All posts by Managing Editor, Experian Software Solutions

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

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

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

With the potential annual value of AI and analytics for global banking estimated to reach $1 trillion,1 financial institutions are seeking out efficient ways to implement insights-driven lending. As regulators continue to supervise risk management, lenders must balance the opportunity presented by AI to determine risk more accurately while growing approval rates and reducing the cost of acquisition, with the ability to explain decisions. The challenge of using AI in building credit risk models In a recent study conducted by Forrester Consulting on behalf of Experian, the top pain points for technology decision makers in financial services were reported to be automation and availability of data.2 The implementation of accessible AI solutions in credit risk management allows businesses to improve efficiency and time-to-market metrics by widening data sources, improving automation and decreasing risk. But the implementation of AI and machine learning in credit risk models can pose other challenges. The study also found that 31% of respondents felt that their organization could not clearly explain the reasoning behind credit decisions to customers.2 Although AI has been proven to improve the accuracy of predictive credit risk models, these advancements mean that many organizations need support in understanding and explaining the outcomes of AI-powered decisions to fulfil regulatory obligations, such as the Equal Credit Opportunity Act (ECOA). Moving from traditional model development methodologies to Machine Learning (ML) As lenders move away from traditional parametric models like logistic regression, to ML models like neural nets or tree-based ensemble methods, explainability becomes more complex. Logistic regression has for many years allowed for a clear understanding of the linear relationships between model attributes and the outcome (approval or decline). Once the model is estimated, it is completely explainable. However, ML models are non-parametric, so there are no underlying assumptions made around the distribution (shape) of the sample. Furthermore, the relationships between attributes and outcomes are not assumed to be linear – they’re often non-linear and complex, involving interactions. Such models are perceived to be black boxes where data is consumed as an input, processed and a decision is made without any visibility around the inner dynamics of the model. At the same time, it is possible for ML models to perform better when accurately classifying good customers and those deemed delinquent. Ensuring transparency and explainability is crucial – lenders must be able to identify and explain the most dominant attributes that contribute towards a decision to lend or not. They must also provide ‘reason codes’ at the customer level so any declined applicants can fully understand the main cause and have a path to remediation. The importance of developing transparent and explainable models By prioritizing the development of transparent and interpretable models, financial institutions can also better foster equitable lending practices. However, fair credit decisioning goes beyond the regulatory and ethical obligations - it also makes business sense. Unfair lending leads to higher default rates if creditworthiness is not accurately assessed, therefore increasing bad debts. Removing demographics considered to be the ‘unscored’ or ‘underserved’ (those who are credit worthy but do not have a traditional data trail, but instead a digital footprint comprised of alternative data) can also limit portfolio opportunity for businesses. For these reasons, it is critical to remove or minimize model bias. Bias is an upstream issue that starts at the data collection stage and model algorithm selections. Models developed using logistic regression or machine learning algorithms can be made fairer through carefully selecting attributes relevant to credit decisioning and avoiding sensitive attributes like race, gender, or ethnicity. Wherever sensitive metrics are used, they should be down-weighted to suppress their impact on lending decisions. Some other techniques to mitigate bias include: Thoroughly reviewing the data samples used in modelling. Fair Model Training - Train models using fairness-aware techniques. This may involve adjusting the training process to penalise any discrimination that creeps in. According to Forrester, an essential component of a decisioning platform is one that can “harness the power of AI while enhancing and governing it with well-proven and trusted human business expertise. The best automated decisions come from a combination of both.”3 Developing explainable models goes some way towards reducing bias, but making the decisions explainable to regulatory bodies is a separate issue, and in the digital age of AI, can require deep domain expertise to fulfil. While AI-powered decisioning can help businesses make smarter decisions, they also need the ability to confidently explain their lending practices to stay compliant. With the help of an expert partner, organizations can gain an understanding of what contributed most to a decision and receive detailed and transparent documentation for use with regulators. This ensures lenders can safely grow approval rates, be more inclusive, and better serve their customers. “The solution isn’t simply finding better ways to convey how a system works; rather, it’s about creating tools and processes that can help even the deep expert understand the outcome and then explain it to others.”McKinsey: why businesses need explainable ai and how to deliver it Experian’s Ascend Intelligence ServicesTM Acquire is a custom credit risk model development service that can better quantify risk, score more applicants, increase automation, and drive more profitable decisions. Find out more Confidently explain lending practices:Detailed, rigorous, and transparent documentation that has been proven to meet the strictest regulatory standards. Breaking Machine Learning (ML) out of the black box:Understand what contributed most to a decision and generate adverse action codes directly from the model through our patent-pending ML explainability.References: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.") In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. 2023_05_Forrester_AI-Decisioning-Platforms-Wave.pdf https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-businesses-need-explainable-ai-and-how-to-deliver-it Contributors:Masood Akhtar, Global Product Marketing Manager

Lenders prioritise automation above all, according to 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. Research from Forrester on behalf of Experian found that automation is the top priority for businesses, and regardless of the specific industry or region, decision-makers consistently identified it as an important area of focus, and the biggest challenge. Lenders are using automation across the credit lifecycle and intend to invest further in the next 12 months, but there are multiple barriers to enhancing automation. We look at the use cases for automation and address the key challenges lenders face when automating decisions. The automation agenda The interpretation and application of automation vary hugely across the maturity spectrum of businesses in our research. While some companies consider automation as a means of simplifying tasks, such as the transition from manual processes to electronic spreadsheets, others are embracing its more advanced forms, such as AI-powered models. Use cases for automation in lending Customer service chatbots using Natural Language Processing (NPL) combined with Robotic Process Automation system (RPA). Remote verification of customers using machine vision and RPA to cross-check data. Data governance - data cleansing of personal information from within data using RPA and NPL. Operational efficiencies using process mining and AI to identify automation opportunities. Credit and fraud risk decisioning, using machine learning. Automation is about making processes as slick and robust as possible, giving the consumer a rapid journey so they can get processed very quickly, while behind the scenes lenders are making the best possible, compliant decisions, that protect them from losses around both credit risk and fraud.Neil Stephenson, Vice President of Experian SOFTWARE SOLUTIONS CONSULTANCY The changing face of automated decision-making in line with rapid tech advancements makes the use of automation by lenders a more complex opportunity than most. On one side there is the chance to enhance models with AI-powered tools to take away manual and subjective decision-making from processes. On the other, there’s the issue of governance and compliance – how to explain models that remove humans altogether. Introducing automation into some parts of the credit lifecycle isn’t always straightforward. Customer management has benefited from a lot of investment in the automation space over the years, particularly Natural Language Processing (NLP), but according to our research, the priority for business investment for Robotic Process Automation (RPA) in the next 12 months is originations. With onboarding playing such a key role in both customer experience and portfolio growth, businesses are looking to enhance this part of the credit lifecycle with automation. Customer experience is driving growth Automation plays a pivotal role in improving the customer journey and experience. The research showed that enhancing customer experience ranked even higher than growth as a priority for many organisations. As businesses strive to deliver seamless and personalised interactions, automation provides the necessary foundation for digital success, which in turn can strengthen competitiveness while retaining valuable customers. "Strategically investing in automation offers businesses the opportunity to scale operations, with a primary focus on growth. In times of economic uncertainty, more targeted, customer-centric strategies, that encompass more accurate predictive models, built on up-to-date samples and executed rapidly, can help mitigate a higher-risk lending backdrop." says Neil Stephenson, Vice President of Experian Software Solutions Consultancy. "Customer experience is the battleground for businesses, where they compete to deliver the best digital journeys in the market. It's a battleground that isn’t just about increasing revenue – the market perception of an organisation can be as important as growth in some portfolios because businesses have a reputation to protect." Automating decisions can ensure customer experience is truly seamless, but businesses face multiple barriers when it comes to credit and risk decision automation. Reducing referred applications  From scoring regression models to the development of machine learning models, better and smarter analytics are critical to drive the processes responsible for making application decisions. Reducing referred applications in turn decreases the need for manual intervention. By minimising the volume of applications in the middle of the credit score, lenders have a clearer and ultimately more automated approach to application accepts and declines. We interviewed decision-makers to understand the numerous challenges faced by lenders when automating decisions: Increasing data sources to allow for a more complete picture of the consumer Improved data quality, and increased volume of data Prevention of model bias The complexity of consumer type attached to some products Redundancy in data input and analytics Training across key roles for a better understanding of automation capabilities Explaining decisions based on machine learning models to regulators Complex fraud referral processes For many respondents, automation is about accuracy and efficiency. By improving automation, there are fewer instances of errors and delays. To ensure scalability can exist in consistent, compliant, and accurate processes that work for both the business and the consumer, here are 10 tips to help tackle the challenges faced by lenders when it comes to automating decisions: Embrace advanced data aggregation tools and technologies that can efficiently collect and integrate data from various sources. Partner with known, trustworthy data providers to enrich datasets. Explore the use of no-code data management tools that allow users to add and remove data sources more quickly and easily. Implement data quality processes. Regularly audit and clean data to remove inconsistencies. Move to cloud-based solutions for scalable data storage and processing of very large datasets. Regularly audit (monitor) machine learning models for bias.  Eliminating sampling bias is not yet possible but using a range of datasets (samples) and various sampling techniques will ensure representation across different demographics to help minimise bias. Develop specific models for different consumer segments or product categories. Regularly update models based on evolving consumer trends and behaviours. Conduct a thorough analysis of data inputs and streamline redundant variables. Use feature selection techniques such as correlations, weight of evidence, and information value to identify the most relevant information. Foster a culture of continuous learning and collaboration for all key stakeholders involved in the credit decisioning and strategy process. Develop transparent models with explainable features. Use interpretable machine learning algorithms that allow for clear explanations of decision factors at the customer level. Streamline identity verification processes by using smart orchestration to reduce false positives and prevent fraud. More on automated decision-making from PowerCurve – North America More on automated decision-making from PowerCurve – UK Related content: Digital decisioning

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

With heightened consumer demand for an improved customer experience online, and the increasing threat of fraud, how can organizations ensure secure and efficient customer onboarding in today's digital landscape? Onboarding the highest number of customers while maintaining compliance and security Digital account opening is in demand. Businesses are competing to create the most effective onboarding experience, while managing the need to draw on multiple sources during account opening. The onboarding stage of the customer lifecycle plays a pivotal role in establishing trust between the customer and the business. Friction during the digital account opening process can lead to customer dropouts, resulting in lower growth for organizations. Moreover, the ever-present threat of fraud necessitates organizations to be vigilant and enhance customer journey with an added layer of verification and protection. Liminal, a leading market intelligence firm specializing in digital identity, cybersecurity, and fintech markets, recently recognized Experian as a market leader for compliance and fraud prevention capabilities and execution in its Liminal Link Index on Account Opening in Financial Services. Download report The report highlights that solution providers in financial services are focused on delivering high levels of assurance while maintaining regulatory compliance and minimizing user friction. Access to real-time verification data, risk analytics and decision-making strategies make it possible for clients to verify identities, detect and prevent fraud, and ensure regulatory compliance. Experian’s identity verification and fraud prevention solutions, including CrossCore® and Precise ID®, received the highest Link Score out of the 32 companies highlighted in the report. It found that Experian was recognized by 94% of buyers and 89% identified Experian as a market leader. “We’re thrilled to be named the top market leader in compliance and fraud prevention capabilities and execution by Liminal’s Link Index Report. We’re continually innovating to deliver the most effective identity verification and fraud prevention solutions to our clients so they can grow their business, mitigate risk and provide a seamless customer experience.”Kathleen Peters, Chief Innovation Officer for Experian’s Decision Analytics business in North America The report offers valuable insights into the market overview, demands, challenges, purchasing criteria, vendor landscape, landscape analysis, and buyer opportunities. Access full 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.