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

Using business and consumer quantitative and qualitative research from the UK, US, Brazil, EMEA, and APAC between 2023 and 2024, we assess the current global impact of fraud. Download now As 2024 draws to a close, businesses face an increasingly hostile environment in the battle against fraud. Driven by rapid technological advancement and evolving regulatory landscapes, organisations seek new ways to prevent and detect highly sophisticated attacks. Experian’s 2024 Global Fraud Report offers a deep dive into the current state of fraud, revealing critical insights and strategies businesses must adopt to stay ahead of fraudsters. Read the report to discover: Why security and customer experience are still in conflict In today’s digital age, businesses face the daunting task of balancing robust fraud prevention with a seamless customer experience. The report highlights that while stringent security measures are essential, unnecessary friction can drive customers away. A multi-layered approach to fraud prevention, integrating advanced technologies with customer-friendly practices, is crucial. The power of data sharing Data sharing has emerged as a powerful tool in the fight against fraud. By collaborating and sharing data across industries, businesses can gain a comprehensive view of fraud patterns and enhance their detection capabilities. Regulatory frameworks in regions like Brazil and the UK increasingly support data-sharing initiatives, which are vital for effective fraud prevention. What the rise in Authorised Push Payment Fraud means for businesses APP fraud has seen a significant rise in some parts of the world due to newly accessible GenAI tools enabling fraudsters to create more convincing scams at scale. Financial institutions are under pressure to implement measures to protect consumers and comply with new regulations that mandate reimbursement for APP fraud victims. How to uncover synthetic identities Synthetic identity fraud is a growing concern. The report reveals that advancements in GenAI have enabled the creation of highly realistic fake identities, making detection more challenging. Businesses need to invest in advanced analytics and alternative data sources to uncover synthetic identities effectively. Why AI and machine learning are critical to fraud prevention AI and machine learning are pivotal in modern fraud prevention strategies. The report underscores the necessity of these technologies in detecting and preventing fraud. AI and machine learning can analyse vast amounts of data to identify patterns and anomalies that may indicate fraudulent activity. Download the report to discover the 5 key takeaways to combat evolving fraud The 2024 Global Fraud Report reinforces the need for businesses to leverage advanced analytics, alternative data insights, data sharing, and a multi-layered approach to combat evolving fraud threats globally. Download report now About the research The 2024 Global Identity and Fraud Report uses the latest research from the United States, the United Kingdom, Brazil, EMEA, and APAC between 2023 and 2024 to examine fraud worldwide. The research provides combined insights globally from over 1,000 businesses and fraud leaders, as well as 4,000 consumers, focusing on fraud management and digital experience. See the report appendix for full details of the research.

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

Download eBook How to deploy a multi-layered approach with a holistic view of the consumer to stay ahead of evolving fraud. Find out how to mitigate against GenAI-enhanced fraud by downloading the eBook GenAI's rise to the top has been rapid. It was only last year that GenAI fully emerged in the public domain as an accessible tool, with the technology's impact and expectations reverberating across businesses worldwide. This massive growth trajectory has led some critics to suggest that GenAI is nearing its hype peak. However, its potential is still unfolding as the technology continues to evolve and be applied to new use cases. Although its positive applications have enormous potential, the technology also poses many risks. In the fraud space, GenAI poses two main threats: The scaling and personalisation of attacks. Criminals today are generating synthetic content with a goal of decieving businesses and individuals. Fraudsters leverage GenAI to produce convincing synthetic identities and deepfakes that include audio, images, and videos that are increasingly sophisticated and practically impossible to differentiate from genuine content without the help of technology. Fraudsters also exploit the power of Large Language Models (LLMs) by creating eloquent chatbots and elaborate phishing emails to help them steal vital information or establish communication with their targets. Mitigation comes in many forms, depending on the business, but the fundamental differentiator in the fight against evolving and increasing fraud attempts is the ability to have a holistic view of the consumer. Businesses today deploy multiple solutions from various vendors to ensure fraud mitigation covers all touchpoints. Although full coverage may exist, businesses often don’t have a holistic offline and digital view of the consumer, meaning losses can accumulate before patterns emerge within these siloed views. Rapidly evolving, highly automated, and large-scale attacks demand an up-to-date cross-industry view of online and offline identity behavior, linkages, and interactions. The flexible solution must similarly leverage GenAI to spot and validate fraud signals, interpret intelligence from fraud analysts, and quickly operationalize new attributes and models to keep pace with attackers. This is where layered fraud and identity controls in real time and a comprehensive offline analytics platform work together Download the eBook to discover: The rise of GenAI GenAI impact by fraud type Deepfakes: The authenticity challenge The challenge of detecting synthetic identoties Scaling up: The emergence of bot-as-a-service Authorised Push Payment Fraud (APP Fraud) Understanding the role of intent and context in fraud prevention A holistic view of the consumer with Ascend Fraud Sandbox Key takeaways: Find out how to mitigate against GenAI-enhanced fraud Businesses that implement these recommendations will be best equipped to manage fraud spikes from GenAI while simultaneously protecting good customer experiences from being negatively impacted by unnecessary friction. Ascend Fraud Sandbox helps businesses to shine a light on the holistic view of consumer activity across the industry, moving far beyond the typical point-in-time, product-specific view of consumers.Mike Gross, Vice President, appled fraud research and analytics, experian Download eBook

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 PowerCurve *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

Experian has been named a leader in Liminal’s Link Index for Account Takeover Prevention in Banking. Download Report Advances in technology have increased the scale and sophistication of fraud attacks for businesses around the globe with a significant increase in recent years in account takeover fraud (ATO). During the pandemic there was a rise in account opening attacks as the world moved in lockstep to digital channels, creating huge growth in online digital accounts. Now fraudsters are attempting to takeover those digital accounts and are leveraging AI tools to convince consumers to give away their login credentials, creating an enormous financial risk and loss for banks and other service providers. In a March 2024 survey of bank buyers across North America, Europe, Latin America, Asia Pacific, and the Middle East, Liminal found that ATO attacks now average $6,232 per incident, while fraud teams have reported a 66.8% increase in social engineering attacks in the past two years. However, Liminal also found that despite the growing exposure, only 44% of banks are leveraging mobile device signals. The opportunity for banks to implement more effective tools is the result of a combination of factors: 96% are worried about balancing ATO prevention with privacy laws. 82% say customization was necessary to comply with regional regulations. 96% have concerns about limitations on device signals stemming from data restrictions with consumer technologies. As a result, banks are faced with a three-pronged problem: simultaneously solving for authentication, identity and fraud prevention. Identity across the customer lifecycle Truly understanding a customer, especially in a digital-first environment where hundreds of billions of events occur each year, requires much more than ensuring a name matches a social security number and a physical address. The customer, their account information, the device they use, the network they are coming from, the geolocation of their device, and the behavior they exhibit are intertwined. Banks must now assess more information than ever before to try to distinguish between a legitimate customer and fraudsters. This challenge only gets harder when businesses require more complex passwords, which users promptly forget. Fraudsters, ever creative, exploit the password reset processes to impersonate the customer and convince businesses to give them the new reset password. In ATO attacks, often the only data presented to a business by the user at the time of login is a username and password. However, there are hundreds of other variables that may be passed back and forth between the device and the business in that digital moment, which can be useful for identifying potential threats or legitimate users. This exercise can be a monumental task that involves capturing vast data sets, knowing the difference between critical data and data that increases workload, analyzing that data and then marrying that back to what you know about the customer, all in a few milliseconds. And this is where one of the biggest hurdles exists. These vast data sets sit across a complex set of systems and technologies that have been implemented (but not fully integrated) over time. And consider within this context, the authentication team managing ATO that would otherwise benefit from a cohesive set of data isn’t usually aligned with the general fraud teams and is even further separated from the credit risk or compliance teams. These gaps in technologies and teams hinder ATO prevention and provide zero support for any interdependencies with other critical functions – and fraudsters are more than happy to exploit this weakness. On the other hand, managing a more complete view of the customer (which allows the business to streamline operational costs, data costs, and infrastructure costs) to prevent more ATO attacks and provide a more seamless experiences for the consumer has never been more possible. A fundamental shift in mindset is required to prevent fraudsters from exploiting gaps between business functions. Legitimate customers do not care about these internal divisions; they only see the inconsistency when one part of the business has no knowledge of them compared to another. This disconnect not only frustrates customers but also undermines trust and security. To effectively combat ATO attacks, financial institutions must leverage comprehensive data insights that cover various touchpoints. Integrating identity verification, device intelligence, and behavioral analytics is essential for distinguishing legitimate users from fraudsters. Breaking down traditional silos and enabling seamless data sharing ensures a holistic approach to fraud prevention, delivering a secure and frictionless customer experience. Liminal, a leading market intelligence firm specialising in digital identity, cybersecurity, and fintech markets, recently named Experian as a leader in its Link Index for ATO Prevention in Banking. Leading in ATO prevention The report highlights vendors that lead in authentication, fraud and identity and based on two primary criteria: product execution and strategic positioning. As a top-ranked vendor overall and in product execution, Experian’s performance underscores the effective integration of identity management in our solutions, positioning us as a leader in shaping strategies for account takeover prevention over the next five years. Download Liminal’s Link Index for ATO Prevention in Banking “When it comes to ATO prevention, banks are prioritizing highly accurate solutions that minimize fraud losses and limit financial loss, while reducing customer abandonment through a seamless user experience. Overall satisfaction is most strongly correlated with scalability. As a leader in this evaluation, Experian not only delivers these capabilities to banks, it also demonstrates an unparalleled ability to meet the market’s growing demand, which is projected to reach $1.5 billion by 2028.” Will Charnley, Chief Operating Officer, Liminal The report details the trends that are fundamentally reshaping the ATO threat landscape and today’s specific challenges, as well as those on the horizon, that banks must overcome, while also meeting an increasing expectation of customer satisfaction. Key statistics detail a prescriptive assessment of the market landscape and total addressable market, as well as findings from a March 2024 survey of banks conducted by Liminal, which includes: Specific key purchasing criteria (KPC). The scale and average cost (by volume and per incident) of ATO attacks. A descriptive methodology for calculating fraud loss opportunity costs. A priority-tiered description of ATO solution capabilities. As banks continue to operate in a competitive digital environment that favours excellent customer experience in parallel with fraud prevention, it is crucial to recognize that the front-end experience mirrors back-end operations; therefore, creating seamless integration on both sides is critical. Download Report CrossCoreR provides a fully-featured toolkit that leverages a wide range of capabilities for highly accurate and scalable ATO prevention.

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

Credit professionals from a range of banks, telcos and financial services businesses gathered in London’s Kings Place in June for one of the highlights of the Experian decisioning community: FutureForum. The forum fosters collaboration, networking, and insight, allowing customers to influence product development whilst staying informed on industry trends. This year’s event, The Art of Decisioning, offered a vibrant mix of insightful talks, thought-provoking discussions, demos of industry-leading capabilties, and, of course, a celebratory awards dinner. Uncovering opportunities in the credit market   FutureForum kicked off with a big-picture look at the state of the economy and some revealing insights into the credit market. Experian’s Chief Economist, Mo Chaudri, was joined by Head of Strategic Propositions and Innovation, Natalie Hammond, to explain how the UK economy has stabilised after a turbulent period, with falling prices, much lower inflation and steady employment rates. Consequently, in recent months, there has been an increase in credit demand, particularly in the unsecured sector of credit cards and loans. As a result, the credit card market has seen its most substantial quarter on record, with over one hundred products now on the market. Additionally, the Buy Now Pay Later (BNPL) sector has experienced an accelerated growth rate of 14% among UK consumers. While this surge has proven beneficial for lenders, Experian's data reveals a significant portion of the population, totaling over 2.75 million individuals, either did not qualify or chose not to proceed with their credit offers. Among this group, 1.57 million individuals, constituting 61%, were assigned a 0% eligibility rating, while 1.08 million individuals, accounting for 26%, achieved a 100% eligibility rating. As a result, the opportunity for lenders to serve those customers and accelerate portfolio growth now exists within the market. But to do that, companies need to better understand their customers. Investing in a Unified Platform  Managing Director of Enterprise Strategy and Innovation Steve Thomas took delegates through Experian’s ongoing investment in innovation and problem-solving. Continuing to evolve the richest, most comprehensive data while developing a unified platform that connects data, machine learning, advanced analytics, decisioning and generative AI, all in one place is central to this. The Ascend Platform advances to decision and outcome monitoring for integrated customer management which can revolutionise the way organisations analyse, test and adopt new data and analytics, independently of Experian. The introduction of GenAI and enhanced RegTech functionaility enhances governance and transparency by efficiently integrating new data sets, enabling real-time monitoring, and ensuring comprehensive compliance through thorough documentation and auditing, removing inefficiencies from processes. Through advancements in data and decisioning, businesses can build and test multiple models, understand customers better and make confident decisions across the customer lifecycle. PowerCurve and data upgrades A key element of Experian’s Ascend Platform is the suite of widely used Experian solutions. Ed Heal, Decisioning Director, presented recent investments in this area, which include migrating more of PowerCurve’s functionality to the cloud for a more agile offering, and a game-changing approach to data integration. New data sources can now be directly integrated into PowerCurve within days instead of months, supporting areas such as affordability, Fincrime, buy-now-pay-later and eligibility. As well as making it much easier to add new data, PowerCurve Originations now comes pre-integrated with over 40 data links, including a number of ID and fraud services. These provide a wealth of sources to help businesses better understand consumers for improved lending decisions and to support regulatory and Consumer Duty obligations. As for Strategy Design Studio, a new ‘lite’ version is being launched that’s faster, more visual and easier to use. With simplified processing, SDS means businesses don’t have to rely on strategy specialists to use it, improving operational efficiency and allowing users to test quickly and with confidence. The rise of GenAI It’s impossible to talk about the future without discussing AI. Chris Fletcher, SVP Decisioning and Cloud Solutions took to the stage looking at the latest developments in this area, with a focus on Generative AI tools such as ChatGPT. Chris explored how businesses can use synthetic data and AI to train models and test strategy simulations based on dynamic changes to the economy that may impact credit risk rules or customer behaviour. He also looked at how GenAI can be used to quickly and easily write and edit lending policies, while supporting regulatory reporting. This led to an interesting roundtable discussion exploring some of the future possibilities of AI in the decisioning process. Decisioning everywhere As technology grows ever more powerful and we continue to converge data, analytics and decisioning into an integrated environment, FutureForum offered a chance to imagine the future of customer management. Neil Stephenson, SVP Software Management, discussed how businesses can currently make customer-level decisions across multiple portfolios to drive collection and limit-management strategies. But, he said, “Experian is also looking at how we can help businesses manage customer interactions more holistically in areas such as affordability or promoting new products. Imagine, knowing that a customer is spending a lot to have their car fixed regularly. Could they be thinking about buying a new car? Would this be the right time to offer a loan you know would be attractive to that customer?” This customer hub approach to better service, made possible by Experian data and a unified platform, could introduce a new age of decisioning everywhere. Celebrating our brilliant clients After the speakers and panel discussions had wrapped up, it was time for delegates to relax, enjoy some good food and network with their peers and Experian experts. The evening was also an opportunity to recognise our clients’ achievements and innovations with the FutureForum Awards. This year, congratulations go to Vanquis and Leeds Building Society for ‘Best Customer Outcomes’, Santander for ‘Best Technical Transformation’ and Principality Building Society for ‘Peoples Award – Best Business Outcome’. Thank you to everyone who came along to FutureForum and made it another memorable event. To hear about Experian Decisioning Community events and experiences, please contact us decisioningcommunity@experian.com. 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How predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. In 2023, the US economy outperformed expectations, with strong job growth, impressive GDP (annual growth rate was 2.5%, up from 1.9% in 2022), and lower inflation. Increased consumer spending and reduced trade deficit highlighted its resilience and adaptability, fostering a stable economic environment. However, the story around consumer debt and delinquencies has not been so positive. In the latest quarterly report on household debt and credit released in February 2024 by the Federal Reserve Bank of New York, total household debt saw a notable increase of $212 billion (1.2%) in the fourth quarter of 2023, reaching $17.5 trillion. Within this surge, credit card balances increased by $50 billion, alongside mortgage balances which rose by $112 billion to hit $12.12 trillion. Auto loans, which have been trending upwards since 2011, saw an additional $12 billion increase, totalling $1.61 trillion. Other balances, encompassing retail cards and various consumer loans, witnessed a growth of $25 billion. Despite the economic recovery post-Covid, the level of debt in credit cards and auto loans, transitioning into delinquency remains higher than pre-pandemic levels. In Q4 2023, aggregated delinquency rates reached 3.1%, signifying a persistent financial strain for many lower income households. Transition rates into delinquency increased across all debt categories except for student loans. Approximately 8.5% of credit card balances and 7.1% of auto loans transitioned into delinquency on an annualised basis. Serious credit card delinquencies (90 days +) surged across all age groups, especially among younger borrowers, surpassing pre-pandemic levels. With such elevated debt and early-stage delinquency rates, lenders face many challenges. We look at how predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. Collections managers and teams within financial institutions face a range of challenges in maintaining portfolio growth while effectively managing increases in early-stage delinquencies. The top five challenges include: 1. High operating costs Contacting delinquent customers, negotiating payments, and managing recovery efforts entail labor-intensive and costly processes. This encompasses expenses related to staffing call centres, sending mailers, and deploying collections management software. 2. Regulatory compliance Navigating federal, state, and local regulations governing debt collection practices presents a complex challenge. Compliance with laws such as the Fair Debt Collection Practices Act (FDCPA) and the Telephone Consumer Protection Act (TCPA) is imperative, dictating the permissible methods and timing of borrower contact. 3. Customer retention and satisfaction Balancing effective debt recovery with maintaining positive customer relationships is essential. Employing aggressive collection tactics risks damaging customer relationships and tarnishing brand reputation, potentially impacting long-term customer retention. 4. Technological integration Incorporating modern technologies like machine learning, and automation into the collections process can enhance efficiency but poses implementation challenges. These technologies require substantial investment and expertise to streamline operations effectively. 5. Data management and predictive analytics Efficiently managing and analyzing vast amounts of data to identify at-risk accounts early and customise collection strategies is a significant endeavour. Accurate data analysis is pivotal for predicting delinquencies likely to self-cure and determining appropriate contact channels, such as; SMS, Email, Phone, Outbound IVR or social media. Applying a customer-centric, strategic approach These challenges underscore the critical need for credit lenders to adopt strategic, compliant, and customer-centric approaches to early-stage delinquency management. Currently, financial institutions use a multitude of strategies to maximize revenue collection. These range from data-driven customer segmentation to profile customers, Regulatory Technology (RegTech) for compliance, proactively identifying vulnerable customers needing financial relief, offering flexible repayment solutions and predictive modelling. Some credit lenders are also using machine learning models, such as Next Best Action (NBA) to personalize collection strategies based on customer behaviour, financial status, and communication preferences. This approach predicts recovery rates by tailoring channel contact to each individual customer in the most effective way. However, NBA models alone are not enough. To maximise collections, within known business constraints (call centre resources, budget, regulations), NBA needs to be augmented with non-linear optimization techniques to ensure not only the right communication preferences are adhered to, but also the business constraints mentioned above. Without the optimisation component businesses are left with NBA modelling that is unadjusted for business constraints. Next Best Action (NBA) Optimization NBA optimization presents a game-changing opportunity for lenders, particularly given the current economic challenges consumers are facing. Here's how NBA optimization can drive value: Personalized communication NBA optimization uses sophisticated customer modelling to pinpoint the most effective communication channels for each borrower, be it email, text, phone, or another preferred method. By personalizing communications, lenders significantly increase the chance of response and engagement from customers, which will also streamline the collections process with greater efficiency and reduced intrusion. Dynamic strategy adjustment NBA solutions continuously learn from outcomes, enabling strategy adjustments. This dynamic capability empowers lenders to swiftly adapt to changing economic conditions, borrower behaviours, and regulatory landscapes, ensuring the maintenance of effective collections practices. Optimized timing Leveraging predictive modelling, NBA optimization empowers lenders to identify the best times to contact their customers. This strategic approach ensures their communication attempts yield higher success rates, minimizing the need for repeated contacts and reducing operational costs. Regulatory compliance NBA optimization solutions can be configured to seamlessly adhere to regulatory requirements, including permissible contact times and frequency limits. This automation ensures compliance, protecting lenders from legal penalties and upholding their standing with regulatory bodies. Operational efficiency Through automated decision-making processes, NBA optimization assists lenders in allocating resources more thoughtfully. By prioritizing accounts with higher payment probabilities and determining the most cost-effective collection strategies, lenders can streamline operations and minimize costs. Improved customer experience (CX) NBA optimization facilitates a tailored approach to debt collection, significantly enhancing the borrower's experience. By considering the borrower's unique circumstances and preferences, lenders can offer more relevant and flexible repayment options, while also boosting customer satisfaction and loyalty. By implementing NBA optimization customised to channel contact preferences and operational constraints, lenders can navigate the complexities of early-stage collections with precision. This strategic approach not only addresses operational challenges but also aligns with the evolving expectations and financial pressures of consumers, leading to improved outcomes for both lenders and borrowers. Businesses can assign the most profitable, cost-effective treatment and channel to contact customers. Ascend Intelligence Services™ Collect delivers an optimized collections decision strategy, driven by predictive analytics, that determines the next best action and contact channel for each individual customer to improve recovery rates, increase efficiency, and stay within day-to-day constraints and regulatory requirements. Find out more

New IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor assessment provides valuable resource as organizations face increased fraud. With fraud scam losses reported to have reached $10bn in 2023*, preventing fraud in today's digital landscape has become increasingly complex. As organizations continue to leverage advanced technologies, fraudsters have also evolved, employing ever more sophisticated techniques. Striking the balance between robust fraud prevention and delivering a seamless digital experience to customers has become a priority for organizations, with customer experience (CX) proving to be a competitive differentiator in a market with high digital expectations. Why real-time detection matters for CX As techniques employed by fraudsters get faster, so does the need for quick and effective fraud detection, making real-time solutions increasingly important during a period of rapid technological advancement. The development of real-time fraud solutions not only minimizes financial losses, but it has also paved the way for frictionless customer journeys, with identity and fraud checks no longer impeding customer experience. Using machine learning to leverage data and enable fraud detection To enable real-time detection, proactive fraud prevention also requires the analysis of vast amounts of data. Deploying static rules to identify anomalies in data does not allow for nuance because the thresholds within the rules are fixed, and therefore real-time patterns cannot be adjusted to within the model. Machine learning not only allows businesses to leverage data more effectively through analysis, allowing for flexibility within the parameters, but it also removes some manual processes, improving efficiency by updating models faster into production. Approving good customers is the number one priority for businesses, and a frictionless digital customer journey is the catalyst for this. To minimize financial losses while reducing the overall number of fraud incidents, organizations are looking to real-time fraud detection, enabled by machine learning. "As fraud risk losses continue to increase, the pursuit of fraud risk management solutions designed to identify, mitigate, and prevent fraud incidents and losses is a topic with increasing focus within financial services.” Sean O'Malley, research director, IDC Financial Insights: Worldwide Compliance, Fraud and Risk Analytics Strategies IDC, the premier global market intelligence firm, released its latest IDC MarketScape: Worldwide Enterprise Fraud Solutions, providing a valuable resource to buyers looking for new solutions in the market. Download excerpt of IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor Assessment The report highlights: Fraud solutions are increasingly moving toward real-time fraud detection and prevention. There are significant enhancements in technological capabilities, particularly with respect to cloud computing. Some newer fraud solutions take advantage of the increased computing power that is available to both expand the data sets being used to identify potential fraud incidents and enhance the models designed to detect, mitigate, and prevent fraud. Experian is recognized as a leader in this report. The IDC MarketScape notes, “In addition to evaluating the transactional data for potential fraud, Experian's CrossCore solution includes identity-authentication tools. The solution uses identity data, device intelligence, email and phone intelligence, alternative identity data, biometrics, behavioral biometrics, one-time passwords, and document verification to confirm identities and aid with identity protection, including synthetic identity protection. Experian utilizes multiple data partnerships in its fraud solution, which often can help provide a more comprehensive understanding of fraud risks and exposures.” To achieve a frictionless and secure customer experience, it is the integration of digital identity and fraud risk that is creating a gold standard for businesses. A siloed approach to fraud prevention not only leaves gaps for criminals to exploit, but it also presents consequences for customer experience too. The ability to layer multiple fraud capabilities together in a synchronized effort to achieve the best analytics-driven output possible can allow businesses to have the flexibility within their user journeys to optimize and control the order in which capabilities are called, removing friction, and ensuring good customers are successfully onboarded. Add in a final layer of machine learning to ensure the deployment of unified decisioning, and businesses are left with cohesive and explainable decisions. At Experian, we are working diligently to stay on the cutting edge of fraud and identity. In addition to our proprietary credit data on over 1.5 billion consumers and over 200 million businesses, Experian leverages a unique curated partner ecosystem to provide a more comprehensive understanding of fraud risks and exposures. Our powerful technology platform enables users to leverage a wide range of tools to combat their customized fraud challenges. Download Report Excerpt More on Crosscore® *IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor Assessment

With the potential annual value of AI and analytics for global banking estimated to reach $1 trillion,1 financial institutions are seeking out efficient ways to implement insights-driven lending. As regulators continue to supervise risk management, lenders must balance the opportunity presented by AI to determine risk more accurately while growing approval rates and reducing the cost of acquisition, with the ability to explain decisions. The challenge of using AI in building credit risk models In a recent study conducted by Forrester Consulting on behalf of Experian, the top pain points for technology decision makers in financial services were reported to be automation and availability of data.2 The implementation of accessible AI solutions in credit risk management allows businesses to improve efficiency and time-to-market metrics by widening data sources, improving automation and decreasing risk. But the implementation of AI and machine learning in credit risk models can pose other challenges. The study also found that 31% of respondents felt that their organization could not clearly explain the reasoning behind credit decisions to customers.2 Although AI has been proven to improve the accuracy of predictive credit risk models, these advancements mean that many organizations need support in understanding and explaining the outcomes of AI-powered decisions to fulfil regulatory obligations, such as the Equal Credit Opportunity Act (ECOA). Moving from traditional model development methodologies to Machine Learning (ML) As lenders move away from traditional parametric models like logistic regression, to ML models like neural nets or tree-based ensemble methods, explainability becomes more complex. Logistic regression has for many years allowed for a clear understanding of the linear relationships between model attributes and the outcome (approval or decline). Once the model is estimated, it is completely explainable. However, ML models are non-parametric, so there are no underlying assumptions made around the distribution (shape) of the sample. Furthermore, the relationships between attributes and outcomes are not assumed to be linear – they’re often non-linear and complex, involving interactions. Such models are perceived to be black boxes where data is consumed as an input, processed and a decision is made without any visibility around the inner dynamics of the model. At the same time, it is possible for ML models to perform better when accurately classifying good customers and those deemed delinquent. Ensuring transparency and explainability is crucial – lenders must be able to identify and explain the most dominant attributes that contribute towards a decision to lend or not. They must also provide ‘reason codes’ at the customer level so any declined applicants can fully understand the main cause and have a path to remediation. The importance of developing transparent and explainable models By prioritizing the development of transparent and interpretable models, financial institutions can also better foster equitable lending practices. However, fair credit decisioning goes beyond the regulatory and ethical obligations - it also makes business sense. Unfair lending leads to higher default rates if creditworthiness is not accurately assessed, therefore increasing bad debts. Removing demographics considered to be the ‘unscored’ or ‘underserved’ (those who are credit worthy but do not have a traditional data trail, but instead a digital footprint comprised of alternative data) can also limit portfolio opportunity for businesses. For these reasons, it is critical to remove or minimize model bias. Bias is an upstream issue that starts at the data collection stage and model algorithm selections. Models developed using logistic regression or machine learning algorithms can be made fairer through carefully selecting attributes relevant to credit decisioning and avoiding sensitive attributes like race, gender, or ethnicity. Wherever sensitive metrics are used, they should be down-weighted to suppress their impact on lending decisions. Some other techniques to mitigate bias include: Thoroughly reviewing the data samples used in modelling. Fair Model Training - Train models using fairness-aware techniques. This may involve adjusting the training process to penalise any discrimination that creeps in. According to Forrester, an essential component of a decisioning platform is one that can “harness the power of AI while enhancing and governing it with well-proven and trusted human business expertise. The best automated decisions come from a combination of both.”3 Developing explainable models goes some way towards reducing bias, but making the decisions explainable to regulatory bodies is a separate issue, and in the digital age of AI, can require deep domain expertise to fulfil. While AI-powered decisioning can help businesses make smarter decisions, they also need the ability to confidently explain their lending practices to stay compliant. With the help of an expert partner, organizations can gain an understanding of what contributed most to a decision and receive detailed and transparent documentation for use with regulators. This ensures lenders can safely grow approval rates, be more inclusive, and better serve their customers. “The solution isn’t simply finding better ways to convey how a system works; rather, it’s about creating tools and processes that can help even the deep expert understand the outcome and then explain it to others.”McKinsey: why businesses need explainable ai and how to deliver it Experian’s Ascend Intelligence ServicesTM Acquire is a custom credit risk model development service that can better quantify risk, score more applicants, increase automation, and drive more profitable decisions. Find out more Confidently explain lending practices:Detailed, rigorous, and transparent documentation that has been proven to meet the strictest regulatory standards. Breaking Machine Learning (ML) out of the black box:Understand what contributed most to a decision and generate adverse action codes directly from the model through our patent-pending ML explainability.References: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.") 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. 2023_05_Forrester_AI-Decisioning-Platforms-Wave.pdf https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-businesses-need-explainable-ai-and-how-to-deliver-it Contributors:Masood Akhtar, Global Product Marketing Manager