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Due to Covid-19 , the focus on analytics and artificial intelligence (AI) has significantly increased. However, while companies have made significant investments in AI, many are struggling to show a tangible impact in return. One executive commented, “We have data science teams and a data lab where advance techniques like neural networks, GANs, etc. are successfully being used. However, less than 10% of our actual operational decisions and products are powered by AI and machine learning (ML). I would like us to be driving greater measurable impact and Covid-19 is exposing some of our execution gaps.” And, he’s not alone. Despite the investment, the true impact is elusive, and many businesses are not getting the desired effect from their efforts. Achieving the results needed to justify continuous investment will take a holistic approach. So, what can companies do to achieve this impact? The four pillars of holistic AI: performance, scaling, adoption and trust Achieving impact from AI requires taking a more holistic approach across four pillars — beyond just the delight of the data scientist producing a better performing model. 1. AI performance — outperforming the status quo and quantifying the impact This pillar is where most data scientists and companies tend to focus first, for example using modern AI techniques to create an underwriting model that performs better than traditional models. The so-called ‘data science moment of truth,’ where the data scientist declares that he has built a model which outperforms the status quo by 10%. However, it’s important to note model performance alone is not sufficient. We should look beyond the model to understand business performance. What quantifiable business impact does the 10% improvement deliver? How many more credit approvals? How much lower will the charge-off rate be? This reasoning provides the important business context around what the incremental performance means. 2. AI scaling — having the right technical infrastructure to operate models at scale This area is often ignored. The risk with data science teams is they can see their job as being completed with creating a better performing model. However, that’s just the beginning. The next important step is to operationally deploy the model and setup the operational infrastructure around it to make decisions at scale. If it is an underwriting model, is it deployed in the right decisioning systems? Does it have the right business rules around it? Will it be sufficiently responsive for real-time decision making, or will users have to wait? Will there be alerts and monitoring to ensure that the model doesn’t degrade? Are there clearly defined, transparent and explainable business strategies, and technology infrastructure and governance to ensure all stakeholders are aware? Is the regulatory governance around this model in place? Does the complexity in the model allow it to scale? Too often we see data scientists and data labs create great models that can’t scale and are impractical in an operating environment. One banking executive shared how her team had developed 5 machine learning models with better performance, but were in ‘cold storage’ verse in use, because they didn’t have the ability to scale and operationally deploy them effectively. 3. AI adoption — ensuring you have the right decisioning framework to help translate business decisions to business impact With better performing predictive models and the right technology, we now need to present the information in a way that is ‘human-consumable’ and ‘human-friendly.’ At one bank, we found they built a customer churn ML model for their front lines, but no one was using it. Why? They didn’t have the contextual information needed to talk to the customer — and the sales force didn’t have faith in it — so didn’t adopt it. Subsequently, they built a model with a simpler methodology and more information available at their fingertips — where decisions could be made. This was immediately adopted. This pillar is where the importance of decisioning tools is highlighted. The workflow and contextual information to allow a decision to be orchestrated and made is critical in driving AI adoption. 4. AI trust – having governance, guardrails and the appropriate explainability mechanisms in place to ensure models are compliant, fair and unbiased This final pillar is probably the most important for the future of AI — getting humans to trust it. In recent times we have seen numerous examples like the Apple Card, where the underlying principles and models have been called into question. For scalable AI impact, we need an entire ecosystem of people who can trust AI. To achieve this effect, you need to consistently apply the right principles over time. You also need the right decisions to be explained — like adverse action calls. Explainability capabilities help manage communication and understanding of advanced analytics, contributing to established AI trust. And, when fairness and bias issues come up, you need to provide good answers as to why decisions were made. AI is poised to fundamentally change the way we do business, and studies show that $3 to 5 trillion in global value annually, up to $15 trillion by 2030, is likely to be created. We believe the four pillars highlighted above will be key to accelerating the journey to driving positive results and capturing this value. At Experian, we are making investments to drive impact for our clients by delivering against these four pillars. Related articles: What is the right approach to AI and analytics for your business? Four fundamental considerationsHow rapidly changing environments are accelerating the Need for AI What’s new in online payment fraud Part 2: How AI and evolving regulation are driving change

In this episode of the Insights in Action podcast we talk to Neil Stephenson, Vice President of Strategic Client Development, about how businesses can address a lack of data. Following an earlier episode tackling business data challenges, we discuss getting value from the data your organization already has access to, tackling legacy software issues, the accelerated shift to customer-centric technology stacks, and an increase in industry partnerships to solve common challenges. Nearly a third of senior business leaders say they don't have enough data to get insights they need, or that the quality of the data they have access to is poor. We take a look at the three steps businesses need to take to address this challenge, starting with the quality of data already in the business. "We see a number of organizations that have pretty powerful data within their own business but don't leverage it as well as they could, so matching data together and making sure they've got a really strong view of their customer across all of their systems is really essential, and then having processes ongoing to make sure that they maintain that view whenever they touch the customer, whether that be through an online channel or face to face." Neil Stephenson, VP, Strategic Client Development Listen to the full episode here, and look back at the previous in the series, Solving key business data challenges - with Bill O'Connell, Experian Global Decision Analytics

In a recent piece for the Forbes Technology Council, Businesses Need to Modernize Their Approach For Delivering Digital Experiences, I shared how the current rapidly changing environment has greatly accelerated the shift from offline to digital interactions. As businesses experience a need for heightened governance and controls, they must look towards technologies such as artificial intelligence (AI) and machine learning, coupled with access to data in real-time, to move forward. According to the report Experian commissioned Forrester Consulting to conduct, 53% of businesses struggle to make consistent customer decisions. Additionally, only 29% of businesses believe they do a good job of connecting analytics to action. When applying AI and machine learning to customer experiences, there are some concerns that businesses must keep in mind. The first is legal implications and privacy protections, which must always be a priority. The second is to combine analytics models with real-time decisions so that predictions can be harnessed and put into action in real-time. As more and more businesses shift to fully digital experiences, they must learn how to apply their vast amounts of data to models that can help inform the newly remote customer experience. If interested in the topic of businesses’ modernized approach to digital experiences, you can find the full article here.

Recently we commissioned Forrester Research to look into senior executives’ perceptions on key business data challenges and the importance of achieving a holistic view of their customers. This research uncovered that nearly a third of business leaders worldwide say they don't have enough data to get the insights they need or that the quality of the data they have access to is poor. While getting the type, quality, and amount of data right is paramount to success in your endeavors to create actionable insights that take your business to the next level, data alone is not enough. To get value from data, there's a whole ecosystem that needs to be in place that enables the business to create, manage and maintain a holistic view of the customer, create analytically driven insights into those customers, and deploy them into production environments that drive optimal customer actions and journeys. Organizations also have the opportunity to explore new data assets from traditional sources or those dynamically created in a myriad of places across mobile devices and the Internet of Things. There must be systems and procedures in place to continuously improve and assess these new data sources, by bringing them into analytical processes where insights are derived and predictive models generated.  The critical task is then to seamlessly ingest and embed the data and models into production environments in a robust and compliant way. And that's got to be a continuous process. Otherwise, businesses will stagnate, and they will lose out to those competitors who are actively doing this. Addressing the lack of data your business needs to get actionable insights: Three practical steps Prior to even considering external or additional data sources, you need to get a solid understanding of the data you currently have access to within your organization, what value those data sets bring in and what are the gaps to be filled.   You should also review your internal processes and technology stack to understand if further IT investment is required to create a more effective ecosystem.  With the right tools and processes, you must be able to easily assess the uplift of new data sources in your analytics environment, as well as ingest those new data sources into production environments, to drive new models, run segmentation rules, and execute customer-centric actions. What are the three steps you need to take to get enough data to gain business insight you can take action on? Look at the quality of your internal data. We see a number of organizations that have powerful data in their own business but don't leverage it as well as they could. So matching data together, making sure that they've got a really, really strong view of that customer across all of their systems is really essential. And then having processes ongoing to make sure that they maintain that view whenever they touch the customer, whether it's through an online channel or face-to-face, so that they always know who that customer is, and they can match them to their existing relationship profile. Getting your internal data process correct is a foundational element to this whole piece. Understanding the value and role of new data. In terms of new data, it’s about understanding if that new data can actually add value to the business rather than plugging it into core systems straight away.  You need to work with the vendor or the source of that data to get hold of a dataset, match it to your customers, and run analytical processes to identify whether the data adds value. If it does, consider what models or segmentations could you create from that data that'll actually drive value in the business.Identify the software and architectures you have in place that allow you to connect to data and drive that data into a tool that can dynamically apply models and rules in a heavily regulated environment.  With the right toolset forming the bridge between your off-line analytics environment and your on-line production environment, you can leverage predictive data to continuously improve your customer-centric decisoning across the lifecycle for all of your portfolios.

In many respects, the explosion in the type and volume of customer data businesses gather to facilitate security, ensure a convenient, user-friendly approach to customer interactions, and personalize interactions is a double-edged sword. In an era when businesses are awash in data, customers' expectations regarding its use continue to grow. Nonetheless, when it comes time to recognize a consumer by utilizing the data, there is a disconnect between how confident businesses are in their ability to recognize the consumer and the consumer's confidence in businesses' ability to do the same. In our latest Global Identity and Fraud Report, where input from over 6,500 consumers and 650 businesses worldwide was gathered, 95% of businesses expressed confidence in their ability to recognize their customers whereas only 55% of consumers reported that they don't feel recognized by businesses. So why do businesses feel they are recognizing their customers better than customers think they are? At the heart of the problem, many businesses fail to appreciate the risks and shortcomings associated with weak or no identity verification and customer authentication tools, including their inability to prevent criminal activity or offer seamless processes that minimize customer friction. And while businesses possess the means of gathering data from customers through a multitude of identity verification and authentication touchpoints, they sometimes struggle to develop an overarching picture of individual customers, in conjunction with their needs during each phase of the customer lifecycle. This, in turn, results in a myopic view of the customer, despite the existence of extensive data. A never-ending torrent of data Due to the rapid increases in the number of connected devices, there is exponential growth occurring in the amount of data generated, with some estimates predicting an excess of 79.5 zettabytes (or 79.5 billion terabytes) of generated data by 2025. With these facts in mind, many companies experience the shortcomings of big data solutions and their ability to make sense of the unprecedented growth in consumer data at the fingerprints. This inability to provide actionable insight means that what started as promising data lakes now resemble data swamps, meaning that companies possess unfathomable amounts of data but struggle with how to put it to good use. The security implications for business and consumers While businesses rush to embrace digitization by gathering all manner of data from customers at every stage of their journey, vast amounts of data continue to be exposed. Furthermore, as stated earlier, when it comes to customer engagement, there are expectations that businesses must meet regarding security, convenience, and personalization, yet many businesses struggle to understand the interrelationship between these three elements. In specific terms, as a customer interacts with a company, they provide additional data, with each interaction. This helps paint a more accurate picture of their identity and behaviors. In turn, this increasingly detailed, data-driven portrait improves an organization's ability to recognize them in subsequent interactions. Moreover, with a more detailed understanding of the customer, the need for burdensome security processes lessens, resulting in less friction for the customer. In a nutshell, security, convenience, and personalization form individual legs of the same stool. Consequently, failing to consider this fact, leads to isolated security measures, peppered throughout the customer lifecycle. For example, while browsing online, a customer may receive recommendations regarding the products or services they may like. However, when they access their account profile during the same session, the company may force them to reauthenticate their access. Using this example, since the company had sufficient data to personalize the customer's experience, in theory, at least, they also possessed sufficient information about the customer and their identity to grant unfettered access to their profile. Was there a genuine need to reauthenticate the customer in this scenario? At the heart of that interaction lies the customer's identity, which forms the basis for any interactions. When disparate systems capture various elements of a customer's digital identity, a mechanism must exist to aggregate the elements, to minimize the friction customers experience when interacting with businesses at different points in the lifecycle. And while relatively sophisticated CRM systems exist to memorialize customer preferences, due to their inability to capture a holistic view of the customer's identity and subsequent activity during all touchpoint in the customer lifecycle, they often fall short as in their ability to deliver a cohesive, consistent and appealing approach when it comes to security. The power of layers and analytics When fractured infrastructures are in place, businesses often subject their customers to a complicated and disjointed approach to security and risk requests, while simultaneously bombarding them with attempts to up-sell or cross-sell products and services. So, while the goal of data gathering and analysis should in part facilitate convenience, that is far from the customer experience when interacting with certain businesses. Conversely, when customer identity and recognition involves layers of data gathered from across business units, coupled with advanced analytics and quality identity verification tools, businesses can present a more compelling, user-friendly approach that minimizes the stress placed on the customer while providing a positive customer experience. With this approach in mind, businesses can do a great deal to foster engagement which is secure and trusted by the customer. Our research determined that 86 percent of businesses state that advanced analytics is a strategic priority. Yet only 67 percent of businesses consider the use of advanced analytics, like artificial intelligence, to be important for fraud prevention, whereas only 57 percent deem advanced analytics as important for identifying customers. Even fewer respondents see a reason to adopt a hybrid approach involving machine learning involving both unsupervised and supervised models with business rule logic – 45 percent globally and with the United States and Japan as the outliers at 58 percent. However, when businesses pursue the adoption of more sophisticated authentication strategies and advanced fraud detection tools, they will improve their ability to identify and their customers, reducing their exposure to risk and ultimately leading to increased trust. Trust is the linchpin for any transaction and while it's easy to underestimate the importance of trust, given how difficult it is to measure and maintain, without it consumers and businesses will part ways. In a world with no shortage of data, with the right tools and methodology in place, businesses can mitigate various forms of risk, refine the customer experience, and foster the trust needed to support a mutually beneficial relationship between businesses and the customers they serve.

I recently had the opportunity to talk to Christian Hubbs and Muhammed Shuaibi from Artificially Intelligent Podcast about the value AI and analytics generate for businesses. We reviewed how a growing number of businesses are seeing a lot of value added in terms of problem-solving when they bring in more sophisticated machine learning models and technology.  The conversation quickly pivoted towards how to determine the analytics and AI that better suit your business needs, as well as understanding what is required to operationalize those promising models. Think of performance, scalability, adoption and trust before embarking on your AI journey Ensuring that AI is right for your business requires a holistic approach, which is fundamentally based on four components:   AI Performance – selecting and framing problems, with a view to demonstrate that what you build outperform traditional methods.  AI Scalability - what starts as an experiment conducted by data scientists needs to be turned into a scalable system that truly impacts the business.   AI Adoption – ensuring that your AI and analytics are embraced and used by consumers and businesses and, ultimately, change the way they make decisions.  AI Trust – explaining decisions in a transparent way so the models and systems you build can be trusted, explainable and stand the test and scrutiny of regulators. Leveraging an outcome-based approach to solve COVID-19 related business challenges At Experian, we are applying this holistic approach to identify and address the most pressing concerns our clients are dealing within the context of COVID-19. The first is helping our clients understand what’s currently happening with different customer segments. We’re creating tools that bring together a series of early warnings and indicators and portraying how different customer segments are seeing various patterns in credit. We’re also identifying those most affected or needing concessions around lending, and understanding what banks are doing in terms of forbearance. Our priority is identifying these needs and quickly get the relevant AI and analytical solutions to our clients.  We are expecting to see a later urge in the industry to recalibrate existing models and to expand the type and volume of decisions they can make. Updating and monitoring them will be also a big area of focus over the next couple of years.  Listen to the podcast

The credit landscape is undergoing a seismic shift.  Consumers expect seamless, lightning-fast digital experiences, but financial institutions must also contend with rising fraud risks and intensifying regulatory pressure. Incremental tweaks won’t cut it – modern lending demands a radical rethink.  In an era defined by AI, automation and data-driven insights, lenders have a unique opportunity: to transform credit operations from rigid and reaction, to agile, intelligent and scalable.   When presenting our research, I’m often challenged to provide evidence of companies that have made this move and embraced this shift, not just to survive, but to lead. “What benefits did they actually see – in dollars and cents?” It’s a fair question, especially when the stakes are high and the path forward can seem uncertain.   To answer it, Experian commissioned Forrester Consulting to conduct a Total Economic Impact™ study on the impact of Experian Ascend Platform with organisations that have made this move. The findings showcase how lending institutions are leveraging advanced analytics and automation to enhance credit operations, reduce fraud, and accelerate business growth.  Download now The need for accurate and efficient credit operations has never been greater The challenges financial institutions face today: Manual, slow credit decisioning: Lengthy approval processes limit scalability and impact customer satisfaction. Lack of up-to-date data: On-premises environments can prevent accurate up-to-date data. Static scorecards and manual checks happen after the fact. Inconsistent underwriting decisions: Manual assessments introduce bias and inefficiencies. Rising fraud risks: The financial ecosystem faces increasing fraud threats that require real-time detection. Market volatility: Institutions must adapt faster to economic changes and regulatory requirements. Key findings from Forrester Total Economic Impact™ of Experian Ascend Platform Forrester’s independent research provides quantifiable insights into the financial impact of Experian Ascend Platform. The results are based on a composite organisation representative of interviewed customers over three years. 183% ROI with a $13.3M Net Present Value (NPV) 12-month payback period 12% improvement in approval rates over three years 5% year-over-year in new revenue from additional applications 67% efficiency gains in credit decisioning 20% reduction in default costs These numbers show institutions investing in Experian Ascend Platform see rapid, measurable returns. Real-world impact: How businesses are benefiting In addition to the data, the study includes real customer successes across industries: Car leasing company: Increased approval rates from 60% to 66%, leading to better risk management and higher conversion rates. Global airline: Reduced fraud chargebacks by 99.9% (from 6,660 cases per year to just 4), preventing financial losses and reputational damage. Fintech lender: Reduced model development time from months to days, improving agility in risk assessment. These organisations transformed their operations by replacing legacy systems with cloud-based, automation and advanced analytics that deliver real-time insights and consistent, scalable decision-making. "Experian Ascend Platform is driving revenue because more business is being accepted on an automated basis. It’s taking the decision away from the underwriters - making decisioning more consistent - and we are seeing less revenue erosion through successful fraud reporting."Credit Manager, Car Leasing How the Forrester Total Economic Impact™ study can help your business Financial leaders can use the Forrester Total Economic Impact™ of Experian Ascend Platform as a strategic decision-making tool to: Explore ROI potential: Start with the specific areas your business could benefit from using the Experian Ascend Platform, such as operational efficiency, faster decisions, or marketing effectiveness. Build a business case that resonates: Back your investment with proven results. Use real-world success metrics from organisations like yours to shape a compelling, data-driven case. Uncover new growth opportunities: Think beyond cost savings and efficiency gains. With automation and advanced analytics, there’s real potential to expand your portfolio, enter new markets, and deepen customer engagement. Mitigate risk with confidence: See how other businesses have successfully reduced fraud, defaults, and compliance risk. This study provides a data-driven framework to help financial institutions understand the value added to their business. Download the full Forrester Total Economic Impact™ of Experian Ascend Platform study to explore the potential financial impact on your business.

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

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

Experian's new global report is now available on how businesses can enhance efficiency, insights, and growth through integration to transform the future of risk strategy. Download report In the ever-evolving financial landscape, the convergence of credit risk, fraud risk, and compliance is becoming a game-changer. Financial institutions (FIs) increasingly recognise the need to integrate these functions to enhance efficiency, gain deeper insights, and drive growth. The 2024 global report on the convergence of credit, fraud, and compliance sheds light on this critical transformation, emphasising how a unified strategy can revolutionise risk management. The report highlights the importance of convergence in shaping the future of financial services. We surveyed 750 leaders in credit risk, fraud risk and compliance in financial services organisations across the world. Inside the report: The need for convergence As technology advances, financial institutions (FIs) face the dual challenge of managing complex systems while simplifying consumer processes. The report reveals that organisations use an average of eight tools across credit, fraud, and compliance, with some using more than ten. This fragmentation leads to inefficiencies and increased risks.In addition, 79% of respondents want to work with fewer vendors to manage credit risk, fraud, and compliance, underscoring the need for streamlined operations. Independent evolution of functions and associated challenges Credit risk, fraud risk, and compliance functions have evolved independently, creating operational silos and technology management challenges. This separation has led to increased fraud and credit losses. The report highlights that only 9% of organisations prioritise these functions equally, with most focusing on fraud. However, 87% of respondents acknowledge the overlap between these areas and are working towards closer collaboration. Regulatory pressures and advanced fraud techniques New regulations in the US, UK, and EU are compelling FIs to reimburse consumers for losses due to scams, increasing the liability for both sending and receiving banks. Penalties for failing to implement effective Anti-Money Laundering (AML) solutions have also intensified. These regulatory demands and advanced fraud techniques necessitate a more integrated approach to risk management. Early stages of convergence While the market is beginning to recognise the benefits of convergence, many FIs are still in the early stages of this journey. The convergence speed varies, but mature organisations have already started or plan to start the process soon. The report shows that 91% of respondents believe that forward-looking companies will centralise these functions within the next three years. However, only 15% prefer a 'point solution', 36% prefer a single integrated solution, and 49% prefer modular integration. The role of technology Technology plays a crucial role in integrating functions and managing risk. Next-generation platforms are essential for adapting to market needs, delivering innovative products, and meeting regulatory requirements. The report emphasises the importance of data aggregation, which combines diverse data for deeper insights, and the integration of credit decisioning and fraud detection solutions to balance risk and growth goals simultaneously. Improving risk management through alignment Correctly identifying consumers, managing fraud risk, making informed credit decisions, and ensuring compliance share common ground. The report shows that 57% of respondents believe aligning credit risk, fraud, and compliance functions leads to better overall risk management. Businesses with more centralised practices report improved risk management effectiveness, operational efficiencies, and data integrity. Benefits of convergence The convergence of credit risk, fraud, and compliance offers numerous benefits, including: Improved risk management effectiveness: Better alignment leads to more effective risk management strategies. Operational efficiencies: Streamlined processes and reduced duplication of efforts enhance operational efficiency. Increased data integrity: Centralised data management ensures consistency and accuracy. Cost reduction: Consolidation of functions and technology reduces costs. Enhanced customer experience: A unified approach improves customer recognition and service across all channels. Read the report to find out how to prove value through integration. Download report

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

In an era where businesses are inundated with data and options for consumer engagement, it is paramount to use sophisticated targeting techniques that reach and resonate deeply with the intended audience. Pre-screen targeting solutions are becoming increasingly sophisticated, offering a strategic advantage by enabling more precise and impactful outreach, especially within the financial services sector. Technological evolution and targeting precision The core innovation behind pre-screened targeting solutions is extensive data analytics and predictive modelling. These systems integrate detailed consumer data, such as purchasing behaviors and credit scores, with advanced algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This methodological approach streamlines campaign efforts and enhances each interaction's accuracy and tactical effectiveness. Effective targeting with direct mail Understanding the dynamics of various targeting channels is crucial for deploying effective strategies. In the competitive landscape of financial services in North America, direct mail has been shown to have distinct advantages. Direct mail offers substantial engagement. For credit products, this is typically 0.2-2% for prime consumers and 1-3% for near prime and subprime consumers**. This channel’s effectiveness stems from its tangible nature, which cuts through digital clutter and captures consumer attention.​ Benefits of pre-screened targeting solutions Maximized response rates—Direct Mail response models can dramatically boost prospect response rates by targeting a well-defined, high-propensity audience likely to be interested in specific offers. Using a custom response model could improve the average response rate of pre-screen direct mail campaigns by 10-25%**. Reduced risk—Traditional broad-spectrum marketing campaigns waste resources on uninterested parties. Pre-screened targeting via direct mail aims to gain the right through-the-door prospects, minimizing the risk of fraud and delinquencies, thus leading to significant cost savings on underwriting costs. Enhanced customer engagement and retention—Targeted and personalized direct mail strengthens customer relationships by making recipients feel valued. This leads to higher engagement and loyalty, essential for long-term business success. Robust compliance and enhanced security—Pre-screened solutions simplify adherence to industry regulations and consumer privacy standards. These systems come equipped with compliance safeguards that help prevent data breaches and ensure that all communications meet regulatory standards, which is especially critical in the highly regulated financial sector. Looking forward: The strategic imperative of advanced targeting and optimization As markets evolve, the strategic importance of deploying precise and efficient marketing techniques will only grow. Financial institutions harnessing pre-screened targeting and optimization solutions gain a significant competitive edge, achieving higher immediate returns and long-term customer loyalty and brand strength. Optimization ensures that the right customer prospects are targeted and done within business constraints such as resources and direct mail budgets. Future enhancements in AI and machine learning are expected to refine the capabilities of pre-screened targeting solutions further, offering even more sophisticated tools for marketers to engage with their target audiences effectively. For businesses aiming to lead in efficiency, customer satisfaction, and innovation, adopting advanced pre-screened targeting solutions is not just an option—it’s a necessity for staying relevant in a crowded and competitive marketplace. About Ascend Intelligence ServicesTM (AIS) Target AIS Target is a sophisticated pre-screening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Find out more ** Experian Research, Data Science Team, July 2024