Tag: AI

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.

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

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.

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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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

We explore four fraud trends likely to be influenced the most by GEN AI technology in 2024, and what businesses can do to prevent them. 2023: The rise of Generative AI 2023 was marked by the rise of Generative Artificial Intelligence (GEN AI), with the technology’s impact (and potential impact) reverberating across businesses around the world. 2023 also witnessed the democratisation of GEN AI, with its usage made publicly available through multiple apps and tools such as Open AI's Chat GPT and DALL·E, Google's Bard, Midjourney, and many others. Chat GPT even held the world record for the fastest growing application in history (until it was surpassed by Threads) after reaching 100 million users in January 2023, just less than 2 months after its launch. The profound impact of GEN AI on everyday life is also reflected in the 2023 Word of the Year (WOTY) lists published by some of the biggest dictionaries in the world. Merriam-Webster’s WOTY for 2023 was 'authentic'— a term that people are thinking about, writing about, aspiring to, and judging more than ever. It's also not a surprise that one of the other words outlined by the dictionary was 'deepfake', referencing the importance of GEN AI-inspired technology over the past 12 months. Among other dictionaries that publish WOTY lists, both Cambridge Dictionary and Dictionary.com chose 'hallucinate' - with new definitions of the verb describing false information produced by AI tools being presented as truth or fact. A finalist in the Oxford list was the word 'prompt', referencing the instructions that are given to AI algorithms to influence the content it generates. Finally, Collins English Dictionary announced 'AI' as their WOTY to illustrate the significance of the technology throughout 2023. GEN AI has many potential positive applications from streamlining business processes, providing creative support for various industries such as architecture, design, or entertainment, to significantly impacting healthcare or education. However, as signalled out by some of the WOTY lists, it also poses many risks. One of the biggest threats is its adoption by criminals to generate synthetic content that has the potential to deceive businesses and individuals. Unfortunately, easy-to-use, and widely available GEN AI tools have also created a low entrance point for those willing to commit illegal activities. Threat actors leverage GEN AI to produce convincing deepfakes that include audio, images, and videos that are increasingly sophisticated and practically impossible to differentiate from genuine content without the help of technology. They are also exploiting the power of Large Language Models (LLMs) by creating eloquent chatbots and elaborate phishing emails to help them steal important information or establish initial communication with their targets. GEN AI fraud trends to watch out for in 2024 As the lines between authentic and synthetic blur more than ever before, here are four fraud trends likely to be influenced most by GEN AI technology in 2024. A staggering rise in bogus accounts: (impacted by: deepfakes, synthetic PII)Account opening channels will continue to be impacted heavily by the adoption of GEN AI. As criminals try to establish presence in social media and across business channels (e.g., LinkedIn) in an effort to build trust and credibility to carry out further fraudulent attempts, this threat will expand way beyond the financial services industry. GEN AI technology continues to evolve, and with the imminent emergence of highly convincing real-time audio and video deepfakes, it will give fraudsters even better tools to attempt to bypass document verification systems, biometric and liveness checks. Additionally, they could scale their registration attempts by generating synthetic PII data such as names, addresses, emails, or national identification numbers. Persistent account takeover attempts carried out through a variety of channels: (impacted by: deepfakes, GEN AI generated phishing emails)The advancements in deepfakes present a big challenge to institutions with inferior authentication defenses. Just like with the account opening channel, fraudsters will take advantage of new developments in deepfake technology to try to spoof authentication systems with voice, images, or video deepfakes, depending on the required input form to gain access to an account. Furthermore, criminals could also try to fool customer support teams to help them regain access they claim to have lost. Finally, it's likely that the biggest threat would be impersonation attempts (e.g., criminals pretending to be representatives of financial institutions or law enforcement) carried out against individuals to try to steal access details directly from them. This could also involve the use of sophisticated GEN AI generated emails that look like they are coming from authentic sources. An influx of increasingly sophisticated Authorised Push Payment fraud attempts: (impacted by: deepfakes, GEN AI chatbots, GEN AI generated phishing emails)Committing social engineering scams has never been easier. Recent advancements in GEN AI have given threat actors a handful of new ways to deceive their victims. They can now leverage deepfake voices, images, and videos to be used in crimes such as romance scams, impersonation scams, investment scams, CEO fraud, or pig butchering scams. Unfortunately, deepfake technology can be applied to multiple situations where a form of genuine human interaction might be needed to support the authenticity of the criminals' claims. Fraudsters can also bolster their cons with GEN AI enabled chatbots to engage potential victims and gain their trust. If that isn’t enough, phishing messages have been elevated to new heights with the help of LLM tools that have helped with translations, grammar, and punctuation, making these emails look more elaborate and trustworthy than ever before. A whole new world of GEN AI Synthetic Identity: (impacted by: deepfakes, synthetic PII)This is perhaps the biggest fraud threat that could impact financial institutions for years to come. GEN AI has made the creation of synthetic identities easier and more convincing than ever before. GEN AI tools give fraudsters the ability to generate fake PII data at scale with just a few prompts. Furthermore, criminals can leverage fabricated deepfake images of people that never existed to create synthetic identities from entirely bogus content. Unfortunately, since synthetic identities take time to be discovered and are often wrongly classified as defaults, the effect of GEN AI on this type of fraud will be felt for a long time. How to prevent GEN AI related fraud As GEN AI technology continues to evolve in 2024, its adoption by fraud perpetrators to carry out illegal activities will too. Institutions should be aware of the dangers they possess and equip themselves with the right tools and processes to tackle these risks. Here are a few suggestions on how this can be achieved: Fight GEN AI with GEN AI: One of the biggest advantages of GEN AI is that while it is being trained to create synthetic data, it can also be trained to spot it successfully. One such approach is supported by Generative Adversarial Networks (GANs) that employ two neural networks competing against each other — a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates the generated data and tries to distinguish between real and fake samples. Over time, both networks fine tune themselves, and the discriminator becomes increasingly successful in recognising synthetic content. Other algorithms used to create deepfakes, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders, can also be trained to spot anomalies in audio, images, and video, such as inconsistencies in facial movements or features, inconsistencies in lighting or background, unnatural movements or flickering, and audio discrepancies. Finally, a hybrid approach that combines multiple algorithms often presents more robust results. Advanced analytics to monitor the whole customer journey and beyond: Institutions should deploy a fraud solution that leverages data from a variety of tools that can spot irregular activity across the whole customer journey. That could be a risky activity, such as a spike in suspicious registrations or authentication attempts, unusual consumer behaviour, irregular login locations, suspicious device or browser data, or abnormal transaction activity. A best-in-class solution would give institutions the ability to monitor and analyse trends that go beyond a single transaction or account. Ideally, that means monitoring for fraud signals happening both within a financial institution’s environment and across the industry. This should allow businesses to discover signals pointing out fraudulent activity previously not seen within their systems or data points that would otherwise be considered safe, thus allowing them to develop new fraud prevention models and more comprehensive strategies. Fraud data sharing: Sharing of fraud data across multiple organisations can help identify and spot new fraud trends from occurring within an instruction's premises and stop risky transactions early. Educate consumers: While institutions can deploy multiple tools to monitor GEN AI related fraud, regular consumers don't have the same advantage and are particularly susceptible to impersonation attempts, among other deepfake or GEN AI related cons. While they can't be equipped with the right tools to recognize synthetic content, educating consumers on how to react in certain situations related to giving out valuable personal or financial information is an important step in helping them to remain con free. Learn more with our latest fraud reports from across the globe: UK Fraud Report 2023 US Fraud Report 2023 EMEA + APAC Fraud Report 2023

What are lenders prioritising when it comes to Gen AI? We take a look at five transformative use cases in lending, and organisational priorities for integrating Gen AI into customer lifecycle processes. Although Generative Artificial Intelligence (Gen AI) only launched publicly in the form of Chat GPT last November, adoption has been widespread and rapid. Even in typically risk-adverse industries like financial services, our research shows that there is widespread recognition that Gen AI could deliver a range of benefits across business functions. We identified five areas of focus for lenders based on our research. 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. The qualitative research showed that lenders are already using a type of Gen AI, Large Language Models (LLMs), in their operations, with a focus on testing across areas such as customer service and internal processes before deploying to credit operations. We look at the potential use cases, and how businesses are using Gen AI now. 1. Personalised customer experience Customers today expect a personalised lending experience that is tailored to their unique needs and preferences. GenAI can leverage customer data to generate personalised loan offers, recommendations, and repayment plans. This helps lenders improve customer satisfaction and loyalty, leading to increased customer retention and revenue growth. This is an area that is front of mind for the companies in our research – nearly half of businesses surveyed are planning to implement or expand technology capabilities to either upsell or retain customers in the next 12 months. Furthermore, 50% of companies believe that offering more tailored underwriting and pricing is a top priority in their credit operations, followed by 44% who also aim to increase personalisation in marketing, products, and services to their customers. According to the research, some organisations have formed alliances with technology providers like OpenAI and Microsoft to investigate and further explore the use of LLMs. These partnerships involve analysing customer data to identify opportunities for cross-selling. 2. Enhancing models with new data sources With new data sources emerging all the time, Gen AI is one of the technologies that will most likely accelerate the opportunity for businesses to incorporate them into models. Lenders could include sources such as social network data into their models by using LLMs. This unstructured data, including customer emotions and behaviours on social networks, would be treated as an additional variable in the models. According to the research social media data and psychometric data is already used across financial services, to varying degrees. It showed that 35% of retail companies use social media data, while 29% of FinTechs use psychometric data. Auto finance companies sit at lower end of the adoption scale, with only 12% using social media data and 15% psychometric data. 3. Operational efficiencies Gen AI can help bring operational efficiencies to customerjourneys across the entire lifecycle, offering lenders theability to automate and streamline various processes,resulting in improved productivity, cost savings, andenhanced customer experiences. One of the top challenges for businesses surveyed isimproving customer journeys during onboarding, and thiswas particularly significant for credit unions / buildingsocieties (53%). 4. Detecting and preventing fraud Gen AI can play a crucial role in fraud detection by analysing patterns and anomalies in vast datasets. By leveraging machine learning techniques, Gen AI models can proactively identify potentially fraudulent activities and mitigate risks. The ability to detect fraud in real-time improves the overall security of lending operations and helps protect lenders and borrowers from financial losses. Detecting and preventing fraud is a constant challenge for lenders. 51% of retailers and 47% of credit unions/ building societies surveyed said that reducing fraud losses is a key challenge for them. 5. Customer service Driven by advances in the machine learning and AI space, the world of customer service has benefited hugely from the adoption of virtual assistants and chatbots in recent years. This looks to continue, with businesses saying that LLMs are being tested for customer service purposes, allowing lenders to identify customer issues and automate actions. What's next for lenders? The research found that lenders are utilising various machine learning techniques like regression, decision trees, neural networks, and random forest, along with LLMs. Businesses are in the early stages of exploring how they can use LLMs in credit risk models, but it will undoubtedly involve a blend of existing and new capabilities. As with any emerging technology, it’s important to look at potential risk. The research indicated that organisations see challenges and concerns when it comes to the use of LLMs in their models. It is crucial to ensure the models are trusted, validated, and properly understood to avoid reliance on outsourced solutions and maintain control and visibility over the models’ functions. The ability to explain decisions in Gen AI to avoid bias can be difficult, and businesses will be watching the regulators to understand how best to proceed. There is no doubt, however, that Gen AI will optimise the credit customer lifecycle, creating vast opportunities for lenders. Download PDF More on Gen AI

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. More on Gen AI

Online fraud has increased at unprecedented levels over the past two and half years, with numerous reports coming from all corners of the world to confirm that. From benefits and unemployment fraud to authorised push payment fraud, and more advanced scams such as synthetic identity fraud and deepfake fraud, cybercrime has been on the rise. Understandably, the increase in criminal activity has had a significant impact on financial services businesses, and it is little wonder that this has been reflected in our recent study: • 48% of businesses reported that fraud is a high concern, and 90% reported fraud as a mid-to-high concern • 70% of businesses said their concern about fraud has increased since last year • 80% of businesses said that fraud is often or always discussed within their organisations High levels of fraud have also raised consumer concern, and their expectations of the protection businesses should offer them. Nearly three-quarters of consumers said that they expect businesses to take the necessary security steps to protect them online. However, only 23% of respondents were very confident that companies were taking steps to secure them online. Businesses need to take additional steps to meet consumer demand, while also protecting their reputation and revenue streams. Businesses are investing in fraud prevention, so why isn’t it working? As a result of the rise in fraud during the pandemic, there has been an increase in spending related to fraud prevention tools and technology, with 89% of businesses surveyed in our latest research indicating that investment in fraud detection software is important to them. However, there is a risk that institutions could take a siloed approach, and funds could be spent on point solutions that solve one or two problems without adding the needed flexibility to fight multiple attack patterns. This gives fraudsters the opportunity to exploit these gaps. Orchestration and automation drive fraudsters away Criminals constantly evolve. They are not new to technology and have multiple attack patterns that they can rely on. They also share information between themselves at a higher rate and pace when compared with financial institutions, banks, and merchants. Fraudsters can learn how to bypass one or two features in an organisation’s fraud prevention strategy if they recognise weak spots or a vulnerability that they can take advantage of. However, when multiple fraud prevention tools and capabilities work harmoniously against them, the chances are higher that they will eventually be blocked or forced to move to a weaker place where they can exploit another system. Synchronizing multiple solutions together is the key to excellent fraud orchestration Fraud orchestration platforms give businesses the chance to layer multiple solutions together. However, taking a layered approach is not only about piling multiple point solutions but also about synchronizing them to achieve the best output possible. Every solution looks at different signals and has its own way of scoring the events, which is why they need to be governed into a workflow to achieve the desired results. This means that institutions can control and optimize the order in which various solutions or capabilities are called, as the output of one solution could result in a different check for a subsequent one or even the need to trigger another solution altogether. It also gives companies the ability to preserve their user journeys while answering different risks presented to them. Some businesses are seeking to build trust with customers but want to stay invisible to remove friction from their digital customer experience. This is where capabilities such as device intelligence, behavioural biometrics, or fraud data sharing could be added as an additional layer in the fraud prevention strategy. Those additional solutions may only be called 30 per cent of the time when there is a real need for an additional check. Excellent orchestration means that organisations can rely on multiple solutions while only calling the services they need, exactly when they need them. Building trust through a secure but convenient customer experience. Machine Learning should be the final layer to rule them all The results from our research revealed the top initiatives that businesses are leveraging to improve the digital customer journey with the top two being: • Improving customer decisioning with AI • New AI models to improve decisioning While our April 2022 Global Insight Report showed that consumers are becoming more comfortable with AI, with 59% saying they trust organisations that use AI. Fraud orchestration platforms allow companies to deploy unified decisioning by leveraging machine learning (ML) on top of multiple fraud prevention tools. This means they can rely on one cohesive output instead of looking at separate, sometimes contradictory results across various platforms and making subjective decisions. ML can also offer explainability by pointing out the attributes that contributed the most to a particular suggestion or decision. These could be attributes coming from a few different tools instead of one. This also means that operational teams, like fraud investigators, have a single view of activity, resulting in operational efficiency - removing the need to log in to different tools and look at multiple screens, views, and scores, while also enabling faster decisions. Stay in the know with our latest research and insights:

Did you miss these March business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Experian partners with Black Opal to bring credit options to US immigrants PYMNTS.com covers the partnership between Experian and Black Opal to boost consumer credit access to immigrants in the US. Using Crosscore and PowerCurve, Block Opal will be able to make real-time credit decisions while also managing using the platform’s tools to better manage identity verification and fraud prevention. Fraud shifting as online activity increases In this CUNA article, Brock Fritz explores Experian's Future of Fraud Index for 2022, with Experian's Chief Innovation Officer, Kathleen Peters, offering up solutions for businesses looking to mitigate the effects of more online fraud. How AI is modernizing online transactions Donna DePasquale, EVP of Global Decisioning Software, writes in Dataversity about the importance of automation and insights as objectives driving modernization through AI for businesses, and what they should focus on in order to increase customer acquisition. Online payment fraud Online payment fraud will reach 206 Billion by 2025. David Britton, Experian VP Industry Solutions Global Identity and Fraud is interviewed by David Cogan, host of the Heroes Show and founder of Eliances entrepreneur community. Stay in the know with our latest research and insights: