AI & Innovation

Every business makes decisions about people and transactions all day long. Should we approve this loan? Is this purchase fraud? Which customer should get this offer, and what should it be? For a long time, those decisions were made in one of two ways: a person reviewed each case by hand, or the company wrote fixed rules, like "approve anyone with a credit score above 700." Both work. Both also leave value on the table. The manual review is slow and hard to scale. The fixed rule can turn away good applicants and is slow to adapt when the market shifts. AI decisioning is a third way. What makes AI decisioning work Instead of relying on a single reviewer or a rigid rule, automated decisioning uses models that learn from data — studying how thousands of past cases turned out, finding the patterns that predict an outcome, and applying them to each new decision, often in real time. The result is faster, more consistent decisions. But a model on its own isn't the whole story. Getting real value from AI decisioning takes good data to learn from, AI analytics to generate insights, the tools to act on it and the governance to keep it compliant. What we've found is that the pieces only pay off when they work together, and that is where we're built differently. A model is only as good as what it learns from, and we pair your data with one of the deepest views of consumer and commercial credit: decades of full-file history and vetted attributes. Then we give you the tools to act on it. Use cases across your business Whether you're trying to grow your customer base, reduce fraud, manage lending risk, or improve collections, automated decisioning brings all the pieces together to make more accurate, consistent and explainable decisions at scale. Fraud and Identity A fraudulent transaction that slips through costs money and erodes trust. Rules are static, and fraudsters move fast. They'll probe boundaries, find the blind spots and move to the next scheme. By the time the rules are updated, they're already three steps ahead. How AI decisioning changes this: AI fraud detection with real-time risk scoring and decisioning across transactions and customer interactions Intelligence that continuously learns from results to help adapt fraud strategies as threats evolve Reduced false positives and less friction for customers at account opening and checkout Identity verification tools that confirm someone is who they say they are without slowing down the experience Credit and Lending Loan approval is where the relationship begins. Credit risk decisioning helps lenders find that delicate balance between approving enough people to grow, but carefully enough to manage risk. Missing that balance means turning away good customers or taking on losses that are difficult to absorb. How AI decisioning changes this: Increased approval opportunities for creditworthy applicants without increasing overall risk Models you can update and deploy quickly as market conditions change, rather than waiting months Ability to run "what-if" scenarios to test how a new strategy would have performed on your historical data before putting it live Collections Which customer should your team reach out to today? Through which channel? What kind of message? If you reach out too aggressively, you push someone who might have recovered into default. If you wait too long, you lose them. If you call someone at work, they resent you; if you text, they might ignore it. If you offer a payment plan, they might accept it, but only if the terms make sense to their financial situation. How AI decisioning changes this: Optimized next-best-action and contact-channel strategies for each individual customer Improved recovery potential through better targeting Less time spent on accounts with a lower propensity to pay, freeing your team for higher-impact cases Ability to segment and test new strategies before rollout Customer Acqusition Finding the right customers is about reaching the right people with the right offer at the right time. To stay competitive, it’s now a requirement to balance growth with risk while creating a seamless experience converting prospects into customers. How AI decisioning changes this: More precise prospect targeting using credit, behavioral, and alternative data, where permitted, to identify consumers most likely to respond Personalized offers delivered in real time Dynamic decision strategies that can be updated quickly as market conditions and customer behavior change Ongoing testing and optimization of acquisition strategies to improve campaign performance and support customer lifetime value Driving results with AI decisioning Every customer interaction is a decision. Businesses that can adapt quickly will be better positioned to grow, manage risk, and deliver the experiences customers expect. The technology will continue to evolve, but the goal remains the same: making informed decisions that balance business objectives, risk, and customer experience. Learn more about our decisioning software

ValidMind CEO Jonas Jacobi shares insights on AI, innovation and why Experian's partnership is helping shape the future of responsible AI.

From innovation to inspiration, Day 2 of Vision 2025 delivered on every front. Attendees experienced a powerful lineup of speakers, engaging breakout sessions and hands-on exploration of technologies shaping the future of finance. Setting the tone: Responsible AI and the future of work The morning opened with an insightful keynote from Sol Rashidi, Chief Strategy Officer of AI & Data at Cyera. With a forward-looking perspective on responsible AI, Sol emphasized the need for data stewardship, workforce readiness and using AI to amplify, not replace, human potential. Her message resonated deeply: Responsible AI means outsourcing tasks, not critical thinking. Challenging convention with Dave Portnoy Next, Dave Portnoy, Founder and Chief of Content at Barstool Sports, brought an energetic conversation on entrepreneurship and disruption in the digital age. His keynote was a reminder that progress is driven by those willing to challenge convention, adapt fast and embrace change with confidence. Exploring the future of financial services After the general session, attendees joined a new wave of breakout sessions exploring the future of financial services. Discussions spanned from how fintech disruptors and embedded finance are reshaping e-commerce and investments to how data and analytics are unlocking new opportunities in housing, lending and fraud prevention. Meanwhile, the Innovation Showcase brought Experian’s cutting-edge capabilities to life, highlighting the power of AI, analytics and modern platforms in driving smarter, faster financial solutions. Closing inspiration: Shaquille O’Neal on reinvention and resilience As the day came to a close, NBA legend Shaquille O’Neal took the stage, captivating the audience with his trademark humor and hard-earned wisdom. He shared stories of his iconic basketball career and business ventures, and his philosophy behind the “business of fun,” leaving us all inspired to think big, act boldly and lead with kindness. A Vision to remember While Vision 2025 has come to a close, the conversations, partnerships and ideas sparked here will continue to shape what’s next in our industry. Thank you for making this year’s event our most inspiring and impactful yet. We can’t wait to see you next year in San Antonio, Texas!

Day 1 of Vision 2025 is in the books – and what a start. From bold keynotes to breakout sessions and networking under the Miami sun, the energy and inspiration were undeniable.  A wave of change: Jeff Softley opens Vision 2025  The day kicked off with a powerful keynote from Jeff Softley, Experian North America CEO, who issued a call to action for the industry: to not just adapt to change, but to lead it.  “It isn’t a ripple – it’s a tidal wave of technology,” Jeff said. “Together we ride this wave with confidence.”  His keynote set the tone for a day centered on innovation and the future of financial services – where technology, insight and trust converge to create lasting impact. Jeff continues this conversation in the latest Experian Exchange episode, where he explores three forces shaping the industry: the rise of AI, the demand for personalized digital experiences and the mission to expand credit access for all.  Turning vision into action: Alex Lintner on agentic AI  Building on Jeff’s message, Alex Lintner, CEO of Experian Software and Technology, took the stage to show how Experian is turning innovation into measurable results. His keynote explored how agentic and advanced AI capabilities are redefining financial services ROI and powering the next generation of the Ascend Platform™.  For a deeper look into how Experian is reshaping the economics of credit and fraud decisioning, read the latest American Banker feature.  Unfiltered insights from “Mr. Wonderful”  The day’s highlight came from Kevin O’Leary, investor, entrepreneur and the always-candid “Mr. Wonderful.” With his trademark wit and honesty, Kevin shared sharp insights on thriving in a disruptive economy, offering candid advice on leadership, risk and opportunity. He even gave attendees a peek behind the Shark Tank curtain, revealing a few surprises and the mindset that drives his bold business decisions.  Breakouts that inspired and informed  The conference floor buzzed with energy as attendees joined breakout sessions on fraud defense, AI-driven personalization, regulatory trends and consumer insights. Sessions highlighted how Experian’s unified value proposition is fueling double-digit growth, how to future-proof credit risk strategies and how data and innovation are redefining customer engagement across the lifecycle.   Hands-on innovation and connection  The Innovation Showcase gave attendees an up-close look at Experian’s latest tools and technologies in action. Meanwhile, friendly competition kept the excitement high through the Vision mobile app leaderboard – with every check-in and connection earning points toward the top spot.  Networking beyond the conference hall walls  As the sun set, Vision 2025 shifted into high gear with unforgettable networking events across Miami – from golf at the Miller Course to art walks, brewery tours and a scenic cruise through Biscayne Bay.   An evening to remember  The day closed with the first-ever Vision Awards Dinner, celebrating standout leaders who are shaping the future of financial services.   Up Next: Day 2  The momentum continues tomorrow as more keynote speakers take the stage. Stay tuned for more insights, innovation, and inspiration from Vision 2025. 

Industry leaders are leveraging GenAI technology to accelerate the modeling lifecycle, streamline workflows and ensure regulatory compliance.

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.

Learn how GenAI is reshaping financial services from customer engagement to compliance, leading to improved decisions and operations.

Experian's latest GenAI solution empowers organizations to increase productivity, improve data visibility, and scale expertise.

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

“Learn how to learn.” One of Zack Kass’, AI futurist and one of the keynote speakers at Vision 2024, takeaways readily embodies a sentiment most of us share — particularly here at Vision. Jennifer Schulz, CEO of Experian, North America, talked about AI and transformative technologies of past and present as she kicked off Vision 2024, the 40th Vision. Keynote speaker: Dr. Mohamed El-Erian Dr. Mohamed El-Erian, President of Queens’ College, Cambridge and Chief Economic Advisor at Allianz, returned to the Vision stage to discuss the labor market, “sticky” inflation and the health of consumers. He emphasized the need to embrace and learn how to talk to AI engines and that AI can facilitate content, creation, collaboration and community Keynote speaker: Zack Kass Zack Kass, AI futurist and former Head of Go-To-Market at OpenAI, spoke about the future of work and life and artificial general intelligence. He said AI is aiding in our entering of a superlinear trajectory and compared the thresholds of technology versus those of society. Sessions – Day 1 highlights The conference hall was buzzing with conversations, discussions and thought leadership. Some themes definitely rose to the top — the increasing proliferation of fraud and how to combat it without diminishing the customer experience, leveraging AI and transformative technology in decisioning and how Experian is pioneering the GenAI era in finance and technology. Transformative technologiesAI and emerging technologies are reshaping the finance sector and it's the responsibility of today's industry leaders to equip themselves with cutting-edge strategies and a comprehensive understanding to master the rapidly evolving landscape. That said, transformation is a journey and aligning with a partner that's agile and innovative is critical. Holistic fraud decisioningGenerative AI, a resurgence of bank branch transactions, synthetic identity and pig butchering are all fraud trends that today's organizations must be acutely aware of and armed to protect their businesses and customers against. Leveraging a holistic fraud decisioning strategy is important in finding the balance between customer experience and mitigating fraud. Unlocking cashflow to grow, protect and reduce riskCash flow data can be used not only across the lending lifecycle, but also as part of assessing existing portfolio opportunities. Incorporating consumer-permissioned data into models and processes powers predicatbility and can further assess risk and help score more consumers. Navigating the economyAmid a slowing economy, consumers and businesses continue to struggle with higher interest rates, tighter credit conditions and rising delinquencies, creating a challenging environment for lenders. Experian's experts outlined their latest economic forecasts and provided actionable insights into key consumer and commercial credit trends. More insights from Vision to come. Follow @ExperianVision and @ExperianInsights to see more of the action.

Lenders who use AI-driven credit risk decisioning can help improve outcomes for borrowers and increase financial inclusion.

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.

Digital transformation in banking reshapes processes, boosts innovation, and delivers personalized experiences to modern financial consumers.

To ensure that decisions are accurate and accountability is maintained, financial institutions must accelerate model velocity.