Tech Today

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

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 

In this episode of the Insights in Action podcast we speak with David Bernard, Senior Vice President of Global Marketing and Strategy, about managing digital transformation in the face of unprecedented challenges such as those originated by the global Covid-19 (Coronavirus) pandemic. While the internet has long been a lifeline, technology companies now appear to be the backbone of a global virtual working and collaboration scheme on a scale never experienced before. David shares his perspective on: How business leaders can help accommodate system stressors caused by evolving needs What actions and technologies can help accelerate or scale digitalization efforts Shifting to the cloud without rushing key strategic decisions Managing virtual teams "There’s a lot of comfort as a leader in seeing a solution that works — even if it’s not completely very sophisticated, and building in a Covid time — rather than doing a big ‘what’s it for’ project to design something from scratch and having a long project before implementing something that has all the bells and whistles. So, it leads to a demand for what I would call more software-as-a-service (SaaS) packages and more pre-configured solutions than the highly configurable world that we have seen in the past." - David Bernard >> Listen now to the full episode of this Insights in Action podcast

It was Dr. Simon Ramo’s vision of a ‘cashless society’, made possible by information and technology, that led to the creation of Experian’s business in the U.S. in the 1960s. He could see how information was going to change the way people lived and envisioned a future where systems would enable the rapid transfer of information to establish patterns of payment and individual creditworthiness. The democratization of digital financial tools and initiatives to improve financial literacy can create promising beginnings for countless disadvantaged individuals. Fast-forward to the present, and the global Covid-19 (Coronavirus) pandemic has taken the world by storm, proving a catalyst for an accelerated path towards a cashless society. Our recent proprietary research indicates that: Since Covid-19, we’ve seen growth in the use of mobile wallets (+8%), such as Apple Pay, and retail apps (+6%), such as Starbucks. The largest areas of growth for using digital payment methods are online grocery shopping (+7% increase) and ordering food (+6%). 50% of consumers globally intend to increase their online activities (banking, payments and shopping) in the next 12-months. Over the past decades, many developing countries such as India and China have recognized the value digital payments deliver to communities. Those governments are fully invested in their cashless society initiatives with a view to increase financial inclusion, improve security, boost trust online, and leverage their high mobile penetration rates to expand the adoption of mobile payments and services. A cashless society brings greater visibility into a larger number of transactions, reducing the potential risk of money laundering, bribery and corruption. It also allows central banks to have a more accurate view into how much money is in circulation, helping them prevent cash hoarding. On the other hand, businesses don’t need to maintain cash reserves, bank their cash payments or pay bank charges for withdrawing physical currency, which means that less ATMs to service and less cash to process leads to more resources to put at the service of their customers. More about our research From June 30 – July 7, 2020, we commissioned an independent research firm to survey consumers and businesses in 10 countries worldwide to understand the impact of Covid-19 on changing consumer trends and behaviors and business strategies and operations. >> See New global research insights: The impact of Covid-19 on consumer behaviors and business strategies for more insights from this study

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

In a recent interview by Irene Ang from Identity Engineering at Microsoft, our own Marika Vilen, SVP of Platform Commercialization, discusses the importance of identity verification solutions and how to seamlessly integrate those across the digital user journey. We are very excited to be working with Microsoft. Identity verification allows organizations to confirm the person they are dealing with online is who they say they are. In light of the ongoing global pandemic, we see an uptick in digital activity and therefore an increased need for organizations to better verify who they are interacting with online, all while minimizing customer friction. Marika Vilen, SVP of Platform Commercialization, Experian Since COVID-19 started, there has been a 20% increase overall in consumer online transaction activities, our recent proprietary research shows. Consumers cite security as the most important factor in their online experience, particularly in regards to managing their financial data. So, what does this digital shift mean for businesses? Identity verification is an important step to take in digital interactions, and some level of friction can invoke a sense of security, but too much for too many customers can have a negative impact on the bottom line. So, while benefits are evident for identity verification, the process must also factor in the impact on the consumer. By taking a holistic approach that integrates across all stages in a customer relationship, customer friction can be minimized – and customer satisfaction and security maximized. We are proud to be working with Microsoft, integrating solutions that provide rich identity data assets and help inform real-time decision making. Related articles: Are traditional online identification methods becoming obsolete?  Q&A: Biometrics as the catalyst for trust in a socially distanced world  Getting to grips with the shifting fraud landscape 

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

In the second part of the Juniper Research and Experian podcast series on online payment fraud, we talk to Nick Maynard from Juniper Research, and David Britton, Vice President of Industry Solutions at Experian, about maturity in artificial intelligence and virtual assistants, and their current ability to respond to current business challenges. "What we're seeing in the consumer space is that AI is powering these virtual assistants and typically Alexa, Siri, Google, are the three big examples. What that's doing is creating an additional channel, it's a new way for users to interact... it mirrors the digital transition and the mobile transition over a number of years."Nick Maynard, Juniper Research "If you consider where artificial intelligence and machine learning are coming together, this is not going to be a big bang launch into market. We're seeing a slow, incremental roll-out." "In the physical world, when we talk about risk and recognition of a consumer, the human to human interaction takes in a tremendous number of variables to ensure that the person you're engaging with is who they claim to be.... in the digital space, that was eliminated overnight, and cosnumers were using a device as a proxy to represent them to another system or set of devices, like bank servers and eCommerce web servers." David Britton, VP of Industry Solutions We also discuss key points around evolving regulatory frameworks, and how they are driving change in identity-based solutions. Listen to the full podcast episode here, and don't forget to listen to What’s new in online payment fraud Part 1: Implications for consumers and businesses if you haven't already.

In this episode of the Insights in Action podcast we talk to Neil Stephenson, Vice President of Strategic Client Development, about how businesses can address a lack of data. Following an earlier episode tackling business data challenges, we discuss getting value from the data your organization already has access to, tackling legacy software issues, the accelerated shift to customer-centric technology stacks, and an increase in industry partnerships to solve common challenges. Nearly a third of senior business leaders say they don't have enough data to get insights they need, or that the quality of the data they have access to is poor. We take a look at the three steps businesses need to take to address this challenge, starting with the quality of data already in the business. "We see a number of organizations that have pretty powerful data within their own business but don't leverage it as well as they could, so matching data together and making sure they've got a really strong view of their customer across all of their systems is really essential, and then having processes ongoing to make sure that they maintain that view whenever they touch the customer, whether that be through an online channel or face to face." Neil Stephenson, VP, Strategic Client Development Listen to the full episode here, and look back at the previous in the series, Solving key business data challenges - with Bill O'Connell, Experian Global Decision Analytics