Strategy & Operations

Why agile data integration is key to profitability and reduced time-to-market for lenders, and how businesses are looking to cloud, alternative data sources and self-serve to enable this opportunity. “Data integration is increasingly critical to companies’ ability to win, serve, and retain their customers. To accelerate their performance in data integration, companies are evaluating and adopting a range of contributing technologies.” The Forrester Tech Tide: Enterprise Data Integration As the digital world expands, new and alternative data sources continue to emerge rapidly. With this exponential growth comes the need for financial services companies to integrate new data sources into models quickly and seamlessly. The ability to respond promptly to market changes that require new data sources can significantly reduce time to market for lenders, improving customer decisions by using a mix of traditional and alternative data that ultimately raises approval rates and, in turn, profitability. Research conducted by Forrester Consulting on behalf of Experian shows that a lack of available data is one of the three top technology pain points for tech decision-makers at financial services businesses.* According to the same research, 29% of respondents said that acquiring new customers that match the businesses’ risk appetite is a current challenge, while simultaneously reporting that credit scores still dominate data in decisioning. As more data becomes available, the gap continues to widen between what is possible, and what the reality is for financial institutions. With more data accessible through APIs, lenders have the opportunity to enhance their data analytics capabilities, leading to more personalised loan offers and cross-selling products. Our research supports this: 47% of banks and 52% of FinTechs say that increasing personalisation is a top priority. However, at the same time, data integration opportunities also pose challenges for lenders, namely around security, compliance, and cost. Data access and integration challenges As the prospect of open banking proliferates, newly proposed rules by government bodies such as the Consumer Financial Protection Bureau (CFPB) around consumer data sharing could significantly open financial data access through APIs, further enabling the potential for partnerships between financial institutions and data aggregators. Although open data access and the integration of third-party services present lenders with challenges around the cost of cloud services and total ownership, according to a recent trends report from Datos**, financial institutions will need to invest in secure, scalable, and compliant cloud infrastructure to handle the increased data flow and integration requirements. Cloud deployment: enabling data integration Adopting new credit operations technology is pivotal to data-driven strategy for lenders and deploying that technology in the right way can be critical. Cloud makes it easier to connect data feeds, allowing different internal departments to safely work with data from a variety of sources. Most respondents in our study prefer cloud-based technology, with 83% citing that a cloud or hybrid solution is the preferred deployment option and just 17% seeking on-premises deployment. Self-serve data integration Another key component of agile data integration is enabling users in-house to manipulate data sources flexibly. By speeding up the data integration process with low-code and no-code platforms and tools, businesses can customise their APIs regardless of in-house team experience, allowing data integration to happen in days instead of weeks. “Increasing use of low-code and no-code capabilities give business users the ability to create more customized and packaged business analytics capabilities with business-centric modularity and embed into applications via APIs to serve their business objectives.”Gartner’s Top Trends in Data Analytics, 2023 Improving data integration is central to the quest for speed and agility in today’s credit risk market. With 25% of business respondents citing that they prioritise investment in initiatives that accelerate time to market in response to business and market changes, organisations are ready to capitalise on the opportunity. According to Datos, in 2024, next-generation core banking platforms are poised to address these challenges, providing flexibility, agility, and configurability, along with cloud-native benefits, ensuring financial services institutions stay competitive in the rapidly evolving technological landscape.** Learn more about PowerCurve *In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. **Datos Top 10 trends Retail Banking Payments 2024

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

As credit card issuers grow, the size of their customer base expands, bringing both opportunities and challenges. One of the most critical challenges is managing growth while controlling default rates. Credit Limit Optimization (CLO) has emerged as a vital tool for banks and credit lenders to achieve this balance. By leveraging machine learning models and mathematical optimization, CLO enables lenders to tailor credit limits to individual customers, enhancing profitability while mitigating risk. Recent trends in credit card debt To understand the significance of Credit Limit Optimization, it is essential to consider the current economic landscape, particularly in North America. The first quarter of 2024 saw total household debt in the U.S. rise by $184 billion, reaching $17.69 trillion. While credit card balances declined slightly (a reflection of seasonal factors and consumer spending patterns), they remain a substantial component of household liabilities, with total credit card debt standing at approximately $1.26 trillion in early 2024. On average, American households hold around $10,479 in credit card debt, which is down from previous years but still significant. The average APR for credit cards in the first quarter of 2024 was 21.59%.* The rising tide of delinquencies In the first quarter of 2024, about 8.9% (annualized) of credit card balances transitioned into delinquency. This trend underscores the need for credit card issuers to adopt more sophisticated methods to assess credit risk and adjust credit limits accordingly. The rising rate of credit card delinquencies is a key driver behind the adoption of CLO strategies. What is Credit Limit Optimization Credit limit optimization uses advanced analytics to assess individual customers' creditworthiness. By analyzing various data points, including payment history, income levels, spending patterns, and economic indicators, these tools can recommend optimal credit limits that maximize customer spending potential while minimizing the risk of default, all within the constraints set by the business in terms of its appetite for risk and capacity. For instance, a customer with a strong payment history and stable income might receive a higher credit limit, encouraging more spending and enhancing the lender's revenue through interest and interchange fees. Conversely, customers showing signs of financial stress might see their credit limit reduced to prevent them from accumulating unmanageable debt. Benefits of Credit Limit Optimization Improved Profitability - By setting credit limits reflecting customers' credit risk and spending potential, lenders can increase their revenue through higher interest and fee income. Reduced Default Rates - Lenders can significantly reduce the incidence of bad debt by identifying customers at risk of default and adjusting their credit limits accordingly. Improved Customer Satisfaction - Personalized credit limits can improve customer satisfaction, as customers are more likely to receive credit that matches their needs and financial situation. Regulatory Compliance - CLO can help lenders comply with regulatory requirements by ensuring that credit limits are set based on objective, data-driven criteria. Economic indicators and CLO Impact Several economic indicators provide context for the importance of CLO in the current market. For instance, the Federal Reserve reported that in 2023, fewer than half of adult credit cardholders carried a balance on their cards, down from previous years. This indicates a more cautious approach to credit use among consumers, likely influenced by economic uncertainty and rising interest rates. Moreover, the disparity in credit card debt across different states highlights the varying economic conditions and the need for tailored credit strategies. States like New Jersey have some of the highest average credit card debts, while states like Mississippi have the lowest. This regional variation underscores lenders' need to adopt flexible, data-driven approaches to credit limit setting. Enhanced profitability and risk mitigation Credit limit optimization is critical for credit card issuers aiming to balance growth and risk management. As economic conditions evolve and consumer behaviors shift, the ability to set personalized credit limits will become increasingly important. By leveraging advanced analytics and machine learning, CLO enhances profitability and contributes to a more stable and resilient financial system. One such solution is Experian’s Ascend Intelligence Services (AIS) Limit™, which provides an optimized strategy designed to enhance the precision and effectiveness of credit limit assignments. AIS Limit™ combines best-in-class bureau data with machine learning to simulate the impact of different credit limits in real time. This capability allows lenders to quickly test and refine their credit limit strategies without the lengthy trial-and-error period traditionally required. AIS Limit™ enables lenders to set credit limits that align with their business objectives and risk tolerance. By providing insights into the likelihood of default and potential revenue for each credit limit scenario, AIS Limit™ helps design optimal limit strategies. This not only maximizes revenue but also minimizes the risk of defaults by ensuring credit limits are appropriate for each customer's financial situation. In a landscape marked by rising delinquencies and varying regional debt levels, the strategic use of CLO like AIS Limit™ represents a forward-thinking approach to credit management, benefiting both lenders and consumers. Ascend Intelligence Services * HOUSEHOLD DEBT AND CREDIT REPORT (Q1 2024) – Federal Reserve Bank of New York

How predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. In 2023, the US economy outperformed expectations, with strong job growth, impressive GDP (annual growth rate was 2.5%, up from 1.9% in 2022), and lower inflation. Increased consumer spending and reduced trade deficit highlighted its resilience and adaptability, fostering a stable economic environment. However, the story around consumer debt and delinquencies has not been so positive. In the latest quarterly report on household debt and credit released in February 2024 by the Federal Reserve Bank of New York, total household debt saw a notable increase of $212 billion (1.2%) in the fourth quarter of 2023, reaching $17.5 trillion. Within this surge, credit card balances increased by $50 billion, alongside mortgage balances which rose by $112 billion to hit $12.12 trillion. Auto loans, which have been trending upwards since 2011, saw an additional $12 billion increase, totalling $1.61 trillion. Other balances, encompassing retail cards and various consumer loans, witnessed a growth of $25 billion. Despite the economic recovery post-Covid, the level of debt in credit cards and auto loans, transitioning into delinquency remains higher than pre-pandemic levels. In Q4 2023, aggregated delinquency rates reached 3.1%, signifying a persistent financial strain for many lower income households. Transition rates into delinquency increased across all debt categories except for student loans. Approximately 8.5% of credit card balances and 7.1% of auto loans transitioned into delinquency on an annualised basis. Serious credit card delinquencies (90 days +) surged across all age groups, especially among younger borrowers, surpassing pre-pandemic levels. With such elevated debt and early-stage delinquency rates, lenders face many challenges. We look at how predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. Collections managers and teams within financial institutions face a range of challenges in maintaining portfolio growth while effectively managing increases in early-stage delinquencies. The top five challenges include: 1. High operating costs Contacting delinquent customers, negotiating payments, and managing recovery efforts entail labor-intensive and costly processes. This encompasses expenses related to staffing call centres, sending mailers, and deploying collections management software. 2. Regulatory compliance Navigating federal, state, and local regulations governing debt collection practices presents a complex challenge. Compliance with laws such as the Fair Debt Collection Practices Act (FDCPA) and the Telephone Consumer Protection Act (TCPA) is imperative, dictating the permissible methods and timing of borrower contact. 3. Customer retention and satisfaction Balancing effective debt recovery with maintaining positive customer relationships is essential. Employing aggressive collection tactics risks damaging customer relationships and tarnishing brand reputation, potentially impacting long-term customer retention. 4. Technological integration Incorporating modern technologies like machine learning, and automation into the collections process can enhance efficiency but poses implementation challenges. These technologies require substantial investment and expertise to streamline operations effectively. 5. Data management and predictive analytics Efficiently managing and analyzing vast amounts of data to identify at-risk accounts early and customise collection strategies is a significant endeavour. Accurate data analysis is pivotal for predicting delinquencies likely to self-cure and determining appropriate contact channels, such as; SMS, Email, Phone, Outbound IVR or social media. Applying a customer-centric, strategic approach These challenges underscore the critical need for credit lenders to adopt strategic, compliant, and customer-centric approaches to early-stage delinquency management. Currently, financial institutions use a multitude of strategies to maximize revenue collection. These range from data-driven customer segmentation to profile customers, Regulatory Technology (RegTech) for compliance, proactively identifying vulnerable customers needing financial relief, offering flexible repayment solutions and predictive modelling. Some credit lenders are also using machine learning models, such as Next Best Action (NBA) to personalize collection strategies based on customer behaviour, financial status, and communication preferences. This approach predicts recovery rates by tailoring channel contact to each individual customer in the most effective way. However, NBA models alone are not enough. To maximise collections, within known business constraints (call centre resources, budget, regulations), NBA needs to be augmented with non-linear optimization techniques to ensure not only the right communication preferences are adhered to, but also the business constraints mentioned above. Without the optimisation component businesses are left with NBA modelling that is unadjusted for business constraints. Next Best Action (NBA) Optimization NBA optimization presents a game-changing opportunity for lenders, particularly given the current economic challenges consumers are facing. Here's how NBA optimization can drive value: Personalized communication NBA optimization uses sophisticated customer modelling to pinpoint the most effective communication channels for each borrower, be it email, text, phone, or another preferred method. By personalizing communications, lenders significantly increase the chance of response and engagement from customers, which will also streamline the collections process with greater efficiency and reduced intrusion. Dynamic strategy adjustment NBA solutions continuously learn from outcomes, enabling strategy adjustments. This dynamic capability empowers lenders to swiftly adapt to changing economic conditions, borrower behaviours, and regulatory landscapes, ensuring the maintenance of effective collections practices. Optimized timing Leveraging predictive modelling, NBA optimization empowers lenders to identify the best times to contact their customers. This strategic approach ensures their communication attempts yield higher success rates, minimizing the need for repeated contacts and reducing operational costs. Regulatory compliance NBA optimization solutions can be configured to seamlessly adhere to regulatory requirements, including permissible contact times and frequency limits. This automation ensures compliance, protecting lenders from legal penalties and upholding their standing with regulatory bodies. Operational efficiency Through automated decision-making processes, NBA optimization assists lenders in allocating resources more thoughtfully. By prioritizing accounts with higher payment probabilities and determining the most cost-effective collection strategies, lenders can streamline operations and minimize costs. Improved customer experience (CX) NBA optimization facilitates a tailored approach to debt collection, significantly enhancing the borrower's experience. By considering the borrower's unique circumstances and preferences, lenders can offer more relevant and flexible repayment options, while also boosting customer satisfaction and loyalty. By implementing NBA optimization customised to channel contact preferences and operational constraints, lenders can navigate the complexities of early-stage collections with precision. This strategic approach not only addresses operational challenges but also aligns with the evolving expectations and financial pressures of consumers, leading to improved outcomes for both lenders and borrowers. Businesses can assign the most profitable, cost-effective treatment and channel to contact customers. Ascend Intelligence Services™ Collect delivers an optimized collections decision strategy, driven by predictive analytics, that determines the next best action and contact channel for each individual customer to improve recovery rates, increase efficiency, and stay within day-to-day constraints and regulatory requirements. Find out more

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

Authorised Push Payment fraud is growing, and as regulators begin to take action around the world to try to tackle it, we look at what financial institutions need to focus on now. APP fraud and social engineering scams In recent years, there has been a significant surge in reported instances of Authorized Push Payment Fraud (APP). These crimes, also known as financial scams, wire fraud scams, or social engineering scams in different parts of the world, refer to a type of fraud where criminals trick victims into authorising a payment to an account controlled by the fraud perpetrator for what the victim believes to be genuine goods or services in return for their money. Because the transactions made by the victim are usually done using a real-time payment scheme, they are often irrevocable. Once the fraudster receives the funds, they are quickly transferred through a series of mule accounts and withdrawn, often abroad. Because APP fraud often involves social engineering, it employs some of the oldest tricks in the criminal's book. These scams include tactics such as applying pressure on victims to make quick decisions, or enticing them with too-good-to-be-true schemes and tempting opportunities to make a fortune. Unfortunately, these tricks are also some of the most successful ones, and criminals have used them to their advantage more than ever in recent times. On top of that, with the widespread adoption of real-time payments, victims have the ability to transfer funds quickly and easily, making it much easier for criminals to take advantage of the process. APP Fraud and social engineering scams - cases and losses across the globe: View map Impact of AI on APP fraud Recent advancements in generative artificial intelligence (Gen AI) have accelerated the process used by fraudsters in APP fraud. Criminals use apps like Chat GPT and Bard to create more persuasive messages, or bot functionality offered by Large Language Models (LLMs) to engage their victims into romance scams and the more sophisticated pig butchering scams. Other examples include the use of face swapping apps or audio and video deepfakes that help fraudsters impersonate someone known to their victims, or create a fictitious personality that they believe to be a real person. Additionally, deepfake videos of celebrities have also been commonly used to trick victims into making an authorised transaction and lose substantial amounts of money. Unfortunately, while some of these hoaxes were really difficult to pull off a few years ago, the widespread availability of easy-to-use Gen AI technology tools has resulted in an increased number of attacks. A lot of these scams can be traced back to social media, where the initial communication between the victim and criminal takes place. According to UK Finance, 78% of APP fraud started online during the second half of 2022, and this figure was similar for the first half of 2023 at 77%. Fraudsters also use social media to research their victims which makes these attacks highly personalised due to the availability of data about potential targets. Accessible information often includes facts related to family members, things of personal significance like hobbies or spending habits, information about favourite holiday destinations, political views, or random facts like favourite foods and drink. On top of that, criminals use social media to gather photos and videos of potential targets or their family members that can later be leveraged to generate convincing deepfake content that includes audio, video, or images. These things combined contribute to a new, highly personalised approach to scams than has never been seen before. What regulators are saying around the globe APP fraud mitigation is a complex task that requires collaboration by multiple entities. The UK is by far the most advanced jurisdiction in terms of measures taken to tackle these types of fraud to help protect consumers. Some of the most important legislative changes that the UK’s Payment Systems Regulator (PSR) has proposed or introduced so far include: Mandatory reimbursement of APP scams victims: A world first mandatory reimbursement model will be introduced in 2024 to replace the previous voluntary reimbursement code which has been operational since 2019. 50/50 liability split: All payment firms will be incentivised to take action, with both sending and receiving firms splitting the costs of reimbursement 50:50. Publication of APP scams performance data: The inaugural report was released in October, showing for the first time how well banks and other payment firms performed in tackling APP scams and how they treated those who fell victim. Enhanced information sharing: Improved intelligence-sharing between PSPs so they can improve scam prevention in real time is expected to be implemented in early 2024. Because many of the scams start on social media or in fake advertisements, banks in the UK have made calls for the large tech firms (for example, Google, Facebook) and telcos to be included in the scam reimbursement process. As a first step to offer more protection for customers, in December 2022, the UK Parliament introduced a new Online Safety Bill that intends to make social media companies more responsible for their users’ safety by removing illegal content from their platforms. In November 2023, a world-first agreement to tackle online fraud was reached between the UK government and some of the leading tech companies - Amazon, eBay, Facebook, Google, Instagram, LinkedIn, Match Group, Microsoft, Snapchat, TikTok, X (Twitter) and YouTube. The intended outcome is for people across the UK to be protected from online scams, fake adverts and romance fraud thanks to an increased security measures that include better verification procedures and removal of any fraudulent content from these platforms. Outside of the UK, approaches to protect customers from APP fraud and social engineering scams are present in a few other jurisdictions. In the Netherlands, banks reimburse victims of bank impersonation scams when these are reported to the police and the victim has not been ‘grossly negligent.’ In the US, some banks provide voluntary reimbursement in cases of bank impersonation scams. As of June 2023, payment app Zelle, owned by seven US banks, has started refunding victims of impersonation scams, thus addressing earlier calls for action related to reported scams on the platform. In the EU, with the newly proposed Payment Services Directive (PSD3), issuers will also be liable when a fraudster impersonates a bank’s employee to make the user authenticate the payment (subject to filling in a police report and the payer not acting with gross negligence). In October 2023, the Monetary Authority of Singapore (MAS) proposed a new Shared Responsibility Framework that assigns financial institutions and telcos relevant duties to mitigate phishing scams and calls for payouts to be made to affected scam victims where these duties are breached. While this new proposal only includes unauthorised payments, it is unique because it is the first such official proposal that includes telcos in the reimbursement process. Earlier this year, the National Anti-Scam Centre in Australia, announced the start of an investment scam fusion cell to combat investment scams. The fusion cell includes representatives from banks, telcos, and digital platforms in a coordinated effort to identify methods for disrupting investment scams to minimise scam losses. To add to that, in November 2023, Australian banks announced the introduction of confirmation-of-payee system that is expected to help reduce scams by ensuring customers can confirm they are transferring money to the person they intend to, similarly to what has been done in the UK a few years ago. Finally, over the past few months, more jurisdictions such as Australia, Brazil, the EU and Hong Kong, have announced either proposals or the roll out of fraud data sharing schemes between banks and financial institutions. While not all of these schemes are directly tied to social engineering scams, they could be seen as a first step to tackle scams together with other types of fraud. While many jurisdictions beyond the UK are still in the early stages of the legislative process to protect consumers from scams, there is an expectation that regulatory changes that prove to be successful in the UK could be adopted elsewhere. This should help introduce better tracking of the problem, to stimulate collaboration between financial insitutions, and add visibility of financial instituitions efforts to prevent these types of fraud. As more countries introduce new regulations and more financial institutions start monitoring their systems for scams occurrences, the industry should be able to achieve greater success in protecting consumers and mitigating APP fraud and social engineering scams. How financial institutions can prevent APP fraud Changing regulations have initiated the first liability shifts towards financial institutions when it comes to APP fraud, making fraud prevention measures a greater area of concern for many leaders in the industry. Now the responsibility is spreading across both the sending and receiving payment provider, they also need to improve monitoring for incoming payments. What’s more, as these types of fraud are a global phenomenon, financial institutions from multiple jurisdictions might consider taking greater fraud prevention steps early on (before regulators impose any mandatory rules) to keep their customers safe and their reputation high. Here are five ways businesses can keep customers safe, while retaining brand reputation: Advanced analytics – advanced data analytics capabilities to create a 360° of individuals and their behaviour across all connected current accounts. This supports more sophisticated and effective fraud risk analysis that goes beyond a single transaction. Combining it with a view of fraudulent behaviours beyond the payment institution's premises by adding the ability to ingest data from multiple sources and develop models at scale allows businesses to monitor new fraud patterns and evolving threats. Behavioural biometrics – used to provide insights on indicators such as active mobile phone calls, session length, segmented typing, hesitation, and displacement to detect if the sender is receiving instructions over the phone or if they show unusual behaviour during the time of the transaction. Transaction monitoring and anomaly detection – required to monitor sudden spikes in transaction activity that are unusual for the sender of the funds as well as mule account activity on the receiving bank’s end. Fraud data sharing capabilities – sharing of fraud data across multiple organisations can help identify and stop risky transactions early, in addition to mitigation of mule activity and fraudulent new accounts opening. Monitoring of newly opened accounts – used to detect fake accounts or newly opened mule accounts. By leveraging a combination of these capabilities, financial institutions will be better prepared to cope with new regulations and support their customers in APP fraud. Identity & Fraud Report 2023 US Identity & Fraud Report 2023 UK Defeating Fraud Report 2023 EMEA & APAC

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

Fraud prevention is a critical concern for businesses today. To help combat this ever-present threat, the consortium approach has emerged as a powerful tool in the fight against fraud. By pooling resources, expertise, and creating visibility, consortium members can be more effective in detecting and preventing fraudulent activities. con-sor-tium noun: A group of people, countries, companies, etc., who are working together on a particular project. What is a consortium? Within business, consortiums are a global concept and can operate under multiple categories, including finance, marketing, and tech. A well-known, successful example is Star Alliance. They are a group of airlines, whose agreement enables their members to share and benefit from flights, airport lounges, and frequent flyer programs. All Star Alliance members are working towards the same goal, which is to offer their customers a seamless travel experience. Key benefits of the consortium approach Resource sharing: Pooling resources like funding, expertise, and infrastructure can lead to cost savings and efficient resource utilisation. Risk mitigation: Shared risks make it easier for organisations to tackle ambitious projects or ventures with reduced individual exposure. Access to expertise: Members can tap into the collective knowledge and skills of the consortium, enhancing their capabilities. Market influence: Consortiums often have more influence in negotiations, regulations, and standards-setting, benefiting all members. Innovation: Collaboration can foster innovation through cross-pollination of ideas and technologies among members. Economies of scale: Consortiums can negotiate better deals on purchases or services due to their combined purchasing power. Reduced competition: In some cases, members can reduce direct competition among themselves by coordinating efforts. Market entry: Consortiums can facilitate market entry, especially in foreign markets, by leveraging each other's networks and knowledge. Shared infrastructure: Access to shared facilities or infrastructure can save costs and accelerate projects. Brand recognition: Being part of a reputable consortium can enhance an organisation's credibility and market presence. However, consortiums also come with challenges such as coordination issues, conflicts of interest, and shared decision-making. Successful consortiums require effective governance structures and clear agreements among members. Consortiums in fraud detection and prevention The success of a consortium relies on the collective commitment of its members to a shared goal. In the context of fraud prevention, this means maintaining consistent and high-quality insights across all members. To achieve this, consortium members adhere to an agreement that covers elements such as data quality and data frequency. These agreements ensure that all participants contribute their best insights and information. By fostering a culture of cooperation and sharing, consortiums create an environment where valuable insights can be harnessed to combat fraud effectively. However, it's crucial to emphasise that the success of consortiums ultimately depends on the active participation and contribution of all its members. Consortiums can only thrive when every member is dedicated to making their quality insights accessible to the group. Read more about how consortiums can revolutionise fraud detection and prevention by sharing data on fraudsters across different product types and industry sectors with Hunter.

With an ever-growing number of data sources, businesses must be able to rapidly access and integrate them into decisioning processes using no-code tools to stay ahead of the competition. Today’s customer journey has become increasingly sophisticated. As most firms that interact with customers can attest, this journey is a dynamic process shaped by a range of decisions. Businesses need to decide what is the most compelling offer to deliver to a new customer. Should you approve their loan application? Could the customer gain more from sustainability-linked loans or greener mortgages? What is rich data? These diverse decisions are ideally informed by rich data. This is all the available data, including new data derived from analytics using advanced techniques such as Machine Learning and using rules to make predictions and to calculate scores. While most firms have this data, it is difficult to gather, prepare and integrate into the decisioning processes. Multiplicity of data sources Data types and sources are growing. With regulatory bodies gradually approving the use of more data globally, businesses are faced with an opportunity dressed up as a challenge. Speedy integration of different data sources gives organizations a competitive edge, so finding vendors that can enable firms to utilize available data will positively impact them from a cost efficiency perspective, while also creating the potential for revenue growth. The future is to empower business users with no-code data management No-code data management capabilities add a whole new meaning to self-sufficiency for businesses. It will enable teams across organizations to rapidly change data-driven strategies without much vendor involvement. Gartner estimates that by 2025, 70% of new applications developed by enterprises will use low-code or no-code technologies, up from less than 25% in 2020.   Moving towards client self-service with no-code capabilties is the goal of most businesses. These capabilities are already allowing teams supporting clients to rapidly integrate data sources into their solutions, providing the perfect test ground for business user enablement. If a decision strategy requires changes and a new data source, PowerCurve users can quickly adapt. They can now gather and prepare the right data and deliver it to the system within days. These changes can be instantly published through secure and easily adjustable APIs that support the latest industry standards and frameworks such as OpenAPI and OAuth. An effective customer journey relies on informed decisions and these decisions rely on the right data and advanced analytics. While Experian's PowerCurve platform is well known for automating a range of decisions across the customer lifecycle, it is the data integration capabilities that ensure these decisions are informed by rich data and insights. Creating a harmonious relationship that produces superior and trustworthy results for businesses. No-code data management enables businesses with easy and rapid data source access to deliver rich and insightful data to decisioning processes.

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

Our latest Global Identity and Fraud Report reveals that fraud has been of high concern for consumers over the past year. In fact, more than half of consumers report that they are worried about online transactions, and 40% say that their concern has increased over this period. Data breaches, well-publicised scams, and direct first-hand experience with fraud have all contributed to these higher levels of concern. Our study shows that 77% of consumers had increased concern after experiencing online fraud, with more than half of consumers surveyed having had a close encounter with fraud: 58% of consumers say they have been a victim of online fraud, know someone who has been a victim, or both 57% of consumers say they have been a victim of identity theft, know someone who has been a victim, or both 53% of consumers say they have been a victim of account takeover, know someone who has been a victim, or both As a consequence, it makes sense that consumers rank security and privacy above convenience and personalisation when evaluating their online experience and expect businesses to take the necessary security steps to protect them online. We look at the main factors that play a role in the high levels of fraud concern among consumers and what businesses should do to address challenges in their fraud strategies. Three contributing factors to increased fraud concern among consumers Identity fraud has increased  Our research also unveils that identity theft has overtaken credit card theft as consumers’ biggest security worry across all age groups. Furthermore, a recent report from the UK showed that recorded cases of identity fraud have grown by 22% over the past year. Fraud prevention and security professionals have been trying to educate consumers for a long time on this topic. Stealing identity data and using it in multiple fraud schemes can be significantly more harmful than criminals having access to someone's credit card numbers, where transactions can be traced quickly and revoked or charged back. While many factors contributed to an increase in concern about identity theft, the most impactful over the past two years were the numerous cases of unemployment and benefits fraud. Multiple countries reported cases where criminals applied for loans in the name of genuine consumers or through synthetic identities, created by combining real stolen information with fake data. The cost of these scams is yet to be discovered, and it could take years to see their full effect, with fraud losses well into the billions (if not trillions) of dollars worldwide. Criminals can access stolen data and fraud tutorials beyond the dark web To commit many types of fraud, criminals need Personal Identifiable Information (PII) that is stolen through techniques such as hacking attacks, credential harvesting, credential stuffing, phishing, or other types of social engineering. For years the knowledge of how to do that, along with the stolen data available after a successful attack, was available mainly on cybercriminal forums accessed through the dark web. However, over the past year, it has become easier than ever to obtain not only PII data but also valuable information on how to bypass some of the security and fraud features in place for a certain institution. Criminals no longer need to go to the dark web to do that - it's available on platforms like Telegram, just a few clicks away, where other fraudsters are selling tutorials (often called 'Sauce') on how to commit fraud, as well as PII data (called 'Fullz') to achieve it. As a result, the entry level for those that want to commit fraud has been set lower than ever before - both in terms of skillset and accessibility. Phishing and scams are at all-time high Another contributing factor to the increase in consumer concern is the number of scams resulting in authorised push payment fraud, which totalled £583.2 million in the UK alone during 2021. Criminals continue to seek out consumer vulnerabilities and use a variety of tactics to apply pressure on their victims and convince them to transfer money out of their bank accounts. This could take many forms - from various types of impersonation scams, romance scams, and investment (fraud) opportunities, to scams related to utility bills and easy loan offers among other types. This wouldn't be possible without numerous phishing/smishing/vishing attempts and the amount of data available through data breaches. One other factor that helps criminals is the direct access to potential victims given by social media and the sheer volume of personal information available in the public domain. These types of scams sometimes get high publicity (and rightly so) which can also contribute to the increased level of concern among the public while also applying additional pressure on financial institutions to improve their fraud screening and transaction monitoring capabilities to protect consumers. How businesses can improve fraud screening capabilities and increase consumer trust To restore consumer trust, businesses need to look for ways to improve their capabilities both at account opening and login to prevent criminals from gaining easy access to their services. There are multiple ways to do that, from introducing online identity document verification or phone-centric identity verification capabilities at the account opening stage, to adding behavioural biometrics, device intelligence, or fraud data sharing capabilities during different stages of the customer journey. By introducing some of these capabilities businesses also can improve the digital customer journey for genuine consumers and increase trust. Online identity document verification and phone-centric identity verification solutions both offer pre-fill capabilities. These tools can streamline registration processes and thus contribute greatly to a positive consumer outlook of the company that offers them. While behavioural biometrics, device intelligence, and fraud data sharing tools are invisible to both fraudsters and genuine consumers creating a more frictionless experience. Businesses should look carefully at the fraud they are experiencing along with fraud trends shared by similar businesses. This should help inform whether to introduce new capabilities as part of the existing strategy. It's common that companies might need a mix of capabilities to mitigate fraud issues, with additional support from machine learning models to blend them into one cohesive output while limiting the number of false positives and building consumer trust. Stay in the know with our latest research and insights: