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Why digital acceleration has created more opportunities for deepfake fraud tactics like voice cloning and what businesses can do about it Digital acceleration has placed information and services in the hands of the masses, connecting individuals on a global level like never-before, and in turn making them increasingly dependent on devices in their daily lives. The argument for technology as an equalizer in society is a strong one. Most people have a voice and a platform, producing millions of virtual interactions and recordings every day. But in this digital world of relative anonymity, it is difficult to know who is really on the other side of the connection. This uncertainty gives fraudsters an opening to threaten both businesses and consumers directly, especially in the realm of deepfakes. What is a deepfake? Deepfakes are artificially created images, video and audio designed to emulate real human characteristics. Deepfakes use a form of artificial intelligence (AI) called deep learning. A deep learning algorithm can teach itself how to solve problems using large sets of data, swapping out voices and faces where they appear in audio and video. This technology can deliver extraordinary outcomes across accessibility, criminal forensics, and entertainment, but it also allows a way in for cybercriminals that hasn’t existed until now. Deepfake fraud tactics A principal tactic among deepfake fraud is voice cloning – the practice of taking sample snippets of recorded speech from a person and then leveraging AI to understand speech patterns from those samples. Based on those learnings, the modeler can then use AI to apply the cloned voice to new contexts, generating speech that was never spoken by the actual voice owner. For businesses, deepfake tactics such as voice cloning means access to points of vulnerability in authentication processes that can put organizations at risk. Fraudsters may successfully bypass biometric systems to access areas that would otherwise be restricted. For government leaders, it can mean the proliferation of misinformation – a growing area of concern with huge repercussions. For consumers, the risk of falling victim to scams involving access to personal information or funds is particularly high when it comes to voice cloning. How to prevent deepfake fraud 1. Vigilance: Stay on top of sensitive personal information that could be targeted. Fraudsters are always at work, relentlessly seeking out opportunities to take advantage of any loophole or weak spot. Pay close attention to suspicious voice messages or calls that may sound like someone familiar yet feel slightly off. In an era of remote work, it is important to question interactions that can impact business vulnerabilities – could it be a phishing or complex social engineering scam? 2. Machine learning and advanced analytics: Deepfake fraud is an emerging threat, which leverages the development and evolution of the technology that fuels it. The flip side is that businesses can in fact use the same technology against the fraudsters, fighting fire with fire by deploying deepfake detection and analysis. 3. Layered fraud prevention strategy: Leveraging machine learning and advanced analytics to fight deepfake fraud can only be effective within a layered strategy of defense, and most importantly, at the first line of defense. Ensuring that the only people accessing the points of vulnerability are genuine means using identification checks such as verification, device ID and intelligence, behavioral analytics, and document verification simultaneously to counter how fraudsters may deploy or distribute deepfakes within the ecosystem. As with many types of fraud, staying one step ahead of the fraudsters is critical. The technology and the tactics continually evolve, which may make the countermeasures on the table right now obsolete, however the fundamentals of sound risk management, with the right layered approach, and a flexible and dynamic solution set, can mitigate these emerging threats.   Stay in the know with our latest research and insights:

Fraud threats continue to rise across the globe as consumers are spending record amounts of time online due to the pandemic. At the same time, emerging threats of fraud are growing, as fraudsters are taking advantage of the globally shifting economic conditions. Fraud prevention remains a top concern for both consumers and businesses alike. Anticipating future fraud risk is critical and companies are adopting more complex technology systems to ensure consumers’ financial safety. To provide a safe and convenient experience, businesses need to take a customer-first approach when evaluating the latest technology and solutions available to them. To ensure they are providing secure online experiences, businesses are turning to verification strategies using data technology and other detection methods. In fact, according to this year’s Global Identity and Fraud Report, customer recognition security strategies have become the new norm for businesses with 82 percent of companies saying they now have one in place, a 26 percent increase since the start of the pandemic. An independent research firm headquartered in Germany, KuppingerCole Analysts, released a report, Leadership Compass: Fraud Reduction Intelligence Platforms, that provides an overview of the market segment, vendor service functionality, prevention measures and innovative solutions to fraud. The report cites Experian as an overall leader, product leader, innovation leader, market leader and technology leader in fraud reduction intelligence platforms. Experian is also credited for taking a client-oriented upgrade approach and delivering other cutting-edge features while maintaining compatibility with our older platform releases. We also scored a strong positive for interoperability, usability, deployment, innovativeness, market position, financial strength and ecosystem; and a positive in security and functionality. We pride ourselves in our digital identity protection services and consumer safety, taking proactive approaches to fraud prevention and providing businesses with the necessary tools to identify risks of fraud. The report discusses fraud prevention measures and innovative solutions to fraud. According to the report, cybercrime costs will reach $10.5 trillion by 2025. The report evaluated 15 different data security and fraud prevention platforms and ranked their products, innovation, market positioning and technology in their report. All of Experian’s fraud detection and prevention services are available through our CrossCore® partner ecosystem. By combining advanced analytics, rich data assets, identity insights and fraud prevention capabilities, businesses can connect any new or existing tools and systems in one place, whether it be Experian’s, Experian’s partners or its own. With its built-in strategy design and enhanced workflow, fraud and compliance teams have more control to quickly adjust strategies based on evolving threats and business needs, which helps to improve efficiency and reduce operational costs. Learn more about the CrossCore platform.   Stay in the know with our latest research and insights:

How elite leaders train analytics teams to unearth and convey the highest quality data insights and better manage risk. It's surprising how much of an art the effective use and analysis of qualitative data in the business world truly is. Too often, data scientists are tasked with turning raw data into insights without ever actually being taught the true art of identifying and reporting the most meaningful insights that address the problems at hand. Instead, data teams often produce reams of summarized information without drawing any useful conclusions – falling short of discovering deeper truths hidden within. I've been fortunate to work for, with, and manage data scientists of various titles, abilities, and personalities over the years. I've found that the true "artists" in this profession can combine technical proficiency, tactical communications with an affinity for the science, and excellent detective skills. Objectivity in Data Analysis As Arthur Conan Doyle wrote in Sherlock Holmes says, "I never guess. It is a capital mistake to theorize before one has correct data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts." As data scientists, we're often sent down a singular path to analyze data to support a narrative. Data is inherently objective; analyzing with subjective intent typically leads to ineffective results when put into practice. However, with the proper guidance, probing questions, and some detective work, scientists can uncover deeper insights leading to effective outcomes in the form of actionable intelligence and forecasts. Early in my career, I was tasked by a business partner to pull data that demonstrated higher customer satisfaction scores for a customer call center. Requests like this – "just get me the data" – are (unfortunately) common. In this case, however, he was open to discussing the "why" behind his ask. As a result, this incident proved a learning opportunity for me on how to satisfy a requirement while simultaneously producing information explicitly valuable to the organization. I've often had to find workable paths through figurative minefields with mandates such as "just get me the data" or "make the numbers work." During this scenario, I diligently asked ancillary questions to build into the data modeling outside the required parameters. I intended to generate value beyond the pre-conceived conclusion I was tasked with finding data for. The resulting report yielded compelling insights, actionable intelligence, and a clear forecasting plan. In this example, it was found that clients had higher satisfaction scores for reasons other than what we initially thought and had nothing to do with the seven million dollars my business partner spent on branding, training, etc. The solution was simple: move a training location. Tactical communication skills were necessary in this scenario as I had to tell my business partner where the efficiency gains were actually coming from and where future budgets could be more effective. Doing so was the catalyst behind an alternative business strategy and focus, resulting in a much more significant impact on our customer relationships. Asking the Right Questions The true purpose of analytics is to discover, interpret, and communicate meaningful patterns in data and the connective tissue between. Most importantly, it exists to aid in effective decision-making within an organization. Under that premise, I teach my teams to be communicative, especially during planning stages and consistently ask questions of the data throughout the analytical process. It's always imperative to identify the specific addressable problems our clients are trying to solve while frequently conversing with them to understand what actions and/or decisions the analysis is meant to inform. This strategy produces more profound results and focuses on solving a problem – not endlessly cycling through various cuts of the same data. As a result, the team will be primed to evaluate results objectively and be ready to dig beyond surface-level data, capturing vital insights hidden deep within. Using the Right Tools Nobody does arithmetic by hand anymore. A data scientist's best friend should be sophisticated model development software that leverages AI and Machine Learning. The efficiency they provide enables us to focus on areas where human intelligence is best applied, such as interpreting model performance within the context of how that model will be used. Elite leaders know how to leverage the right tools to maximize speed and efficiency. Ignoring the sheer processing power of cloud computing and other advancements places your organization at a distinct competitive disadvantage in performance and accuracy. I shudder when thinking about the dark days when it would take six to nine months to develop a new model. It reminds me of watching NASA mathematicians do advance calculations with slide rules in movies like Apollo 13 and Hidden Figures. Strategy optimization is a perfect example; how do I ensure that my portfolio is holistically delivering the highest value within risk constraints? I could grow my portfolio endlessly, but that likely means taking on too much back-end risk. Instead, mathematical optimization can be used to determine the right balance between growth, return, and risk. To do this successfully requires a vast amount of processing power. Gradient boosting, a Machine Learning technique that helps build far more accurate models, is another excellent example of what's possible with modern technology. Some of the operations we perform daily were literally not possible 10-15 years ago as we did not have access to such processing power. Thus, we're able to solve problems not previously solvable. What has also changed is our ability to process volumes of data and highly complicated, multi-tiered models, with extreme speed and efficiency. Organizations don't need to take all of this on, as companies like Experian effectively provide data science services where AI/ML solutions are delivered rapidly and digitally. A well-equipped, efficient, curious, and well-trained data team whose data analysis consistently helps corporate leaders make informed decisions is true art. The answers they provide to challenging business questions is their magnum opus. Read about topics related to this article Stay in the know with our latest research and insights:

In this opinion piece on CEO World, David Britton, VP of Industry Solutions, Global ID & Fraud, discusses why, in today's increasingly digital world, it is much easier for fraudsters to operate on a global scale. As commerce and financial services ramped up their online offerings due to the pandemic, it enabled criminals to take advantage of people in vulnerable situations. There has been a significant shift away from previously prevalent fraud schemes such as account takeover, account opening and card-not-present, towards the direct manipulation of individuals to get to their personal information and payment details. "Not only have they been taking over the world, but fraudsters have been taking advantage of the growing digital environment, and as recent research from Experian found, 55% of consumers say security is the most important factor in their digital experience. It is important for individuals to know what to do to ensure that their information is secure and to have technology to utilize in order to fight against this issue. For both personal and businesses, there are ways to combat the scandals of fraudsters." Business fraud prevention With a focus on ransomware and email compromise, there are many things businesses can do to minimise vulnerability to fraud. A layered approach to defence is key, along with device intelligence and strong employee training. Personal financial fraud Although there is a common misconception that credit card details pose the biggest fraud opportunity, identity theft is by far the one to watch for consumers today. Fraudsters can use personal information for credit or payments. "Businesses must invest in new technologies in order to give people the added security they desire when accessing their accounts. In fact, according to our most recent Global Identity & Fraud Report, consumers no longer believe passwords are the most secure method for authentication. Since the pandemic, consumers have an increasing level of comfort and preference for physical and behavior-based – or invisible – methods of security." Read the full article Stay in the know with our latest insights:

A recent industry-leading analyst report looking at loan origination solutions found that lenders are experiencing high volumes of new loan applications, but many are struggling to process them. This alongside increased consumer demand for improved digital experience, and a shifting credit landscape means lenders are trying transform both to keep operating costs down and meet the needs of a changing market. This tracks closely to findings from our Global Decisioning Report 2021. We look at what is changing, and how the Now Tech: Loan Origination Solutions report advises lenders to move forward. Consumers went online, and have high expectations of the digital experience The pandemic shut down banking and retail locations around the world. Amidst the lockdown, consumers turned online to manage finances, connect with lenders, and buy essential goods and services. The crisis especially accelerated digital adoption for older consumers and created a new digital imperative for lenders wanting to meet customers’ evolving needs. The rise of self-service and new payment methods There was also an increase in the already growing demand for digital self-service in terms of applying for credit and seeking out repayment support. Consumers expect to be able to apply for credit when and where they need it, often using a mobile-friendly device. In return for convenience and security, consumers report that they’re more willing to provide additional personal data. Timely, meaningful credit and repayment offers, convenient interactions, and improved communication with lenders make the exchange worth it. The convenience of digital channels is also creating the opportunity for new payment methods, such as subscription models and Buy Now Pay Later (BNPL). Both are occurring across a range of products and services, from cars to clothes to beauty essentials. Our Global Decisioning Report found that 27% of consumers reported purchasing products using BNPL programs. Traditional lenders will need to consider the needs that the emerging BNPL market meets. This includes making purchases easier for consumers by providing increased payment flexibility. APIs, security, integration, and explainable AI According to the Now Tech report, lenders should look for solutions that allow access to data via APIs for credit decisioning, have strong data security and privacy practices, integrate with third-party technology products and services, and leverage explainable AI for underwriting. Allowing lenders to acquire customers digitally is key, and loan origination solutions provide a digital portal that can be accessed across devices and which supports real-time customer input, document uploads, data aggregation and analysis, and digital signatures. Want to read the full 2021 Global Decisioning Report?

Financial institutions have long been dependent on technology for business operations, resulting in a long history of tech additions, upgrades and vendors. Changes made to legacy IT systems can not only impact customers, but in many cases, the economy too. Often these systems feel safe and familiar, so it can be a difficult choice to make a change. However, over the last year the pandemic has highlighted the need for agility within the market. Responding to changing customer needs in an increasingly digital environment is number one priority. What do we mean by legacy tech? The term legacy tech has a lot of negative connotations. It refers to a set of computer systems, software and technologies that can no longer be maintained or easily updated. The system could be out of support or in extended support. Integration becomes a challenge because different technologies have accumulated over the lifespan of the business, and the associated support levers around it are all different. There is also the challenge of finding the skills to maintain these systems – in-house or outsourced from providers. Maintenance costs can be high – security and resilience test costs will add to this, while performance will drop with the increasing need for work-arounds. Upgrades can be complex, expensive or even impossible on legacy systems, generating extra costs. Financial institutions create their own legacy systems when they start integrating various data sets from different sources. It can happen when the business grows to new locations, new lines of product, extended consumer services, while using different tech from different vendors. Cloud as an enabler for business transformation From the moment code is written and deployed, it becomes legacy. Cloud integration allows for daily code releases and automated upgrades meaning that businesses are constantly adjusting and responding to client needs, regulation and strategic changes. They can instead focus on their business model and innovation, staying relevant and up to date. Budget is directed towards improvements and innovation instead of maintaining the legacy tech. It brings an interesting level of agility, with the ability to respond to the market much more quickly and effectively. How cloud can benefit the customer Cloud-based services have allowed banks to revolutionize onboarding processes and timescales. Processes like KYC (Know Your Customer) can be carried out by partners for a fast and efficient experience. Throughout the lifecycle of a customer, banks can leverage third parties for every part of the journey and ultimately improve customer experience. Beyond the onboarding process, the entire customer lifecycle, from originations to collections, can be transformed by removing friction and using AI to create interest, and ML to make decisions for quick results. Experian has partnered with Open Banking Expo TV to produce a series on Cloud-based solutions. Sign up to watch. Related content

Did you miss these July 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. Anthony Bourdain Deepfake Controversy Points to Pitfalls of AI-Generated Content As synthetic media grows more popular as a creative tool, creators must navigate new ethical ground, reports Adweek's Patrick Kulp. David Britton, VP of Industry Solutions explains why we will see more deepfakes as the technology embeds, and how the ID & Fraud prevention and detection industry is responding. Tech-driven credit management solutions are key for businesses in post-Covid world, says new report The Business Times takes a look at how, according to Experian's latest Global Decisioning report, businesses need to respond and adapt to consumers' growing digital needs after an acceleration in digital interactions during the pandemic. Selfie biometrics for online pet sales and financial services among latest remote onboarding launches Biometric Update uncovers the latest in remote onboarding implementations, reporting on its promising steady pace and highlighting Juniper Research's recognition of Experian's industry-leading ID & Fraud solutions in this field among its peers. Validating Identities In A Digital World Bloomberg's Paul Sweeney and Matt Miller talk to David Britton, VP of Industry Solutions, in this radio piece about the importance of validating identities in a growing digital world, and what we might need to see from ID & Fraud solutions in the future to ensure that individuals are secure online. How Businesses Can Adapt to Rising Customer Expectations Donna DePasquale, EVP of Global Decisioning Software writes in CXO Today about the importance of customer journey in credit risk decision-making, taking businesses through the customer lifecycle to show how to avoid disruption during each digital interaction and touchpoint, while implementing the best decision analytics. Stay in the know with our latest insights:

Credit providers have long relied on data to lend insights into how their customers are faring—and help predict what's to come. The pandemic, however, introduced unexpected anomalies that have made understanding the actual credit landscape far more challenging. For example, while government assistance programs have enabled customers to stay up-to-date on their payments, they've also made it harder to discern the true financial impacts of the crisis. Our recent research gives voice to these challenges. We surveyed businesses around the world three times from July 2020 through January 2021 for our annual Global Decisioning Report. The results reveal that business confidence in credit risk analytics models has declined over the pandemic, dropping by nine percentage points for Tier 1 lenders, and 15 percentage points for Tier 3 lenders. As we look ahead, credit providers need ways to improve confidence in their analytics models so that that they can make smarter, faster decisions on behalf of their customers and businesses. This is where synthetic and alternative data are beginning to make a real difference. The rise of AI and machine learning solutions has opened the door for lenders to leverage this data. Understanding how to put it to use—and why it's imperative to do so—will help lenders navigate the end of this crisis and prepare them for any economic volatility in the future. The data differentiator  Before we dive into how lenders can best utilize alternative and synthetic data, let's quickly define what we're discussing. Credit providers have traditionally used credit bureau data to assess their portfolio risk and inform credit decisions. But as noted, in times of crisis, supplementing that data with additional context can significantly improve its effectiveness. Alternative data does just that. Alternative data refers to primarily unstructured data from non-traditional sources. For example, social media data can help paint a more complete picture of customer behavior. And location data can provide information about customer geography, such as opportunities for travel-related purchases. Synthetic data complements alternative data but is not the same. Synthetic data is new data created by altering existing data. So a lender might change the profile of its customer base and then use that dataset in analytics models to better understand what the future may hold. Both types of data work together, with alternative data providing a more complete customer view and synthetic data allowing lenders to account for additional variables and offset their risk accordingly. New data in action  Confidence in analytics models may have dropped during the crisis. But lenders aren't resting on their laurels. Instead, nearly half of businesses report that they are dedicating resources to enhance their analytics efforts. Those that include alternative and synthetic data in their improved models have the opportunity to leverage the information in multiple ways. Some of the most exciting applications of alternative and synthetic data include: Anticipating purchasing behavior New data sources, especially from social media, help lenders understand what's happening in their customers' lives and how that may translate into purchases. For example, a customer who has recently moved into a new home may be considering purchasing furniture or home décor. Or customers who are celebrating life milestones such as birthdays or graduations may be buying gifts or spending on events. Predicting credit risk In this realm, synthetic data can be beneficial. Lenders can use synthetic data to understand how credit profiles may change in specific circumstances, such as modeling a higher unemployment rate or dramatic income shifts. They can then use analytics models to determine the related impact on customer affordability. Improving fraud detection With an improved customer view, fraud prevention teams can more easily identify unusual patterns in customer behavior and spending. For instance, does a customer's current location (per location data) match their most recent transactions? Or has the number of contacts on their phone dramatically changed (it may not be their phone)? Enhancing pricing Both types of data are useful in improving pricing models across company portfolios and at a personal level. The additional context can help everyone from lenders to insurers to banks assess customer needs and provide products that meet them—at prices that make sense. What's more, machine learning automates that pricing, allowing companies to scale personalization across the organization. Improving marketing In the same vein, new sources of data can also give marketing efforts a boost. The ability to access more real-time information about customer behaviors uncovers opportunities to provide them with credit, insurance, and other lending products that may prove immediately helpful. Data can also help identify new markets entirely or highlight rising needs that may demand the development of additional products or services. The past year was an anomaly in so many ways. However, as we ease out of the crisis, financial service companies have the opportunity to strengthen their data models—and leverage new types of data to reduce their risk and provide improved decisioning no matter what the future holds.

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

Did you miss these June 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. Fintech Interview with Executive Vice President, Experian – Eric Haller FinTech Buzz talks to Eric Haller, VP & General Manager, Identity, Fraud & DataLabs, about various key trends and challenges in the Digital Identification domain and how fintech prevents fraud in the financial market. Experian on Fraud: 2020 Was a Big Year for Nefarious Actors and Illicit Activity in Financial Services David Britton, VP, Industry Solutions, Global ID and Fraud at Experian, recently shared with Crowdfund Insider Experian’s insight into fraud and what the business is doing to combat nefarious activity in financial services. Evolution of digital identity Professional Security Magazine Online talks to David Britton, VP of Industry Solutions, about how the preference for digital-first was sparked and then accelerated through the Covid-19 pandemic, but the concept of digital identity and the need for its evolution will remain prevalent for both consumers and businesses way beyond that. Preventing fraud without compromising the customer experience Finextra looks at Experian's Global Identity and Fraud Report 2021 to uncover why customer experience is central to the approach for businesses when balancing fraud prevention and revenue. Three things lenders need to do to navigate today’s complex lending and credit landscape Business Information Industry Association covers Experian's Global Decisioning Report 2021 highlighting the dual-lane economy and what lenders can do to succeed in this complex and changing landscape. Stay in the know with our latest insights:

Donna DePasquale, EVP, Global Decisioning Software, talks to Bloomberg Quicktakes about the key findings of the latest Global Decisioning Report. Some key takeaways from the interview: Covid-19 has created an even wider two-lane economy 1 in 3 consumers remain concerned about their finances Lenders need to prepare for a wave of potential delinquencies Around 50% of lenders are investing in model recalibration to deal with the changes caused by the pandemic One third of lenders are creating new credit models We surveyed 9,000 consumers and 2,700 businesses across ten countries worldwide. Download the Global Decisioning Report 2021:

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: