Innovation

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. The forum fosters collaboration, networking, and insight, allowing customers to influence product development whilst staying informed on industry trends. This year’s event, The Art of Decisioning, offered a vibrant mix of insightful talks, thought-provoking discussions, demos of industry-leading capabilties, and, of course, a celebratory awards dinner. Uncovering opportunities in the credit market   FutureForum kicked off with a big-picture look at the state of the economy and some revealing insights into the credit market. Experian’s Chief Economist, Mo Chaudri, was joined by Head of Strategic Propositions and Innovation, Natalie Hammond, to explain how the UK economy has stabilised after a turbulent period, with falling prices, much lower inflation and steady employment rates. Consequently, in recent months, there has been an increase in credit demand, particularly in the unsecured sector of credit cards and loans. As a result, the credit card market has seen its most substantial quarter on record, with over one hundred products now on the market. Additionally, the Buy Now Pay Later (BNPL) sector has experienced an accelerated growth rate of 14% among UK consumers. While this surge has proven beneficial for lenders, Experian's data reveals a significant portion of the population, totaling over 2.75 million individuals, either did not qualify or chose not to proceed with their credit offers. Among this group, 1.57 million individuals, constituting 61%, were assigned a 0% eligibility rating, while 1.08 million individuals, accounting for 26%, achieved a 100% eligibility rating. As a result, the opportunity for lenders to serve those customers and accelerate portfolio growth now exists within the market. But to do that, companies need to better understand their customers. Investing in a Unified Platform  Managing Director of Enterprise Strategy and Innovation Steve Thomas took delegates through Experian’s ongoing investment in innovation and problem-solving. Continuing to evolve the richest, most comprehensive data while developing a unified platform that connects data, machine learning, advanced analytics, decisioning and generative AI, all in one place is central to this. The Ascend Platform advances to decision and outcome monitoring for integrated customer management which can revolutionise the way organisations analyse, test and adopt new data and analytics, independently of Experian. The introduction of GenAI and enhanced RegTech functionaility enhances governance and transparency by efficiently integrating new data sets, enabling real-time monitoring, and ensuring comprehensive compliance through thorough documentation and auditing, removing inefficiencies from processes. Through advancements in data and decisioning, businesses can build and test multiple models, understand customers better and make confident decisions across the customer lifecycle. PowerCurve and data upgrades A key element of Experian’s Ascend Platform is the suite of widely used Experian solutions. Ed Heal, Decisioning Director, presented recent investments in this area, which include migrating more of PowerCurve’s functionality to the cloud for a more agile offering, and a game-changing approach to data integration. New data sources can now be directly integrated into PowerCurve within days instead of months, supporting areas such as affordability, Fincrime, buy-now-pay-later and eligibility. As well as making it much easier to add new data, PowerCurve Originations now comes pre-integrated with over 40 data links, including a number of ID and fraud services. These provide a wealth of sources to help businesses better understand consumers for improved lending decisions and to support regulatory and Consumer Duty obligations. As for Strategy Design Studio, a new ‘lite’ version is being launched that’s faster, more visual and easier to use. With simplified processing, SDS means businesses don’t have to rely on strategy specialists to use it, improving operational efficiency and allowing users to test quickly and with confidence. The rise of GenAI It’s impossible to talk about the future without discussing AI. Chris Fletcher, SVP Decisioning and Cloud Solutions took to the stage looking at the latest developments in this area, with a focus on Generative AI tools such as ChatGPT. Chris explored how businesses can use synthetic data and AI to train models and test strategy simulations based on dynamic changes to the economy that may impact credit risk rules or customer behaviour. He also looked at how GenAI can be used to quickly and easily write and edit lending policies, while supporting regulatory reporting. This led to an interesting roundtable discussion exploring some of the future possibilities of AI in the decisioning process. Decisioning everywhere As technology grows ever more powerful and we continue to converge data, analytics and decisioning into an integrated environment, FutureForum offered a chance to imagine the future of customer management. Neil Stephenson, SVP Software Management, discussed how businesses can currently make customer-level decisions across multiple portfolios to drive collection and limit-management strategies. But, he said, “Experian is also looking at how we can help businesses manage customer interactions more holistically in areas such as affordability or promoting new products. Imagine, knowing that a customer is spending a lot to have their car fixed regularly. Could they be thinking about buying a new car? Would this be the right time to offer a loan you know would be attractive to that customer?” This customer hub approach to better service, made possible by Experian data and a unified platform, could introduce a new age of decisioning everywhere. Celebrating our brilliant clients After the speakers and panel discussions had wrapped up, it was time for delegates to relax, enjoy some good food and network with their peers and Experian experts. The evening was also an opportunity to recognise our clients’ achievements and innovations with the FutureForum Awards. This year, congratulations go to Vanquis and Leeds Building Society for ‘Best Customer Outcomes’, Santander for ‘Best Technical Transformation’ and Principality Building Society for ‘Peoples Award – Best Business Outcome’. Thank you to everyone who came along to FutureForum and made it another memorable event. To hear about Experian Decisioning Community events and experiences, please contact us decisioningcommunity@experian.com. 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Lenders prioritise automation above all, according to research. In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. Research from Forrester on behalf of Experian found that automation is the top priority for businesses, and regardless of the specific industry or region, decision-makers consistently identified it as an important area of focus, and the biggest challenge. Lenders are using automation across the credit lifecycle and intend to invest further in the next 12 months, but there are multiple barriers to enhancing automation. We look at the use cases for automation and address the key challenges lenders face when automating decisions. The automation agenda The interpretation and application of automation vary hugely across the maturity spectrum of businesses in our research. While some companies consider automation as a means of simplifying tasks, such as the transition from manual processes to electronic spreadsheets, others are embracing its more advanced forms, such as AI-powered models. Use cases for automation in lending Customer service chatbots using Natural Language Processing (NPL) combined with Robotic Process Automation system (RPA). Remote verification of customers using machine vision and RPA to cross-check data. Data governance - data cleansing of personal information from within data using RPA and NPL. Operational efficiencies using process mining and AI to identify automation opportunities. Credit and fraud risk decisioning, using machine learning. Automation is about making processes as slick and robust as possible, giving the consumer a rapid journey so they can get processed very quickly, while behind the scenes lenders are making the best possible, compliant decisions, that protect them from losses around both credit risk and fraud.Neil Stephenson, Vice President of Experian SOFTWARE SOLUTIONS CONSULTANCY The changing face of automated decision-making in line with rapid tech advancements makes the use of automation by lenders a more complex opportunity than most. On one side there is the chance to enhance models with AI-powered tools to take away manual and subjective decision-making from processes. On the other, there’s the issue of governance and compliance – how to explain models that remove humans altogether. Introducing automation into some parts of the credit lifecycle isn’t always straightforward. Customer management has benefited from a lot of investment in the automation space over the years, particularly Natural Language Processing (NLP), but according to our research, the priority for business investment for Robotic Process Automation (RPA) in the next 12 months is originations. With onboarding playing such a key role in both customer experience and portfolio growth, businesses are looking to enhance this part of the credit lifecycle with automation. Customer experience is driving growth Automation plays a pivotal role in improving the customer journey and experience. The research showed that enhancing customer experience ranked even higher than growth as a priority for many organisations. As businesses strive to deliver seamless and personalised interactions, automation provides the necessary foundation for digital success, which in turn can strengthen competitiveness while retaining valuable customers. "Strategically investing in automation offers businesses the opportunity to scale operations, with a primary focus on growth. In times of economic uncertainty, more targeted, customer-centric strategies, that encompass more accurate predictive models, built on up-to-date samples and executed rapidly, can help mitigate a higher-risk lending backdrop." says Neil Stephenson, Vice President of Experian Software Solutions Consultancy. "Customer experience is the battleground for businesses, where they compete to deliver the best digital journeys in the market. It's a battleground that isn’t just about increasing revenue – the market perception of an organisation can be as important as growth in some portfolios because businesses have a reputation to protect." Automating decisions can ensure customer experience is truly seamless, but businesses face multiple barriers when it comes to credit and risk decision automation. Reducing referred applications  From scoring regression models to the development of machine learning models, better and smarter analytics are critical to drive the processes responsible for making application decisions. Reducing referred applications in turn decreases the need for manual intervention. By minimising the volume of applications in the middle of the credit score, lenders have a clearer and ultimately more automated approach to application accepts and declines. We interviewed decision-makers to understand the numerous challenges faced by lenders when automating decisions: Increasing data sources to allow for a more complete picture of the consumer Improved data quality, and increased volume of data Prevention of model bias The complexity of consumer type attached to some products Redundancy in data input and analytics Training across key roles for a better understanding of automation capabilities Explaining decisions based on machine learning models to regulators Complex fraud referral processes For many respondents, automation is about accuracy and efficiency. By improving automation, there are fewer instances of errors and delays. To ensure scalability can exist in consistent, compliant, and accurate processes that work for both the business and the consumer, here are 10 tips to help tackle the challenges faced by lenders when it comes to automating decisions: Embrace advanced data aggregation tools and technologies that can efficiently collect and integrate data from various sources. Partner with known, trustworthy data providers to enrich datasets. Explore the use of no-code data management tools that allow users to add and remove data sources more quickly and easily. Implement data quality processes. Regularly audit and clean data to remove inconsistencies. Move to cloud-based solutions for scalable data storage and processing of very large datasets. Regularly audit (monitor) machine learning models for bias.  Eliminating sampling bias is not yet possible but using a range of datasets (samples) and various sampling techniques will ensure representation across different demographics to help minimise bias. Develop specific models for different consumer segments or product categories. Regularly update models based on evolving consumer trends and behaviours. Conduct a thorough analysis of data inputs and streamline redundant variables. Use feature selection techniques such as correlations, weight of evidence, and information value to identify the most relevant information. Foster a culture of continuous learning and collaboration for all key stakeholders involved in the credit decisioning and strategy process. Develop transparent models with explainable features. Use interpretable machine learning algorithms that allow for clear explanations of decision factors at the customer level. Streamline identity verification processes by using smart orchestration to reduce false positives and prevent fraud. More on automated decision-making from PowerCurve – North America More on automated decision-making from PowerCurve – UK Related content: Digital decisioning

What are lenders prioritising when it comes to Gen AI? We take a look at five transformative use cases in lending, and organisational priorities for integrating Gen AI into customer lifecycle processes. Although Generative Artificial Intelligence (Gen AI) only launched publicly in the form of Chat GPT last November, adoption has been widespread and rapid. Even in typically risk-adverse industries like financial services, our research shows that there is widespread recognition that Gen AI could deliver a range of benefits across business functions. We identified five areas of focus for lenders based on our research. In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. The qualitative research showed that lenders are already using a type of Gen AI, Large Language Models (LLMs), in their operations, with a focus on testing across areas such as customer service and internal processes before deploying to credit operations. We look at the potential use cases, and how businesses are using Gen AI now. 1. Personalised customer experience Customers today expect a personalised lending experience that is tailored to their unique needs and preferences. GenAI can leverage customer data to generate personalised loan offers, recommendations, and repayment plans. This helps lenders improve customer satisfaction and loyalty, leading to increased customer retention and revenue growth. This is an area that is front of mind for the companies in our research – nearly half of businesses surveyed are planning to implement or expand technology capabilities to either upsell or retain customers in the next 12 months. Furthermore, 50% of companies believe that offering more tailored underwriting and pricing is a top priority in their credit operations, followed by 44% who also aim to increase personalisation in marketing, products, and services to their customers. According to the research, some organisations have formed alliances with technology providers like OpenAI and Microsoft to investigate and further explore the use of LLMs. These partnerships involve analysing customer data to identify opportunities for cross-selling. 2. Enhancing models with new data sources With new data sources emerging all the time, Gen AI is one of the technologies that will most likely accelerate the opportunity for businesses to incorporate them into models. Lenders could include sources such as social network data into their models by using LLMs. This unstructured data, including customer emotions and behaviours on social networks, would be treated as an additional variable in the models. According to the research social media data and psychometric data is already used across financial services, to varying degrees. It showed that 35% of retail companies use social media data, while 29% of FinTechs use psychometric data. Auto finance companies sit at lower end of the adoption scale, with only 12% using social media data and 15% psychometric data. 3. Operational efficiencies Gen AI can help bring operational efficiencies to customerjourneys across the entire lifecycle, offering lenders theability to automate and streamline various processes,resulting in improved productivity, cost savings, andenhanced customer experiences. One of the top challenges for businesses surveyed isimproving customer journeys during onboarding, and thiswas particularly significant for credit unions / buildingsocieties (53%). 4. Detecting and preventing fraud Gen AI can play a crucial role in fraud detection by analysing patterns and anomalies in vast datasets. By leveraging machine learning techniques, Gen AI models can proactively identify potentially fraudulent activities and mitigate risks. The ability to detect fraud in real-time improves the overall security of lending operations and helps protect lenders and borrowers from financial losses. Detecting and preventing fraud is a constant challenge for lenders. 51% of retailers and 47% of credit unions/ building societies surveyed said that reducing fraud losses is a key challenge for them. 5. Customer service Driven by advances in the machine learning and AI space, the world of customer service has benefited hugely from the adoption of virtual assistants and chatbots in recent years. This looks to continue, with businesses saying that LLMs are being tested for customer service purposes, allowing lenders to identify customer issues and automate actions. What's next for lenders? The research found that lenders are utilising various machine learning techniques like regression, decision trees, neural networks, and random forest, along with LLMs. Businesses are in the early stages of exploring how they can use LLMs in credit risk models, but it will undoubtedly involve a blend of existing and new capabilities. As with any emerging technology, it’s important to look at potential risk. The research indicated that organisations see challenges and concerns when it comes to the use of LLMs in their models. It is crucial to ensure the models are trusted, validated, and properly understood to avoid reliance on outsourced solutions and maintain control and visibility over the models’ functions. The ability to explain decisions in Gen AI to avoid bias can be difficult, and businesses will be watching the regulators to understand how best to proceed. There is no doubt, however, that Gen AI will optimise the credit customer lifecycle, creating vast opportunities for lenders. Download PDF More on Gen AI

In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. More on Gen AI

In today's fast-paced digital landscape, businesses are inundated with an unprecedented amount of data and information. Making informed decisions with the data quickly and effectively has become a crucial factor for success. Enter digital decisioning—a transformative approach that harnesses the power of data, analytics, and automation to drive reliable and expedited decision-making. This article delves into the world of digital decisioning, exploring its significance, components, and benefits.  The Essence of Digital Decisioning  At its core, digital decisioning is the process of leveraging software solutions that use digital decisioning platforms or custom-built engines to author decision logic; use decision intelligence technologies such as machine learning and AI; use digital decisions in vertical and horizontal use cases; and manage the full decision logic lifecycle, including feedback loops, to continuously improve decision logic. It enables organizations to make well-informed choices by automating and optimizing complex decision processes. By amalgamating data from various sources in real-time, including credit data, user behavior, market trends, historical data, and external factors, digital decisioning ensures that timely decisions are not only data-driven but also contextually relevant.  Components of Digital Decisioning  Continuous Data Feed: This is the lifeblood of digital decisions. Organizations normalize data from disparate sources to form comprehensive and accurate datasets. Customer data might include income, credit history, transactional data, bill payment, or digital footprint data; however, regardless of the sources, it’s critical that data is coalesced into a single, virtualized view.   Advanced Analytics and Machine Learning: Analytics and machine learning algorithms are deployed to extract meaningful insights from the collected data. These insights are used to model decision scenarios, predict outcomes, and uncover hidden patterns.  Decision Models: Decision models are created based on the insights derived from data analysis. These models define the rules and logic for making decisions, incorporating factors such as risk tolerance, business goals, and regulatory compliance.   Direct Feedback Loop: Every decision has an outcome. For example, an automated loan offer is either accepted or declined by the customer. These outcomes — good and bad — automatically feed into the decisioning model, which enables the machine learning technology to “learn” which decisions are optimal, given the circumstances and customer profile. This enables the model to adapt and grow more accurately and precisely over time.  Automation: Automation engines execute the decision models in real time, allowing for rapid and consistent decision-making without human intervention. This enhances efficiency and minimizes the risk of errors.  According to a 2022 Gartner poll, the CIO Agenda, more than 80% of companies plan to keep or grow their investment in automation solutions.  Benefits of Digital Decisioning  Enhanced Accuracy: Digital decisioning eliminates human biases and inconsistencies, resulting in more accurate and objective decisions.  Improved Efficiency: Automation reduces decision-making time from hours or days to milliseconds, enabling organizations to respond swiftly to market changes and customer demands.  Hyper Personalization: By considering individual preferences, behaviors, and history, digital decisioning facilitates the creation of tailored experiences for customers, leading to higher satisfaction and engagement.  Scalability: The automated nature of digital decisioning ensures that it can handle a high volume of decisions seamlessly, making it ideal for businesses experiencing rapid growth.  Regulatory Compliance: Explainable decision models can be designed to incorporate regulatory guidelines and compliance requirements, reducing the risk of legal complications. Use Case: Respond faster to credit card applications and personalize cross-sell offers  Customers apply online for a credit card from a bank. As they’re being pre-qualified, digital decisioning will instantly analyze the customers’ accounts with the bank including disclosed and undisclosed cash flow. A digital decisioning software solution enables the bank to assess risk exposure and anticipate the customer’s immediate need(s), thereby automating the application assessment and approval steps to reduce approval times from weeks to minutes. Based on the bank’s comprehensive understanding of that customer at that moment, it triggers a personalized cross-sell offer for another relevant financial product, automatically boosting incremental revenue.  Conclusion Digital decisioning marks a pivotal advancement in how choices are made in business. By harnessing the power of data, analytics, and automation, organizations can make faster, more accurate decisions that are aligned with their goals and market realities. As this technology continues to evolve, it will reshape industries and empower individuals to navigate the complex digital landscape with confidence.   Experian’s decisioning management platform allows clients to operationalize the power of rich data, advanced analytics, and automated decisioning software to support the customer lifecycle. Its key differentiators include credit risk, fraud risk, and strategy expertise, fast deployment of strategies into test and production, empowerment of business users, and proactive monitoring of strategy performance by users. Its key use cases include reducing acquisition costs, credit risk, and fraud risk, and improving acceptance rate and the customer journey.  Experian has been named a Technology Leader in the August 2023 SPARK Matrix on Digital Decisioning Platforms report published by Quadrant Knowledge Solutions.  The report highlights the growth of decisioning platforms and the changing market trends that are driving adoption, including the role machine learning and AI are playing in the technology market. This placement is proof that Experian offers best-in-class capabilities through market-leading data, orchestration and automation, advanced analytical models, decision performance, and reporting. Our cloud-based infrastructure enables a scalable and modular platform that allows our solutions to be suitable for customers of all sizes.   Read the report Experian’s Decisioning Management Platform: Accelerating analytics, decisioning, and fraud detection automation Continuous improvement loop: Advanced machine learning models improve decisioning quality 

As economic uncertainty continues to loom, the threat of fraud continues to grow and is becoming more sophisticated. It’s only going to get worse. Due to intensifying inflationary pressures, prices and costs have been increasing which has led to financial hardship impacting individuals and businesses. This provides an opportunity and motive for bad actors to figure out new ways to commit fraud. Federal Trade Commission data shows that consumers reported losing nearly $8.8 billion to fraud in 2022, an increase of more than 30 percent over the previous year. PwC’s Global Economic Crime and Fraud Survey 2022 shows 51% of surveyed organisations say they experienced fraud in the past two years, the highest level in their 20 years of research. Additional investments in fraud prevention technology are a priority for businesses to combat these evolving threats, according to Experian's Sept. 2022 Global Insights report, which states that 94% of businesses report it as the top priority. Since fraud is becoming more sophisticated, part of the challenge that businesses face is to constantly evaluate multiple solutions so that they can continuously improve their fraud detection and prevention capabilities. Investments that can deliver the highest ROI are the solutions that are integrated and orchestrated in a comprehensive fraud reduction intelligence platform. This gives businesses the flexibility to manage evolving strategies and mitigate threats with real-time decisioning. Experian’s CrossCore is an integrated digital identity and fraud risk platform. It offers global solutions to help protect businesses from fraud and maintain compliance with regulatory requirements, using real-time risk analytics and decision-making strategies. The platform aggregates various fraud and identity verification sources to consolidate risk and trust decisions for Experian clients throughout the consumer journey.   Experian’s CrossCore has been recognized as an Overall Leader, Innovation Leader, Product Leader, and Market Leader in KuppingerCole’s Fraud Reduction Intelligence Platform Leadership Compass 2023. This recognition highlights Experian's comprehensive approach to combating fraud. It validates that CrossCore offers best-in-class capabilities by augmenting Experian’s industry-leading identity and fraud offerings with a highly curated ecosystem of partners which enables further optionality for our clients based on their specific needs.  Read the report CrossCore's Capabilities

The ecosystem of credit lending platforms and technologies has rapidly grown in the past year. Lenders now find themselves in an increasingly competitive market with new players emerging on the scene. More companies now have access to advanced analytics and automation capabilities, and this is helping businesses improve the accuracy and inclusivity of consumer lending decisions – a giant step toward achieving their growth ambitions. Our recent research shows that one of the top priorities for businesses has been to invest in new artificial intelligence and machine learning models for smarter customer decisions. But how effective is building new AI models without considering the data? What is data-centric AI? Building AI models on fixed data has already become an outdated approach. But by coupling data with the best model, better outcomes can be achieved. The concept of data-centric AI was coined by leading thinker in the AI space, Andrew Ng. Ng believed that models in production are only as good as the point-in-time data used to build them. As businesses continue to receive new data, this data needs to feed back into the model if it’s going to continue delivering the best results. This continuous loop of enriching the model with new data can be applied across use cases. The value of data-centric AI models for acquiring new customers By using the latest available data, rather than from 6-12 months ago or longer when the model was originally developed, data-centric AI models can: • More rapidly account for changes in the economy and consumer finances • Reach under-represented populations and provide greater access to credit • Take advantage of newly available types of information from data providers The value of data-centric AI in existing frameworks More observations AI is often limited by the data that was used to create the model. By using a more fluid open-source alternative, different data sets can be inputted to get more observations based on different characteristics and findings. For example, if a business wants to acquire a new type of customer, traditional AI would require a new model with new data sets to be in order to target this new customer. With data-centric AI, businesses can use an existing model and simply expand the data, thus allowing the model to work far more efficiently and target a new consumer base. It is a shared view that businesses should not build models with just their own data, because those data sources are too limited. At the very least, businesses want to combine data with a peer sample. However, an even better way is to use hybrid data sets in order to get the most observations. Data-centric AI makes that process easy without the need to create different models to see different outcomes. Up-to-date data The world is in a state of flux—populations change, people change. This means that the data pools AI models draw on may be compromised, no longer relevant, or have new meaning over time. It’s important to keep AI data sets recent and up to date, and not assume that the models used two years ago still apply today. For AI models to operate efficiently they need current, relevant data. Having a data-centric approach and sweeping through collected observations is essential for any business relying on their AI solutions. Businesses must have processes to understand and test their data to be sure the values are still adding up to what they should be. Being disciplined about data hygiene, all the way back to the source, is a necessity. Enriched and expanded data With model-centric AI, businesses are limited by the data they start with. Data-centric AI makes it possible to expand on the current customer base, which already includes data on customer attributes, with new potential customers that might mimic characteristics of a business's current base. Expanded data can also play a role with financial inclusion and credit worthiness. Having a low credit score does not necessarily mean the consumer is a bad risk or that they shouldn't be allowed access to credit—sometimes, it could mean there is simply a lack of data. Expanding data to include varied sources and adding it to current models without changing their structure, enables businesses to provide credit for individuals who may not have originally been accepted. This new approach in AI is creating solutions that are far more inclusive than previously possible. Data has massively expanded and is constantly evolving. By using data combined with advanced analytics, such as AI, there will be more sophistication in the observations that come from the data. This will allow businesses to better decide what data they choose to rely on while ensuring accuracy. By using expanded data sources, the outcomes of models are changed, leading to more inclusive models better fit for decision making and improving performance. "Models in production are only as good as the point-in-time data used to build them." Andrew Ng Infographic: Why data-centric AI leads to more accurate and inclusive decisions Stay in the know with our latest research and insights:

The pandemic may have accelerated digital transformation across the world of financial services , but behind the scenes, banks and lenders still face a significant tech debt, and many organizations are committed to continuing the innovation. That's for good reason. Today's consumers increasingly expect a digital-first customer experience. The days of visiting a local bank branch to access financial services and products are fading away. Fintechs have risen to the occasion, transforming the market and meeting the growing digital demand. For traditional banks and lenders, waiting to innovate is no longer an option—it's a must to remain competitive. So what comes next? Here's a look at the technology trends that stand to impact and transform financial services as we advance. 1. The rapid rise of low-code/no-code solutions According to a recent survey from TechRepublic1, nearly half of companies are already using low-code/no-code solutions (LCNC). The same report also notes that among companies not using LCNC solutions, one in five plans to begin within the year. The driving force behind this trend is the global shortage of digital skills, from software development to data analytics to information security. The pool of technical talent has long been smaller than the demand, and the Great Resignation has only exacerbated the problem. For instance, 75% of software developers2 report they're currently looking for other jobs. Amidst this ongoing talent shortage, there's another stressor—the need to deploy technology products to market faster and faster. LCNC solutions answer these challenges by making doing so easier and quicker. The technology democratizes software development, allowing business users—or citizen developers—in different functions to design and deploy applications. With the skills gap likely to continue, the interest in LCNC solutions will too. LCNC solutions enable financial institutions to keep pace with technology changes and meet the digital demand, even with limited technical resources. 2. Leveraging data will require adding value—and engendering trust Financial service organizations have used advanced data analytics to provide consumers with more personalized products. And consumers have been on board as long as they see the benefit. For example, a 2021 consumer survey by Experian showed that 42% of consumers would share personal data, and 56% would share contact information, if it improves their experience. However, this research speaks to growing tension between consumers and financial service providers. The first want more personalized services, but they are also more selective about which companies they share data with. Consider a recent McKinsey study that revealed that 44% of consumers don't fully trust digital services3. As we advance, organizations that want to build and keep consumer trust will need to be thoughtful about the data they ask for and increasingly transparent about how they plan to use it. 3. Doubling down on AI but looking for ROI in the process AI has proven helpful in multiple ways, from powering recommendation engines and chatbots within the retail world to improving fraud analysis and prevention in the banking industry. But there's still so much more organizations can do, especially with the AI they already have. Financial service and fintech companies have funneled massive resources into AI solutions. However, only 20% of AI models4 are ever used in widespread deployment. What’s more, the current average return on AI investments hovers around 1%. This year, expect to see more organizations examining the ROI of AI-powered technology and looking to get more from the investments they've made. Technology partners can help by identifying additional opportunities for AI models to drive customer engagement, validate credit scoring, and protect businesses against fraud. 4. Banking-as-a-Service will yield even more choices and more competition There have long been high barriers that protect traditional financial service organizations from much new competition. But the advent of open APIs and Banking-as-a-Service (BaaS) is knocking these barriers down, yielding a considerable influx of startups that provide banking-like services. And this wave of new fintech has captured consumer interest. Consumers have shown that they’re willing to try financial service products from an array of providers; they're not married to sticking with traditional banks. In fact, 27% of global consumers5 have relationships with neobanks, and 40% report using financial apps6 outside of their primary banking app. However, the gold rush towards BaaS will yield a few winners and a lot of losers. The question for the near-term is who will survive in this crowded market. Consumers will also begin to figure out what makes sense in terms of how many financial organizations they want to connect with and when to say enough is enough. 5. Embedded finance is the new black in retail In a similar theme, the influx of embedded finance products into retail experiences continues to gain traction. There's only more to come. Multiple leading retailers, both longstanding and new D2C brands, have incorporated Buy Now Pay Later (BNPL) payment options into their checkout process, and shoppers are rapidly adopting these new payment methods. One-third of consumers report they've used BNPL before7. Though the payment method still lags far behind other forms of credit, awareness of BNPL and other embedded finance solutions is rising, especially among younger consumers. Looking forward, expect to see embedded finance make inroads not only with more retailers but also across other industries such as hospitality or entertainment. These pressing tech trends are reshaping financial services. In the process, they're bringing new solutions to consumers and new opportunities to banks and non-traditional lenders. Organizations that keep pace with these trends will lay the foundation for their next generation of customers as well as the future of their business. More 2022 trends and predictions Stay in the know with our latest research and insights: 1.TechRepublic Survey: Low-code and no-code platform usage increases 2.Stack Overflow: The Great Resignation is here. What does that mean for developers? 3.McKinsey: Are you losing your digital customers? 4.ESI ThoughtLab: Driving ROI through AI 5.EY: How can banks transform for a new generation of customers? 6.Axway: Consumers are starting to sense an open banking transformation 7.PYMNTS.com: No slowdown in sight for surging BNPL as consumers want it, retailers need it

Steve Wagner, Managing Director, Global Decision Analytics on Redesigning the future of consumer lending with data and analytics. Find Steve Wagner's interview in Raconteur's Future of data report to discover what businesses need to do to succeed in an increasingly digital world. “The good thing is that technology and data now allow businesses to put the customer journey at the heart of what they’re doing. With the advanced technologies available today, businesses can access relevant data and deliver on customer expectations in their moment of need. Whether it’s access to a loan or mortgage, or to consolidate debts, a real-time view of the consumer is possible.” Read the full article and find out about: Why the digital customer experience, enabled by both data and analytics, is the new battleground for many industries. Consumers reporting they were online 25% more in 2021 compared to a year before. Online retail sales saw four years of growth in just 12 months during the Covid pandemic. Demand for frictionless journeys through biometrics or multimodal authentication mean customers can see the value exchange in sharing personal data. Behavioural biometrics is the next frontier in tackling fraud and providing a seamless customer journey. Technology is allowing us to analyse far more data sources in real time, providing a comprehensive picture of an individual. Open Banking and the democratisation of data are part of the progressive change around data. Importance of extracting the insight lenders and fintech providers need to implement the best customer journey and make the best decisions. Businesses can make credit-risk decisions using automation and advanced analytics. This will lead to more opportunities for credit and better financial inclusion. Harnessing the power of 'insight everywhere' for better knowledge bases. "The application of advanced analytics, artificial intelligence and machine learning is allowing businesses to tailor their services to an audience of one - at scale." Stay in the know with our latest research and insights:

*Stats from Experian Global Insights Research Read related content The evolution of data: Unlocking the potential of data to transform our world Be more open: Results of the 2021 Open Banking survey - Experian Academy Full text: The future of consumer lending in a digital economy With the advanced technologies available today, businesses can access relevant data and deliver on customer expectations in their moment of need. As more people go online and use digital channels, your business must do more to create a seamless and secure experience. Online activity has increased by 25% globally Online retail sales saw 4 years of growth in 12 months Now online, consumers have high expectations for digital experience without sacrificing security, convenience, and privacy. 64% of consumers have abandoned an online transaction in the past 12 months Consumers, regardless of age, now prefer online banking and payments over in-person transactions The future of credit and fraud risk management is integrating data and technology seamlessly to put the customer at the centre of it all. 74% of businesses are adopting AI (2021), up from 69% the year before Businesses can embrace customer-centricity at scale through: Behavioural biometrics within a layered strategy of defence to make it easier to tackle fraud and maintain a seamless customer journey Open source data so businesses of all sizes can build a view of potential customers, minimise credit risk, and bring more people into mainstream financial services Advanced analytics, AI, and machine learning for real-time underwriting, fraud detection and a truly personalised service “The market is now driven by consumer demand for digital services. Those companies that are able to tailor the digital customer journey – so it reflects the best-in-class consumer experience – are the ones that will win.” – Steve Wagner, Managing Director of Global Decision Analytics

Dr Mark D. Spiteri writes on the Forbes Technology Council about how Experian has embraced DevOps culture to not only improve internal IT processes, but also to reshape the mindset of product development teams. What is DevOps? DevOps is the hybrid of development operations - a combination of software development and IT operations that shortens the product lifecycle and delivers a higher quality operational performance, benefiting the company and customer alike. The shift towards a service-centric culture As tech businesses move away from on-premises, product-centric culture, they are seeking alternatives that enable a service-centric approach. DevOps helps to do this by expanding upon agile and lean software development principles that ultimately lead to a cultural shift towards SaaS. The goal is to improve efficiency and accelerate the distribution of product enhancements, but it's all in the integration of these new ways of working. "It’s not a question if DevOps can help your company upgrade its product cycle; it’s a question of how well you can implement it into your organization." Foundations of DevOps People: Small, autonomous teams with a focus on collaboration and achieving system-orientated outcomes. Processes: End-to-end agile, lean practices for rapid IT service delivery. Technology: Automation tools that make the complete flow and pipeline of development and testing repeatable and reliable. "Improving the DevOps process can make a sea of change across every part of your product’s lifecycle, and what’s most fascinating is that the most important elements do not require a huge IT investment." Read the full article   Stay in the know with our latest research and insights: