Tag: ai

Model inventories are rapidly expanding. AI-enabled tools are entering workflows that were once deterministic and decisioning environments are more interconnected than ever. At the same time, regulatory scrutiny around model risk management continues to intensify. In many institutions, classification determines validation depth, monitoring intensity, and escalation pathways while informing board reporting. If classification is wrong, every downstream control is misaligned. And, in 2026, model classification is no longer just about assigning a tier, but rather about understanding data lineage, use case evolution, interdependencies, and governance accountability in a decentralized, AI-driven environment. We recently spoke with Mark Longman, Director of Analytics and Regulatory Technology, and here are some of his thoughts around five blind spots risk and compliance leaders should consider addressing now. 1. The “Set It and Forget It” Mentality The Blind Spot Model classification frameworks are often designed during a regulatory remediation effort or inventory modernization initiative. Once documented and approved, they can remain largely unchanged for years. However, model risk management is an ongoing process. “There’s really no sort of one and done when it comes to model risk management,” said Longman. Why It Matters Classification is not merely descriptive, it’s prescriptive. It drives the depth of validation, the frequency of monitoring, the intensity of governance oversight and the level of senior management visibility. As Longman notes, data fragmentation is compounding the challenge. “There’s data everywhere – internal, cloud, even shadow IT – and it’s tough to get a clear view into the inputs into the models,” he said. When inputs are unclear, tiering becomes inherently subjective and if classification frameworks are not reviewed regularly, governance intensity can become misaligned with real exposure. Therefore, static classification is a growing risk, especially in a world of rapidly expanding AI use cases. In a supervisory environment that continues to scrutinize model definitions, particularly as AI tools proliferate, a dynamic, periodically refreshed classification process can demonstrate institutional vigilance. 2. Assuming Third-Party Models Reduce Governance Accountability The Blind SpotThere is often an implicit belief that vendor-provided models carry less governance burden because they were developed externally. Why It Matters Vendor provided models continue to grow, particularly in AI-driven solutions, but supervisory expectations remain firm. “Third-party models do not diminish the responsibility of the institution for its governance and oversight of the model – whether it’s monitoring, ongoing validation, just evaluating model drift” Longman said. “The board and senior managers are responsible to make sure that these models are performing as expected and that includes third-party models.” Regulators consistently emphasize that institutions remain responsible for the outcomes produced by models used in their decisioning environments, regardless of origin. If a vendor model influences credit approvals, pricing, fraud decisions, or capital calculations, it directly affects customers, financial performance and compliance exposure. Treating third-party models as inherently lower risk can also distort internal tiering frameworks. When vendor models are under-classified, validation depth and monitoring rigor may be insufficient relative to their true impact. 3. Limited Situational Awareness of Model Interdependencies The Blind SpotModern decisioning environments are interconnected ecosystems. Forecasting models may influence reserve calculations. Marketing models may be repurposed across product lines. Data transformations may feed multiple downstream models simultaneously. Why It Matters Risk often flows across interdependencies. When upstream models degrade in performance or introduce bias, downstream models inherit that exposure. If multiple material decisions depend on the same data transformation or feature engineering process, concentration risk emerges. Without visibility into these dependencies, tiering assessments may underestimate cumulative risk, and monitoring frameworks may fail to detect systemic vulnerabilities. “There has to be a holistic view of what models are being used for – and really somebody to ensure there’s not that overlap across models,” Longman said. Supervisors are increasingly interested in understanding how model risk propagates through business processes. When institutions cannot articulate how models interact, it raises broader concerns about situational awareness and control effectiveness. Therefore, capturing interdependencies within the classification framework enhances more than documentation. It enables more accurate tiering, more targeted monitoring and more informed governance oversight. 4. Excluding Models Without Defensible Rationale The Blind SpotGray-area tools frequently sit outside formal inventories: rule-based engines, spreadsheet models, scenario calculators, heuristic decision aids, or emerging AI tools used for analysis and summarization. These tools may not neatly fit legacy definitions of a “model,” and so they are sometimes excluded without robust documentation. Why It Matters Regulatory definitions of “model” have broadened over time. What creates risk is the absence of defensible reasoning and documentation. Longman describes the risk clearly: “Some [teams] are deploying AI solutions that are sort of unbeknownst to the model risk management community – and almost creating what you might think of as a shadow model inventory.” Without visibility, institutions cannot confidently characterize use, trace inputs, or assign appropriate tiers, according to Longman. It also undermines the credibility of the official inventory during examinations. A well-governed program can articulate why certain tools fall outside model risk management scope, referencing documented criteria aligned with regulatory guidance. Without that evidence, exclusions can appear arbitrary, suggesting gaps in oversight. 5. Inconsistent or Subjective Classification Frameworks The Blind SpotAs inventories scale and governance teams expand, classification decisions are often distributed across reviewers. Over time, discrepancies can emerge. Why It Matters Inconsistency undermines both risk management and regulatory confidence. If two models with comparable use cases and impact profiles are assigned different tiers without clear justification, it signals that the framework is not being applied uniformly. AI adds even more complexity. When it comes to emerging AI model governance versus traditional model governance, there’s a lot to unpack, says Longman: “The AI models themselves are a lot more complicated than your traditional logistic or multiple regression models. The data, the prompting, you need to monitor the prompts that the LLMs for example are responding to and you need to make sure you can have what you may think of as prompt drift,” Longman said. As frameworks evolve, particularly to incorporate AI, automation, and new regulatory interpretations, institutions must ensure that changes are cascaded across the entire inventory. Partial updates or selective reclassification introduce fragmentation. Longman recommends formalizing classification through a structured decision tree embedded in policy to ensure consistent outcomes across business units. Beyond clear documentation, a strong classification program is applied consistently, measured objectively, and periodically reassessed across the full portfolio. BONUS – 6. Elevating Classification with Data-Level Visibility Some institutions are extending classification discipline beyond models to the data layer itself. Longman describes organizations that maintain not only a model inventory, but a data inventory, mapping variables to the models they influence. This approach allows institutions to quickly assess downstream effects when operational or environmental changes occur including system updates or even natural disasters affecting payment behavior. In an AI-driven environment, traceability may become a competitive differentiator. Conclusion Model classification is foundational. It determines how risk is measured, monitored, escalated, and reported. In a rapidly evolving regulatory and technological environment, it cannot remain static. Institutions that invest now in transparency, consistency, and data-level visibility will not only reduce supervisory friction – they will build a governance framework capable of supporting the next generation of AI-enabled decisioning. Learn more

Three winners were announced at Experian’s inaugural Vision Awards ceremony held on Tuesday, October 7 in front of more than 800 attendees at Experian’s Vision Conference held in Miami, Fla. Figure, PREMIER Bankcard and Members First Credit Union were recognized for their work in artificial intelligence, innovation and financial empowerment. The four-day gathering provided a dynamic forum for exploring the latest innovations shaping the future of data-driven decisioning. “Our Vision Awards celebrate the unique impact financial industry leaders can have when data, technology and purpose align,” said Jeff Softley, CEO, Experian North America. “We are proud to recognize these three organizations with whom we collaborate to drive opportunities and help create change for society as a whole.” The Vision Awards recognize the achievements of organizations that accelerate action. These forward-thinking institutions leverage artificial intelligence, innovation and financial empowerment to drive opportunities and create actionable change for consumers, businesses and society. Recognizing Leaders in AI, Innovation, and Financial Empowerment A panel of interdisciplinary judges reviewed nominations from across industries across the regions, evaluating submissions based on rigor, originality, and impact. The 2025 winners reflect how organizations are leveraging data and technology to advance innovation and inclusion. Excellence in AI: Figure Figure’s submission showcased how it has redefined consumer lending outreach through an AI-driven targeting engine powered by more than 90 machine learning models and 5,000+ behavioral and financial features. By combining Experian’s prescreen data with proprietary insights, Figure delivers highly precise, cost-efficient firm offers of credit — helping it become one of the top three home equity line of credit lenders in the U.S. “This win reflects more than just a successful application of AI. It represents the broader innovative culture deeply embedded in our company’s DNA,” said Ruben Padron, Chief Data Officer at Figure. “Our work with Experian has been instrumental in helping us assess creditworthiness and predict borrower intent with greater precision.” Excellence in Innovation: PREMIER Bankcard PREMIER Bankcard continues to demonstrate how financial inclusion and innovation go hand in hand. From modernizing its technology to reimagining its product suite, PREMIER has made bold strides to serve the underserved and democratize access to credit. “This award affirms our belief that financial inclusion and innovation must go hand in hand,” said Chris Thornton, Senior Vice President of Credit at PREMIER Bankcard. “We’re committed to reaching those who need it most, and Experian has proven to be an exceptional partner in that mission.” With more than 30 million customers served, PREMIER has become a leader in first-time and second-chance credit, while also giving back more than $4 billion to charitable causes through its partnership with First PREMIER Bank and founder Denny Sanford. “We’re here to change lives,” Thornton added. “That’s how we measure success — and that’s ultimately what we’re investing in.” Excellence in Financial Empowerment: Members First Credit Union Members First Credit Union was honored for its commitment to inclusive lending and community development across Michigan. In 2024 alone, the credit union’s programs helped thousands of members access fair and affordable credit, supported 166 community organizations, and contributed nearly $230,000 in donations — backed by 2,000 volunteer hours from its employees. “Our impact demonstrates how mission-driven financial institutions can meaningfully expand access, strengthen communities, and foster long-term financial health,” said Carrie Iafrate, CEO/President at Members First Credit Union. “We’re honored to receive this recognition and inspired to continue helping individuals thrive financially.” Honoring the Judges Behind the Vision The 2025 Vision Awards were evaluated by a distinguished panel of judges representing both Experian and external associations and partners in the financial inclusion community, including: Lisa Cantu-Parks, Vice President of Resource Development, Unidos Jean Carlos Rosario Mercado, Juntos Avanzamos Program Officer, Inclusiv Ian P. Moloney, Senior Vice President, Head of Policy and Regulatory Affairs, American Fintech Council Marc Morial, President and CEO, National Urban League Kevin O’Connor, Senior Vice President, Membership and Sponsorship, Consumer Bankers Association Their expertise ensured that the winners reflect the industry’s highest standards of innovation, integrity, and impact. Ian P. Moloney, Senior Vice President, Head of Policy and Regulatory Affairs, American Fintech Council, and Rhonda Spears Bell, Senior Vice President and Chief Marketing Officer, National Urban League, were at the recognition session at Vision and shared about their organizations and experience serving as a judge. Video messages were also shared from Jean Carlos Rosario Mercado of Inclusiv and Kevin O’Connor of Consumer Bankers Association, who were unable to attend the live event. “I greatly appreciated the opportunity to participate as a judge in the Experian Vision Awards because it provided me a chance to look beyond my usual day-to-day, and understand the myriad of innovations and projects going on to help consumers and the industry,” Moloney said. “The award winners tonight showcase the best of our industry, and I appreciate the opportunity to take part in highlighting their success.” “I’m inspired by the outstanding organizations we’re celebrating tonight - each making a lasting impact in our country and globally,” Spears Bell said. “I want to take a moment to recognize Experian - not only as a valued corporate partner, but as a true ally in our mission to advance financial literacy, stability, and generational wealth.” Looking Ahead: Vision Awards 2026 Experian will continue to champion progress in financial services and across all industries, and the Vision Awards offers one of the avenues through which the industry can recognize organizations driving change through responsible innovation. Submissions for the 2026 Vision Awards open on June 1, 2026. To learn more about this year’s winners and how to apply for next year’s program, visit the Vision Awards page.

AI credit scoring addresses traditional limitations by introducing more advanced, data-driven techniques. Learn the benefits and challenges.

Fake IDs have been around for decades, but today’s fraudsters aren’t just printing counterfeit driver’s licenses — they’re using artificial intelligence (AI) to create synthetic identities. These AI fake IDs bypass traditional security checks, making it harder for businesses to distinguish real customers from fraudsters. To stay ahead, organizations need to rethink their fraud prevention solutions and invest in advanced tools to stop bad actors before they gain access. The growing threat of AI Fake IDs   AI-generated IDs aren’t just a problem for bars and nightclubs; they’re a serious risk across industries. Fraudsters use AI to generate high-quality fake government-issued IDs, complete with real-looking holograms and barcodes. These fake IDs can be used to commit financial fraud, apply for loans or even launder money. Emerging services like OnlyFake are making AI-generated fake IDs accessible. For $15, users can generate realistic government-issued IDs that can bypass identity verification checks, including Know Your Customer (KYC) processes on major cryptocurrency exchanges.1 Who’s at risk? AI-driven identity fraud is a growing problem for: Financial services – Fraudsters use AI-generated IDs to open bank accounts, apply for loans and commit credit card fraud. Without strong identity verification and fraud detection, banks may unknowingly approve fraudulent applications. E-commerce and retail – Fake accounts enable fraudsters to make unauthorized purchases, exploit return policies and commit chargeback fraud. Businesses relying on outdated identity verification methods are especially vulnerable. Healthcare and insurance – Fraudsters use fake identities to access medical services, prescription drugs or insurance benefits, creating both financial and compliance risks. The rise of synthetic ID fraud Fraudsters don’t just stop at creating fake IDs — they take it a step further by combining real and fake information to create entirely new identities. This is known as synthetic ID fraud, a rapidly growing threat in the digital economy. Unlike traditional identity theft, where a criminal steals an existing person’s information, synthetic identity fraud involves fabricating an identity that has no real-world counterpart. This makes detection more difficult, as there’s no individual to report fraudulent activity. Without strong synthetic fraud detection measures in place, businesses may unknowingly approve loans, credit cards or accounts for these fake identities. The deepfake threat AI-powered fraud isn’t limited to generating fake physical IDs. Fraudsters are also using deepfake technology to impersonate real people. With advanced AI, they can create hyper-realistic photos, videos and voice recordings to bypass facial recognition and biometric verification. For businesses relying on ID document scans and video verification, this can be a serious problem. Fraudsters can: Use AI-generated faces to create entirely fake identities that appear legitimate Manipulate real customer videos to pass live identity checks Clone voices to trick call centers and voice authentication systems As deepfake technology improves, businesses need fraud prevention solutions that go beyond traditional ID verification. AI-powered synthetic fraud detection can analyze biometric inconsistencies, detect signs of image manipulation and flag suspicious behavior. How businesses can combat AI fake ID fraud Stopping AI-powered fraud requires more than just traditional ID checks. Businesses need to upgrade their fraud defenses with identity solutions that use multidimensional data, advanced analytics and machine learning to verify identities in real time. Here’s how: Leverage AI-powered fraud detection – The same AI capabilities that fraudsters use can also be used against them. Identity verification systems powered by machine learning can detect anomalies in ID documents, biometrics and user behavior. Implement robust KYC solutions – KYC protocols help businesses verify customer identities more accurately. Enhanced KYC solutions use multi-layered authentication methods to detect fraudulent applications before they’re approved. Adopt real-time fraud prevention solutions – Businesses should invest in fraud prevention solutions that analyze transaction patterns and device intelligence to flag suspicious activity. Strengthen synthetic identity fraud detection – Detecting synthetic identities requires a combination of behavioral analytics, document verification and cross-industry data matching. Advanced synthetic fraud detection tools can help businesses identify and block synthetic identities. Stay ahead of AI fraudsters AI-generated fake IDs and synthetic identities are evolving, but businesses don’t have to be caught off guard. By investing in identity solutions that leverage AI-driven fraud detection, businesses can protect themselves from costly fraud schemes while ensuring a seamless experience for legitimate customers. At Experian, we combine cutting-edge fraud prevention, KYC and authentication solutions to help businesses detect and prevent AI-generated fake ID and synthetic ID fraud before they cause damage. Our advanced analytics, machine learning models and real-time data insights provide the intelligence businesses need to outsmart fraudsters. Learn more *This article includes content created by an AI language model and is intended to provide general information. 1 https://www.404media.co/inside-the-underground-site-where-ai-neural-networks-churns-out-fake-ids-onlyfake/

Romance scams target individuals of all ages and backgrounds. Financial institutions need to protect their customers from these schemes.

Using AI in debt collection can help financial institutions leverage technology to ensure more accurate and timely collections.

Scott Brown, Group President at Experian, recently presented at Reuters Next on the power of AI innovation in financial services.

Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights.

AI is significantly transforming the landscape of real estate fraud, enabling criminals to execute schemes like deed theft with greater ease.

With the advent of AI and ML, optimizing credit prescreen campaigns has never been easier or more efficient.

Experian’s award-winning platform now brings together market-leading data, generative AI and cutting-edge machine learning solutions.

Being able to explain how an ML model works and what drives its decisions is important if you want to use ML-powered models for underwriting.

Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

From science fiction-worthy image generators to automated underwriting, artificial intelligence (AI), big data sets and advances in computing power are transforming how we play and work. While the focus in the lending space has often been on improving the AI models that analyze data, the data that feeds into the models is just as important. Enter: data-centric AI. What is a data-centric AI? Dr. Andrew Ng, a leader in the AI field, advocates for data-centric AI and is often credited with coining the term. According to Dr. Ng, data-centric AI is, ‘the discipline of systematically engineering the data used to build an AI system.’1 To break down the definition, think of AI systems as a combination of code and data. The code is the model or algorithm that analyzes data to produce a result. The data is the information you use to train the model or later feed into the model to request a result. Traditional approaches to AI focus on the code — the models. Multiple organizations download and use the same data sets to create and improve models. But today, continued focus on model development may offer a limited return in certain industries and use cases. A data-centric AI approach focuses on developing tools and practices that improve the data. You may still need to pay attention to model development but no longer treat the data as constant. Instead, you try to improve a model's performance by increasing data quality. This can be achieved in different ways, such as using more consistent labeling, removing noisy data and collecting additional data.2 Data-centric AI isn't just about improving data quality when you build a model — it's also part of the ongoing iterative process. The data-focused approach should continue during post-deployment model monitoring and maintenance. Data-centric AI in lending Organizations in multiple industries are exploring how a data-centric approach can help them improve model performance, fairness and business outcomes. For example, lenders that take a data-centric approach to underwriting may be able to expand their lending universe, drive growth and fulfill financial inclusion goals without taking on additional risk. Conventional credit scoring models have been trained on consumer credit bureau data for decades. New versions of these models might offer increased performance because they incorporate changes in the economic landscape, consumer behavior and advances in analytics. And some new models are built with a more data-centric approach that considers additional data points from the existing data sets — such as trended data — to score consumers more accurately. However, they still solely rely on credit bureau data. Explainability and transparency are essential components of responsible AI and machine learning (a type of AI) in underwriting. Organizations need to be able to explain how their models come to decisions and ensure they are behaving as expected. Model developers and lenders that use AI to build credit risk models can incorporate new high-quality data to supplement existing data sets. Alternative credit data can include information from alternative financial services, public records, consumer-permissioned data, and buy now, pay later (BNPL) data that lenders can use in compliance with the Fair Credit Reporting Act (FCRA).* The resulting AI-driven models may more accurately predict credit risk — decreasing lenders' losses. The models can also use alternative credit data to score consumers that conventional models can't score. Infographic: From initial strategy to results — with stops at verification, decisioning and approval — see how customers travel across an Automated Loan Underwriting Journey. Business benefit of using data-centric AI models Financial services organizations can benefit from using a data-centric AI approach to create models across the customer lifecycle. That may be why about 70 percent of businesses frequently discuss using advanced analytics and AI within underwriting and collections.3 Many have gone a step further and implemented AI. Underwriting is one of the main applications for machine learning models today, and lenders are using machine learning to:4 More accurately assess credit risk models. Decrease model development, deployment and recalibration timelines. Incorporate more alternative credit data into credit decisioning. AI analytics solutions may also increase customer lifetime value by helping lenders manage credit lines, increase retention, cross-sell products and improve collection efforts. Additionally, data-centric AI can assist with fraud detection and prevention. Case study: Learn how Atlas Credit, a small-dollar lender, used a machine learning model and loan automation to nearly doubled its loan approval rates while decreasing its credit risk losses. How Experian helps clients leverage data-centric AI for better business outcomes During a presentation in 2021, Dr. Ng used the 80-20 rule and cooking as an analogy to explain why the shift to data-centric AI makes sense.5 You might be able to make an okay meal with old or low-quality ingredients. However, if you source and prepare high-quality ingredients, you're already 80% of the way toward making a great meal. Your data is the primary ingredient for your model — do you want to use old and low-quality data? Experian has provided organizations with high-quality consumer and business credit solutions for decades, and our industry-leading data sources, models and analytics allow you to build models and make confident decisions. If you need a sous-chef, Experian offers services and has data professionals who can help you create AI-powered predictive analytics models using bureau data, alternative data and your in-house data. Learn more about our AI analytics solutions and how you can get started today. 1DataCentricAI. (2023). Data-Centric AI.2Exchange.scale (2021). The Data-Centric AI Approach With Andrew Ng.3Experian (2021). Global Insights Report September/October 2021.4FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context. 5YouTube (2021). A Chat with Andrew on MLOps: From Model-Centric to Data-Centric AI *Disclaimer: When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

Lenders are increasingly using artificial intelligence (AI) and machine learning (ML) throughout the customer lifecycle and access to these advanced tools is expanding.