Best Practices: Diversity and Inclusion in the Financial Services Industry

by Corliss Hill 5 min read March 9, 2023

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.

Related Posts

Ask the Expert: A Closer Look at Modern Lending with Jeff Hops and Erin Haselkorn

In this first episode of Ask the Expert, Experian's Jeff Hops, Senior Director of Data Platform and Product, and Erin Haselkorn, Senior Director of Analyst Relations, explore how broader data and new signals can help lenders better understand today’s consumers, while maintaining responsible decisioning. Lending is changing  Interest rates, regulation, embedded finance and AI are reshaping the lending landscape. Consumer behavior is evolving just as quickly. But the core job hasn’t changed. Lenders are still making decisions about people they don’t fully know, and that makes data more important than ever. "There are periods where nothing changes, and periods where it seems like everything changes. We’re in the latter … but the core premise hasn’t changed. You’re still trying to lend to somebody you don’t know."Jeff Hops, Senior Director of Data Platform and Product To make those decisions with confidence, lenders need a strong foundation of identity, history and reliable signals. In a period of rapid change, the quality and completeness of that data become even more critical. A more complex view of today’s consumer What has changed is the consumer. Traditional credit data is foundational but can be further enhanced with visibility on how people earn, manage and move money. Income may come from multiple sources, and financial activity often spans bank accounts, applications (apps) and digital channels. Cash flow data, for example, can provide a clearer view of what’s actually coming into a consumer’s account, beyond what traditional records may show.These additional signals can help lenders better understand: Income variability across multiple earning sources Current financial behavior through cash flow activity Digital and identity-linked activity across channels These signals don’t replace traditional data; they expand it. The result is a more complete and current view of the consumer. From exploration to real-world application The conversation around broader data signals has moved beyond theory. Lenders are no longer just asking whether these signals are useful. They’re asking where, how and under what governance they can be applied across the lending lifecycle. Lenders are actively researching, testing and implementing new data sources across the lending lifecycle. What was once experimental is now operational. Institutions are progressing through a clear path: Research Understanding available signals and use cases Testing Evaluating performance in controlled environments Implementation Applying insights in production Today, alternative data is being used in areas like analytics, channel scoring and decisioning, often within governed environments that allow for safe testing and validation. AI may accelerate this shift by helping institutions identify patterns at scale, but its value depends on the strength of the underlying data: quality, governance, context and clear business use cases. More signal, more responsibility As data availability expands, lenders have access to more granular insights than ever before. That creates opportunity, but also responsibility. The institutions that lead won’t be the ones that use the most data. They’ll be the ones that know which signals to use, how to validate them and how to apply them in ways that are fair, explainable and aligned to consumer outcomes. “Institutions can unlock more granular and powerful decisions, but they have to do it responsibly.”Erin Haselkorn, Senior Director, Analyst Relations The future of lending will be shaped not just by how much data is available, but by how thoughtfully it’s applied. Keeping the consumer at the center of decisioning is essential to building trust and long-term success. Explore alternative data with us A more complete understanding of today’s consumers starts with better data. We help lenders responsibly incorporate broader data signals and advanced analytics into decisioning strategies, enhancing visibility into today’s consumers while strengthening risk assessment and expanding access to credit. Let’s work together to build more confident, more responsible lending decisions. Learn more Contact us About our experts Jeff Hops Senior Director, Data Platform and Product, Experian Jeff Hops is a Senior Director in Experian’s Financial Services and Data business with over eight years of experience driving innovation in credit and data solutions. He has led product development for Experian’s Credit Report and played a key role in launching Ascend Identity Platform™, a leading identity resolution platform. Erin Haselkorn Senior Director, Analyst Relations, Experian Erin Haselkorn is responsible for analyst relations for Experian. She has developed an understanding of key marketing trends across a broad range of verticals. Her market research around data strategy, AI, fraud, identity and data management, paired with her broad Experian product knowledge, gives her a unique understanding of business automation and data trends. Erin is a frequent spokesperson and guest blogger.

Published: June 22, 2026 by Julie Lee
How Consumer Vehicle Choices Are Shaping Automotive Loan Trends

Conversations about rising auto loan balances and higher monthly payments has often centered around increasing vehicle prices and elevated interest rates; and while those factors have undoubtedly played a role, another important piece of the puzzle is the type of vehicles consumers are choosing to purchase. According to Experian’s Automotive Consumer Trends Report: Q1 2026, consumers are continuing to opt for SUVs over other vehicle types, a trend that may be contributing to higher average loan amounts and monthly payments. SUVs accounted for 63.5% of all new retail vehicle registrations over the last 12 months, up from 62.8% a year ago. Additionally, more than 117 million SUVs were in operation across the United States in the first quarter of 2026, making up 42.2% of the market share. At the same time, traditional passenger cars continue to fall in share, coming in at 16.5%, a decrease from 18.4% last year. As consumers increasingly gravitate towards the larger vehicle segment, it reflects the ongoing desire for versatility, cargo capacity, and family-friendly functionality. Electrification’s growing role in consumer purchasing behavior Interestingly, electrified SUVs continue to gain traction, representing 27.7% of all new SUV registrations, these vehicles include battery-electric, hybrids, plug-in hybrids, and other alternative fuel types. Diving a bit deeper, the Tesla Model Y was the market share leader for new, retail electrified SUV registrations in the last 12 months, coming in at 15.8%. Rounding out the top five were Honda CR-V (9.6%), Toyota RAV4 (7.2%), Chevrolet Trax (7.2%), and Toyota Grand Highlander (3.4%). As model availability and familiarity with the electrification segment grows, the broader adoption of these vehicles are playing an increasingly important role in vehicle pricing and overall consumer demand. While average loan amounts and monthly payments are being driven by a combination of factors such as financing costs and consumer purchasing behavior, data in Q1 2026 demonstrates the continued interest in SUVs. This suggests that the industry’s shift toward larger vehicles is likely playing a meaningful role in today’s financing environment. To learn more about SUV insights, view the full Automotive Consumer Trends Report: Q1 2026 presentation.

Published: June 17, 2026 by Kirsten Von Busch
When New Data Impacts MBS Pricing: Student Loan Debt

In our previous post, we described the Current Second Lien Balance field, which is one of over 2,000 fields in the new Experian Mortgage Loan Performance (MLP) dataset. We showed that the Current Second Lien Balance field meets our three-pronged materiality standard for new data delivery: New: Provides information not available in existing datasets (i.e., orthogonal to currently available data). Material: Impacts a sizeable portion of the MBS universe. Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions. In this article, we discuss another field that satisfies the above criteria: Student Loan Balance.  We evaluate this field in the context of these criteria. First, however, we provide a summary of the MLP dataset and how it compares to standard GSE loan-level data available today. Standard GSE Data vs. Experian Mortgage Loan Performance (MLP) Data The MLP dataset contains thousands of fields describing mortgage performance from each borrower, loan, and property perspective, all refreshed monthly (including, amongst other things, new credit scores and refinance inquiry activity, loan performance, filed junior liens, and AVM values).  MLP differs from loan-level data provided by Freddie Mac, Fannie Mae, and Ginnie Mae, which the vast majority of market participants solely rely on, in a number of ways: Standard data provided by the GSEs and GNMA does not contain all the information necessary for accurate forecasting of mortgage prepayment and credit performance. Basic, critical fields like borrower’s current credit score and current junior liens on the property are missing. The new Mortgage Loan Performance (MLP) dataset from Experian contains borrower, loan, and property data fields covering the entire mortgage universe, including Agency, Non-Agency, and Esoteric mortgage products (CES, HELOC, Reverse), both securitized and non-securitized. MLP enables full three-dimensional (borrower + loan + property) tracking with persistent keys for borrower (before and after refinancing), loan (in securities/deals even after exit due to payoffs or buyouts, including before and after MSR sales), and property.  This enables end-to-end analysis of each borrower’s (and property’s) mortgage experience throughout their credit lifecycle. New, Material and Significant Field:  Student Loan Debt MLP contains a number of fields describing each mortgage borrower’s student debt load, including amounts in repayment, forbearance and collections; estimated interest rate, time remaining until forbearance expiration, and more. In the interest of simplicity, for this article we’ll focus on a single student loan-related field within MLP: Student Loans Balance, which is defined as the total balance on open non-deferred student trades reported in the last 3 months. Is Information Regarding Student Loans New to Markets? Standard loan-level data disclosed by the GSEs and GNMA contain no student-loan-specific fields. Theoretically, fields related to DTI at origination might capture some aspect of student loan debt. So, in the best-case scenario for an investor relying solely on standard disclosure, a DTI value as of origination is provided -- yet is never updated as the loan seasons and the borrower’s debt and income change (see more here).  But in the case of federal student loan debt attached to mortgages originated from early 2020 to late 2023, the level of detail provided by disclosure may be even more unknown due to COVID-era repayment and reporting moratoriums. The student loan repayment moratorium was a temporary federal policy that paused required payments, set interest rates to 0%, and suspended collections on most federally-held student loans. The moratorium began in March 2020, with payments resuming in October 2023, making it approximately 3.5 years in duration—the longest consumer credit payment pause in U.S. history. (Source: NCUA ) During the moratorium, student loan-related debt loads may have been understated as federal loans were in a temporary state of $0 repayment.  As an alternative to leaving student loan debt completely out of DTI calculations, an imputed payment equal to only 0.50% of the outstanding balance was often used as a placeholder for a borrower’s DTI calculation. As the Wall Street Journal recently reported, a new report from the Federal Reserve of New York shows a rise in student loan default rates by age group.  Student loan delinquencies have been broadly trending higher across all age groups.  Also, the average age of a borrower in default has risen to 40, and borrowers age 50 and older are now at a higher risk of default than younger groups. This 40 to 50-year-old age group represents prime home ownership years. Defaulted borrowers are also struggling to make other debt payments, too. The same report stated that almost 40% of past due student loan borrowers with auto loans are past due, 56% have at least one credit card past due, and 20% have a past due mortgage.  In addition to increased delinquency risk on their mortgage, borrowers with student loan debt also have fewer mortgage refinance options, as their elevated DTI may prevent qualification for a refinance, or increase the offered rate of a refinance and thereby reduce their incentive.  These dampening effects of student loan debt on mortgage CPR are clearly evident in the data, as described further below. Standard data only reports information related to the primary mortgage and does not include any details on the borrower’s other debts, with the exception of DTI at origination, which is never updated throughout the life of the loan. In contrast, MLP provides a comprehensive view of the borrower’s full credit profile, including other obligations such as credit cards, mortgages on other properties, student loan balances, and much more. Is Student Loan debt material to the residential mortgage market? Approximately $11 trillion of residential mortgage loans were originated during the student loan payment moratorium (Source: Experian MLP Dataset), a period marked by historically low mortgage rates during the COVID era.  As discussed above, DTI data contained in standard market disclosure may be particularly inaccurate for these loans.   As the Wall Street Journal recently reported, a new report from the Federal Reserve of New York shows a rise in student loan default rates by age group.  Student loan delinquencies have been broadly trending higher across all age groups.  Also, the average age of a borrower in default has risen to 40, and borrowers aged 50 and older are now at a higher risk of default than younger groups. This 40 to 50-year-old age group represents prime home ownership years.  Defaulted borrowers are also struggling to make other debt payments, too.   The same report stated that almost 40% of past due student loan borrowers with auto loans are past due, 56% have at least one credit card past due, and 20% have a past due mortgage.  In addition to increased delinquency risk on their mortgage, borrowers with student loan debt also have fewer mortgage refinance options, as their elevated DTI may prevent qualification for a refinance, or increase the offered rate of a refinance and thereby reduce their incentive.  These dampening effects of student loan debt on mortgage CPR are clearly evident in the data, as described further below.  Of today’s $13 trillion in outstanding mortgage debt, more than 10% of that debt ($1.5 trillion) is associated with borrowers who carry student loan debt.  For these borrowers, the average amount of student loan debt outstanding is approximately $50,000, versus a mortgage balance of approximately ~$289,000. In other words, the average student loan debt balance is almost 20% of the mortgage balance for the average borrower who carries both. For this set of borrowers, the average monthly payment is approximately $400 for student loan vs. approximately $2,200 for 1st lien mortgage—so that monthly student loan payments are a significant debt load, approximately 20% of the monthly mortgage payment.  (Source:  Experian MLP Dataset)  Is the effect of student loan debt a significant driver of performance? Figure 1 illustrates prepayments by student loan balance for a sample of loans drawn from MLP. The chart illustrates that borrowers with larger student loan balances prepay much more slowly, likely because some are effectively locked out of refinancing once student loan payments resume due to elevated DTI. The debt-to-income (DTI) ratio calculated using actual student loan payments may be significantly higher than the DTI calculated during the moratorium, in some cases exceeding GSE eligibility thresholds. As illustrated in Figure 1, for in-the-money (ITM) collateral, the differential between loans with material student loan balances (greater than $200,000) and loans with no student debt can reach up to 5 CPR. Notably, even for out-of-the-money (OTM) collateral, loans with student debt prepay 1 to 3 CPR slower, likely reflecting reduced mobility due to tighter financing constraints when purchasing a new home. Pools with otherwise similar prepayment characteristics may exhibit different prepayment behavior depending on the distribution of student loan exposure within their collateral. In addition, because loans with student debt tend to prepay more slowly, this effect increases over time due to burnout: loans without student debt prepay and exit the pools more quickly, leaving a higher concentration of slower-paying loans behind.  Given that 10% of the $13 trillion outstanding mortgage market is associated with borrowers who have student loans (Source:  Experian MLP dataset)—and that student loans have a meaningful impact on prepayments—many pools issued between March 2020 and October 2023 may be subject to this student loan debt CPR throttle, and therefore mispriced by investors relying exclusively on standard market data. Fig 1. Prepayment S-Curve: Student Loans Balance Source:  Experian MLP dataset hosted on IVolatility Data-Driven Platform _____________________________________________________ Michael Pyatski advises MBS traders, portfolio managers, quants, risk managers, loan originators, and technology professionals on making informed, data-driven business decisions that drive revenue growth, enhance risk management, and reduce trading costs. With more than 15 years of experience as an Agency RMBS trader—including serving as Head of the Proprietary Trading Desk at BNP Paribas—Michael developed and successfully implemented relative-value, data-driven profitable trading strategies to capture market opportunities embedded in data but not fully priced by the market. His trading experience, combined with a Ph.D. in econometrics, led him to found the Data-Driven Portal (https://datadrivenportal.com/), a platform that provides advanced technology for MBS trading and risk management. The platform’s No-Model Data-Driven technology leverages big data, econometric analysis, and AI to help traders identify relative-value opportunities in RMBS markets and generate above-market, risk-adjusted returns. _____________________________________________________

Published: June 17, 2026 by Perry DeFelice, Michael Pyatski