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In the second part of the Juniper Research and Experian podcast series on online payment fraud, we talk to Nick Maynard from Juniper Research, and David Britton, Vice President of Industry Solutions at Experian, about maturity in artificial intelligence and virtual assistants, and their current ability to respond to current business challenges. "What we're seeing in the consumer space is that AI is powering these virtual assistants and typically Alexa, Siri, Google, are the three big examples. What that's doing is creating an additional channel, it's a new way for users to interact... it mirrors the digital transition and the mobile transition over a number of years."Nick Maynard, Juniper Research "If you consider where artificial intelligence and machine learning are coming together, this is not going to be a big bang launch into market. We're seeing a slow, incremental roll-out." "In the physical world, when we talk about risk and recognition of a consumer, the human to human interaction takes in a tremendous number of variables to ensure that the person you're engaging with is who they claim to be.... in the digital space, that was eliminated overnight, and cosnumers were using a device as a proxy to represent them to another system or set of devices, like bank servers and eCommerce web servers." David Britton, VP of Industry Solutions We also discuss key points around evolving regulatory frameworks, and how they are driving change in identity-based solutions. Listen to the full podcast episode here, and don't forget to listen to What’s new in online payment fraud Part 1: Implications for consumers and businesses if you haven't already.

In a recent piece for the Forbes Technology Council, Businesses Need to Modernize Their Approach For Delivering Digital Experiences, I shared how the current rapidly changing environment has greatly accelerated the shift from offline to digital interactions. As businesses experience a need for heightened governance and controls, they must look towards technologies such as artificial intelligence (AI) and machine learning, coupled with access to data in real-time, to move forward. According to the report Experian commissioned Forrester Consulting to conduct, 53% of businesses struggle to make consistent customer decisions. Additionally, only 29% of businesses believe they do a good job of connecting analytics to action. When applying AI and machine learning to customer experiences, there are some concerns that businesses must keep in mind. The first is legal implications and privacy protections, which must always be a priority. The second is to combine analytics models with real-time decisions so that predictions can be harnessed and put into action in real-time. As more and more businesses shift to fully digital experiences, they must learn how to apply their vast amounts of data to models that can help inform the newly remote customer experience. If interested in the topic of businesses’ modernized approach to digital experiences, you can find the full article here.

I recently had the opportunity to talk to Christian Hubbs and Muhammed Shuaibi from Artificially Intelligent Podcast about the value AI and analytics generate for businesses. We reviewed how a growing number of businesses are seeing a lot of value added in terms of problem-solving when they bring in more sophisticated machine learning models and technology.  The conversation quickly pivoted towards how to determine the analytics and AI that better suit your business needs, as well as understanding what is required to operationalize those promising models. Think of performance, scalability, adoption and trust before embarking on your AI journey Ensuring that AI is right for your business requires a holistic approach, which is fundamentally based on four components:   AI Performance – selecting and framing problems, with a view to demonstrate that what you build outperform traditional methods.  AI Scalability - what starts as an experiment conducted by data scientists needs to be turned into a scalable system that truly impacts the business.   AI Adoption – ensuring that your AI and analytics are embraced and used by consumers and businesses and, ultimately, change the way they make decisions.  AI Trust – explaining decisions in a transparent way so the models and systems you build can be trusted, explainable and stand the test and scrutiny of regulators. Leveraging an outcome-based approach to solve COVID-19 related business challenges At Experian, we are applying this holistic approach to identify and address the most pressing concerns our clients are dealing within the context of COVID-19. The first is helping our clients understand what’s currently happening with different customer segments. We’re creating tools that bring together a series of early warnings and indicators and portraying how different customer segments are seeing various patterns in credit. We’re also identifying those most affected or needing concessions around lending, and understanding what banks are doing in terms of forbearance. Our priority is identifying these needs and quickly get the relevant AI and analytical solutions to our clients.  We are expecting to see a later urge in the industry to recalibrate existing models and to expand the type and volume of decisions they can make. Updating and monitoring them will be also a big area of focus over the next couple of years.  Listen to the podcast

Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI, speaks to Forbes' Peter High on his Technovation podcast about Experian's Analytics and AI solutions. During times of crisis, innovation accelerates. What was once considered innovation, suddenly falls into the realms of the necessary, with businesses seeking quick, smart solutions to emerging challenges. Although this conversation took place before COVID19 reached the levels of a global pandemic, Santhanam discusses how advanced analytics and AI can be a game-changer for businesses. Key topics include how businesses need to bring together data, tech and analytics to formulate best in class products and services using AI in the form of examples such as Experian Boost. What it takes to run the global analytics and AI function at Experian. How high-profile consulting positions within previous businesses have placed Santhanam in an ideal position to problem-solve. And what he considers to be the two stand-out developments in the analytics and AI space. Listen to the podcast, or read the interview.

Over the past decade, advances in data availability, modelling techniques and machine learning have materially improved predictive performance across retail banking. Credit risk is more finely segmented, fraud detection more adaptive, and customer insight more granular. At the same time, expectations of analytics are changing. Chartis Research, in its inaugural Retail Banking Analytics50, evaluates providers based on how they help financial institutions use analytics to inform strategy, modelling and go-to-market decisions. Experian was recognised as one of the top vendors in the Retail Banking Analytics 50, receiving awards for Best Overall Strategy, Retail Analytics as a Service, and its Retail Analytics Governance Framework. This reflects a broader shift in emphasis – from model performance in isolation to how analytics is applied across the business. From analytical capability to decisioning systems Most financial institutions now operate with a substantial portfolio of models across acquisition, underwriting, fraud and customer management. Many of these models perform well in isolation. The challenge is how they are applied in practice. In many organisations, analytics still sits across fragmented environments, with separate data layers, different deployment approaches and limited feedback between decisions and outcomes. This does not prevent progress, but it does make it harder to achieve consistency at scale. What is emerging instead is a more integrated approach, where analytics is treated as a continuous system across the lifecycle. In its summary, Chartis notes, "Financial institutions are increasingly prioritizing platforms that are straightforward to implement and offer tightly integrated capabilities across the value chain.” Strategy: aligning analytics to outcomes Aligning analytics to business outcomes remains one of the more complex aspects to execute. Different functions optimise for different objectives. Data definitions are not always consistent. Decision strategies evolve independently over time. Even where underlying models are strong, this can lead to divergence in how decisions are made. Addressing this depends on shared data foundations, reusable features and clearer feedback loops between decisions and outcomes. In practice, achieving this level of alignment remains a work in progress for many organisations. Delivery: enabling scalable execution Cloud-based, API-enabled environments are making it easier to deploy models, update them more frequently and embed decisioning into operational workflows. They also allow multiple models to be applied within a single decision process. However, adoption remains uneven. Many financial institutions continue to operate hybrid environments, where newer capabilities sit alongside legacy infrastructure. This can introduce friction, particularly when scaling changes across multiple decision points. Chartis highlights "financial institutions increasingly are adopting a modular approach to retail analytics, seeking best-in-class external solutions rather than relying solely on legacy systems.” This changes how analytics is consumed, but also increases the importance of how it is integrated into decisioning processes. Governance: supporting scale and confidence As analytics becomes more embedded in decisioning, governance is becoming more operational. Expectations around data quality, model explainability and regulatory compliance continue to increase. At the same time, governance approaches are evolving, moving from periodic validation towards more continuous monitoring and control. The challenge is how governance is implemented. When embedded into development and deployment workflows, it can support scale and consistency. When applied retrospectively, it often introduces delay. Chartis states, “solutions that strengthen governance across data, model risk, controls and compliance also streamline regulatory alignment, reducing the operational burden.” For many institutions, embedding this consistently across the lifecycle remains an area of ongoing development. A more realistic benchmark for analytics success Improvements in model performance increase the need for consistent deployment. Faster deployment introduces new governance requirements. Greater alignment depends on more standardised data and features. As a result, analytics success is increasingly defined at the system level. More broadly, this points to a shift towards evaluating how effectively analytics can be applied across the lifecycle, rather than how individual models perform in isolation. For most institutions, progress will be incremental rather than immediate. The next phase of value will come not from isolated advances in modelling, but from the ability to apply those advances consistently across the business, with the right balance of scalability, control and flexibility. Winner's Summary Chartis’ Retail Banking Analytics50 and the winner’s summary provide additional detail on the capabilities shaping retail banking analytics, including integration across the value chain, analytics as a service and governance.

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

Identity verification (IDV) is evolving from a gating function at onboarding into an adaptive capability that underwrites digital trust across every interaction. In today’s digital world, identity has become the new battleground. Fraudsters are no longer just exploiting payments or accounts; they’re infiltrating identities by layering synthetic profiles, spoofing biometrics, and launching AI-driven deception. For organisations that depend on consumer trust, the question is no longer whether to double down on identity verification, but how to do so in a way that preserves customer experience, supports regulatory compliance, and scales effectively. But how can organisations prepare for this complex set of challenges? To answer that, we examine what industry experts say is required in today’s environment. Experian has been named as a Leader in the IDC MarketScape: Worldwide Identity Verification Financial Services 2025 Vendor Assessment (doc # US52985325, September 2025). The report evaluates vendors on the depth and breadth of their identity verification capabilities and on how effectively they align with current and future customer needs. Read the excerpt IDV must evolve from a gateway to a trust layer For many years, IDV has been a hurdle to face at onboarding. Prove who you are, then proceed. But that paradigm is no longer fit for purpose: Fraud is identity-first. Attacks now are focused on synthetic identity, identity layering, account takeover, and sophisticated imitation. 72% of US business leaders expect AI-generated fraud and deepfakes to be major challenges by 2026. Experian’s 2025 US ID & Fraud Report User expectations and trust sensitivity are rising. Consumers abandon flows when verification feels arbitrary, opaque, or overly burdensome. 40% of US consumers and nearly 30% of UK consumers have considered abandoning a new account setup. Experian Global Fraud Snapshot 2025 Regulatory, audit, and governance pressures are intensifying. Digital ID trust frameworks, privacy regulations, and auditability demands mean that IDV must be auditable, explainable, and modular. Identity is a living construct. People move, change, re-register, and identity data degrades over time, so fraudsters have the opportunity repurpose credentials. Static identity checks become stale - IDV must be woven into ongoing interactions, not just at the starting point. Agentic AI is redefining trust. As humans are increasingly removed from decision loops, IDV frameworks must adapt to ensure integrity in autonomous interactions. New protocols will be needed to extend identity verification into the trust and authorisation requirements of the Human-to-Agent (H2A) environment. IDV must no longer be a checkpoint but a core pillar on which other functions (fraud, KYC, onboarding, collections) lean. How to support continual trust across the full lifecycle Layered, multi-modal signal orchestrationDocument verification, biometrics, device intelligence, behavioural analytics, and external identity attributes are now baseline elements. What differentiates is how these signals are orchestrated: when to escalate, when to fallback, and how dynamically they interact in varying risk scenarios. Orchestration solutions must now extend beyond the technical integration of multiple services to incorporate an intelligence layer that interprets all signals to derive the best outcome for each event. Risk-adaptive trust scoringBinary checks (pass/fail) create either friction or exposure. Businesses need adaptive trust scores that allow for light touch for low-risk journeys and stronger verification for unusual or high-risk interactions. Continuous monitoring & reverificationIdentity is not static. Behaviour drifts, credential changes, and devices often alternate, all of which require ongoing scrutiny. Systems must detect anomalies and trigger re-verification where necessary, embedding verification into usage, recovery, and even offboarding stages. Auditability, explainability & governanceDecisions in identity systems increasingly draw regulatory, audit, and compliance scrutiny. Transparency, decision explainability, and audit trails are vital. For enterprises, this means every verification result should be justifiable, traceable, and defensible. Interoperability & trust networksSiloed identity systems are declining. The future lies in credential attestation, federated identity, and interoperable trust networks. Readiness to support these trust frameworks through integration is key. Resilience to AI-driven identity attacksGenerative AI and synthetic identity tactics stress-test traditional identity checks. To counter these threats, systems now require liveness detection, cross-signal consistency, anomaly detection, and defences against deepfakes. New dynamics shaping identity verification“Not only are solutions more intelligent, but the level of ease with which they can be implemented and flexibly adapted has improved. What was previously a drawn-out process of customization is now facilitated by low-code configuration, AI-backed recommendations, and modular plug-ins.”IDC MarketScape: Worldwide Identity Verification Financial Services 2025 Vendor Assessment (doc # US52985325, September 2025) The IDC MarketScape shares market insights The IDC MarketScape: Worldwide Identity Verification Financial Services 2025 Vendor Assessment reflects the core concerns of organisations investing in identity, fraud, and onboarding capabilities. The study noted the following strengths for Experian: Access to a broad and diverse range of proprietary identity and credit data sources enables multi-layered verification across different financial services use cases. The platform incorporates risk-based authentication, progressive onboarding and behavioural analytics that enable fraud detection with reduced friction. NeuroID integration expands capabilities in behavioural monitoring, including detection of fraud rings and bot behaviour during digital onboarding. Read the IDC MarketScape: Worldwide Identity Verification Financial Services 2025 Vendor Assessment excerpt Learn more about Experian’s fraud solutions

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