Accordion Test

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

Credit professionals from a range of banks, telcos and financial services businesses gathered in London’s Kings Place in June for one of the highlights of the Experian decisioning community: FutureForum. We take a look at the highlights.

Lenders are using automation across the credit lifecycle and intend to invest further in the next 12 months. We look at the use cases for automation and address the key challenges lenders face when automating decisions.

Did you miss these February business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Experian launches new anti-fraud platform for digitally accelerated world Financial IT covers the latest on tools to help businesses safely meet the rapid increase in demand for digital services and online accounts. Eduardo Castro, Head of Identity & Fraud, speaks to gaining confidence in preventing fraud while meeting these new business challenges. Experian helps Atlas Credit double approval rates while reducing credit losses by up to 20 percent This Global FinTech Series article provides insights on efforts to make the power of artificial intelligence accessible for lenders of all sizes. Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI, shares background on constantly-changing economic conditions impacting credit models and how to rapidly develop and deploy models to keep up. 60 percent of consumers are using a universal mobile wallet New research shows a continuing trend toward digital transactions and mobile wallet payments. Steve Wagner, Global Managing Director of Decision Analytics, speaks to consumer and business insights on the increased demand and what businesses need to consider to ensure positive customer journeys that support these shifts. Why digital identity and the customer journey is crucial for today’s businesses Steve Pulley, Managing Director of Data Analytics, explores business opportunities stemming from the massive increase in consumers accessing services online. Taking the right steps not only helps ensure business survival but sustainable success. The key is fundamentals including the customer journey and digital identity. How modern data strategies underpin the digital identity and authentication practices critical to digital transformation In this Datanami article covering our progression toward a ‘contactless world,’ modern fraud prevention is explored. Dealing with a tremendous amount of data to offer security, while bearing in mind customer convenience, requires sophisticated technology. Holistic approaches both improve operations and helps keep pace with fraudsters to protect customers. Stay in the know with our latest insights:

Artificial Intelligence (AI) offers people and companies many advantages, and we interact with it every day. From the technology we use to do simple things like heating and cooling our homes, to more advanced tools that map potential disease outbreaks across the globe. AI is also being used more and more in the financial services sector – from matching new customers with the right loan and terms to assisting with transactions in real-time online. In a recent study, we found two-thirds of businesses surveyed globally are using AI to help manage their businesses today. More businesses are keen to use AI but are challenged to fulfill requirements for decision explainability – a must-do for ensuring consumers are treated fairly. The history of AI AI stems from the realization of the potential of computation. The father of theoretical computer science and AI, Alan Turing, introduced a theoretical mathematical model of computation – aptly named the Turing Machine – in 1936. He described this machine as being capable of computing anything computable. By 1950, his work posed the question “Can machines think?” He introduced the Turing Test, still in use today to subjectively evaluate whether a machine is intelligent based on its ability to have a conversation. Six years later, in 1956, prominent computer scientists proposed the famous Dartmouth Summer Project. Advanced concepts were introduced and discussed and the term “artificial intelligence” was first coined. Over the following two decades, AI flourished. Computers became not only faster, cheaper, and more accessible, but they were progressively able to store more information. Meanwhile, machine learning algorithms continued to improve, getting the interest of experts in different fields and industries and taking the realm of artificial intelligence to a tipping point in the early ’80s. Back then, John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn from experience. Meanwhile, Edward Feigenbaum introduced expert systems which mimicked the decision-making process of a human expert, allowing the program to ask an expert in a field how to respond in a given situation and to learn from it. How can AI benefit both businesses and consumers? Following these early milestones, the advanced analytics sector has experienced explosive growth – with AI impacting many aspects of our lives today. While most people have come to realize that AI can be beneficial, even since the early days, there have been many different views on how those involved in programming the algorithms must take the necessary steps to prevent AI from reinforcing stereotypes, widening wealth and educational gaps, or providing incorrect answers at critical junctures such as in a medical setting. As an example of what not to do: a famous language model was trained using 8 million pages sourced directly from the web. So, implicit in this model are the preconceptions and biases included in its training data. In this case, it led to a model with a trend towards greater male bias in more senior, higher-paying jobs. How to determine fairness in AI models So how can we ensure that the use of AI does not reinforce societal racism, sexism, or other stereotypes? That leads us to define fairness. It’s the impartial and just treatment of people without favoritism or discrimination; when no unjustified distinctions occur based on groups, classes, or other categories to which they are perceived to belong. But, within the world of AI, there are varying approaches to fairness associated with different metrics to evaluate and adopt this sought-after algorithmic fairness. Any solution requires defining dimensions of fairness, but realistically, it’s extremely hard to capture all these very sensitive variables and risky to store and process them. To truly determine if an AI system is fair requires an enormous amount of data and expertise. Additionally, promoting fairness requires an approach across the entire data science life cycle and modeling life cycle. All areas must be considered from the approach to data collection to ongoing evaluation of decisions. And, while fairness in AI is not ‘once and done’ or easily solved, the good news is that it is an area of great focus for regulators, academics, and data and analytics industry experts, like our peers at Experian. The growing importance of transparency and explainability Models generally compute calculations that are complex and involve more dimensions than we can directly comprehend. Given this processing step from model-input-to-model-output is unclear, it leads to questions around how a model has come to a decision. Importantly, how can one be sure that the model is behaving as expected? There are different ways to address explainability. One includes an understanding of how different inputs of a model affect its outputs. Shapley values, introduced by Nobel prize winner Lloyd Shapely, consider an aggregate of marginal contributions for all possible combinations. Another technique involves explaining the behavior of a decision by identifying model constants verse variables to extract what drove a decision and how. Yet another method uses counterfactual explanations, identifying the precise boundary where a decision changes. This method is easy to communicate since it involves statements such as if X had not occurred, Y would not have happened. As in the case of fairness, there’s an on-going dialogue around explainability, underpinned by current and yet to emerge new techniques that maintain model accuracy and improve explainability. Artificial intelligence is past its infancy stage. It’s already had an impact on our daily lives and is becoming increasingly ubiquitous. Fairness, along with a transparent and explainable approach are key ingredients to help this field continue its transition to maturity.

If the past year revealed the rising demand for everything digital—it also highlighted key aspects of the online customer journey that organizations have neglected. Until now, most companies have prioritized their digital investments around the revenue-generating aspects of the customer experience. Online account onboarding, e-commerce, and credit lending are prime examples. However, when consumers require different outcomes such as payment support, the interactions are often still handled by call centers. We saw the consequences of this play out during the pandemic. As stay-at-home orders left call centers closed or understaffed, customers who needed help found themselves spending hours on the phone—or worse, were left without guidance. This digital disconnect may be common, but hopefully not for long. Forward-looking companies are creating end-to-end digital customer experiences that benefit customers and the business. From improved customer LTV (Life Time Value) to cost savings, the results reveal that prioritizing customers at every turn pays. The future of digital experiences revealed The last year yielded many insights into how, why, and when customers engage with a business digitally. Faced with few options, customers turned to online resources in droves. And unlike years past, when younger generations have driven digital adoption, the crisis forced customers of all ages to engage with businesses online. Around the world, companies rose to the challenge. According to Experian research, nine out of 10 businesses currently have a strategy for serving their digital customers, with 47% implementing their strategies since Covid-19 began. The rapid shift to digital uncovered opportunities to reach new markets. For example, online grocer Instacart™ launched support services specifically for seniors interested in grocery delivery. However, the sudden spike in consumer demand for engaging with businesses online also revealed significant gaps in the digital customer journey. Consider that only one in four consumers report that they can get help when they need it from a customer representative while online. We saw this play out in real-time after a global bank reached out because it needed a digital solution for payment support. The company had traditionally routed customers to a call center for help. But during the crisis, it was overwhelmed by demand. While working with this bank on a self-serve solution that enabled customers to address their payment concerns online, the experience revealed something important about the future of digital experiences: Regardless of where they are in their journey, customers expect—and deserve—the same experience. True digital yields a true value   Investing in a seamless digital customer journey is a long game, but it’s one that can pay off exponentially. As we’ve seen, organizations have prioritized digital investment that brings in near-term revenue. These include mobile capabilities that increase customer conversions or personalized offerings that boost the average spend. And make no mistake, digital solutions that meet these needs are essential. But the customer who applies for a vacation loan may be the same one who later needs a payment holiday. Meeting these needs digitally, no matter what they are, engenders deep loyalty. Companies that support their customers during downtimes will gain customers for life and reap the benefits when those individuals are inevitably back on their feet again. The happy result is not only increased financial stability for customers but also improved advocacy scores and customer lifetime value. Additionally, an end-to-end digital customer experience can yield unexpected cost savings. For instance, an organization we recently worked with was spending an average of $35 per customer interaction. Their customers accessed customer service representatives via many independent channels, incurring costs each time they connected. We provided them with a multi-channel digital solution that reduced the cost to between $5 and $7 that allowed several interactions and provided consistent experience in the process. In the end, the company was able to deliver better service at a much lower cost point. From here to there The goal of building an end-to-end digital experience is a worthy one, and there are a few components of a forward-looking digital strategy that will help ensure success. First, companies need to create systems and cultures that allow them to respond to changing customer demands. No one predicted that a pandemic would rapidly accelerate our digital shift. But the companies improved their digital capabilities to meet the need came out ahead. Also, while customers experience the technology’s front-end, orchestrating and supporting that journey across a range of consumer touchpoints driven by different events is equally as important. Implementing decisioning tools that leverage data across systems allows you to create advanced analytical models that predict customer behavior, potential problems, and more. Organizations can then make decisions in real-time to support customers and the business when they need help, be it a global pandemic or an environmental event. Lastly, taking advantage of emerging technologies can ensure your company keeps pace with the rapidly evolving expectations customers have for their digital experience. For example, AI-powered virtual assistants learn from every interaction and provide more personalized service than a standard chatbot that uses decision trees. These virtual assistants won’t replace humans but leveraging them to augment customer experiences offers additional support to customers and creates continuity across the experience. The shift we’ve seen is about more than meeting the digital demand. At the core, it’s about leveraging digital capabilities to see, understand and prioritize customers at all points of their journey. Then we can offer them proactive solutions that make their lives better and strengthen our businesses along the way. Related stories: New research available: Global Insights Report, February 2021  The role of the virtual assistant: Meet consumer demand for the digital experience Cloud-based decision management software is a must for re-imagining the customer journey