Tag: credit risk management

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 Experian Decisioning – North America More on automated decision-making from PowerCurve – UK Related content: Digital decisioning

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

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

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

*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

Did you miss these November 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. Online retailers work to turn pandemic buyers into loyal customers Digital Commerce 360 cites that only 73% of U.S. consumers say they're loyal to the brands they shopped with before the pandemic, down from 79% last year, according to Experian's latest wave of Global Insights research. So what does this mean for businesses? Donna DePasquale on Using Tech to Modernize Financial Services In this podcast, Donna DePasquale, EVP Global Decisioning Software, talks to eWeek about how the use of data analytics has evolved in the financial sector, the challenges involved, where we are at now, and what the future might look like. Was that for real? Delving into the deepfake reality Digital Journal spoke to David Britton, VP of Industry Solutions, on deepfake learning benefits and risks, focusing on how bad actors can deceive or manipulate consumers and businesses - and what they can both do to mitigate the dangers. Experian Finds 25 Percent Increase in Online Activity Since Covid-19 Business Information Industry Association looks at Experian's latest research and why the pandemic-accelerated increase in digital transactions is here to stay and how businesses must continue to transform their operations as they head into 2022. Stay in the know with our latest research and insights:

Businesses with priorities to acquire and retain customer loyalty should be prioritizing technology investments that improve the digital customer experience as well as prevent fraud and better manage consumer credit risk.  In our latest survey of consumers globally, we found that the increase in online activity between June and October 2020 has sustained itself for the past year with little sign of digital fatigue. Consumers report that they’re online 25% more today than they were just a year ago. Many lenders and retailers have transformed their operations and met consumers’ needs for accessing goods and services online throughout the pandemic; however, customer expectations for their digital experience may be outpacing those efforts.  Our same study found that customer loyalty toward businesses during the pandemic was at an all time high, but now starting to slip. 61% of consumers reported continuing to engage with the same companies they did a year ago, down 6% in twelve months. Consumers cite security, privacy, and convenience as their top priorities for engaging online. As companies adopt more digital processes and automation to deliver on the real-time financial transactions of their customers, they’re looking to access advanced capabilities for more accurate fraud prevention and credit risk management. Globally, the adoption of artificial intelligence in credit risk decisions is trending up, and 60% of businesses intend on increasing their analytics budgets. Similarly, 65% of companies are increasing their fraud prevention Scalable solutions are creating opportunities for businesses of all sizes to compete for the digital customer. What this means to a mid-size bank, credit union, building society, Fintech and neo-bank is greater accessibility to cloud-based credit risk decision management software. Decades of decisioning best practices coupled with leading edge analytics and technology can help more companies achieve their growth ambitions by attracting, acquiring, and engaging more customers. In fact, confidence in on-demand, cloud-based decisioning has grown to 81%, up from 72% in the past twelve months. Access more insights from our latest research here Other key insights: Consumers report that they are online 25% more now than they were just a year ago 42% of consumers have increased concern for the safety banking and shopping transactions 61% of consumers say they’re transacting with the same businesses, down 6% from last year Consumers rank their priorities online: security #1, privacy #2, convenience #3 Business adoption of advanced analytics has increased over last year – AI is up from 69% to 74% Confidence in on-demand, cloud-based credit risk decisioning is trending up from 72% to 81% Businesses globally say improving digital engagement and customer acquisition is their top priority 75% of consumers feel the most secure using physical biometrics #1 Digital investment is decisioning software, followed by AI and digital enablement for staff Businesses plan to increase budgets for fraud prevention (65%) and consumer credit analytics (60%) In our latest research, we surveyed 3,000 consumers and 900 businesses across Australia, Brazil, Germany, India, Italy, Japan, Singapore, Spain, United Kingdom, and United States. This report is part of a longitudinal study and published series that started in June 2020 through October 2021 exploring the major shifts in consumer behavior and business strategy throughout Covid-19.   Stay in the know with our latest research and insights:

Did you miss these September 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. Lending in a Two-Lane Economy Harry Singh, Senior VP, Global Decisioning, features on this CU Management podcast, discussing ways in which Credit Unions can best serve their customers with loans and other products within what Experian's latest research refers to as the two-lane economy. The deepfake-scape: How to fight fraud in the digital age This Biometric Update article by David Britton, VP of Industry Solutions, looks at why deepfakes are a big risk to businesses and consumers, and how fighting fire with fire in the form of artificial intelligence and machine learning can be the best form of defence for organizations. Focus on Data, Advanced Analytics and Decisioning Creates a Winning Strategy for Experian Global Banking and Finance announce that Experian has been ranked number 11 in the IDC FinTech Rankings Top 100 which highlights the top 100 global providers of financial technology, with the piece referring to Experian as a “rising star.” The Rise Of Voice Cloning And DeepFakes In The Disinformation Wars Forbes's Jennifer Kite-Powell uncovers that although deepfake fraud is dominant in social media, it is quickly moving into business sectors. Kite-Powell talks to David Britton, VP of Industry Solutions, about what businesses can do to counteract deepfake fraud tactics like voice-cloning. Shri Santhanam talks AI in lending On this Fintech One to One podcast from Lendit FinTech News, Shri Santhanam, Global Head of Advanced Analytics and AI, talks about how lenders in the FinTech space should be using AI and machine learning, and what key trends he has encountered through the years, and what we might expect to see in the future. Stay in the know with our latest insights:

Donna DePasquale, EVP and General Manager of Global Decisioning Software at Experian, talks to Experian’s Insights in Action Podcast about the different ways businesses of all sizes can navigate a new era of credit risk decisioning, always with a view to assisting consumers with their credit needs when they need it most. Based on the latest Global Decisioning Report, Donna discusses the four key areas of focus that have come out of the findings: • The pandemic has not impacted everyone in the same way. 1 in 3 consumers say they are still concerned about their finances, while others are ready to start spending again. • Accelerating the movement to online credit and banking. 50% of consumers said they applied for credit online, up from 33% at the start of the pandemic. • The shift increased in investment businesses are making in advanced analytics. • Importance of delivering fast, safe, efficient, and high-quality credit experiences. How we define decisioning “To make decisioning real, it’s really about the experience that someone goes through when they’re applying for credit. When they’re managing their existing accounts and maybe asking for a credit line increase. And it’s the whole experience from providing the information to getting that answer back and then getting that outcome back. From a consumer perspective we want that to be fast and easy and simple, and also from a lenders' perspective you want a comprehensive set of information and rules that allow you to make the right decision for the business and for your consumers.” Donna DePasquale, EVP and General Manager of Global Decisioning Software

As we enter the beginning of the end of this global crisis, the role of data, analytics, and credit risk decisioning takes on even greater significance than before. Consumers face uneven roads to recovery, with some ready to spend again and others still mired in pandemic-related financial stress. And businesses of all sizes report their operations are recovering but there’s still a way to go. A key difference we saw is that companies that adapted to serve customer needs digitally are faring much better. Our 2021 Global Decisioning eBook, Navigating a new era of credit risk decisioning, looks at how consumers are stabilizing their finances and how businesses are returning to growth. A recent survey among 9,000 consumers and 2,700 businesses across ten countries worldwide reveals the importance of lenders prioritizing digital transformation, and the role of advanced data and analytics in enhancing the customer experience. The pandemic fall-out is impacting everyone differently: 1 in 3 consumers remains concerned about their finances – paying bills and managing credit Whereas high-income households are no longer reducing their discretionary spending Navigating this varied credit landscape requires a deep understanding of customer needs on both ends of the spectrum. However, business confidence in the consumer credit risk management analytics models dropped over the past year from 71 percent to 61 percent. Smaller lenders with revenues ranging from $10M to $49M have seen the sharpest decline from 72 percent to 57 percent in the past six months. Adapting data and analytics to a rapidly changing customer base: Almost 50% of businesses surveyed said their dedicate more resources to enhance analytics One-third of businesses are planning to re-build their models from scratch Recalibrating credit models is one thing, but lenders also need to rethink their data sources to better understand current customer profiles. The data inputs generated by the pandemic have impacted credit risk models and machine learning applications in unexpected ways. For example, widespread payment holidays and government stimulus programs may be masking customers’ true financial circumstances. According to Recovery Insights, a separate study published by Experian North America: Delinquency prior to the pandemic is a strong indicator of future risk. Accounts exiting an accommodation period are 2x more likely to become delinquent than are accounts that never received an accommodation. Payment on debt during accommodation indicated a reduced risk for subsequent delinquency. Amidst the pandemic lockdown, consumers turned online to manage finances and connect with lenders – including older consumers.  And while the pandemic pushed consumers online out of necessity, now that they’re there – it’s become a preference – as overall digital gains are holding above pre-pandemic levels. Lenders have a new digital imperative to meet consumers’ evolving needs for continued digital engagement. Consumer expectations of digital experiences 55% of consumers have higher expectations of their digital experience since Covid-19 began 43% of consumers surveyed age 70+ reported digital banking throughout the pandemic 14% of consumers surveyed age 60-69 applied for a new loan or card online The importance of a digital-first approach has revealed itself and many companies have put a digital customer journey in place since Covid-19 began. The future, however, is more than providing online services. It’s about knowing your customers well enough to anticipate their credit needs and using tools to automate the process and reduce risk. Adapt or lose customers 9 in 10 businesses have a digital customer journey in place 1 in 4 consumers have taken their business elsewhere because a company didn’t adapt to their digital needs Online customer experience and credit risk management are more connected than ever before. And, businesses need technology that supports the entire customer journey, from onboarding to customer management to collections. Five digital investments businesses are prioritizing the new era of credit risk management: Implement new machine learning models for customer decisions Increase digital acquisitions and engagement Understand their customer base (affordability, value, behavior) Automate customer decisions Increase value of existing customers Access the report here to get more consumer trends and find out what the future of decisioning means for businesses looking to return to growth. Stay in the know with our latest insights:

There's been lots of discussion about what a return to normal will look like as we transition out of the global pandemic—and much remains up the air. However, our recent consumer and business surveys paint a picture that merits the attention of financial service and credit companies. The big takeaway: The Covid-19 crisis has bifurcated consumers, created extremes on both sides. On the one hand, many individuals coming out of the pandemic have more cash than they had going in. The crisis didn't impact their income, and instead, they've spent the year spending less than they usually would due to work-from-home mandates and local lock-downs. Our consumer survey from January 2021 shows that financial challenges have eased for younger consumers and higher-income households. Yet, at the same time, there's also a contingent of consumers who continue to struggle. One in three of our survey respondents reported that they still have financial concerns and a similar percentage are worried about their employment. We anticipate that the demand for support, service, and credit will be high from each side. So how can companies respond to the heightened need for credit products while continuing to service consumers who may need support? This is where digital solutions make all the difference. By employing digital onboarding and decision automation tools, you can rapidly increase your capabilities while also improving the online customer experience for all. A return to spending The U.K. provides a glimpse of what a staggered return to normalcy may look like. When shops and restaurants re-opened for business in mid-April, lines of people streamed out the doors and flooded the streets. With the country's re-opening culminating in June, many consumers will be looking to resume spending on items and projects that they've neglected since the pandemic's start. For example, our survey data reveals that consumers are becoming less cautious with their finances in general. Fewer people report that they're cutting back on discretionary spending and there's a decline in consumers putting money toward emergency funds and drawing funds from savings accounts. These consumers may be gearing up to spend more. And companies that can anticipate their needs and meet them proactively will be positioned to win and keep their business. Solutions for pent-up demand Many businesses are already preparing for this new wave of demand. Consider that eight out of 10 businesses report that they're turning to cloud-based decisioning applications to improve the customer journey. In doing so, companies are giving themselves much-needed flexibility right when it's needed most. They can dial up their online capabilities based on demand and then dial down if it drops. At the same time, these automated solutions enable companies to deploy their staff to customers who do require personal attention. It's a divide-and-conquer model that keeps the customer at the center. In addition to utilizing the cloud, more than 40% of companies say they leverage AI to improve the customer experience. The AI component enables companies to provide personalized options for consumers and create customer journeys that are far more relevant. The timing for such personalization couldn't be better. In our research, a growing percentage of consumers indicate they're willing to share more personal data about themselves in exchange for improved experiences and added value. Building solutions that work—for everyone The pending volume creates a significant growth opportunity and highlights why digital solutions are a must. Companies that provide the best digital service to customers will garner their trust, loyalty, and even referrals. This yields more demand, increasing the need for scalable, cloud-based onboarding and decisioning even more. Amid this activity, you'll want to focus on getting the most from your digital tools. To do so, consider: Leveraging data for improved credit outcomes Evaluate your end-to-end customer journey, looking for ways to utilize data and increase personalization at every juncture. You'll improve the customer experience and provide more relevant offers. The right data also provides a holistic picture of customer credit risk and ensures you're not creating problems for the future. Utilizing low-code solutions so employees can dive in Digital onboarding and decision automation can be game-changing for the customer experience. But if it's hard for employees to use, then that effectiveness takes a hit. Look for solutions that your employees can use off the shelf. The ability to generate customizable reports and execute on ideas and strategies without involving IT at every turn is essential. Recognizing limitations and potential bias Evaluate your analytics models and look for areas of limitation or potential bias. You want to ensure that you're providing access to credit to all eligible customers and not inadvertently excluding specific demographics. Building capabilities that put you ahead of the market The pandemic provided many lessons—and the value of anticipating demand or potential problems was one of the most important. The crisis is waning, but the financial consequences will continue to reverberate, especially as various government aid programs come to an end. Focus on improving your analytics so that they can better describe what's happening now and predict pending changes in demand and shifts in your portfolio. By and large, consumers are moving forward after a challenging year. Prioritize your digital solutions to make sure you can meet their needs regardless of what the future holds. Stay in the know with our latest insights: