Tag: credit risk management

In this series, we explore what businesses are telling us about AI adoption in lending, examining the key barriers and challenges shaping progress. When ambition meets friction Artificial intelligence (AI) in lending now spans far more than predictive models. It includes Machine Learning (ML) systems that power credit and fraud analytics, Generative AI (GenAI) that automates insights and content creation, and increasingly, Agentic AI – autonomous systems that can reason, act, and adapt across workflows. Yet, as these forms of AI evolve, one reality remains constant: integration complexity can be the hidden cost of AI adoption. Experian’s research on the perceptions of AI in lending shows that 84% of lenders plan to prioritise AI in the next two years, and 89% believe it will play a critical role across the lending lifecycle. However, despite this ambition, only 38% report achieving meaningful ROI from existing implementations. 15% of respondents also said that integration complexity is the top barrier to adopting AI in lending, ahead of uncertain ROI and lack of expertise, revealing a widening gap between strategic ambition and operational execution. The disconnect isn’t about belief in AI’s value; it’s about how to make it work at scale. Many lenders have discovered that their biggest challenge isn’t model performance, but rather the web of integrations that connect everything together. Why is integration so complex? Deploying AI in lending involves connecting a complex network of systems, including data repositories, decision engines, compliance frameworks, and vendor tools. Each must interact flawlessly while meeting stringent standards for fairness, transparency, and governance. This complexity arises from several reinforcing factors: Legacy infrastructureCore banking and credit systems were never designed to host AI workloads. Integrating ML and GenAI into these legacy environments creates costly dependencies and slows deployment. Data silosCustomer, credit, and fraud data often reside in disconnected environments, resulting in inconsistent insights and less explainable AI outcomes. Fragmented vendor ecosystemsEach vendor brings its own APIs and integration standards. The result is duplicated work, escalating costs, and a patchwork of partial connections. Compliance and governance demandsEvery AI model must be auditable and explainable. Integrating these requirements into multiple systems compounds complexity. Talent and expertise gapsFew lenders have teams skilled across data engineering, data science, ML Ops, GenAI, and integration design, leaving critical bridges incomplete. The result is an invisible drag on innovation. AI projects stall not because the models fail, but because the ecosystem around them cannot connect. The hidden cost of AI adoption Integration complexity is the silent tax on every AI initiative. It doesn’t appear in budgets, yet it inflates costs, extends timelines, and erodes returns. For data science teams, it means less time innovating and more time fixing pipelines. For IT, it means managing brittle integrations that break with every system change. For business leaders, it means projects that take longer and deliver less than promised. And for the organisation, it creates a trust issue in both the technology and its outcomes. When AI decisions can’t be traced or reproduced because data or model integrations are opaque, confidence collapses. Overcoming Integration Complexity The path forward isn’t about replacing everything old with something new. It’s about connecting intelligently, building infrastructure that unites data, analytics, and decisioning under a common, interoperable framework. Build AI-ready infrastructureAn AI-ready infrastructure is modular and connected. It utilises APIs, shared data models, and orchestration layers to enable ML, GenAI, and Agentic systems to collaborate effectively. This allows lenders to scale responsibly without disrupting legacy operations. Embed governance and transparencyRegulation and responsibility must be built into the architecture, not added later. AI-ready systems ensure models are explainable, decisions are auditable, and governance is consistent across all integrations. Collaborate with trusted ecosystem partnersNo lender can solve integration complexity alone. Collaboration with trusted data and analytics partners provides proven frameworks, shared standards, and interoperability that accelerate transformation. Experian’s research highlights this dynamic. 72% of lenders trust Experian to deliver reliable AI, and 66% already view it as a strategic AI partner, reinforcing that trust is the foundation of scalable innovation. From fragmented to unified Lenders that overcome integration complexity move from fragmented systems to connected ecosystems, and the benefits are transformative: Speed to Value - Unified data and model pipelines cut deployment times dramatically. Consistency and Explainability - Decisions are made on complete, traceable data. Scalability - One integration foundation supports multiple AI use cases across credit, fraud, marketing, and collections. Each integration solved accelerates future value, turning AI adoption into a flywheel for enterprise-wide agility. Futureproofing the AI Ecosystem As AI matures from model to ecosystem, integration becomes an ongoing capability, not a one-time challenge. Future-ready lenders will: Adopt open standards for interoperability across ML, GenAI, and Agentic tools. Invest in modularity, allowing teams to innovate without destabilising core systems. Rethink workflows, embedding AI into decisioning processes, not just data pipelines. The ability to integrate rather than replace will define a competitive advantage, protect past investments, and enable continuous learning and adaptation. The industry leaders in AI will not be those with the most advanced models but those with the most connected ecosystems, where ML, GenAI, and AI Agents operate in concert, powered by simplicity beneath the surface. Learn more about Experian Ascend Platform

In 2025, financial institutions grappled with acceleration - faster digital journeys, faster fraud, and faster AI adoption. As we go into 2026, the challenge has shifted. Speed is no longer the differentiator. Instead, control, confidence, and accountability are driving investment. Experian’s Global Insights 2026: Predictions for credit and fraud risk explores how organisations are responding to rapid change by moving from experimental innovation to accountable intelligence that is connected, governed and trusted at scale. Grounded in analyst insight, Experian research, and market signals, the report identifies seven shifts that will define the year ahead. Download report 1. Making AI deliver through accountability and governance The optimism that fuelled generative AI (GenAI) adoption throughout 2024 and 2025 has evolved into a more disciplined focus on performance, return on investment, and operational integrity. In 2026, organisations are no longer asking what AI can do; they are asking whether it delivers measurable value, integrates safely into core workflows, and can be governed with confidence. For many organisations, this reality check has already arrived. AI that cannot demonstrate value or withstand governance scrutiny will struggle to scale. 2.The agentic ecosystem transforms enterprise automation Early agentic AI deployments show promise in automating tasks and accelerating workflows. But as adoption grows, so does complexity. In 2026, organisations will focus less on individual agents and more on how agents are orchestrated, governed and integrated. Agent frameworks will become commonplace, but differentiation will come from platforms that can orchestrate data, enforce policy and maintain consistency across every automated workflow. 3. Fraud and identity risks intensify, demanding layered, intelligent orchestration Consumers are increasingly relying on AI tools to guide financial decisions, marking the early stages of AI-mediated customer journeys. Fraudsters are already exploiting the gaps created as AI and automation accelerate. In 2026, identifying who (or what) is on the other side of a digital interaction is becoming harder. GenAI has rendered voice authentication unreliable through advanced voice cloning, while deepfake videos undermine visual trust. Autonomous AI agents are beginning to act on behalf of humans in everyday transactions, often indistinguishable from malicious bots. In this environment, traditional identity checks simply cannot keep pace. Identity verification must evolve from static checks to continuous, contextual validation. 4. Quality and connectivity of data define intelligent credit AI is only as effective as the data that powers it. In 2026, data quality, lineage and governance take centre stage. Businesses building explainable AI require reliable, well-structured data that can be traced, audited, and updated in real-time. The ability to orchestrate all data sources within a single, high-quality ecosystem will determine the success of every credit and fraud decision. In an AI-driven world, data quality and integration are the most powerful levers of performance and trust. 5. Credit, fraud and compliance converge into unified intelligence Historically separate risk functions are now converging as financial institutions pursue consistent decisions, lower operating costs and stronger governance. This shift is driven by both regulatory pressure and operational necessity. By breaking down silos, financial institutions can not only strengthen risk management but also unlock better financial opportunities for their customers, creating a more secure and seamless ecosystem. 6. Partnerships and integration drive growth In 2026, platform strategy becomes inseparable from partnership strategy. No lender can access every data source or defend against every threat alone. The winning model is collaborative, modular and interoperable. Businesses are recognising that partnerships are no longer optional; they define coverage, scalability and the ability to participate in agent-enabled ecosystems where identity, data and payment layers work together seamlessly. 7. The credit lifecycle becomes frictionless and human-verified The credit lifecycle is evolving in response to shifting consumer behaviour and rapidly changing technology, becoming both automated and human-centred. In 2026, this evolution begins to take shape through customer journeys, where identity is continuously verified, fraud controls adapt in real time, decisions are explainable, and consumers experience interactions that feel both effortless and trustworthy. The capabilities that will drive success in 2026 As 2026 unfolds, organisations face an environment defined by accountable AI, converging risk functions, rising fraud threats, and the emergence of agent-driven ecosystems. Success will depend on the ability to seamlessly orchestrate data, identity and intelligence, while keeping people at the centre of oversight and accountability. Download the full Global Insights 2026: Predictions for credit and fraud risk report

The Global Fraud Snapshot 2025 draws on extensive research from across the UK, US, EMEA, APAC, and Brazil to uncover how consumer behaviour, technological change, and business strategies are shaping this year’s fraud landscape. Global Fraud Snapshot 2025 Here’s what we found: Fraudsters are exploiting new technologies at unprecedented scale, while consumers demand stronger, more transparent protections. As a result, businesses are caught between defending against increasingly complex attacks and delivering seamless digital experiences. Consumer awareness is high while trust remains low Almost 80% of consumers in the UK and US are aware of online scams, and in Brazil, more than half of consumers report having been victims of fraud. Despite this, confidence is low. Fewer than a third of UK and US consumers believe businesses are transparent about how their data is used, or that they can reliably identify them online. The trust gap is widening, and businesses are under pressure to respond. Security over convenience: what consumers expect Consumers consistently place security above convenience or personalisation in digital experiences. Globally, consumers believe businesses must act decisively to protect their identities and online transactions, and many are willing to share more personal data if it results in stronger safeguards – 76% of UK consumers and 65% of US consumers say they would do so if it improved protection. Biometrics continues to be the most trusted authentication method, signalling the continued expectation for frictionless yet robust security. AI investment priorities in a GenAI-driven fraud landscape For businesses, fraud attack volumes are increasing, driven by the rise in generative AI (GenAI), which is accelerating the creation of synthetic identities, fuelling a surge in identity theft, and enabling more sophisticated authorised push payment (APP) scams. Across every region, AI and machine learning (ML) have moved to the top of the investment agenda to tackle these growing fraud threats, with the UK and US citing GenAI as a key priority for investment in authentication. Both the UK and US are also prioritising the adoption of new analytics methods and the development of new AI models to enhance customer decision-making. In addition, more than half of UK business respondents are targeting investment in the detection and prevention of synthetic identity fraud, while in the US, 63% cite APP fraud prevention as a priority. In EMEA and APAC, the liability shift for APP fraud is accelerating investment in stronger defences. Convergence of fraud, credit risk and anti-money laundering (AML) The snapshot also highlights a structural shift: fraud prevention is no longer a standalone discipline. 65% of businesses in EMEA/APAC and 60% in the UK are now integrating fraud and anti-money laundering (AML) operations. Increasingly, credit risk is being brought into the fold as well, reflecting a broader move toward unified risk management. The convergence of typically siloed functions helps businesses build a more accurate picture of risk exposure - detecting fraud disguised as defaults or chargebacks, identifying credit risk patterns linked to financial crime, and improving overall decisioning across the customer lifecycle. What businesses should do next Closing the trust gap requires bold, strategic action. Businesses must: Invest in AI and advanced analytics to remove false positives and stay ahead of increasingly complex threats. Adopt a multi-layered strategy through fraud orchestration to remove silos and reduce friction while strengthening protection. Integrate fraud, credit risk, and AML functions for a unified view of risk. Prioritise transparency around data use to rebuild consumer confidence. Educate consumers to raise awareness of scams and reinforce trust in digital experiences. Click to read the full Global Fraud Snapshot 2025:

Moving into the agentic era of fraud and credit risk decisioning In the next five years, underwriting will change more than it has in the past two decades. That’s the clear message from Experian’s latest global study. Based on research with more than 700 senior decision-makers across 10 countries and over 70 expert interviews. The findings point to an underwriting future that is more automated, contextual, and seamlessly embedded into customer journeys. But while technology is the enabler, the driving force is the consumer. Download report Consumers want more, and technology is heightening expectations Today’s consumers want credit experiences that are faster, more transparent, and tailored to their real financial lives. Over 40% of respondents told us that a frictionless borrowing experience is now the top priority for consumers, especially younger generations. They’re no longer willing to tolerate difficult journeys, unclear terms, or long waits for a decision. This means underwriting can no longer remain a back-office function. It must become a front-line service that operates in real time, embedded within digital channels. Already, 83% of industry leaders expect lending to become an embedded part of commercial transactions, not a standalone product. Technology is transforming how risk is assessed Automation and AI are changing how we assess risk and make decisions. But crucially, most experts do not expect AI to fully replace human underwriters. Instead, AI is seen as an enabling tool, handling lower-ticket cases at scale, while humans remain essential in high-value or complex decisions. What is changing is the scale and speed. By 2030, decisions will increasingly be powered by agentic AI – digital agents that act on behalf of customers to compare products, submit applications, and verify identity. This will radically simplify the journey but will also mean that businesses must rethink how they secure trust and verify identity in a world of invisible interactions. Alternative data is essential, but orchestration is the key More data is available now than ever before, but access alone is not enough. The winners in 2030 will be those who can orchestrate data in real time, from traditional credit files to behavioural, open banking, and synthetic sources, to build a holistic, explainable view of each customer. In fact, 80% of respondents expect to rely more on alternative data than traditional sources by the end of the decade. This shift will improve credit inclusion and model precision, but also demands investment in platforms that can ingest and manage data with full transparency. Fraud is not going away As underwriting becomes more seamless, the traditional touchpoints for fraud detection start to disappear. In low-friction journeys, fraud doesn’t vanish; it hides. That’s why 94% of leaders told us that cybersecurity and fraud prevention will remain a top priority into the next decade. Trust will need to be embedded into the journey with dynamic risk signals, behavioural biometrics, and AI-based identity verification working in the background to spot anomalies and synthetic profiles. Risk and fraud functions, once siloed, are now converging. What should businesses do now? The insight from this research is clear: businesses must evolve their approach to underwriting to better tackle a rapidly evolving environment. Here are the five priorities to focus on now: Invest in orchestration: Data access alone isn’t enough. Tools must connect, validate, and use alternative, behavioural and consented data in real time. Embed trust: Adopt continuous risk monitoring, robust digital identity, and explainable models to ensure fraud is detected and decisions remain accountable. Ensure platform-readiness : Cloud-native, API-first architecture will be essential for agility, scalability and compliance. Rethink human contribution: The role of the underwriter will shift from manual decision-making to exception handling, oversight, and governance. Train staff for exception handling, oversight, and governance, not manual processing. Adopt a partnership mindset: Success will depend on building and managing high-performing ecosystems and leveraging trusted partners for infrastructure, insights, and innovation. Want to lead in 2030? Download report

Across the financial sector, model risk management is no longer just a regulatory obligation; it’s a strategic imperative. As financial institutions face rising pressure from regulators and the growing complexity of AI, one area that has emerged as particularly difficult to get right is model documentation. Experian recently conducted a global study of 511 financial institutions in the United States, the United Kingdom, and Brazil to explore how firms are navigating this challenge. Download full report now Regulatory requirements are increasing According to our research, 95% of financial institutions report a rise in the number of regulations they need to comply with. Nearly seven in ten expect regulatory changes to increase even further over the next two years. What’s changing isn’t just the volume of regulation, it’s the frequency and specificity of regulatory feedback. 79% of institutions say supervisory concerns from regulators have increased, often requiring immediate attention or formal remediation plans. This is not just a compliance issue. When documentation is unclear or inconsistent, it raises doubts about the model itself. This leads to rework, slower approvals, and reputational risk. Manual processes are still the norm Despite these rising pressures, 60% of institutions still rely entirely on manual processes for model documentation, involving an average of 29 people. Larger institutions report 50 or more individuals involved across teams. This approach is time-consuming, error-prone, and unsustainable. Regulatory requirements evolve constantly, and manual documentation simply cannot keep pace. Financial institutions report spending up to one-third of their time on documentation tasks that could be automated. Hybrid tools aren’t enough: Businesses need end-to-end automation Many firms have tried to tackle the challenge with partial automation, but these approaches are falling short. 68% of respondents say their current technology doesn’t meet compliance needs, and most still require manual effort to stitch together outputs from multiple tools. The research found that 28 different third-party tools are being used by respondents, often fragmented and duplicated. What’s needed is an integrated solution across the full model lifecycle and can produce regulator-ready documentation. The future is automated Recognising a need for change, 87% of respondents plan to implement automated model documentation within the next two years, with a near-even split between third-party solutions and internally developed tools. But automation alone isn’t the answer. Success requires strong data foundations, responsible AI frameworks, and modern governance. Financial institutions are prioritising improvements in three key areas over the next 6–12 months: What can financial institutions do now? To meet these challenges, businesses must shift from tactical fixes to strategic transformation: Establish clear documentation standards across model types Embed explainability and responsible AI principles Enable seamless handoffs between model developers and validators Modernise operations to reduce time-to-market and regulatory risk Download the full report now to understand how the compliance landscape is evolving, and what your business can do about it Download now

The credit landscape is undergoing a seismic shift.  Consumers expect seamless, lightning-fast digital experiences, but financial institutions must also contend with rising fraud risks and intensifying regulatory pressure. Incremental tweaks won’t cut it – modern lending demands a radical rethink.  In an era defined by AI, automation and data-driven insights, lenders have a unique opportunity: to transform credit operations from rigid and reaction, to agile, intelligent and scalable.   When presenting our research, I’m often challenged to provide evidence of companies that have made this move and embraced this shift, not just to survive, but to lead. “What benefits did they actually see – in dollars and cents?” It’s a fair question, especially when the stakes are high and the path forward can seem uncertain.   To answer it, Experian commissioned Forrester Consulting to conduct a Total Economic Impact™ study on the impact of Experian Ascend Platform with organisations that have made this move. The findings showcase how lending institutions are leveraging advanced analytics and automation to enhance credit operations, reduce fraud, and accelerate business growth.  Download now The need for accurate and efficient credit operations has never been greater The challenges financial institutions face today: Manual, slow credit decisioning: Lengthy approval processes limit scalability and impact customer satisfaction. Lack of up-to-date data: On-premises environments can prevent accurate up-to-date data. Static scorecards and manual checks happen after the fact. Inconsistent underwriting decisions: Manual assessments introduce bias and inefficiencies. Rising fraud risks: The financial ecosystem faces increasing fraud threats that require real-time detection. Market volatility: Institutions must adapt faster to economic changes and regulatory requirements. Key findings from Forrester Total Economic Impact™ of Experian Ascend Platform Forrester’s independent research provides quantifiable insights into the financial impact of Experian Ascend Platform. The results are based on a composite organisation representative of interviewed customers over three years. 183% ROI with a $13.3M Net Present Value (NPV) 12-month payback period 12% improvement in approval rates over three years 5% year-over-year in new revenue from additional applications 67% efficiency gains in credit decisioning 20% reduction in default costs These numbers show institutions investing in Experian Ascend Platform see rapid, measurable returns. Real-world impact: How businesses are benefiting In addition to the data, the study includes real customer successes across industries: Car leasing company: Increased approval rates from 60% to 66%, leading to better risk management and higher conversion rates. Global airline: Reduced fraud chargebacks by 99.9% (from 6,660 cases per year to just 4), preventing financial losses and reputational damage. Fintech lender: Reduced model development time from months to days, improving agility in risk assessment. These organisations transformed their operations by replacing legacy systems with cloud-based, automation and advanced analytics that deliver real-time insights and consistent, scalable decision-making. "Experian Ascend Platform is driving revenue because more business is being accepted on an automated basis. It’s taking the decision away from the underwriters - making decisioning more consistent - and we are seeing less revenue erosion through successful fraud reporting."Credit Manager, Car Leasing How the Forrester Total Economic Impact™ study can help your business Financial leaders can use the Forrester Total Economic Impact™ of Experian Ascend Platform as a strategic decision-making tool to: Explore ROI potential: Start with the specific areas your business could benefit from using the Experian Ascend Platform, such as operational efficiency, faster decisions, or marketing effectiveness. Build a business case that resonates: Back your investment with proven results. Use real-world success metrics from organisations like yours to shape a compelling, data-driven case. Uncover new growth opportunities: Think beyond cost savings and efficiency gains. With automation and advanced analytics, there’s real potential to expand your portfolio, enter new markets, and deepen customer engagement. Mitigate risk with confidence: See how other businesses have successfully reduced fraud, defaults, and compliance risk. This study provides a data-driven framework to help financial institutions understand the value added to their business. Download the full Forrester Total Economic Impact™ of Experian Ascend Platform study to explore the potential financial impact on your business.

Financial institutions have no shortage of data — but transforming it into actionable insights remains a challenge. Siloed systems, inconsistent workflows, and compliance concerns often slow progress and limit the impact of AI and analytics initiatives. Transforming raw data into valuable insights hinges significantly on feature building —selecting, modifying or creating new custom features based on existing data to enhance model performance. Download the eBook to understand the key challenges businesses face today and what they can do in response: Data silos and poor lineage tracking Disconnected teams and manual processes Difficulty scaling model development efficiently By centralising and automating feature development, financial institutions can reduce risk, boost agility, and improve time to market. It’s not just about better tools — it’s about creating smarter workflows that align people, data, and strategy. Download now

By leveraging insights from leading industry analysts, Experian's expertise, extensive market studies, and market sentiment, we identified four key themes shaping the financial services sector this year. Read now Four themes impacting financial services this year: 1. Fraud evolution driven by AI Tracking synthetic identities is a big challenge for FIs in 2025, exacerbated by fraudsters' use of Gen AI tools to scale activities. Investment in AI is a growing priority as banks seek to strengthen identity verification. Account takeover (ATO) and Authorised Push Payment Fraud (APP) are also growing problems very much linked to advanced AI methods employed by criminals. Collaboration across institutions and the adoption of advanced analytics will be critical in staying ahead of fraudsters. 2. Advanced AI will improve operational efficiencies in new ways GenAI and Agentic AI (an orchestration tool connecting multiple AI models) are unlocking new levels of efficiency and personalisation. The emphasis on adoption is twofold: first, automating steps to accelerate development and delivery, and second, ensuring transparency, compliance, and governance. Businesses need to take an incremental approach to GenAI adoption, with centralised governance and a focus on explainability. AI will improve mid-office processes where internal manual inefficiencies impact downstream customer interactions. 3. Emergence of RegTech to meet complexities of compliance Heightened regulatory scrutiny is driving the adoption of innovative compliance technologies. Adopting cloud-native, modular systems supports more agile compliance strategies and reduces the cost and complexity of updating solutions. Explainable AI is increasingly essential for demonstrating compliance and fostering regulator confidence in automated decision-making. 4. Convergence of risk management The integration of fraud prevention, credit risk assessment, and compliance is a growing trend among financial institutions. Digital identity frameworks and unified data analytics are becoming essential for holistic risk management. Banks need to embrace collaborative approaches and consortium-level partnerships to address interconnected challenges. Read the report

Experian's new global report is now available on how businesses can enhance efficiency, insights, and growth through integration to transform the future of risk strategy. Download report In the ever-evolving financial landscape, the convergence of credit risk, fraud risk, and compliance is becoming a game-changer. Financial institutions (FIs) increasingly recognise the need to integrate these functions to enhance efficiency, gain deeper insights, and drive growth. The 2024 global report on the convergence of credit, fraud, and compliance sheds light on this critical transformation, emphasising how a unified strategy can revolutionise risk management. The report highlights the importance of convergence in shaping the future of financial services. We surveyed 750 leaders in credit risk, fraud risk and compliance in financial services organisations across the world. Inside the report: The need for convergence As technology advances, financial institutions (FIs) face the dual challenge of managing complex systems while simplifying consumer processes. The report reveals that organisations use an average of eight tools across credit, fraud, and compliance, with some using more than ten. This fragmentation leads to inefficiencies and increased risks.In addition, 79% of respondents want to work with fewer vendors to manage credit risk, fraud, and compliance, underscoring the need for streamlined operations. Independent evolution of functions and associated challenges Credit risk, fraud risk, and compliance functions have evolved independently, creating operational silos and technology management challenges. This separation has led to increased fraud and credit losses. The report highlights that only 9% of organisations prioritise these functions equally, with most focusing on fraud. However, 87% of respondents acknowledge the overlap between these areas and are working towards closer collaboration. Regulatory pressures and advanced fraud techniques New regulations in the US, UK, and EU are compelling FIs to reimburse consumers for losses due to scams, increasing the liability for both sending and receiving banks. Penalties for failing to implement effective Anti-Money Laundering (AML) solutions have also intensified. These regulatory demands and advanced fraud techniques necessitate a more integrated approach to risk management. Early stages of convergence While the market is beginning to recognise the benefits of convergence, many FIs are still in the early stages of this journey. The convergence speed varies, but mature organisations have already started or plan to start the process soon. The report shows that 91% of respondents believe that forward-looking companies will centralise these functions within the next three years. However, only 15% prefer a 'point solution', 36% prefer a single integrated solution, and 49% prefer modular integration. The role of technology Technology plays a crucial role in integrating functions and managing risk. Next-generation platforms are essential for adapting to market needs, delivering innovative products, and meeting regulatory requirements. The report emphasises the importance of data aggregation, which combines diverse data for deeper insights, and the integration of credit decisioning and fraud detection solutions to balance risk and growth goals simultaneously. Improving risk management through alignment Correctly identifying consumers, managing fraud risk, making informed credit decisions, and ensuring compliance share common ground. The report shows that 57% of respondents believe aligning credit risk, fraud, and compliance functions leads to better overall risk management. Businesses with more centralised practices report improved risk management effectiveness, operational efficiencies, and data integrity. Benefits of convergence The convergence of credit risk, fraud, and compliance offers numerous benefits, including: Improved risk management effectiveness: Better alignment leads to more effective risk management strategies. Operational efficiencies: Streamlined processes and reduced duplication of efforts enhance operational efficiency. Increased data integrity: Centralised data management ensures consistency and accuracy. Cost reduction: Consolidation of functions and technology reduces costs. Enhanced customer experience: A unified approach improves customer recognition and service across all channels. Read the report to find out how to prove value through integration. Download report

In an era where businesses are inundated with data and options for consumer engagement, it is paramount to use sophisticated targeting techniques that reach and resonate deeply with the intended audience. Pre-screen targeting solutions are becoming increasingly sophisticated, offering a strategic advantage by enabling more precise and impactful outreach, especially within the financial services sector. Technological evolution and targeting precision The core innovation behind pre-screened targeting solutions is extensive data analytics and predictive modelling. These systems integrate detailed consumer data, such as purchasing behaviors and credit scores, with advanced algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This methodological approach streamlines campaign efforts and enhances each interaction's accuracy and tactical effectiveness. Effective targeting with direct mail Understanding the dynamics of various targeting channels is crucial for deploying effective strategies. In the competitive landscape of financial services in North America, direct mail has been shown to have distinct advantages. Direct mail offers substantial engagement. For credit products, this is typically 0.2-2% for prime consumers and 1-3% for near prime and subprime consumers**. This channel’s effectiveness stems from its tangible nature, which cuts through digital clutter and captures consumer attention.​ Benefits of pre-screened targeting solutions Maximized response rates—Direct Mail response models can dramatically boost prospect response rates by targeting a well-defined, high-propensity audience likely to be interested in specific offers. Using a custom response model could improve the average response rate of pre-screen direct mail campaigns by 10-25%**. Reduced risk—Traditional broad-spectrum marketing campaigns waste resources on uninterested parties. Pre-screened targeting via direct mail aims to gain the right through-the-door prospects, minimizing the risk of fraud and delinquencies, thus leading to significant cost savings on underwriting costs. Enhanced customer engagement and retention—Targeted and personalized direct mail strengthens customer relationships by making recipients feel valued. This leads to higher engagement and loyalty, essential for long-term business success. Robust compliance and enhanced security—Pre-screened solutions simplify adherence to industry regulations and consumer privacy standards. These systems come equipped with compliance safeguards that help prevent data breaches and ensure that all communications meet regulatory standards, which is especially critical in the highly regulated financial sector. Looking forward: The strategic imperative of advanced targeting and optimization As markets evolve, the strategic importance of deploying precise and efficient marketing techniques will only grow. Financial institutions harnessing pre-screened targeting and optimization solutions gain a significant competitive edge, achieving higher immediate returns and long-term customer loyalty and brand strength. Optimization ensures that the right customer prospects are targeted and done within business constraints such as resources and direct mail budgets. Future enhancements in AI and machine learning are expected to refine the capabilities of pre-screened targeting solutions further, offering even more sophisticated tools for marketers to engage with their target audiences effectively. For businesses aiming to lead in efficiency, customer satisfaction, and innovation, adopting advanced pre-screened targeting solutions is not just an option—it’s a necessity for staying relevant in a crowded and competitive marketplace. About Ascend Intelligence ServicesTM (AIS) Target AIS Target is a sophisticated pre-screening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Find out more ** Experian Research, Data Science Team, July 2024

Why agile data integration is key to profitability and reduced time-to-market for lenders, and how businesses are looking to cloud, alternative data sources and self-serve to enable this opportunity. “Data integration is increasingly critical to companies’ ability to win, serve, and retain their customers. To accelerate their performance in data integration, companies are evaluating and adopting a range of contributing technologies.” The Forrester Tech Tide: Enterprise Data Integration As the digital world expands, new and alternative data sources continue to emerge rapidly. With this exponential growth comes the need for financial services companies to integrate new data sources into models quickly and seamlessly. The ability to respond promptly to market changes that require new data sources can significantly reduce time to market for lenders, improving customer decisions by using a mix of traditional and alternative data that ultimately raises approval rates and, in turn, profitability. Research conducted by Forrester Consulting on behalf of Experian shows that a lack of available data is one of the three top technology pain points for tech decision-makers at financial services businesses.* According to the same research, 29% of respondents said that acquiring new customers that match the businesses’ risk appetite is a current challenge, while simultaneously reporting that credit scores still dominate data in decisioning. As more data becomes available, the gap continues to widen between what is possible, and what the reality is for financial institutions. With more data accessible through APIs, lenders have the opportunity to enhance their data analytics capabilities, leading to more personalised loan offers and cross-selling products. Our research supports this: 47% of banks and 52% of FinTechs say that increasing personalisation is a top priority. However, at the same time, data integration opportunities also pose challenges for lenders, namely around security, compliance, and cost. Data access and integration challenges As the prospect of open banking proliferates, newly proposed rules by government bodies such as the Consumer Financial Protection Bureau (CFPB) around consumer data sharing could significantly open financial data access through APIs, further enabling the potential for partnerships between financial institutions and data aggregators. Although open data access and the integration of third-party services present lenders with challenges around the cost of cloud services and total ownership, according to a recent trends report from Datos**, financial institutions will need to invest in secure, scalable, and compliant cloud infrastructure to handle the increased data flow and integration requirements. Cloud deployment: enabling data integration Adopting new credit operations technology is pivotal to data-driven strategy for lenders and deploying that technology in the right way can be critical. Cloud makes it easier to connect data feeds, allowing different internal departments to safely work with data from a variety of sources. Most respondents in our study prefer cloud-based technology, with 83% citing that a cloud or hybrid solution is the preferred deployment option and just 17% seeking on-premises deployment. Self-serve data integration Another key component of agile data integration is enabling users in-house to manipulate data sources flexibly. By speeding up the data integration process with low-code and no-code platforms and tools, businesses can customise their APIs regardless of in-house team experience, allowing data integration to happen in days instead of weeks. “Increasing use of low-code and no-code capabilities give business users the ability to create more customized and packaged business analytics capabilities with business-centric modularity and embed into applications via APIs to serve their business objectives.”Gartner’s Top Trends in Data Analytics, 2023 Improving data integration is central to the quest for speed and agility in today’s credit risk market. With 25% of business respondents citing that they prioritise investment in initiatives that accelerate time to market in response to business and market changes, organisations are ready to capitalise on the opportunity. According to Datos, in 2024, next-generation core banking platforms are poised to address these challenges, providing flexibility, agility, and configurability, along with cloud-native benefits, ensuring financial services institutions stay competitive in the rapidly evolving technological landscape.** Learn more about Credit Decisioning *In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. **Datos Top 10 trends Retail Banking Payments 2024

As credit card issuers grow, the size of their customer base expands, bringing both opportunities and challenges. One of the most critical challenges is managing growth while controlling default rates. Credit Limit Optimization (CLO) has emerged as a vital tool for banks and credit lenders to achieve this balance. By leveraging machine learning models and mathematical optimization, CLO enables lenders to tailor credit limits to individual customers, enhancing profitability while mitigating risk. Recent trends in credit card debt To understand the significance of Credit Limit Optimization, it is essential to consider the current economic landscape, particularly in North America. The first quarter of 2024 saw total household debt in the U.S. rise by $184 billion, reaching $17.69 trillion. While credit card balances declined slightly (a reflection of seasonal factors and consumer spending patterns), they remain a substantial component of household liabilities, with total credit card debt standing at approximately $1.26 trillion in early 2024. On average, American households hold around $10,479 in credit card debt, which is down from previous years but still significant. The average APR for credit cards in the first quarter of 2024 was 21.59%.* The rising tide of delinquencies In the first quarter of 2024, about 8.9% (annualized) of credit card balances transitioned into delinquency. This trend underscores the need for credit card issuers to adopt more sophisticated methods to assess credit risk and adjust credit limits accordingly. The rising rate of credit card delinquencies is a key driver behind the adoption of CLO strategies. What is Credit Limit Optimization Credit limit optimization uses advanced analytics to assess individual customers' creditworthiness. By analyzing various data points, including payment history, income levels, spending patterns, and economic indicators, these tools can recommend optimal credit limits that maximize customer spending potential while minimizing the risk of default, all within the constraints set by the business in terms of its appetite for risk and capacity. For instance, a customer with a strong payment history and stable income might receive a higher credit limit, encouraging more spending and enhancing the lender's revenue through interest and interchange fees. Conversely, customers showing signs of financial stress might see their credit limit reduced to prevent them from accumulating unmanageable debt. Benefits of Credit Limit Optimization Improved Profitability - By setting credit limits reflecting customers' credit risk and spending potential, lenders can increase their revenue through higher interest and fee income. Reduced Default Rates - Lenders can significantly reduce the incidence of bad debt by identifying customers at risk of default and adjusting their credit limits accordingly. Improved Customer Satisfaction - Personalized credit limits can improve customer satisfaction, as customers are more likely to receive credit that matches their needs and financial situation. Regulatory Compliance - CLO can help lenders comply with regulatory requirements by ensuring that credit limits are set based on objective, data-driven criteria. Economic indicators and CLO Impact Several economic indicators provide context for the importance of CLO in the current market. For instance, the Federal Reserve reported that in 2023, fewer than half of adult credit cardholders carried a balance on their cards, down from previous years. This indicates a more cautious approach to credit use among consumers, likely influenced by economic uncertainty and rising interest rates. Moreover, the disparity in credit card debt across different states highlights the varying economic conditions and the need for tailored credit strategies. States like New Jersey have some of the highest average credit card debts, while states like Mississippi have the lowest. This regional variation underscores lenders' need to adopt flexible, data-driven approaches to credit limit setting. Enhanced profitability and risk mitigation Credit limit optimization is critical for credit card issuers aiming to balance growth and risk management. As economic conditions evolve and consumer behaviors shift, the ability to set personalized credit limits will become increasingly important. By leveraging advanced analytics and machine learning, CLO enhances profitability and contributes to a more stable and resilient financial system. One such solution is Experian’s Ascend Intelligence Services (AIS) Limit™, which provides an optimized strategy designed to enhance the precision and effectiveness of credit limit assignments. AIS Limit™ combines best-in-class bureau data with machine learning to simulate the impact of different credit limits in real time. This capability allows lenders to quickly test and refine their credit limit strategies without the lengthy trial-and-error period traditionally required. AIS Limit™ enables lenders to set credit limits that align with their business objectives and risk tolerance. By providing insights into the likelihood of default and potential revenue for each credit limit scenario, AIS Limit™ helps design optimal limit strategies. This not only maximizes revenue but also minimizes the risk of defaults by ensuring credit limits are appropriate for each customer's financial situation. In a landscape marked by rising delinquencies and varying regional debt levels, the strategic use of CLO like AIS Limit™ represents a forward-thinking approach to credit management, benefiting both lenders and consumers. Ascend Intelligence Services * HOUSEHOLD DEBT AND CREDIT REPORT (Q1 2024) – Federal Reserve Bank of New York