Tag: fair lending

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

Conventional credit scoring systems are based on models developed over six decades. As consumer behavior evolves, it's important to seek newer, fresher sources of data to assess creditworthiness. Because the data used by conventional credit scoring models does not provide the full picture of a consumer's financial health, a large population segment of the United States is excluded from accessing credit.With changing times and new technology, forward-thinking financial institutions are using alternative data1 to gain a more holistic consumer view. A move toward inclusive finance, including incorporating alternative data in credit scoring models, is a crucial step towards promoting financial inclusion and helping millions of consumers achieve their financial and personal goals. More importantly, it provides the insight needed for lender confidence, which can help fuel business growth. Understanding limitations of the conventional scoring system Credit scores can be obtained from any one of the major credit bureaus based on information found in a consumer's credit report and are incorporated into a lender's credit-decisioning process. While there are various credit scoring models based on lender preference that could yield slightly different scores, all traditional scores are comprised of credit characteristics within these categories: payment history, credit mix, credit history length, amounts owed and new credit account inquires. Lenders use past credit performance to predict whether extending credit is a risk, posing a major challenge for credit invisible and thin-file consumers and leaving millions at a disadvantage. This dilemma also limits business growth for lenders. Consumers who are unable to access mainstream credit often turn to the alternative financial services (AFS) industry, a $140 billion market that continues to grow by 7-10 percent each year.2 The AFS industry offers consumers additional products, like payday loans, cash advances, short-term installment loans, and rent-to-own loans, none of which are included in a traditional credit file. With alternative credit data, lenders can obtain a more holistic view of creditworthiness and risk, helping to enhance inclusive lending by broadening their pool of potential loan candidates. Why conventional scoring models simply aren't enough Because of the criteria used to assess creditworthiness, conventional credit scoring models do not accurately capture an individual's financial behavior or health. Indeed, many people demonstrate financial responsibility in other legitimate ways that are not reported to the major credit bureaus.In contrast, non-traditional data considers a consumer's everyday financial behavior to provide a more accurate score for lenders. It can include a range of indicators, such as: Bill payments: Consistent payment history on typical household bills (which may have been paid from a debit account). Bank account data: Shows average balance and withdrawal activity and recurring payroll deposits (indicating that a consumer is employed and receives a regular income). Rental data: Indicates a consumer's long-term stability in making regular, on-time monthly rent payments. Registered licenses: Registered licenses or membership with a skilled  trade or profession can indicate the likelihood to generate income. Including this type of data can benefit both lenders and applicants. According to an Experian report, by adding alternative credit data to a near-prime population, lenders could see an increase in approvals for consumers historically being left behind. When Clear Early Risk Score™ is paired with the VantageScore® credit score, approvals climb to 16 percent of the population inside the same risk criteria, representing a 60 percent lift in credit approvals for near-prime consumers.2 The pool of people from whom this type of alternative data can reliably be collected is growing, with 70 percent of consumers willing to provide additional financial information to a lender if it increases their chance for approval or improves their interest rate for a mortgage or car loan.3 Plenty of available yet untapped data exists that can add value to a consumer's profile and lead to greater inclusive lending. For example, 95 percent of Americans own a cell phone and about two-thirds of households headed by young adults are being rented. Reporting on this data could potentially "thicken" a credit file and provide deeper insight into a consumer's credit behavior.3Indeed, turning to non-traditional data can expand the credit universe and lead to more inclusive credit scoring models, especially by leveraging existing technology and financial inclusion solutions. Research shows that with Lift Premium™, virtually all of the 21 million conventionally unscorable consumers would become scoreable, and over 1 million of them would have scores in the near-prime range or better. Of these, 1.7 million would be Black American and Hispanic/Latino people.3 For lenders, these numbers reveal potential opportunities to grow their businesses. Of the 255 million adults in the U.S., 19 percent of credit eligible adults are left out of mainstream scoring systems. 28 million are considered credit invisible – meaning they have no credit history (11%). 21 million are considered unscorable – have partial credit history but not enough to generate a score using conventional models (8%). Of the remaining credit eligible adults, 57 million were considered subprime (22%). 106 million U.S. adults can't get mainstream credit rates (42%). Adopting inclusive finance lending practices is not only the right thing to do but also provides financial institutions with the chance to reach untapped markets, grow their business and promote a healthier economy. Financial inclusion is not a destination, but an ever-evolving journey. Don't miss out on this critical opportunity to join the movement. Learn more about our financial inclusion tools to help enhance your inclusive lending approach. 1"Alternative Credit Data,” 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.2Experian: 2020 State of Alternative Credit Data.3Oliver Wyman white paper, “Financial Inclusion and Access to Credit," January 12, 2022.

Nearly 28 million American consumers are credit invisible, and another 21 million are unscorable.1 Without a credit report, lenders can’t verify their identity, making it hard for them to obtain mortgages, credit cards and other financial products and services. To top it off, these consumers are sometimes caught in cycles of predatory lending; they have trouble covering emergency expenses, are stuck with higher interest rates and must put down larger deposits. To further our mission of helping consumers gain access to fair and affordable credit, Experian recently launched Experian GOTM, a first-of-its-kind program aimed at helping credit invisibles take charge of their financial health. Supporting the underserved Experian Go makes it easy for credit invisibles and those with limited credit histories to establish, use and grow credit responsibly. After authenticating their identity, users will have their Experian credit report created and will receive educational guidance on improving their financial health, including adding bill payments (phone, utilities and streaming services) through Experian BoostTM. As of January 2022, U.S. consumers have raised their scores by over 87M total points with Boost.2 From there, they’ll receive personalized recommendations and can accept instant card offers. By leveraging Experian Go, disadvantaged consumers can quickly build credit and become scorable. Expanding your lending portfolio So, what does this mean for lenders? With the ability to increase their credit score (and access to financial literacy resources), thin-file consumers can more easily meet lending eligibility requirements. Applicants on the cusp of approval can move to higher score bands and qualify for better loan terms and conditions. The addition of expanded data can help you make a more accurate assessment of marginal consumers whose ability and willingness to pay aren’t wholly recognized by traditional data and scores. With a more holistic customer view, you can gain greater visibility and transparency around inquiry and payment behaviors to mitigate risk and improve profitability. Learn more Download white paper 1Data based on Oliver Wyman analysis using a random sample of consumers with Experian credit bureau records as of September 2020. Consumers are considered ‘credit invisible’ when they have no mainstream credit file at the credit bureaus and ‘unscorable’ when they have partial information in their mainstream credit file, but not enough to generate a conventional credit score. 2https://www.experian.com/consumer-products/score-boost.html

Chatbots, reduction of manual processes and explainability were all hot topics in a recent discussion between Madhurima Khandelwal, Vice President and Head of DataLabs at American Express®, and Eric Haller, Executive Vice President and head of Experian DataLabs. The importance of AI’s role in innovation in the financial services space was the focus of the recent video interview. In the interview, Khandelwal highlighted some of the latest in what American Express DataLabs is working on to continue to solve complex challenges by building tools driven by AI and Machine Learning: Natural language processing has come a long way in even the last few years. Khandelwal discussed how chat bots and conversational AI can automate the simple to complex to enhance customer experience. Document recognition and processing is another leading-edge innovation that is useful for extracting and analyzing information, which saves staff countless manual hours, Khandelwal said. Fairness and explainability are consistently brought to the forefront especially in financial services as regulators are looking at ways to prevent AI/ML from causing bias for the consumer. Khandelwal showcased how there is extreme rigor in each part of creating their models and how human oversight and training are primary drivers for how they stay on top of this. As for innovation advice, Khandelwal points out that it’s important to be aware that AI and innovation are not always interchangeable, and companies need to think through whether a problem needs to be solved through AI/ML models before charting ahead. Another major key to the equation is the data. In all use cases, the undercurrent of innovation in any form is dependent on the data being used. Learn more about this topic and what Harry Potter has to do with women in data science. Watch the Interview

Last week, artificial intelligence (AI) made waves in the news as the Vatican and tech giants signed a statement with a set of guidelines calling for ethical AI. These ethical concerns arose as the usage of artificial intelligence continues to increase in all industries – with the market for AI technology projected to reach $190.61 billion by 2025, according to a report from MarketsandMarkets™. In the “Rome Call for Ethics,” these new principles require that AI systems must adhere to ethical AI guidelines to protect basic human rights. The doctrine says AI must be developed with a focus on protecting and serving humanity, and that all algorithms should be designed by the principles of transparency, inclusion, responsibility, impartiality, reliability, security and privacy.  In addition, according to the document, organizations must consider the “duty of explanation” and ensure that decisions made as a result of these algorithms are explainable, transparent and fair. As artificial intelligence becomes increasingly used in many applications and ingrained into our everyday lives (facial recognition, lending decisions, virtual assistants, etc.), establishing new guidelines for ethical AI and its usage has become more critical than ever. For lenders and financial institutions, AI is poised to shape the future of banking and credit cards. AI is now being used to generate credit insights, reduce risk and make credit more widely available to more credit-worthy consumers. However, one of the challenges of AI is that these algorithms often can’t explain their reasoning or processes. That’s why AI explainability, or the methods and techniques in AI that make the results of the solution understandable by human experts, remains a large barrier for many institutions when it comes to AI adoption. The concept of ethical AI goes hand-in-hand with Regulation B of the Equal Opportunity Act (ECOA), which protects consumers from discrimination in any aspect of a credit transaction and requires that consumers receive clear explanations when lenders take adverse action. Adverse action letters, which are intended to inform consumers on why their credit applications were denied, must be transparent and incorporate reasons on why the decision was made – in order to promote fair lending. While ethical AI has made recent headlines, it’s not a new concept. Last week’s news highlights the need for explainability best practices for financial institutions as well as other organizations and industries. The time is now to implement these guidelines into algorithms and business processes of the present and future. Join our upcoming webinar as Experian experts dive into fair lending with ethical and explainable AI. Register now