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The artificial intelligence (AI) market is expected to grow 159% by 2025 to $190.61 Billion, according to Markets and Markets, and there’s considerable value for businesses and consumers. In our July global survey of businesses and consumers, we found that 60% of businesses planned to invest in advanced analytics and AI to better support their customers' financial needs during Covid-19. As more businesses adopt AI, processing their vast amounts of data with advanced analytics for automated decisions, human oversight is and will remain key to ensure transparency and explainability. This “human element” in AI was the inspiration for our latest “game changers” series. We recently sat down with five industry experts to get their view on how AI is making the world a better place, and how its use in financial services can be realized. Yi He, Deeba Kazmi, Jennifer Kung, Kathleen Peters, and Laura Stoddart are visionaries and leaders in data science and innovation making a real difference in how advanced technologies are helping consumers and businesses engage more meaningfully. Q: What excites you most about the AI Industry? He:  "As AI is more involved in our lives, it provides benefits we couldn’t imagine before – such as using your face to unlock your phone security. With the development of AI and machine learning, we can find patterns in data or in behaviors of people to solve complicated problems. That’s really it; helping people make life easier." Kazmi: "The main thing is that AI is not only transforming the way we live and communicate, it's changing the way almost every industry around the world is going to operate. To positively contribute to this growth, it’s not just that you need to learn and then deliver, but to keep innovating and coming up with new solutions that others learn from." Kung: "The technology improvement excites me. Things are getting easier, giving us more time to focus on what really matters. We usually don’t have time to focus on some of these areas because we are used to doing things manually. Now with AI, we have a machine to do a job that is manual, so we can focus on analysis and improvement." Peters: "What’s most exciting for me are ways AI technology can augment human decisions and innovation, in new directions that we historically run out of horsepower for. And, it can be applied to virtually every industry — the ways that it can better help us leverage big data, robotics, the Internet of Things — there are so many directions we can go with AI." Stoddart: "One of the most exciting things about AI is that people benefit from it every day — using social media, or maps to get to the shops, sometimes without even realizing it. And, if you can create an algorithm that can help somebody get credit who previously couldn't, you can have a real impact on the world that actually changes people's lives for the better." Q: What concerns you most about the AI industry? He: "I think the key things are data security and privacy protection. People are more and more sensitive about their information being used and released, which is understandable, and why opportunities exist to opt-out of information being used or sold to third parties. The key is to offer comfort by building in how to secure the data and protect privacy." Kazmi: "There are pros and cons of everything, especially with a stream of faster evolutions in prominent areas affecting our day-to-day lives. Since it’s still so innovative, when AI is introduced, there’s bound to be reluctance. But, to progress, we need acceptability, encouragement and patience; an understanding between AI research and stakeholders that these developments are going to bring huge positive change." Kung: "My main concern is that we need to keep in mind that AI is just a tool to help us. The machine will not replace humans and it cannot tell you what to do. An algorithm can give you a number based on its design. You need to analyze that result and ensure decisions make sense for your business." Peters: "The more we know and learn about AI, the better we can anticipate potential risk areas. These include the ethical aspects of technology, and striving to be consciously unbiased. As we progress, explainability and other model governance practices will help us stay within the right guardrails and mold the necessary regulations." Stoddart: "Lack of diversity concerns me – both in the boardroom and on the programming side. Decisions that we make in our programming are based on assumptions as human beings and our lived experience. If the people writing the code are not diverse, you’re missing out on whole groups of people in the wider society." Q: Can you share with us the “backstory” of how you decided to pursue this career path? He: "My educational background includes cognitive science, neuroscience, and psychology, and it involved a lot of data analysis and modeling. I wanted to understand how humans behave. In my first job, I did essentially the same work — understanding human behavior from large amounts of data — but to detect fraud. That amazed me and driving my focus today." Kazmi: "My education included subjects around analytics, and had a lot of flavor of data science, predictive modeling, mathematics and statistics. AI was very new at the time. I studied these topics and began to understand how data science is developing, and what's the future of it. I really got excited and interested into it. And once I started my career, there was no looking back." Kung: "As a child, I thought I wanted to be an engineer. Statistics was my second choice. But, I am really glad I had the opportunity to follow this path, because statistics and data analysis are amazing. When I started my course, I was so amazed at how data analysis can help you discover a world. You can do anything with data. I realized that this was my true passion." Peters: "I became interested in AI from the business aspects – working in a big data environment, we really needed machine learning and AI to handle data at scale. When joining Experian in the identity and fraud area, our mission was clear – harnessing the power of one of the largest data assets in the world to make a difference; finding new ways to stop fraud." Stoddart: "I studied physics at university and attained a master's in particle physics. But, during my final year, I started to learn about AI and machine learning. It was inspiring, especially how quickly they can have an impact on the world compared to academic research, which can be over many years. Realizing how quickly it was progressing, I thought it would be really exciting to get involved." Q: Can you tell our audience about the most interesting projects you’re working on now? He: "Recently, I’ve been working on use cases and projects surrounding identity. We have been working to link identity data from various sources – online and offline. Here at Experian, we have information from many sources, across different business areas. This project is providing a platform to link all this data together, which in the past was not very easy to accomplish. With this platform to provide linkages, it provides a 360-degree view of a person and helps provide conclusions such as whether two identities are the same person. To do this, we utilize machine learning techniques and AI. It’s very exciting." Kazmi: "I would like to mention something I'm very proud of, which has been a turning point in the way I look at data science solutions. I have the privilege of playing a prominent role in solving for a crucial economic and societal problem of the world, financial inclusion. This issue has historically blocked growth for financially weak and less established sections of society. I am leading data science as part of the initiative, exploring different sources of information beyond credit history, to increase access to financial products. This is the beauty of data science and how it helps us." Kung: "At Experian, I work in a consulting area, so I advise our customers and show them the power of data. Often, it’s not easy for a client to recognize this power. That’s our job – showing them how data can help their business or their decisions. We developed a credit decisioning model for one client using machine learning. This showed them how powerful it can be to use the data we make available to them. They were so amazed with the results. It was a really great experience." Peters: "The newest aspect of my role is leading innovation and strategy for decision analytics in North America. I am constantly on the watch for opportunities to incubate and try to apply Experian’s data and analytics and AI capabilities to solve new problems. We are looking at the role of identity and how we might apply capabilities in new ways. There is an expansion of needs, especially as the world evolves, and how we’re identified is evolving. So the application of Experian’s differentiated capabilities to new areas and markets is an area of focus of mine that I'm really excited about right now." Stoddart: "One of the most interesting projects I've worked on since joining the lab is around fairness of machine learning algorithms, decision-making. It’s about tackling the bias that can come when you use machine learning in a real world scenario. This happens when an algorithm is not being checked properly and it's discriminating against a certain group. To be part of building this vision about treating everybody fairly is great. Especially to be part of a company that values this effort and recognizes that it's going to be increasingly important going forward." Related stories: What is the right approach to AI and analytics for your business? Four fundamental considerations Maximizing impact from AI investment: 4 pillars of holistic AI Forbes: Are we comfortable with machines having the final say? Yi He Yi He works as a data scientist in the Experian NA DataLab. She is dedicated to using machine learning and AI to extract information from large amounts of data to identify, understand and help people, and prevent fraud. She aims to bridge online and offline worlds by linking identity data from these unique sources. With a focus on minimizing friction to customers, Yi’s work helps organizations identify synthetic identities to avoid fraudulent applications. Recently, she contributed to a Covid Outlook & Response Evaluator (CORE) Model – a “heat map” of geographic populations across the U.S. most susceptible to severe cases of Covid-19. Deeba Kazmi In her role as a data scientist at the Experian APAC DataLab, Deeba Kazmi is focused on solving business problems with analytics, including the development of consumer and small to medium enterprise credit risk models that leverage alternative data. Deeba is passionately focused on leveraging AI to create solutions that can help address issues faced by developing markets. Most prominently, this work includes her data science leadership contributions to solving a crucial economic and societal problem – financial inclusion. This effort is helping disadvantaged socio-economic consumer groups gain access to vital credit and financial services by leveraging the power of technology to deliver better outcomes. Jennifer Kung Jennifer Kung is an analytics consultant for Serasa Experian Decision Analytics, where she combines her knowledge of financial services with her data analysis expertise. Jennifer aims to harness the power of data through robust, descriptive and predictive analytical solutions to help clients realize the benefits of the massive amounts of data available to them. She recognizes the magnificence in powering discoveries through data analysis and enjoys revealing these capabilities to businesses who can benefit from these robust, yet approachable solutions. Jennifer enjoys knowing that her work helps to simplify and accelerate decisions that consumers rely on at important times in their life. Kathleen Peters Kathleen Peters leads innovation and business strategy for Decision Analytics in North America. As the prior Head of North America Fraud & Identity business, Kathleen is well-recognized as an identity industry innovator, being named a “Top 100 Influencer in Identity” by One World Identity the last two years. As of 2020, Kathleen was named Chief Innovation Officer for Decision Analytics. Kathleen and her team rely on the power of AI to continuously find new ways to solve customer challenges by defining product strategies, new paths to market and investment priorities. Underlying these efforts is a key focus on the ethical use of technology and the need to be consciously unbiased. Laura Stoddart Laura Stoddart is a physicist turned data scientist who works at the Experian DataLab in London. From her first exposure to AI, she recognized how quickly it can have an impact on the world, which has driven her to get and stay involved in the industry – both professionally and personally. Laura’s recent work has focused on ethical AI, having recently contributed to her first paper addressing the removal of bias from models. In addition, she is concentrated on leveraging emerging datasets to evaluate risk. Outside the DataLab, Laura also volunteers her data science skills to good causes such as Bankuet and helps expose others to the world of AI through mentoring.

In the not so distant past, consumers mostly interacted with their banks in person. Retail customers, for instance, waited in line to make a deposit or talk to a banker. And though the branch may have been busy, a moving line gave comfort to customers that the wait wouldn't be much longer. However, customer expectations in the digital era are dramatically different. According to Experian's new research, one in three customers will abandon a transaction if they have to wait more than 30 seconds, especially when accessing bank accounts. And that's just the tip of the iceberg. When it comes to the digital experience, consumers increasingly want seamless service at every point of their journey. Now, as the Covid-19 crisis continues to accelerate digital demand, financial institutions face more and more customers with similar if not greater expectations. Expectations for things like personalized products, contextual lending decisions, and offline-online seamlessness. And those organizations that understand these evolving needs and deploy cloud-based decision management to ensure they meet them will likely be the winners in this new world. Right here, right now Banking digital transformation was already underway before the pandemic began. Most retail banks provided some customer-facing app. In efforts to automate and streamline business processes, many organizations have also started to migrate their backend infrastructure from on-premise software to the cloud. The pandemic, though, ramped up the demand for everything digital seemingly overnight. Consider that consumer adoption of mobile wallets has jumped 11% since July, largely due to increased contactless in-payments. In the height of the crisis, customers turned to online platforms for financial assistance, from federal loans and grants to mortgage relief and credit applications to small business loans. Businesses that had already migrated to cloud-based solutions were able to scale their response to meet that growth. But that those hadn't? They faced the combined challenge of needing to scale existing services to serve the influx of online customers while simultaneously adding new digital capabilities. As a result, some organizations have ended up playing catch up with their digital offerings. Experian research shows, though, that it's a race worth finishing. Sixty percent of customers say they have higher expectations of their digital experience now than they did before the pandemic. To be sure, the crisis will end. Those expectations, however, are here to stay. A glimpse of the future Banks may see fewer customers in person, but that doesn't mean their service can't be personal. The data analytics features of cloud-based decision management software allow businesses to know more about their customers, providing personalized offers and services right when customers need them most. One bank we work with in India provides an ideal example. They've leveraged deep analytics and decisioning solutions to accelerate their online loan approval process from days down to seconds. They're no longer turning people away who are good candidates for loans. And they've increased their lending without having to take on additional risk. It's a win-win that reveals how organizations can leverage technology to satisfy customer expectations during the height of a crisis and continue to in a post-Covid reality. With cloud-based solutions, organizations can become 100% customer-centric, both in convenience and personalization. The data gives financial institutions a holistic view of their customers, enabling them to anticipate needs and tailor solutions to the individual. Transformation and soon No organization is going to digitally transform overnight. But given the urgency of the demand, there are proven ways to improve their digital customer experience sooner rather than later. Small-to-mid-sized organizations, for instance, should consider out-of-the-box Software-as-a-Service (SaaS) solutions. These offer pre-determined, high-demand use cases such as online eligibility checks and customer acquisition tools. Organizations can modify these solutions to meet specific market needs while saving time on ramping up a fully custom solution. Additionally, even with the imperative to meet the digital demand, it's important to remember that proper planning leads to successful cloud migrations. Consider all the possibilities of what could go wrong and right in terms of incident management, customer service, links to data sources, and more. Rehearse your transition as much as feasible. The preparation may add a bit of time on the front end, but you'll decrease the likelihood of significant disruption when you do migrate and that's worth the effort. The march toward an increasingly digital customer experience only moves in one direction: forward. The pandemic may have pushed financial institutions to speed up their transition to cloud-based decision management, perhaps a bit earlier than some anticipated. But the outcome of a proactive, data-driven organization centered on serving customers promises to be better for everyone. Related stories: New research available: The continued impact of Covid-19 on consumer behaviors and business strategies  Automating fairness: Using analytics to help consumers in a pandemic era In digital transformation, small wins lead to big outcomes 

For executives and teams across the financial services sector, the question isn't should we digitally transform—but how. That's where things get tricky. According to the Financial Brand’s Digital Banking Report, when asked about the progress of their digital transformation journey, only 17% of organizations reported that their transformation was deployed “at scale” — and a scant 7% said their transformation was deployed at scale and working. Tackling an enterprise-wide transformation effort is no small feat; it requires significant investment and time. Still, many organizations become understandably discouraged when transformation efforts don't yield the anticipated results. And experts contend that transformation initiatives fail not because of products but because organizations need wholesale culture changes to sustain innovation. All that may be true. However, a boil the ocean approach can dramatically increase the timeline of an already lengthy process. By building a strategy based on small iterative wins, businesses can break down the process and deliver interim tangible successes. In doing so, organizations sustain momentum for the broader digital transformation vision and benefit from feedback along the way. Your north star In concept, digital transformation suggests that we are in a finite time and place going from point A to point B. At some point, every financial institution will be digitally transformed. Manual processes, on-premise software, and siloed data will start to disappear. And conversations about transformation will give way to discussions of how to sustain and further advance the bank's digital capabilities. There actually isn’t a “finished state”, but a continuous progress towards a better customer experience. But establishing a long-term objective for transformation initiatives is critical. The leadership team needs to have a vision, and relay the overall goal to the rest of the organization. For instance, in the Financial Brand survey, banks and credit unions noted that improving risk management and security, improving the customer experience, and reducing costs were their top areas of focus. (Unfortunately, the same study revealed that less than half of the organizations surveyed reported high success levels in transforming these areas). In establishing a digital transformation north star, you ensure that smaller projects align with the broader vision. The path there may not be perfectly straight, but leaders can prioritize initiatives that point in the same direction. Small wins, big results As noted, it's challenging to complete a digital transformation journey in one fell swoop. Most organizations can't change technologically and culturally at a rapid pace. Yet, there's a pressing need for innovation. Creating a roadmap of incremental projects and wins can ensure your organization is making steady progress toward that north star goal. I often advise digital transformation teams to start with a small project that seems achievable. That may be transitioning a non-cloud offering to the cloud or introducing an existing interface to a new geography. You solve that problem, and then you evangelize the success; even if it's a small win, you want to shout about it. It's not about nourishing your ego. Instead, the celebration helps build momentum with your frontline staff and clients. It also provides proof points for executive stakeholders. The latter makes it easier to continue funding projects once your leadership sees that the initiative produces results. Then you can begin to expand your transformation perimeter, building on each win with another digital project. Dialing in your customer recognition and improving authentication, for instance, offers areas that are ripe for innovation—especially at a time when online transactions are on the rise and customer expectations are high. The right team for the job Successful digital transformation initiatives require leadership by a core team that's well-networked across the organization. They need to be highly visible to other teams and committed to promoting the cause and selling the vision, and making noise about any success because that's a core part of their job. Leveraging data and analytics along the way is also essential. Data can help you determine which problems to prioritize. And advanced analytics offers critical insights into what's working for customers and the areas that merit attention sooner rather than later. The process of digital transformation is an evolution. Organizations that view it as such should strive for strategies that deliver wins early. That way, they can build momentum, align near-term projects around long-term goals, and reap the rewards of digital transformation throughout the entire journey. Related stories: Impact of technology on changing business operations New global research: The impact of Covid-19 on consumer behaviors and business strategies Digital transformation through cloud-first decisioning

Several months into the global pandemic and we know that general indicators of risk or stress don’t reveal enough about what’s really going on within your customer portfolios. We also know that most institutions heavily use statistical models in identifying and capturing risk drivers in order to make decisions. Active model calibration in current circumstances can have a measurable effect on approvals and expected loss within a few weeks of being implemented. Banks have managed through economic recessions and other stressed scenarios by adjusting various levers for liquidity and risk. None, however, have ever had to predict consumer behavior in a pandemic. How can credit risk executives regain control over disrupted risk models at a time of constant change? Four key actions to enact now for immediate and sustainable impact:  1. Increase the frequency of model health monitoring Many of the predictive models that financial institutions rely on aren’t stable enough to handle real-world disruptions. Nor are the models re-calibrated frequently enough to appropriately assess risk in the rapidly changing situation we currently face. Monitoring models on a quarterly basis isn’t enough, but that tends to be the average frequency for most financial institutions. Increasing the frequency of model monitoring processes and identifying the need for a change in models sooner leads to significant financial impact. Depending on the asset size of the institution and the specific use case, financial institutions can potentially save millions of dollars in lost revenue or avoided credit losses. Automating the process supports an increased frequency of monitoring while requiring less effort from your analytics team. 2. Carry out ex-ante stress testing for your models Businesses should consider using ex-ante stress testing, in light of the difficulty in maintaining the accuracy of model predictions in changing conditions as well as to meet the heavy governance requirements of new models before their actual use. Traditional ex-post processes are effective in simulating what would have happened historically had a new model been in place. This is an extremely valuable exercise but isn’t very helpful in the current stress environment which is both unique and highly uncertain. Risk managers would like to have a go-forward view on model performance for decisions being made right now, not just a look-back view on decisions made historically. Applying ex-ante stress testing allows us to simulate and analyze a range of possible outcomes based on changing macro conditions, evolving consumer behaviors, and other uncertainties like the quality of underlying data. 3. Make practical, short-term adjustments We’ve seen in previous economic downturns that models can rapidly become unfit for purpose, and the consequences may not be fully apparent until long after the start of the downturn. In such circumstances, you shouldn’t necessarily attempt to make changes that you expect to be robust for many months into the future. There’s a strong case for making adjustments that are designed to address temporary circumstances and reviewing them at an increased frequency. Some businesses are taking a conservative strategy by tightening their credit policies and decisioning strategies. Other businesses are overlaying their models with certain attributes. For example, one could look at the number of open inquiries in the past 30 days. Since we know that attribute is unstable, we can pair it with an attribute that will give you more population stability – such as average open inquiries over the past 6 months. 4. Setup for rapid re-calibration or re-build of models The decision to re-calibrate or re-build a model during the pandemic would depend on multiple factors including the business need and model use case, the performance of the existing model, and the confidence in the quality and relevance of data for the model build. However, it is important that financial institutions and other businesses are set up to rapidly update their models. They should be actively working on re-calibrating/re-building their models in a test environment, evaluate the impact, and be prepared to deploy.  The ability to rapidly update models will be a key differentiator as businesses compete to grow their portfolios and manage losses during and in the aftermath of this pandemic. As with many other aspects of our lives, credit risk management is being challenged by the new reality created by a global pandemic. Whether our response is temporary, or whether the crisis is accelerating an existing trend to be more active in model management, we need to react to maximize our portfolio performance. At the end of the day, none of us have been through a pandemic but we know our models can still work. It’s all about model accuracy and model governance and reducing error rates. By increasing the frequency and efficiency of model monitoring and re-calibration, we can drive business outcomes with more impact than ever before. Learn more: For many organizations, navigating and recovering from these volatile times will remain top priorities as they begin strategizing for the future. Get details on accelerating your digital transformation.

The Covid-19 crisis has been a bit like existing inside a shaken snow globe—it disrupted everything, and a lot remains up in the air. However, amidst the uncertainty of the pandemic, one thing has become evident: Cultivating customer trust is more critical than ever. Trust naturally generates loyalty. This is especially true during and after a crisis. For example, Experian's latest global research from July 2020 shows 52% of customers who felt that businesses treated them fairly during the pandemic plan to give those companies more of their business. That fairness bred trust and that trust will undoubtedly lead to more business. As consumers continue to increase their digital transactions, companies need to work hard to enhance customer trust. Improved identity authentication and recognition, for example, will play a key role. As everything begins to settle, those that succeed will find their business on far more steady ground. Does trust even matter? It's a good question—and the answer may be evolving in real-time. Consider that in 2019, Experian's global identity & fraud study showed that digital adoption did not indicate consumer trust of the business. "Consumers still adopt digital channels despite being highly skeptical of the businesses," the study noted. Social media provides an excellent example. Overall, most consumers distrust many of the popular social media platforms, yet they continue to use them regularly. Interestingly, widespread adoption is linked more to convenience than trust. However, this comes with a real caveat: Customers are less concerned about trust when the product is more frivolous. For instance, not trusting a media outlet or social media platform is very different from not trusting a financial institution. Also, a lack of adoption doesn't always mean that customers don't trust the business. In the financial service and payments realm, low adoption may simply reflect that customers use the platforms less regularly. Now, as consumers increase their reliance on online services, maintaining trust will be paramount. For instance, since Coronavirus began, consumers have increased their use and awareness of mobile wallets by 8%, and their use of retail payment apps by 6%. Balancing the convenience that people have needed with the necessary trust will go a long way towards keeping usage high once the crisis subsides. A virtuous cycle Within any digital experience, several components inform customer trust. You want to ensure accurate customer recognition, as well as transparency with your authentication. Robust fraud protection and positive digital experiences also play essential roles. These form the Cycle of Trust, a virtuous circle that ultimately encourages customers to share more information with your company and pursue more transactions. Our 2019 study reveals the importance of each part of this cycle, and we see it playing out now. For example, 90% of consumers are willing to participate in a more thorough identity verification process early on to have easier account access in the future. The ability to routinely and accurately recognize your customers helps build their trust in your technology and products. Also, 76% of customers have more confidence in companies that use biometrics over passwords to protect their information. That means that you can use advanced authentication strategies to enhance trust even more. Transparency also comes into play. Letting people know how you're using their information and whom you're sharing it with makes them more apt to trust your organization—and continue to share their data. The future of trust This cycle represents the goal. In practice, though, there are still quite a few challenges that prevent organizations from getting that wheel spinning. For instance, many have separated the risk assessment processes of verifying customers at signup, logging in, and transacting, so there's no seamless experience. Instead, customers navigate different solutions to onboard, authenticate their identity, and complete transactions. A company may recognize a customer at one point in the process, but not all the way through. What's more, organizations often still place the onus on the consumer for how they represent themselves in the digital world. Authentication processes require them to remember passwords or retrieve codes from their phone. But as noted, the pandemic has opened an opportunity for dramatic improvement. Consumers are at a rare moment in which they're open to change—and they're even looking for it. For example, since the beginning of the pandemic, 60% of customers say they have higher expectations for online experiences. More than half of customers are also more willing to provide organizations they trust with personal information and financial data. Finally, 44% of customers note that since Covid, they are more trusting of companies that demonstrate security. So how can you increase trust while also meeting evolving customer expectations? Organizations that pave the way will likely assume more responsibility for recognizing and authenticating customers. This starts with becoming more creative in using the data they already have access to recognize and authenticate customers. Extending this passive and continuous recognition across channels will also be necessary. Doing so connects the disparate processes and creates a more seamless digital experience. Such initiatives also remove the identity burden from the customer and kickstart that virtuous cycle. No one anticipated the Covid-19 crisis. But it's opened up the chance to create fairer, more trusting, more transparent digital experiences for everyone—and companies shouldn't pass that up. Related stories: Latest global research: The impact of Covid-19 on consumer behaviors and business strategies Better identifying your customers leads to greater trust Covid-19 as a Gateway to Fraud: Top 5 Global Fraud Trends to Watch Out for in 2020 Podcast: Securing online identity

In case you’ve missed these September headlines, we’ve compiled the top global news you need to stay in-the-know on the latest hot topics and insights from our experts. Transforming analytics into business impact CIO.com shares insight on using analytics to maximize business outcomes from IT leaders, including Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI. Global shudder: How businesses and customers are reacting to Covid-19 This MediaPost article covers global research findings on the impact of the Covid-19 pandemic, as well as perspective on the trends and what’s to come, from Steve Wagner, Global Managing Director of Decision Analytics. Experian touts Biocatch behavioral biometrics, adds Onfido face authentication for onboarding Biometric Update shares the latest on enhanced fraud detection for new account openings through a layered approach. Marika Vilen, SVP Platform Commercialization, Global Identity and Fraud, speaks to optimizing operations in today’s environment. Experian’s cloud-based solutions adapt to today’s evolving customer needs In this AiThority article covering cloud-based solutions for automating decisions, Donna DePasquale, General Manager, Executive Vice President of Global Decisioning, shares her perspective on businesses meeting the needs of today’s changing market. Why businesses need to meet the challenge of digital acceleration Steve Pulley, Managing Director of Data Analytics, offers global insights on continuing operations through an evolving digital marketplace impacted by Covid-19 in this Bdaily, United Kingdom, article. Stay in the know with our latest insights:

Whether you work for a small or big company, chances are you’ve seen budgets contract in the wake of Covid-19.  There are a lot of factors contributing to it: fluctuating economic outlooks, building up loan loss reserves, and re-directing expenditures to keep employees and customers safe and secure. A recent global study of banks and retailers found that the top area of short-term investment was securing the mobile and digital channels. In fact, it also showed that 80% of businesses put a digital identity strategy in place, a 30-point increase since Covid-19 began and 60% of businesses are planning to increase their budgets for credit risk analytics and fraud prevention, respectively. So why is it that only 32% of banks and retailers feel operationally ready for their customer’s continued demand for digital engagement? The Capex required to invest in new technology these days requires a fiercely competitive business case. Not forgetting to mention, if approved, it could be a while before you see a return on your investment. But it doesn’t mean the latest advancements and innovation available for managing credit risk or fraud risk is out of reach. Getting more out of your existing tools and technologies is easier to implement and quick to deliver results. In fact, since Covid-19 began, hundreds of clients have optimized their use of credit and fraud risk software and analytics, helping them focus on creating more meaningful customer relationships and saving them millions in potential losses. Here are two examples of how you can get the most out of your existing technologies today and a checklist for evaluating your current tools. Device recognition Beyond securing systems against Cybersecurity threats, businesses need to think like the criminals they’re trying to deflect. If it seems like the world all went digital overnight because of Covid-19, then you can bet fraudsters were one step ahead exploiting the blind spots in the customer relationships you quickly moved online. But how do you recognize your customer behind their mobile device or computer screen? One way is to discern a fraudulent (or “mimic”) device from a genuine one. Having access to this information allows you to swiftly see the same device repeating both good and bad behavior and thus have a better chance of isolating the mimic device and mitigating fraud attacks. This is done by creating a strong probabilistic measure to determine whether two events are from the same device or not. How does this help? It helps to reduce over-firing fraud velocity rules and more precisely out-sort fraud events for manual review. It’s not as complicated as it sounds, and many businesses already have access to this device intelligence data which simply requires them to either turn it on or upgrade their fraud management systems to its latest version. In fact, additional device data points are always being added, and upgrading this layer is often recommended as it can provide up to 85% improvement in performance. Bottom-line: Device data bolster the effectiveness of your customer identity and fraud defenses with little impact on operational resources and reduces friction on your customer’s digital experience. Machine learning Innovations in decision management are having an impact on areas traditionally associated with predicting consumer behavior, such as credit risk, collections, and fraud detection. The ubiquity of data nowadays requires the methods used to derive actionable insights to evolve and most lenders globally have started to adopt advanced analytics. Nearly 70% of businesses increasing their use of machine learning for determining creditworthiness since Covid-19 began. For the collections process, it has helped to determine the best way to contact a delinquent customer or the best treatment to use as a customer exits Covid-induced forbearance?  For card, mortgage, and automotive portfolios, machine learning has played a strategic role in creating and implementing pricing strategies to determine the most accurate decisions for financing terms. Perhaps it’s in fraud detection where machine learning is having the biggest impact. Unlike how it’s applied in credit risk decision strategies, machine learning used for fraud detection can be trained to learn and improve with experience without explicitly being told to do so. It excels at solving problems where the “problem space” cannot be defined easily by rules, which makes it a great complement to mature rules-based fraud management systems. Furthermore, machine learning models can take advantage of the different data points from all backing applications at the time of any single transaction, login, or submission. This produces a final decision that’s more accurate than that produced by a simple rules-based approach or manual decision matrix. Attributes that once provided minimal lift when analyzed in a silo may now provide a substantial lift to predict credit risk or prevent a fraud attack when combined with multiple data elements. Conversely, legitimate events that were inadvertently triggered by traditional fraud detection methods can be identified as authentic before having a negative impact on the customer’s experience. Bottom-line: A layered approach continues to be a key component in any credit decision or fraud detection solution and machine-learning models are the final call in your decision workflow strategy so they can leverage all the previous decision data. Checklist: Evaluate whether you’re getting the most from your decision technology Is your current solution providing the results you need? Avoid comfort in patterns and request a business review of your current solution to analyze performance. It may reveal unknown gaps and opportunities to improve your business results. How do your results compare to your peers? Some peer benchmarking is publicly available, but most vendors offer peer (blind) benchmarking using your specific performance data. It’s worth the ask! Are you using all the functionality your tool has to offer? Sometimes decision technology is implemented with a myopic focus on solving a specific problem or used in a specific area despite a broad range of functionality available that covers more use cases. Are you using the most up-to-date version of your tools? Check with your vendor right away and stay informed regarding newer versions. Upgrades generally require less effort and cost than a new solution and by continuously monitoring for the latest version, you’re able to meet current regulatory and policy standards. Are there any ‘add-ons’ available? Your existing decision technology may offer add-ons to enhance your current solution. Add-ons such as new or enriched data sets, updated scores or models or new software features may extend the business usage of a solution to different processes and within additional departments. Are your technologies integrated to enhance your credit risk and fraud risk decision workflow? Integrating your technologies can help you to execute credit and fraud strategies seamlessly with less chance for error, manual intervention, or duplicating actions across disparate systems. Technology is critical in meeting customer demand and staying competitive in any market. It can help balance the demand for internal resources while providing the service your customers deserve. But as organizations look to stay competitive, and agile through a volatile economic time, remember the importance and tangible benefits of optimizing what you already have in place. Related articles: Global research study: The impact of Covid-19 on consumer behaviors and business strategies Podcast: Banking trends and opportunities in the post-Covid-19 era Are traditional online identification methods becoming obsolete? Case study: Layered behavioral biometrics, device intelligence and machine learning 

As businesses continue to figure out the best way to operate through the global pandemic, I’ve been asked by leaders across industries to provide my thoughts and insights around the path forward for businesses, specifically around where to invest and how to manage distributed teams. While my experience drives how I answer these common questions, Experian recently released the results of a global study which helps to demonstrate where businesses are focusing their resources. In a recent global survey among financial services and eCommerce businesses, we found that most companies are focusing on the health and safety of employees and customers, with 42% of those surveyed saying this was their primary focus. Following closely was 32% of businesses who said making operational changes and managing increases in demand across channels and functions is their greatest challenge. That’s a shift from pre-pandemic times when firms were spending more on mobile and digital advancements with intent to strengthen the security of mobile/digital channels, invest advanced analytics (e.g. creation of artificial intelligence models), and improving customer digital account opening and engagement. Top questions I’ve received in the past few months:  Q: As someone with extensive experience managing technology for a distributed team, what advice would you impart to other leaders addressing this for the first time?  A: I don't think there is a single answer, but there are a few things that are mostly common sense. For example, there is a lot of ad-hoc interaction happening in an office. Therefore, consider increasing frequency of any common team and wider meetings, remotely(all-hands, daily stand-ups, staff meetings, or ask-me-anything type of meetings). To compensate for the increase in frequency, consider making these meetings shorter. Another thing is to encourage people to be on video - it adds presence and makes it much easier to collaborate. Also, make sure you have efficient comms-channels (Slack, Teams, Skype, or whatever tool your company uses) which helps with the asynchronous flow and lets everyone jump in. And put the effort in to get good tools. Poor quality connections and audio saps energy and makes it frustrating instead of being useful. It also helps during larger meetings: That way everyone can comment and jump in through a different means, without interrupting. It is also useful to be a bit more disciplined when running meetings. There are many non-verbal cues when we communicate, so to compensate for this a bit more structure (somebody moderating the discussion) may help. Conducting surveys afterward to find out what people find interesting is useful and I also think it is important to talk about the situation, making sure that people can be transparent and recognizing challenges. Finally, in the current situation, where many people have had to adjust their daily lives, we’ve seen a lot of innovation amongst the teams. Anything from virtual coffee breaks outside of regular meetings to virtual curry nights and meet-ups. I think it depends on your team's circumstances but what matters is to stay in close contact. Q: What areas do you believe are most in need of advancement in light of the ongoing global crisis and why? It is hard to predict all of the lasting changes, but I think we will see a continuing acceleration to digital, and some industries that have not had to may now be forced to shift faster — and leaders will need to balance such focus with their priority to best assist employees in a remote environment. According to a recent survey, we know that 50% of consumers anticipate increased spending on items purchased online versus in-person – both in the short-term and within the next 12 months. So, we’ll continue to see people using both remote and digital ways of working, shopping and entertainment, and that will of course continue to drive the need for companies to think about their digital offerings.  And,  by extension how to appropriately secure those transactions for the associated risk and how to make a smooth customer onboarding journey that can be fully digital. I also believe we will see a lot of new and creative use cases from software and analytics, specifically the role of AI. Specifically, we’re seeing rapid changes in behaviors and volumes, and this again emphasizes how important it is to have resilient and scalable systems that can turn around quickly. The current circumstances also highlight the importance and opportunity to take the data we have and apply analytics to drive insight into what impacts we may see and adjust our plans accordingly. This is also an area where businesses are investing. 60% of businesses we surveyed plan to increase their budget for analytics and credit risk management and businesses in the U.K., U.S., Australia, and Spain have already increased adoption of AI and advanced analytics, since Covid-19 began. I’ll continue to monitor these key areas and share significant findings, especially as the pandemic plays out longer than any of us hoped and as businesses start re-opening offices while disparate employees make the best use of resources to support customers. For more about our recent study, check out some highlights here. If you'd like to submit a question to Birger, please email GlobalInsights@experian.com

As consumer organizations settle into the so-called new normal, behaviors have dramatically changed and expectations have been redefined. Speed, convenience, and choice have gained a different meaning, accelerating digitalization efforts and demands virtually overnight. Recently, we spoke with our internal experts – Derek Garriock, Design & Innovation Director at Experian and David Bernard, SVP of Global Marketing & Strategy at Experian Decision Analytics – about the future of banking and trends and opportunities arising in the post-crisis era. Here’s highlights of that discussion: A different way of understanding and doing banking – a viewpoint by Derek Garriock Industries are redefined by changes in consumer behavior, and certainly, the crisis that’s been unfolding across the globe has had a big impact in terms of how we live our day-to-day lives. These changes are reflected in the demands made of banks, as we try to manage our money in a different way. The challenge that the banks and lenders have seen across the globe is obviously different levels of reaction from consumers and businesses — depending on the jurisdiction that they’re in and the immediate need that’s created. This challenge is more about how you are able to adapt given that going forward this behavioral change will be no doubt be one of the lasting impacts of the crisis. At a very basic level for banks, we still have some of the pre-existing challenges around how they deliver change in a digital world to really serve customers and give them the best possible experience and journeys to serve their needs. Obviously, there’s a lot of regulation banks are required to observe and follow as an organization doing the type of business that they do — but the current needs shine a light on big areas of focus that probably haven’t changed in the last decade — around how do you digitize your business to reduce cost, to better serve your customers, and to be in a place where you drive deeper share of wallet with customers to grow your business. What we’ve seen through the crisis is really a spotlight shone on this area and in the context of how to move quicker, faster, better, and to deliver against some of those core objectives. Current areas of focus for the global banking industry – a viewpoint by David Bernard Thinking about the immediate reaction to the crisis, we have a number of banks that are still focused on coping with lockdowns and business continuity across the globe — managing going into lockdown and out of lockdown across different countries. For example, we had banks in the UK that have call centers in India. When the India lockdown happened, very suddenly they lost their ability to respond to clients over the phone — so we see some immediate impacts of the crisis with banks coping with a situation where different parts of the globe are challenged from a business continuity perspective. Banks also had to adapt to a number of government programs such as government-sponsored loans for small businesses and individuals. They had to adapt details like their scorecards for lending, or their scorecards for debt collections — evaluating their approach to debt collections since suddenly you have a lot of people that lost their jobs. Asking for last month’s bank statements doesn’t quite give you the right view of their personal situation. There is adaptation to the current crisis, but even as we start to progressively get out of lockdown in a number of countries, banks have realized there are a number of deeper things around their use of analytics, the fine-tuning of their scorecards, lending strategies and risk strategies that have to be redone. Also, there’s the general, longer-term trend towards moving some of their banking structure to the cloud, making sure that their decision strategies are fit for purpose, that they are flexible enough, building attributes into the system. So, there are a number of programs that are continuing and sometimes accelerating. There is also the example of digital interfaces where it looks like you can do something in an app on the website, but behind the scenes, a number of banks have analog processors — non-digital processors — where there are people reading data internal in the system or doing some manual task behind the scenes and the whole crisis is shedding light on those examples and forcing more complete digitization across the board. Listen to the full podcast: https://bit.ly/IIA_FutureFS Related articles: Digital transformation through cloud-first decisioning by Chris Fletcher, SVP Decision Management & Cloud Services & David Britton, VP Of Industry Solutions Maximizing impact from AI investment: 4 pillars of holistic AI by Shri Santhanam, Global Head Of Advanced Analytics & AI & Birger Thorburn, Chief Technology Officer, Global Decision Analytics How rapidly changing environments are accelerating the need for AI and Machine Learning in business by Birger Thorburn, Chief Technology Officer, Global Decision Analytics

The global pandemic led to swift and unexpected shifts in consumer behavior, from the significant increase in the use of digital channels, to the decrease in ability to pay for many. Based on this environment, we’ll highlight where senior financial services executives should focus their analytics and decisioning teams’ efforts to provide a bit of certainty in an uncertain time: Confidence and demand for credit First off, it’s important that lenders consider current dynamics when monitoring and measuring the effect of fluctuating market conditions on their portfolio. Overall lower consumer confidence in the ability to access credit is not surprising, but the true impact on demand for credit is yet to be concluded. “As a result of both the pandemic itself and the changed economic conditions it caused, consumers’ appetite for new credit and the ways in which they are using existing credit are in flux.” – Leslie Parrish, “Uncertainty Is Certain: Consumers’ Financial Outlook at Mid-Year 2020,” Aite Group, July 2020 From late June to early July 2020, we surveyed 3,000 consumers and 900 businesses in 10 countries. This research indicates some consumers are responding to economic uncertainty by reducing spend and tapping into financial reserves, while other consumers are using credit to make strategic decisions such as refinancing, buying a new house, or opening new lines of credit for access to money. Regardless of customer sentiment, it's important for businesses to understand these realities: Consumer demand for digital is increasing — our research shows it's gone up 20% since Covid-19 Digital channels will help fuel new business — with a marked 40% increase in consumers opening new loans digitally based on our research These indicators should drive investment in solutions to secure the digital channel and improve digital onboarding, including data, analytics, and technology. Such investments help meet consumers’ digital demands, safeguarding your ability to retain existing customers and win new business. >> Download the Global Insights Report Ability to pay Lenders should also be mindful of the volatility of the current environment and ensure their teams rely on data and analytics that enable accurate decisions based on a consumer’s current financial situation. Given active programs established to supplement a decline in consumer income, we are still enjoying a nourished economic environment. However, our research shows that globally, since Covid-19 began, the number of consumers having difficulty paying their bills has doubled, and according to Aite Group, half of consumers in the U.S. reported their household has suffered a loss of employment income since mid-March.1 These conditions enforce the need to have the right tools in place to best assess consumer creditworthiness. Decisioning in the new norm As lenders continue to focus on business health, it’s key to consider operational efficiency and ongoing optimization. Given there is no precedent to the current global pandemic, lenders will need to rely on innovative solutions to learn and adapt in real-time. Our research shows that many businesses know change is needed and are seeking solutions to tackling this challenge. One in five businesses globally lack confidence in the effectiveness of their credit risk and collection decisions since Covid-19 began. Sixty percent plan to increase budget for analytics and credit risk management. Meanwhile, the top three solutions businesses believe will improve operational efficiency when supporting customers’ financial needs are: automated decision management, cloud-based applications, and artificial intelligence. To keep pace and be successful through this unchartered territory, lenders must leverage innovative technologies such as cloud-enabled solutions, artificial intelligence, and machine learning. Though today’s lending environment is likely to include levels of volatility for some time, making the right adjustments now can help lenders support consumers and business performance in the long term. >> Get more insights on the impact of Covid-19 on consumer behaviors and business strategies _____ 1 “Uncertainty Is Certain: Consumers’ Financial Outlook at Mid-Year 2020,” Aite Group, July 2020

Download the report People’s changing behaviors to safeguard their health during the ongoing global Coronavirus pandemic has fueled a massive shift to digital channels. As people’s day-to-day routines and behaviors shift, so too is the attention on businesses to find new ways of staying relevant to their customers. Two-thirds of consumers are staying loyal to the businesses they preferred prior to Covid-19. 20% increase in overall online transactions – a 41% increase in online grocery shopping, 40% increase in applying for loans online, and a 22% increase in food delivery or takeout. 50% of consumers surveyed expect to increase their online transactions even more in the next 12-months. Uncertainty for what the next 6-12 months will hold has people and businesses vacillating between optimism and pessimism.  Some likely contributing factors could be public health gains and setbacks for containing the virus, some businesses opening only to close again, and the prospect of some students returning to school in-person and while others go remote – and what all of that means for economic recovery. At the time of our study (June 30 -July 7, 2020), some lenders and retailers are demonstrating more confidence than others, while consumers - many already feeling depleted - are expecting and bracing for an expected second wave of Covid-19.  Consumer financial hardship 65% of people believe their country has not yet recovered from the economic impact of the pandemic. 30% of consumers reported a decline in household income; India saw the largest household decline at 43%. The number of people having difficulty paying their bills has doubled since Covid-19 began. Businesses operational challenges 53% of businesses believe their operational processes have mostly or completely recovered since Covid-19 began. The U.S. (80%) is the most confident and Germany (27%) is the least. Top challenges faced by most businesses globally are the health and safety of their employees and customers, adjusting operations to support customers, and managing increased demand across channels and functions. 1 in 5 businesses surveyed lacks confidence in the effectiveness of their credit risk and collection decisions since Covid-19 began.  Beyond their intense focus on the safety and security of their employees and customers, our research shows that businesses are making strategic investments – to give consumers greater access to goods and services, and to better manage their customer relationships. They’re also exploring automation and cloud technology to relieve operational constraints. Whether it’s a lender providing financial assistance to small businesses and loan re-payment options to customers or it’s a retailer providing essential supplies and services to people who need it most, helping people and delivering on expectations for secure, relevant customer experience is top of mind. Top areas of investment: strengthening the security of mobile and digital channels, new credit risk analytics, and the creation of artificial intelligence (AI) models and increasing digital customer acquisition and engagement. Top 3 solutions businesses believe will improve operational efficiency when supporting customers’ financial needs are automated decision management, cloud-based applications, and artificial intelligence. 60% of businesses plan to increase the budget for analytics and credit risk management. Businesses in the UK, U.S., Australia, and Spain have already increased the adoption of AI and advanced analytics. To solve for the lack of economic precedent, 51% of businesses say they’re asking customers to contribute more information/data and 49% say they’re exploring new or alternative data sources. Download Experian's Decision Analytics Global Insights Report July/August 2020 and learn more about the impact of Covid-19 on consumer behaviors and business strategies

Chris Ryan, Senior Fraud Business Consultant, talks to Nick Zulovich at the Auto Remarketing podcast about the new ways we are seeing fraud surface as the global pandemic evolves. "The pattern of activity that we're seeing that has really attracted my interest is this notion of human farming. A human farm is a pool of paid labor who research information on potential fraud victims using data that's been stolen through data breaches and using information that people publish through social media and other outlets. The objective of a human farmer is to be able to assemble a detailed profile of a potential fraud victim so that the perpetrator can better impersonate them and navigate around potential security measures and other obstacles that would normally be in the way." Chris Ryan, Senior Fraud Business Consultant Why the opportunity for human farming? People are out of work so there's a recruitment opportunity for those in need of an income. There is a flood of people into the online space who might not ordinarily engage digitally. This demographic may not be tech-savvy and maybe more susceptible to fraud methods such as phishing. Resources that typically screen for fraud are suffering due to office closures. The combination of high tech fraud to find potential victims plus skilled human intelligence makes these methods highly effective. New trend amidst new circumstances - the rise of synthetic ID Remote transactions combined with the high-value nature of the auto industry makes it a very attractive prospect to fraudsters. Even though purchases are down, the fraudsters are still active. Synthetic identity fraud, in particular, continues to be attractive because the identities are not real and therefore not suffering from the same downturn as genuine profiles. Listen to the full podcast here. Related articles: Getting to grips with the shifting fraud landscape Infographic: Top Global Fraud Trends 2020 Covid-19 as a Gateway to Fraud: Top 5 Global Fraud Trends to Watch Out for in 2020