureau Consumer Profile tool can provide detailed insight into a lease applicant's history of meeting their lease obligations, which is invaluable information during the lease application process. Having a tool to report rental payment histories to credit bureaus can be a powerful financial amenity. By reporting these payments, operators can help residents build credit histories and improve financial well-being. Such an amenity can attract and retain residents and provide them with a powerful incentive to pay rent on time and in full. In the end, tools that seek to manage risk and create improved experiences for prospective renters have a multitude of benefits. They create meaningful efficiencies for onsite staff by greatly reducing the time, resources and paperwork required to process applications and verify applicant information. This gives overextended associates more time to handle their many other responsibilities. Beyond just efficiency savings, these technologies and solutions also can help operators avoid the complications and loss of income that result from evictions. In fact, the National Association of Realtors estimates that average eviction costs $7,685. Managing risk and providing the best possible customer experience should always be top of mind for rental housing operators. And with the solutions outlined above, they can effectively accomplish those goals in 2023 and beyond.

Alternative credit scoring has become mainstream. Lenders that use alternative credit scores can find opportunities to expand their lending universe without taking on additional risk and more accurately assess the credit risk of traditionally scoreable consumers. Obtaining a more holistic consumer view can help lenders improve automation and efficiency throughout the customer lifecycle. What is alternative credit scoring? Alternative credit scoring models incorporate alternative credit data* that isn't typically found on consumer credit reports. These scores aren't necessarily trying to predict alternative outcomes. The goal is the same — to understand the likelihood that a borrower will miss payments in the future. What's different is the information (and sometimes the analytical techniques) that inform these predictions.Traditional credit scoring models solely consider information found in consumer credit reports. There's a lot of information there — Experian's consumer credit database has data on over 245 million consumers. But although traditional consumer data can be insightful, it doesn't necessarily give lenders a complete picture of consumers' creditworthiness. Alternative credit scores draw from additional data sources, including: Alternative financial services: Credit data from alternative financial services (AFS) can tell you about consumers' experiences with small-dollar installment loans, single-payment loans, point-of-sale financing, auto title loans and rent-to-own agreements. Buy Now Pay Later: Buy Now Pay Later (BNPL) borrowing is popular with consumers across the scoring spectrum, and lenders can use access to open BNPL loans to better assess consumers' current capacity. Rental payments: Landlords, property managers, collection companies, rent payment services and consumer-permissioned data can give lenders access to consumers' rent payment history. Full-file public records: Credit reports generally only include bankruptcy records from the previous seven to ten years. However, lenders with access to full-file public records can also learn about consumers' property deeds, address history, and professional and occupational licenses. READ: Take a deep dive into Experian's State of Alternative Credit Data report to learn more about the different types of alternative credit data and uses across the loan lifecycle. With open banking, consumers can now easily and securely share access to their banking and brokerage account data — and they're increasingly comfortable doing so. In fact, 70% would likely share their banking data for better loan rates, financial tools or personalized spending insights.Tools like Experian Boost allow consumers to add certain types of positive payment information to their Experian credit reports, including rent, utility and select streaming service payments. Some traditional scores consider these additional data points, and users have seen their FICO Score 8 from Experian boosted by an average of 13 points.1 Experian Go also allows credit invisible consumers to establish a credit report with consumer-permissioned alternative data.  The benefits of using alternative credit data The primary benefit for lenders is access to new borrowers. Alternative credit scores help lenders accurately score more consumers — identifying creditworthy borrowers who might otherwise be automatically denied because they don't qualify for traditional credit scores. The increased access to credit may also align with lenders' financial inclusion goals.Lenders may additionally benefit from a more precise understanding of consumers who are scoreable. When integrated into a credit decisioning platform, the alternative scores could allow lenders to increase automation (and consumers' experiences) without taking on more credit risk. The future of alternative credit scoring Alternative credit scoring might not be an alternative for much longer, and the future looks bright for lenders who can take advantage of increased access to data, advanced analytics and computing power.Continued investment in alternative data sources and machine learning could help bring more consumers into the credit system — breaking barriers and decreasing the cost of basic lending products for millions. At the same time, lenders can further customize offers and automate their operations throughout the customer lifecycle. Partnering with Experian Small and medium-sized lenders may lack the budget or expertise to unlock the potential of alternative data on their own. Instead, lenders can turn to off-the-shelf alternative models that can offer immediate performance lifts without a heavy IT investment.Experian's Lift PlusTM score draws on industry- leading mainstream credit data and FCRA-regulated alternative credit data to provide additional consumer behavior insights. It can score 49% of mainstream credit-invisible consumers and for thin file consumers with a new trade, a 29% lift in scoreable accounts. Learn more about our alternative credit data scoring 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 (2023). Experian Boost

Today’s changing economy is directly impacting consumers’ financial behaviors, with some individuals doing well and some showing signs of payment stress. And while these trends may pose challenges to financial institutions, such as how to expand their customer base without taking on additional risk, the right credit attributes can help them drive smarter and more profitable lending decisions. With Experian’s industry-leading credit attributes, organizations can develop precise and explainable acquisition models and strategies. As a result, they can: Expand into new segments: By gaining deeper insights into consumer trends and behaviors, organizations can better assess an individual’s creditworthiness and approve populations who might have been overlooked due to limited or no credit history. Improve the customer experience: Having a wider view of consumer credit behavior and patterns allows organizations to apply the best treatment at the right time based on each consumer’s specific needs. Save time and resources: With an ongoing managed set of base attributes, organizations don’t have to invest significant resources to develop the attributes themselves. Additionally, existing attributes are regularly updated and new attributes are added to keep pace with industry and regulatory changes. Case study: Enhance decision-making and segmentation strategies A large retail credit card issuer was looking to grow their portfolio by identifying and engaging more consumers who met their credit criteria. To do this, they needed to replace their existing custom acquisition model with one that provided a granular view of consumer behavior. By partnering with Experian, the company was able to implement an advanced custom acquisition model powered by our proprietary Trended 3DTM and Premier AttributesSM. Trended 3D analyzes consumers’ behavior patterns over time, while Premier Attributes aggregates and summarizes findings from credit report data, enabling the company to make faster and more strategic lending decisions. Validations of the new model showed up to 10 percent improvement in performance across all segments, helping the company design more effective segmentation strategies, lower their risk exposure and approve more accounts. To learn how Experian can help your organization make the best data-driven decisions, read the full case study or visit us. Download case study Visit us

Whether your goal is to gain new business or create cross-sell opportunities, being proactive in your credit marketing approach can help drive higher response rates and more meaningful customer experiences. But without knowing when your ideal customers are actively seeking credit, you may risk losing business to lenders who have already engaged. So, how can you identify new opportunities when they occur? Given that 91% of consumers say they’re more likely to shop with brands that provide relevant offers, you’ll need to reach the right consumers at the right moment to increase response rates and stay ahead of competitors. Event-based credit triggers can help you identify new tradelines, inquiries and certain loans nearing term to locate highly responsive, credit-active individuals. By receiving updates on consumers’ recent credit activities, you can make firm credit offers immediately so you never miss an opportunity. Case Study: Deliver timely offers with credit trigger leads Vantage West Credit Union serves over 170,000 members across Arizona. With their members looking elsewhere for their mortgage needs, Vantage West aimed to drive as many of these members back to the credit union as possible. To do this, they looked for a solution that could help them identify and target members who are in the market for a new mortgage. By augmenting their prescreen process with Experian’s Prospect Triggers for mortgages, the credit union was able to quickly pinpoint consumers that not only met their credit criteria but were also likely to respond to their credit offers. Within two years of implementing Prospect Triggers, Vantage West funded an additional $18 million in mortgages and is continuing to grow by making timely offers to credit-active prospects. Prospect Triggers is available for banks, credit card issuers, mortgage lenders, retailers and automotive lenders. To learn how Experian can help bring precision and profitability to your credit marketing campaigns, read the full case study or visit us. Download the case study Visit us

Even before the COVID-19 pandemic, many Americans lacked equal access to financial products and services — from tapping into affordable banking services to credit cards to financing a home purchase. The global pandemic likely exacerbated those existing issues and inequalities. That reality makes financial inclusion — a concerted effort to make financial products and services affordable and accessible to all consumers — more crucial than ever. The playing field wasn't level before the pandemic The Federal Reserve reported that in 2019, Black and Hispanic/Latino families had median wealth that was just 13 to 19 percent of that of White families — $24,100 and $36,100, respectively, compared to $188,200 for White families. That inequity is also reflected in credit score disparities. While credit scores, income, and wealth aren't synonymous, the traditional credit scoring system leads marginalized communities to be disproportionately labeled unscoreable or credit invisible, and face challenges in accessing credit. New research from Experian shows that in over 200 cities, there can be more than a 100-point difference in credit scores between neighborhoods — often within just a few miles from each other. Marginalized communities bore the financial brunt Minority communities were also disproportionately impacted by COVID-19 in terms of infections, job losses, and financial hardship. In mid-2020, the Economic Policy Institute (EPI) reported Black and Hispanic/Latino workers were more likely than White workers to have lost their jobs or to be classified as essential workers — leading to economic or health insecurity. Government initiatives — including the Coronavirus Aid, Relief, and Economic Security (CARES) Act, the Paycheck Protection Program (PPP) and the American Rescue Plan — created expanded unemployment benefits, paused loan payments, eviction moratoriums, and direct cash payments. These helped consumers' immediate financial well-being. The National Bureau of Economic Research found that, on average, U.S. households spent approximately 40 percent of their first two stimulus checks, with about 30 percent used for savings and another 30 percent used to pay down debt. In some communities highly affected by COVID-19, consumers were able to pay down nearly 40 percent of their credit card balances and close more than 9 percent of their bank card accounts, according to recent data. Stimulus payments have been credited with reducing childhood poverty and helping families save for financial emergencies. That being said, people on the upper end of the income scale were able to improve their financial situation even more. Their wealth grew at a much faster pace than people at the bottom end of the income distribution scale, according to data from the Federal Reserve. How the pandemic deepened financial exclusion Although hiring has picked up in low-wage industries, research indicates that low-wage jobs have been the slowest to return. According to a survey by the Pew Research Center, among respondents who said their financial situation worsened during the pandemic, 44 percent believe it will take three years or more to get back to where they were a year ago. About 10 percent don't think their finances will ever recover. Recent Experian data shows that consumers in certain communities that were already struggling to pay their debts fell into an even bigger hole. These consumers missed payments on 56 percent more accounts in the period between spring 2019 to spring 2020 compared to the year prior. Credit scores in these neighborhoods fell by an average of over 20 points during the first 18 months of COVID-19. That being said, U.S. consumers overall increased their median credit scores by an average of 21 points from the end of 2019 to the end of 2021. When consumers with deteriorating credit encounter financial stresses, often their only recourse is to pile on additional debt. Even worse, those who can't access traditional credit often turn to alternative credit arrangements, such as short-term loans, which may charge significantly higher interest rates. READ MORE: More Than a Score: The Case for Financial Inclusion What can the financial sector do? Without access to affordable financial services and products, subprime or credit invisible consumers may not get approved for a mortgage or car loan — things that might come much easier for consumers with better scores. This is just one reason why financial inclusion is so important — and why financial services companies have a big role to play in driving it. One place to start is by taking a broader view of what makes a creditworthy consumer. In addition to traditional credit scoring models, new tools can leverage artificial intelligence and machine learning, along with alternative data, to analyze the creditworthiness of consumers. By qualifying for credit, more consumers can access affordable mortgages, car loans, business loans and insurance - freeing up money for other expenses and allowing them to grow their wealth.. READ MORE: What Is Alternative and Non-Traditional Data? Last word Marginalized communities were already struggling economically before the pandemic, and the impact of COVID-19 has made the wealth disparities worse. With the pandemic waning, now is the time for financial institutions to take action on financial inclusion. Not only does it help improve your customers' lives and make them better prepared for the next crisis, but it also fuels your business's growth and bottom line.

There's no magic solution to undoing the decades of policies and prejudices that have kept certain communities unable to fully access our financial and credit systems. But you can take steps to address previous wrongs, increase financial inclusion and help underserved communities. If you want to engage consumers and keep them engaged, you could start with the following four areas of focus. 1. Find ways to build trust Historical practices and continued discriminatory behavior have created justifiable distrust of financial institutions among some consumers. In February 2022, Experian surveyed more than 1,000 consumers to better understand the needs and barriers of underserved communities. The respondents came from varying incomes, ethnicity and age ranges. Fewer than half of all the consumers (47 percent) said they trusted their bank's personal finance advice and information, and that dropped to 41 percent among Black Americans. In a follow-up webinar discussion of financial growth opportunities that benefitted underserved communities, we found that many financial institutions saw a connection between their financial inclusion efforts and building trust with customers and communities. Here is a sample question and a breakdown of the primary responses: What do you think is the greatest business advantage of executing financial inclusion in your financial institution or business?1 Building trust and retention with customers and communities (78%) Increasing revenue by expanding to new markets (6%) Enhancing our brand and commitment to DEI (14%) Staying in alignment with regulator and compliance guidelines (2%) Organizations may want to approach financial inclusion in different ways depending on their unique histories and communities. But setting quantifiable goals and creating a roadmap for your efforts is a good place to start. 2. Highlight data privacy and mobile access If you want to win over new customers, you'll need to address their most pressing needs and desires. Consumers' top four considerations when signing up for a new account were consistent, but the specific results varied by race. Keep this in mind as you consider messaging around the security and privacy measures. Also, consider how underserved communities might access your online services. Having an accessible and intuitive mobile app or mobile-friendly website is important and likely carries even more weight with these groups. According to the Pew Research Center, as of 2021, around a quarter of Hispanic/Latino and 17% of Black Americans are smartphone-dependent — meaning they have a smartphone but don't have broadband access at home. Low-income and minority communities are also less likely to live near bank branches or ATMs. 3. Offer lower rates and fees Low rates and fees are also a top priority across the board — everyone likes to save money. However, fewer Black and Hispanic households have $1,000 in savings or more compared to white households, which could make additional savings opportunities especially important. There have been several recent examples of large banks and credit unions eliminating overdraft fees. And the Bank On National Account Standards can be a helpful framework if you offer demand deposit accounts. Lowering interest rates on credit products can be more challenging, particularly when consumers don't have a thick (or any) credit file. But by integrating expanded FCRA-regulated data sources and new scoring models, such as Experian's Lift PremiumTM, creditors can score more applicants and potentially offer them more favorable terms. 4. Leverage credit education tools and messaging For consumers who've had negative credit experiences, are new to credit, or are recent immigrants with little understanding of the U.S. credit system, building and using credit can feel daunting. About 80% of women have little or no confidence in getting approved for credit or worry that applying could hurt them further. Only 20% of consumers who make less than $35,000 a year say they're "extremely" or "very" confident they'll be approved for credit. While most consumers haven't used credit education tools before, they're willing to try. More than 60 percent of Black and Hispanic respondents said they're likely to sign up for free credit education tools and resources from their banks. Offering these tools could be an opportunity to strengthen trust and help consumers build credit, which can also make it easier for them to qualify for financial products and services in the future. Moving forward with financial inclusion Broadening access to credit can be an important part of financial inclusion, and financial institutions can grow by expanding outreach to underserved communities. However, the relationship must be built on trust, security, and offerings that meet these consumers' needs. Through our Inclusion Forward™ initiative, Experian can support your financial inclusion goals — helping you empower underserved communities by helping them grow their financial futures. Learn more about Experian financial inclusion solutions and financial inclusion tools.

As competition for used vehicles remains fierce, dealers must make quick decisions on whether to acquire a potential vehicle—or someone else will. Whether you need to evaluate a trade-in or want to make a flat-out offer on a vehicle, quickly accessing the vehicle’s history is only the first step. What if you could determine the likelihood that a vehicle will be on the road in five years and compare it to other similar vehicles? Would this help in your decision-making? Use the AutoCheck Score to evaluate vehicles for your lot The patented AutoCheck ScoreSM was developed to help dealers determine the likelihood that a vehicle will be on the road in five years. The AutoCheck Score summarizes vehicle history data into an easy-to-understand “score” and provides an equivalent score range. Understanding the likelihood that a unit will still be on the road in five years gives dealers more perspective on a vehicle’s desirability and can help you accurately price it. Use the AutoCheck Score to more accurately merchandise vehicles The AutoCheck Score range will give you similarly aged and classed vehicles for comparison that you can use to help merchandise the vehicle for market and help manage consumer conversations regarding the vehicle’s price. How does the AutoCheck Score work? The AutoCheck Score analyzes various characteristics, including age, segment, mileage, number of owners, vehicle use, vehicle events, accidents, theft, and title brands. Essentially, this score summarizes the AutoCheck Vehicle History Report fields and delivers a “score range” that allows you to compare the actual vehicle (score) to other similarly aged and classed vehicles. The score is based on a scale of 1 to 100, but reading the score in conjunction with the score range is essential since looking at the score without knowing the range will reveal only half the story. Check out the example below. Car #1 has a lower score than Car #2. However, look at the score ranges (73-86). For Car #1, other comparable vehicles should fall into a range from 73 to 86, and this car scored an 84. That means the vehicle is within the AutoCheck Score range compared to other similar cars of the same age and class. Car #2 has a score of 89. Because this is higher than Car #1’s score, you might assume this car has a more favorable vehicle history, but that’s not entirely accurate. Comparable vehicles should score in a range from 90 to 95, and this car falls short. That means the car’s history is slightly less favorable compared to similar vehicles of the same age and class. In today’s competitive environment though, dealers may decide to take in both trades, and the AutoCheck Score can also help make decisions on how to price the vehicles for market. Become an AutoCheck member today As an AutoCheck subscriber, you’ll have access to the AutoCheck Score. Between the large graphic display and the simple number comparison, the AutoCheck Score can help you make decisions on vehicle acquisition much quicker and easier!

These days, the call for financial inclusion is being answered by a disruptive force of new financial products and services. From fintech to storied institutional players, we're seeing a variety of offerings that are increasingly accessible and affordable for consumers. It's a step in the right direction. And beyond the moral imperative, companies that meet the call are finding that financial inclusion can be a source of business growth and a necessity for staying relevant in a competitive marketplace. A diaspora of credit-invisible consumers To start, let's put the problem in context. A 2022 Oliver Wyman report found about 19 percent of the adult population is either credit invisible (has no credit file) or unscoreable (not enough credit information to be scoreable by conventional credit scoring models). But some communities are disproportionately impacted by this reality. Specifically, the report found: Black Americans are 1.8 times more likely to be credit invisible or unscoreable than white Americans. Recent immigrants may have trouble accessing credit in the U.S., even if they're creditworthy in their home country. About 40 percent of credit invisibles are under 25 years old. In low-income neighborhoods, nearly 30 percent of adults are credit invisible and an additional 16 percent are unscoreable. Younger and older Americans alike may shy away from credit products because of negative experiences and distrust of creditors. Similarly, the Federal Deposit Insurance Corporation (FDIC) reports that an estimated 5.4 percent (approximately 7.1 million) households, were unbanked in 2019 — often because they can't meet minimum balance requirements or don't trust banks. Credit invisibles and unscoreables may prefer to deal in a cash economy and turn to alternative credit and banking products, such as payday loans, prepaid cards, and check-cashing services. But these products can perpetuate negative spirals. High fees and interest can create a vicious cycle of spending money to access money, and the products don't help the consumers build credit. In turn, the lack of credit keeps the consumers from utilizing less expensive, mainstream financial products. The emergence of new players Recently, we've seen explosive growth in fintech — technology that aims to improve and automate the delivery and use of financial services. According to market research firm IDC, fintech is expected to achieve a compound annual growth rate (CAGR) of 25 percent through 2022, reaching a market value of $309 billion. It's reaching mass adoption by consumers: Plaid® reports that 88 percent of U.S. consumers use fintech apps or services (up from 58 percent in 2020), and 76 percent of consumers consider the ability to connect bank accounts to apps and services a top priority. Some of these new products and services are aimed at helping consumers get easier and less expensive access to traditional forms of credit. Others are creating alternative options for consumers. Free credit-building tools. Experian Go™ lets credit invisibles quickly and easily establish their credit history. Likewise, consumers can use Experian Boost™ to build their credit with non-traditional payments, including their existing phone, utility and streaming services bills. Alternative credit-building products. Chime® and Varo® , two neobanks, offer credit builder cards that are secured by a bank account that customers can easily add or withdraw money from. Mission Asset Fund, a nonprofit focused on helping immigrants, offers a fee- and interest-free credit builder loan through its lending circle program. Cash-flow underwriting. Credit card issuers and lenders, including Petal and Upstart, are using cash-flow underwriting for their consumer products. Buy now, pay later. Several Buy Now Pay Later (BNPL) providers make it easy for consumers to pay off a purchase over time without a credit check. Behind the scenes, it's easier than ever to access alternative credit data1 — or expanded Fair Credit Reporting Act (FCRA)-regulated data — which includes rental payments, small-dollar loans and consumer-permissioned data. And there are new services that can help turn the raw data into a valuable resource. For example, Lift PremiumTM uses multiple sources of expanded FCRA-regulated data to score 96 percent of American adults — compared to the 81 percent that conventional scoring models can score with traditional credit data. While we dig deeper to help credit invisibles, we're also finding that the insights from previously unreported transactions and behavior can offer a performance lift when applied to near-prime and prime consumers. It truly can be a win-win for consumers and creditors alike. Final word There's still a lot of work to be done to close wealth gaps and create a more inclusive financial system. But it's clear that consumers want to participate in a credit economy and are looking for opportunities to demonstrate their creditworthiness. Businesses that fail to respond to the call for more inclusive tools and practices may find themselves falling behind. Many companies are already using or planning to use alternative data, advanced analytics, machine learning, and AI in their credit-decisioning. Consider how you can similarly use these advancements to help others break out of negative cycles. 1When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

Credit reports and conventional credit scores give lenders a strong starting point for evaluating applicants and managing risk. But today's competitive environment often requires deeper insights, such as credit attributes. Experian develops industry-leading credit attributes and models using traditional methods, as well as the latest techniques in machine learning, advanced analytics and alternative credit data — or expanded Fair Credit Reporting Act (FCRA)-regulated data)1 to unlock valuable consumer spending and payment information so businesses can drive better outcomes, optimize risk management and better serve consumers READ MORE: Using Alternative Credit Data for Credit Underwriting Turning credit data into digestible credit attributes Lenders rely on credit attributes — specific characteristics or variables based on the underlying data — to better understand the potentially overwhelming flow of data from traditional and non-traditional sources. However, choosing, testing, monitoring, maintaining and updating attributes can be a time- and resource-intensive process. Experian has over 45 years of experience with data analytics, modeling and helping clients develop and manage credit attributes and risk management. Currently, we offer over 4,500 attributes to lenders, including core attributes and subsets for specific industries. These are continually monitored, and new attributes are released based on consumer trends and regulatory requirements. Lenders can use these credit attributes to develop precise and explainable scoring models and strategies. As a result, they can more consistently identify qualified prospects that might otherwise be missed, set initial limits, manage credit lines, improve loyalty by applying appropriate treatments and limit credit losses. Using expanded credit data effectively Leveraging credit attributes is critical for portfolio growth, and businesses can use their expanding access to credit data and insights to improve their credit decisioning. A few examples: Spot trends in consumer behavior: Going beyond a snapshot of a credit report, Trended 3DTM attributes reveal and make it easier to understand customers' behavioral patterns. Use these insights to determine when a customer will likely revolve, transact, transfer a balance or fall into distress. Dig deeper into credit data: Making sense of vast amounts of credit report data can be difficult, but Premier AttributesSM  aggregates and summarizes findings. Lenders use the 2,100-plus attributes to segment populations and define policy rules. From prospecting to collections, businesses can save time and make more informed decisions across the customer lifecycle. Get a clear and complete picture: Businesses may be able to more accurately assess and approve applicants, simply by incorporating attributes overlooked by traditional credit bureau reports into their decisioning process. Clear View AttributesTM uses data from the largest alternative financial services specialty bureau, Clarity Services, to show how customers have used non-traditional lenders, including auto title lenders, rent-to-own and small-dollar credit lenders. The additional credit attributes and analysis help lenders make more strategic approval and credit limit decisions, leading to increased customer loyalty, reduced risk and business growth. Additionally, many organizations find that using credit attributes and customized strategies can be important for measuring and reaching financial inclusion goals. Many consumers have a thin credit file (fewer than five credit accounts), don’t have a credit file or don’t have information for conventional scoring models to score them. Expanded credit data and attributes can help lenders accurately evaluate many of these consumers and remove barriers that keep them from accessing mainstream financial services. There's no time to wait Businesses can expand their customer base while reducing risk by looking beyond traditional credit bureau data and scores. Download our latest e-book on credit attributes to learn more about what Experian offers and how we can help you stay ahead of the competition.  Download e-book  Learn more 1When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

From awarding bonus points on food delivery purchases to incorporating social media into their marketing efforts, credit card issuers have leveled up their acquisition strategies to attract and resonate with today’s consumers. But as appealing as these rewards may seem, many consumers are choosing not to own a credit card because of their inability to qualify for one. As card issuers go head-to-head in the battle to reach and connect with new consumers, they must implement more inclusive lending strategies to not only extend credit to underserved communities, but also grow their customer base. Here’s how card issuers can stay ahead: Reach: Look beyond the traditional credit scoring system With limited or no credit history, credit invisibles are often overlooked by lenders who rely solely on traditional credit information to determine applicants’ creditworthiness. This makes it difficult for credit invisibles to obtain financial products and services such as a credit card. However, not all credit invisibles are high-risk consumers and not every activity that could demonstrate their financial stability is captured by traditional data and scores. To better evaluate an applicant’s creditworthiness, lenders can leverage expanded data sources, such as an individual’s cash flow or bank account activity, as an additional lens into their financial health. With deeper insights into consumers’ banking behaviors, card issuers can more accurately assess their ability to pay and help historically disadvantaged populations increase their chances of approval. Not only will this empower underserved consumers to achieve their financial goals, but it provides card issuers with an opportunity to expand their customer base and improve profitability. Connect: Become a financial educator and advocate Credit card issuers looking to build lifelong relationships with new-to-credit consumers can do so by becoming their financial educator and mentor. Many new-to-credit consumers, such as Generation Z, are anxious about their finances but are interested in becoming financially literate. To help increase their credit understanding, card issuers can provide consumers with credit education tools and resources, such as infographics or ‘how-to’ guides, in their marketing campaigns. By learning about the basics and importance of credit, including what a credit score is and how to improve it, consumers can make smarter financial decisions, boost their creditworthiness, and stay loyal to the brand as they navigate their financial journeys. Accessing credit is a huge obstacle for consumers with limited or no credit history, but it doesn’t have to be. By leveraging expanded data sources and offering credit education to consumers, credit card issuers can approve more creditworthy applicants and unlock barriers to financial well-being. Visit us to learn about how Experian is helping businesses grow their portfolios and drive financial inclusion. Visit us

Many financial institutions have made inclusion a strategic priority to expand their reach and help more U.S. consumers access affordable financial services. To drive deeper understanding, Experian commissioned Forrester to do new research to identify key focal points for firms and how they are moving the needle. The study found that more than two-thirds of institutions had a strategy created and implemented while one-quarter reported they are already up and running with their inclusion plans.1 Tapping into the underserved The research examines the importance of engaging new audiences such as those that are new to credit, lower-income, thin file, unbanked and underbanked as well as small businesses. To tap into these areas, the study outlines the need to develop new products and services, adopt willingness to change policies and processes, and use more data to drive better decisions and reach.2 Expanded data for improved risk decisioning The research underlines the use of alternative data and emerging technologies to expand reach to new audiences and assist many who have been underserved. In fact, sixty-two percent of financial institutions surveyed reported they currently use or are planning to use expanded data to improve risk profiling and credit decisions, with focus on: Banking data Cash flow data Employment verification data Asset, investments, and wealth management data Alternative financial services data Telcom and utility data3 Join us to learn more at our free webinar “Reaching New Heights Together with Financial Inclusion” where detailed research and related tools will be shared featuring Forrester’s principal analyst on Tuesday, May 24 from 10 – 11 a.m. PT. Register here for more information. Find more financial inclusion resources at www.experian.com/inclusionforward. Register for webinar Visit us 1 Based on Forrester research 2 Ibid. 3 Ibid.

It's one thing to make a corporate commitment to financial inclusion, but quite another to set specific goals and measure outcomes. What goals should lenders set to make financial inclusion a reality? How can success be quantified? What actionable steps must be taken to put policy into practice? The road to financial inclusion may feel long, but this step-by-step checklist can help you measure diversity and achieve goals to become more inclusive as an organization. Step 1: Set quantifiable goals with realistic outcomes Start by defining what you plan to achieve with a financial inclusion strategy. When setting goals, Alpa Lally, Experian's Vice President of Data Business at Consumer Information Services, recommends organizations "assess the strategic opportunity at the enterprise level." "It is important that KPIs are aligned across each business unit and functional groups in order to understand the investment opportunity and what the business must achieve together," said Lally. "The key focus here is 'together', the path to financial inclusion is a journey for all groups and everyone must participate, be committed and be aligned to be successful." Figuring out your short- and long-term goals should be the first step to kickstarting a financial inclusion strategy. But equally important is driving towards outcomes. For instance, if the goal is to increase the number of loans made to previously overlooked or excluded consumers, you may want to start by examining your declination population to better understand who is being left out. Or if financial inclusion is tied to a wider strategy or vision on corporate social responsibility, your goals may include an education component, community outreach, and a re-examination of your hiring practices. No matter what KPIs you're using, here are relevant questions to ask in four key areas – which will help draw out your organizational goals and priorities: Organizational awareness: What action is your organization taking to enhance Diversity, Equity and Inclusion and embrace Corporate Social Responsibility (CSR) around financial inclusion? If you already have financial inclusion programs in place, what are the primary goals? Barriers: What barriers prevent the organization from pursuing equity, diversity and inclusion programs? Education: How do you create awareness and education around financial inclusion? Which community or third-party organizations can help you reach consumers who aren't aware of ways to access financial services? Markers of success: What benchmarks will your organization use to measure and analyze success? Step 2: Do a financial inclusion audit Before developing and implementing a robust financial inclusion program, Lally recommends conducting a financial inclusion audit – which is a "detailed assessment of where you are today, relative to the goals and results you've outlined". In a nutshell, it allows you to assess your current systems and results within your financial institution. According to Lally, a financial inclusion audit should address the following key areas: Roadmap: What are your strategic priorities and how will financial inclusion fit within them? Tracking: Track the actual volume and distribution of different underserved populations (e.g., young adults, low-income communities, immigrants, etc.) within your book of business. Look at the applications and the approval rates by segment. In addition, assess the interest rates these consumers are offered by credit score bands for each group: “Benchmarking is critical. Understanding how they compare to national averages? How do they compare to the rest of your portfolio?" said Lally. Hiring practices: Is diversity, equity and inclusion (DEI) central to your talent management strategy? Is there a link between a lack of DEI in hiring practices and the level of financial inclusion within an organization? Affordability and access: Determine if the products and services you offer are easily accessible, can be understood by a reasonable consumer and are affordable to a broad base. Internal practices: What policies exist that influence the culture and behavior of employees around financial inclusion? Partnerships: Identify outside organizations that can help you develop financial literacy programs to promote financial inclusion. Advertising: Does your advertising promote equal and diverse representation across a wide range of consumer groups? Tools to measure: Are you financially inclusive as a company? How can you improve? The Bayesian Improved Surname Geocoding (BISG) method used by the Consumer Financial Protection Bureau (CFPB) predicts the probability of an individual's race and ethnicity based on demographic information associated with the consumer's surname. Lenders can use this type of information to conduct internal audits or set benchmarks to help ensure accountability in their diversity goals. Step 3: Tap into technology New technology is emerging that gives lenders powerful tools to evaluate a wider pool of prospective borrowers while also mitigating risk. For instance, scoring models that incorporate expanded FCRA-regulated data provide greater insight into 'credit invisible' or 'unscorable' consumers because they look at a wider set of data assets (or 'alternative data'), which allows lenders to assess a larger pool of applicants. It also improves the accuracy of those scores and better assesses the creditworthiness of consumers. Consider these resources, among others: Lift Premium™: Experian estimates that lenders using Lift Premium™ can score 96 percent of U.S. adults, a vast improvement over the 81 percent that are scorable today with conventional scores relying on mainstream data. Such enhanced scores would enable six million consumers who are considered subprime today to qualify for “mainstream" (prime or near-prime) credit. Experian® RentBureau®: RentBureau collects rent payment data from landlords and management companies, which allows consumers to leverage positive rent payment history similarly to how consumers leverage consistent mortgage payments. Clarity Credit Data: Clarity Credit Data allows lenders to see how consumers use alternative financial products and examine payment behaviors that might exist outside of the traditional credit report. Clarity's expanded FCRA -regulated data provides a deeper view of the consumer, allowing lenders to identify those who may not have previously been classified as "at risk" and approve consumers that may have previously been denied using a traditional credit score. Income Verification: Consumers can grant access to their bank accounts so lenders can assess their ability to pay based on verified income and cash flow. In addition, artificial intelligence (AI) and greater automation can reduce operational costs for lenders, while increasing the affordability of financial products and services for customers. AI and machine learning (ML) can also improve risk profiling and credit decisioning by filling in some of the gaps where credit history is not available. These are just a few examples of a wide range of cutting-edge solutions and technologies that enable lenders to promote greater financial inclusion through their decisioning processes. As new solutions are introduced to the market, it is imperative that lenders look into these technologies to help grow their business. Step 4: Monitor and measure Measuring your progress on financial inclusion isn't a one-and-done proposition. After you've set your goals and created a roadmap, it's important to continue monitoring and measuring your progress. That means your performance to gauge the impact of financial inclusion at both the community and business levels. Lally recommends the following examples: Compare your lending pool to the latest population data from the United States census. Is your portfolio representative of the U.S. population or are there segments that should have greater access? How does it compare against other lenders competing in the same space? Keep in mind that it has been widely reported that certain populations were undercounted, so you may want to factor this reality into your assessments. Work to understand how traditionally underserved consumers are performing in terms of their payment behaviors, purchase patterns and delinquencies. Measure the impact of financial inclusion on your company's overall revenue growth, ROI and brand reputation. Conduct an analysis to better understand your company's brand reputation, how it's perceived across different groups and what your customers are saying. Last word Financial inclusion represents a big step towards closing the wealth gap and helping marginalized communities build generational wealth. Given the prevalence of socioeconomic and racial inequality in our country today, it's a complex issue that disproportionately impacts marginalized groups, such as consumers of color, low-income communities and immigrants. Adopting more financially inclusive practices can help improve access to credit for these groups. For financial institutions and lenders, the first step is to identify realistic, quantifiable goals. A successful financial inclusion initiative also hinges on completing a financial inclusion audit, tapping into the right technology and continually monitoring and measuring progress. "It is paramount that financial institutions hold themselves accountable and demonstrate their commitment to make these practices a part of their DNA." - Alpa Lally. Learn more

For decades, the credit scoring system has relied on traditional data that only examines existing credit captured on a credit report – such as credit utilization ratio or payment history – to calculate credit scores. But there's a problem with that approach: it leaves out a lot of consumer activity. Indeed, research shows that an estimated 28 million U.S. adults are “credit invisible," while another 21 million are “unscorable."1 But times are changing. While conventional credit scoring systems cannot generate a score for 19 percent of American adults,1 many lenders are proactively turning to expanded FCRA-regulated data – or "alternative data" – for solutions. Types of expanded FCRA-regulated data By tapping into technology, lenders can access expanded FCRA-regulated data, which offers a powerful and complete view of consumers' financial situations. Expanded public record data This can include professional and occupational licenses, property deeds and address history – a step beyond the limited public records information found in standard credit reports. Such expanded public record data is available through consumer reporting agencies and does not require the customer's permission to use it since it's a public record.1 “Experian has partnerships with these agencies and can access public records that provide insight into factors like income and housing stability, which have a direct correlation with how they'll perform," said Greg Wright, Chief Product Officer for Experian Consumer Information Services. “For example, lenders can see if a consumer's professional license is in good standing, which is a strong correlation to income stability and the ability to pay back a loan." Rental payment data Experian RentBureau draws updated rental payment history data every 24 hours from property managers, electronic rent payment services and collection companies. It can also track the frequency of address changes. “Such information can be a good indicator of risk," said Wright. “It allows lenders to make informed judgments about the financial health and positive payment history of consumers." Consumer-permissioned data With permission from consumers, lenders can look at different types of financial transactions to assess creditworthiness. Experian Boost™, for example, enables consumers to factor positive payment history, such as utilities, cell phone or even streaming services, into an Experian credit file. “Using the Experian Boost is free, and for most users, it instantly improves their credit scores," said Wright. “Overall, those 'boosted' credit scores allow for fairer decisioning and better terms from lenders – which gives customers a second chance or opportunity to receive better terms." Financial Management Insights Financial Management Insights considers data that is not captured by the traditional credit report such as cash flow and account transactions. For instance, this could include demand deposit account (DDA) data, like recurring payroll deposits, or prepaid account transactions. “Examining bank account transaction data, prepaid accounts, and cash flow data can be a good indicator of ability to pay as it helps verify income, which gives lenders insights into consumers' cash flow and ability to pay," Wright added. Clarity Credit Data With Experian's Clarity Credit Data, lenders can see how consumers use expanded FCRA-regulated data along with their related payment behavior. It provides visibility into critical non-traditional loan information, including more insights into thin-file and no-file segments allowing for a more comprehensive view of a consumer's credit history. Lift Premium™ By using multiple sources of expanded FCRA-regulated data to feed composite scores, along with artificial intelligence and machine learning, Lift Premium™ can vastly increase the number of consumers who can be scored. For example, research shows that Lift Premium™ can score 96 percent of American adults ­– a significant increase from the 81 percent that are scorable with conventional scores relying on only traditional credit data. Additionally, such enhanced composite scores could enable 6 million of today's subprime population to qualify for “mainstream" (prime or near-prime) credit.1 How is expanded FCRA-regulated data changing the credit scoring system? The current credit scoring system is rapidly evolving, and modern technology is making it easier for lenders to access expanded FCRA-regulated data. Indeed, this data disruption is changing lender business in a positive way. “When lenders use expanded credit data assets, they see that many unscorable and credit invisible consumers are in fact creditworthy," said Wright. “Layering in expanded FCRA-regulated data gives a clearer picture of consumers' financial situation." By expanding data assets, tapping into artificial intelligence and machine learning, lenders can now score many more consumers quickly and accurately. Moreover, forward-thinking lenders see these expanded data assets as offering a competitive edge: it's estimated that modern credit scoring methods could allow lenders to grow their pool of new customers by almost 20 percent.1 Case study: Consumer-permissioned data To date, over 9 million people have used Experian Boost. The technology uses positive payment history as a way to recognize customers who exhibit strong credit behaviors outside of traditional credit products. “Boosted" consumers were able to add on average 14 points to their FICO scores in 2022 so far, making many eligible for additional financial products with better terms or better product offerings. Active Boost consumers, post new origination performed on par or better than the average U.S. originator, consistently over time. “In other words, having this additional lens into a consumer's financial health means lenders can expand their customer base without taking on additional credit risk," explains Wright. The bottom line The world of credit data is undergoing a revolution, and forward-thinking lenders can build a sound business strategy by extending credit to consumers previously excluded from it. This not only creates a more equitable system, but also expands the customer base for proactive lenders who see its potential in growing business. Learn more 1Oliver Wyman white paper, “Financial Inclusion and Access to Credit,” January 12, 2022.