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

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

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

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

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

Latest Global Insights Report: How supporting consumers in a time of uncertainty can help businesses adapt and grow A changing economic landscape needs a new approach The new digital consumer is here to stay and they expect businesses to support them with the products and services they need to navigate the rising cost of living, in a secure digital world personalised to them. Find out how: Our latest research reveals how economic uncertainty is evolving the experiences and expectations of digital consumers. From increasing the demand for credit options and financial inclusion, to deepening the need for trust, security and being seen. Read the report to find out how businesses can benefit from responding to changing consumer needs - including the additional tools and resources consumers and businesses may need to maintain financial health: What do digital consumers want? The global economy is under pressure with inflation raising prices across the world. In response, consumer behaviour is shifting, as people tackle the increased cost of living, and the prospect of an economic downturn. Digital consumers are continuing to manage their lives online and are expecting businesses to take the lead on improving the digital environment. A quality online experience is paramount, or consumers will move on. 1 in 4 businesses lost more than 10% of their customers in 2021, due to “suboptimal” digital experiences. A range of payment options including BNPL As prices rise, consumers are expecting to spend more online and are looking for varied credit options to help manage their finances. The demand for buy-now-pay-later (BNPL) options is also growing, with more consumers using BNPL to buy household staples. Consumers look favourably on companies that offer BNPL, but companies will have to find the right balance between supporting customers and managing credit risk. 32% of BNPL purchases were for groceries, up from 27% in March. Financial inclusion Economic uncertainty is accelerating the need for greater financial inclusion. Businesses need to find more creditworthy consumers and support them with responsible and sustainable products and services. 1 in 3 businesses is in the process of rolling out financial inclusion initiatives Security and trust As consumer need increases, so does fraud, including cost of living scams. Security is now a top priority for consumers around the world, alongside privacy, convenience and personalisation. 50% of consumers say they’re concerned about their online transactions. However, trust in emerging customer recognition tools is increasing, with consumers’ top three including physical biometrics, PIN codes and behavioural biometrics. Personalisation Consumers who trust businesses are more willing to share their data, enabling companies to create more personalised experiences, which in turn, improves consumer trust. 46% of consumers say that personalisation (receiving offers that fit their needs) is the most important aspect of their online experience. Read our report to discover the challenges and opportunities facing consumers and businesses and the tools, resources and strategies that can help your company get ahead. The survey results represent 6,000 consumers and 2,000 businesses across 20 countries, including Australia, Brazil, Chile, China, Columbia, Denmark, Germany, India, Indonesia, Ireland, Italy, Malaysia, Netherlands, Norway, Peru, Singapore, South Africa, Spain, UK, and US.   Read our report

The survey underpinning these insights encompasses 1,849 business respondents and 6,062 consumers from 20 countries, including Australia, Brazil, China, Chile, Colombia, Denmark, Germany, India, Indonesia, Ireland, Italy, Malaysia, The Netherlands, Norway, Peru, Singapore, South Africa, Spain, UK, and US. We’ve also included interviews with consumers from Brazil, Germany, the UK, and US.

The survey underpinning these insights encompasses 1,849 business respondents and 6,062 consumers from 20 countries, including Australia, Brazil, China, Chile, Colombia, Denmark, Germany, India, Indonesia, Ireland, Italy, Malaysia, The Netherlands, Norway, Peru, Singapore, South Africa, Spain, UK, and US. We’ve also included interviews with consumers from Brazil, Germany, the UK, and US.