Tag: Digital Transformation

Experian is honored to be recognized as a winner of the Artificial Intelligence AI Excellence Award by Business Intelligence Group. Experian was recognized for its credit and collections decisioning solution, PowerCurve, which features intelligent agent-customer assist that processes complex, regulated, and subjective interactions with customers, revolutionizing how they digitally interact, on their terms, with lenders. This AI virtual assistant offers customers 24/7 access to support from their credit provider - on a financially sensitive transaction such as collections. We embedded over 30 years of collections knowledge and experience delivered to major clients across the globe into a credit risk decision management solution that delivers this domain expertise, data, analytics, decisioning, and workflow to clients of all sizes via cloud technology. Harnessing the power of AI has enabled lenders to easily connect with customers on their terms in a fair and transparent way, and to recover losses in an operationally efficient way but also improve customer satisfaction. When’s the last time you heard a customer share a positive story about collections?  This digital self-service channel allows customers to engage with a lender on their terms and allows the lender to fulfill key objectives for quickly recovering losses, using the company’s strategic parameters for segmentation and treatment paths. The intelligent agent customer-assist feature within PowerCurve continuously learns which distinguishes it from the traditional decision-tree structure of a chatbot. It remembers interactions and learns from them, has short-term and long-term conversation goals, and recognizes small talk. It treats customers fairly and transparently. The result feels more empathetic and allows for an always-on and real-time consumer interaction. It can also offer a future-forward benefit to lenders, by allowing their employees to strategically focus on other areas of the business, potentially increasing the capacity for more credit products and services innovation. This Business Intelligence Group awards program sets out to recognize those organizations, products and people who bring Artificial Intelligence (AI) to life and apply it to solve real problems. Related stories: Going the last mile: Improving the End-to-End Digital Customer Journey Technology today: Focus on innovation drives award-winning, AI-powered consumer lending Podcast: Driving product vision with the customer front and center, a software architect’s view

As consumers shop, bank, and pay online during the global pandemic, steps are being taken to return to more business as usual. But, what should businesses expect around consumer digital preferences post-pandemic? Steven Wagner, Global Managing Director of Decision Analytics, recently spoke with Jill Malandrino of Nasdaq Trade Talks about recent survey findings and overall trends to watch out for. Here are highlights of that discussion: Research trends indicate a continued and persistent surge in online transactions and digital payment mechanisms The current environment has been a tipping point for consumer trust in online transactions A secure environment through continuous and passive authentication is key to meeting online consumer demand There is a direct correlation between consumer trust in their online environment and their willingness to provide data to secure a transaction Businesses need to invest in AI that provides a good consumer experience -- from chatbots to machine-learning models that impact consumer treatment in real-time Watch the full episode now: Related stories: New research available: 2021 Global Insights Report Parting ways with old forms of managing credit risk online Why the new era of customer experience includes passive authentication

Given market dynamics, technology today is an important topic for businesses to track — whether beginning a technology transformation journey or looking to continuously improve by understanding the latest advancements available — including AI-powered consumer lending. With innovation at the forefront of practices, our experts help power important topics around this subject — and fuel breakthrough technologies. Recently, this focus on innovation was acknowledged, with Ascend Intelligence Services™, winning both honors from The CIO 100 Awards and The FinTech Breakthrough Awards, premiere and notable programs that recognize breakthrough technology worldwide. ML technology helps drive customer satisfaction and increased bookings The CIO 100 Awards recognized Atlas Credit, a midsized lender headquartered in Texas for their use of the Experian Ascend Intelligence Platform. The platform enabled them to double loan application acceptance rates while reducing credit losses by up to 20 percent. Atlas Credit uses the tools and data to make instant decisions, resulting in improved customer satisfaction and higher booking rates. Using Ascend Intelligence Services, Experian data scientists rapidly built a machine learning (ML) custom credit risk model, optimized a decision strategy, and deployed the model in production, reducing time to impact by six months. “Winners are chosen by a team of external judges, many of them former CIOs, on their use of leading-edge IT practices that produce measurable results. The award is an acknowledged mark of enterprise excellence. This year's honorees exemplify what it means to deliver business value through the innovative use of technology. This elite group is creating competitive advantage in their organizations, improving business processes, enabling growth and improving relationships with customers," according to CIO 100 2021 winners. The CIO 100 awards are presented by IDG's CIO — the executive-level IT media brand providing insight into business technology leadership, each year, CIO recognizes the premier organizations and executives driving IT innovation with these prestigious awards. Honorees are inspiring examples of how IT leadership, business partnerships, and customer engagement are reshaping the future. Atlas Credit was the recipient of the Optimal Loan Underwriting with Machine Learning for Underserved Consumers project. Better results, delivered faster to help power FinTech evolution Experian's Ascend Intelligence Services was also selected as a winner of The FinTech Breakthrough Honors, which recognizes Standout FinTech Companies and Solutions in 2021. Ascend Intelligence Services was recognized as the FinTech Breakthrough Award winner in the Consumer Lending Innovation Award category. The FinTech Breakthrough Awards is the premier awards program founded to recognize the FinTech innovators, leaders, and visionaries from around the world in a range of categories, including Digital Banking, Personal Finance, Lending, Payments, Wealth Management, Investments, RegTech, InsurTech, and many more. The 2021 FinTech Breakthrough Award program attracted more than 3,850 nominations from across the globe. "This past year has been unlike any time period ever seen before for FinTech growth and disruption, with FinTechs maturing to become respected, global players throughout the financial services value chain," said James Johnson, Managing Director, FinTech Breakthrough. "FinTech is a digital force that has clearly entered a new phase of its evolution, moving out of niche use cases to operate at scale, and we are thrilled to recognize the 'breakthrough' FinTech innovators in this market evolution for our fifth annual FinTech Breakthrough Awards program." Ascend Intelligence Services is a fully managed analytics service delivered digitally by Experian data scientists. It helps lenders leverage advanced technology, utilizing AI to deliver better results up to 5x faster, accelerating time-to-market all while allowing them to make sound business decisions. Related stories: Insights in Action Podcast: Identifying the core capabilities your business needs to get MLOps right Fair and explainable artificial intelligence is accelerating industry transformation Going the last mile: Improving the End-to-End Digital Customer Journey

As the demand for digital exploded over the past year, companies responded in kind. Those who were prepared rapidly scaled their digital capabilities to accommodate the sudden influx of customers. And those who were caught off guard? Many found themselves scrambling to meet the moment. For both parties, the result has been an accelerated digital transformation that's benefiting businesses and customers alike. The focus has been primarily on improving the front-facing customer experience. But as we look ahead, the dramatic shift toward digital has also opened up opportunities to enhance the security and authentication experience too. By weaving authentication into the customer micro journey—the subsets of tasks that comprise the customer journey—we can strengthen security and decrease fraud. And the data collected along the way creates wholly new opportunities for personalization that improve the experience that much more. A brittle solution   The most common authentication approach requires customers to create usernames and passwords and provide personal information to verify their identities. Customers increasingly expect that companies will require them to provide this personal information to secure their accounts. In fact, in a recent Experian survey, 45% of customers said they'd be willing to share more personal data with companies. However, as long as passwords serve as the primary security tool, the approach remains vulnerable. First, it's unlikely that companies would (or should) continuously ask customers to provide passwords and verify their identity at various stages in the customer journey. This means that there's one big gate for fraudsters to scale and limited hurdles once they've gained access to an account. Certainly, passwords are unique to an individual, which is a positive. But they're also brittle, so they're easily broken or compromised. They don't flex with the user or the customer experience, nor do they offer security beyond a specific juncture. As we look to improve the customer experience continuously, we also need to provide end-to-end authentication. Doing so ensures you can recognize customers at every point of their journey, whether they're logging in or checking out. Securing the micro journey An end-to-end approach requires an understanding of customer micro journeys. It's not enough to provide a great digital experience, say via your account onboarding, but then have a completely different experience when a customer needs access to payment support. Considering micro journeys allows you to dive deeper into each component of the consumer lifecycle, and to understand the nuanced interactions that occur within each of those stages. Rather than just focusing on general approaches across Onboarding, Login Access, Transactions, etc., each one of these stages can be broken into smaller discrete steps (micro journeys), where opportunities exist to simultaneously delight the customer, and to create a much more nuanced risk management strategy. Then you can ensure that each task is seamless, easy, and personalized to the individual. Such a strategy can create deep and lasting customer loyalty. And identification remains a crucial part of every micro journey. No one likes to be at a party and have the host ask them their name repeatedly. The concept applies to security as well. Passive or invisible approaches to authentication eliminate this friction. For example, companies can continually authenticate the customer by employing physical or behavioral biometrics as they progress through the journey. The technology considers: How does the customer hold their device? What time are they usually active? How much pressure do they apply to the screen? Such data paints a much more nuanced picture of an individual—and one that's exceptionally hard to impersonate. And while privacy concerns arise, the type of data required to authenticate customers in this way is far less intrusive than asking for personal information. Customers are increasingly amenable as well. In our research, consumers cited physical biometrics, OTP, and behavioral biometrics as their preferred authentication methods. Passwords didn't even make the top 5. A holistic approach We're at a point in which forward-looking companies can rethink the complicated security dance they've been asking customers to do and move toward a more passive approach. It's an evolution that doesn't just improve the security experience; it also opens up massive opportunities for increased personalization. The data gathered across these micro journeys enables you to design experiences that truly meet customers' individual needs. That capability can become a significant differentiator and driver of growth. Getting there, however, requires a holistic view of your customer experience—one that includes security as a critical element. Our past three research waves show organizations are starting to deprioritize fraud prevention in favor of customer care and online offerings. This is a concerning trend: companies cannot forgo one for the other. Instead, organizations will need to consider both security and customer experience and creatively explore how to bring them together. It's a long-term strategy for customer retention and growth, one that requires a deep understanding of your audience as well as the solutions needed to enable passive authentication. For organizations, the journey toward passive authentication as part of the customer experience is more of a marathon than a sprint. But by focusing now on melding recognition and the customer experience together, organizations can ensure they're ready to deliver high-quality, less intrusive, and more secure experiences that customers are beginning to demand. Related stories: The evolution of digital identities What your customers say about opening new accounts online during Covid-19 and impacts on how you handle customer authentication 2021 Predictions: Consumer demand for digital will persist and the customer journey will be redefined  

Did you miss these February business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Experian launches new anti-fraud platform for digitally accelerated world Financial IT covers the latest on tools to help businesses safely meet the rapid increase in demand for digital services and online accounts. Eduardo Castro, Head of Identity & Fraud, speaks to gaining confidence in preventing fraud while meeting these new business challenges. Experian helps Atlas Credit double approval rates while reducing credit losses by up to 20 percent This Global FinTech Series article provides insights on efforts to make the power of artificial intelligence accessible for lenders of all sizes. Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI, shares background on constantly-changing economic conditions impacting credit models and how to rapidly develop and deploy models to keep up. 60 percent of consumers are using a universal mobile wallet New research shows a continuing trend toward digital transactions and mobile wallet payments. Steve Wagner, Global Managing Director of Decision Analytics, speaks to consumer and business insights on the increased demand and what businesses need to consider to ensure positive customer journeys that support these shifts. Why digital identity and the customer journey is crucial for today’s businesses Steve Pulley, Managing Director of Data Analytics, explores business opportunities stemming from the massive increase in consumers accessing services online. Taking the right steps not only helps ensure business survival but sustainable success. The key is fundamentals including the customer journey and digital identity. How modern data strategies underpin the digital identity and authentication practices critical to digital transformation In this Datanami article covering our progression toward a 'contactless world,' modern fraud prevention is explored. Dealing with a tremendous amount of data to offer security, while bearing in mind customer convenience, requires sophisticated technology. Holistic approaches both improve operations and helps keep pace with fraudsters to protect customers. Stay in the know with our latest insights:

Artificial Intelligence (AI) offers people and companies many advantages, and we interact with it every day. From the technology we use to do simple things like heating and cooling our homes, to more advanced tools that map potential disease outbreaks across the globe. AI is also being used more and more in the financial services sector – from matching new customers with the right loan and terms to assisting with transactions in real-time online. In a recent study, we found two-thirds of businesses surveyed globally are using AI to help manage their businesses today. More businesses are keen to use AI but are challenged to fulfill requirements for decision explainability – a must-do for ensuring consumers are treated fairly. The history of AI AI stems from the realization of the potential of computation. The father of theoretical computer science and AI, Alan Turing, introduced a theoretical mathematical model of computation – aptly named the Turing Machine – in 1936. He described this machine as being capable of computing anything computable. By 1950, his work posed the question “Can machines think?” He introduced the Turing Test, still in use today to subjectively evaluate whether a machine is intelligent based on its ability to have a conversation. Six years later, in 1956, prominent computer scientists proposed the famous Dartmouth Summer Project. Advanced concepts were introduced and discussed and the term "artificial intelligence" was first coined. Over the following two decades, AI flourished. Computers became not only faster, cheaper, and more accessible, but they were progressively able to store more information. Meanwhile, machine learning algorithms continued to improve, getting the interest of experts in different fields and industries and taking the realm of artificial intelligence to a tipping point in the early ’80s. Back then, John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn from experience. Meanwhile, Edward Feigenbaum introduced expert systems which mimicked the decision-making process of a human expert, allowing the program to ask an expert in a field how to respond in a given situation and to learn from it. How can AI benefit both businesses and consumers? Following these early milestones, the advanced analytics sector has experienced explosive growth – with AI impacting many aspects of our lives today. While most people have come to realize that AI can be beneficial, even since the early days, there have been many different views on how those involved in programming the algorithms must take the necessary steps to prevent AI from reinforcing stereotypes, widening wealth and educational gaps, or providing incorrect answers at critical junctures such as in a medical setting. As an example of what not to do: a famous language model was trained using 8 million pages sourced directly from the web. So, implicit in this model are the preconceptions and biases included in its training data. In this case, it led to a model with a trend towards greater male bias in more senior, higher-paying jobs. How to determine fairness in AI models So how can we ensure that the use of AI does not reinforce societal racism, sexism, or other stereotypes? That leads us to define fairness. It’s the impartial and just treatment of people without favoritism or discrimination; when no unjustified distinctions occur based on groups, classes, or other categories to which they are perceived to belong. But, within the world of AI, there are varying approaches to fairness associated with different metrics to evaluate and adopt this sought-after algorithmic fairness. Any solution requires defining dimensions of fairness, but realistically, it’s extremely hard to capture all these very sensitive variables and risky to store and process them. To truly determine if an AI system is fair requires an enormous amount of data and expertise. Additionally, promoting fairness requires an approach across the entire data science life cycle and modeling life cycle. All areas must be considered from the approach to data collection to ongoing evaluation of decisions. And, while fairness in AI is not ‘once and done’ or easily solved, the good news is that it is an area of great focus for regulators, academics, and data and analytics industry experts, like our peers at Experian. The growing importance of transparency and explainability Models generally compute calculations that are complex and involve more dimensions than we can directly comprehend. Given this processing step from model-input-to-model-output is unclear, it leads to questions around how a model has come to a decision. Importantly, how can one be sure that the model is behaving as expected? There are different ways to address explainability. One includes an understanding of how different inputs of a model affect its outputs. Shapley values, introduced by Nobel prize winner Lloyd Shapely, consider an aggregate of marginal contributions for all possible combinations. Another technique involves explaining the behavior of a decision by identifying model constants verse variables to extract what drove a decision and how. Yet another method uses counterfactual explanations, identifying the precise boundary where a decision changes. This method is easy to communicate since it involves statements such as if X had not occurred, Y would not have happened. As in the case of fairness, there’s an on-going dialogue around explainability, underpinned by current and yet to emerge new techniques that maintain model accuracy and improve explainability. Artificial intelligence is past its infancy stage. It’s already had an impact on our daily lives and is becoming increasingly ubiquitous. Fairness, along with a transparent and explainable approach are key ingredients to help this field continue its transition to maturity.

The world is still grappling with the mental, emotional, and financial toll of the Covid-19 pandemic but there are clear signs of hope and resolution ahead. Consumer concerns about their personal finances have started to ease for the first time since June 2020. And there’s a groundswell of opportunity for businesses to serve the growing ranks of “connected customers”—putting the consumer truly at the heart of the relationship. Download Global Insights Report – January/February 2021 issue Key insights: 60% of consumers are using a universal mobile wallet - for online and/or in-person contactless transactions Top 2 activities among consumers online are personal banking (58%) and ordering groceries and takeout food (56%) 90% of businesses have a strategy in place related to the digital customer journey; 47% of businesses put this strategy into place since Covid-19 41% of businesses intend to use AI to acquire and onboard new customers 55% of consumers say security is the most important factor in their digital experience – this is highest in the UK (65%), followed by Japan (64%) Fraud is the biggest challenge among businesses; 55% of businesses plan to increase fraud management budgets In this report, we continue our examination of consumer behavior and business strategy throughout the pandemic. For our third wave of insights, we surveyed 3,000 consumers and 900 businesses in January 2021. Our respondents span 10 countries, including Australia, Brazil, France, Germany, India, Japan, Singapore, Spain, the United Kingdom, and the United States. Over the past 12 months, we’ve observed consumer demand for the digital channel increase at a rate that few could have predicted. The most recent survey shows that these trends are persisting. Looking ahead, we expect that as people get more comfortable with the security and convenience of the digital channel, it will become the preferred—if not permanent—way to bank and shop. Part of what’s driving the continued demand: Positive digital experiences. Most consumers report they’ve been satisfied with their online transactions, especially when they secure and their financial information is protected. This is remarkable, given the challenges businesses faced to meet online demand while simultaneously adapting their employee and customer operations to the crisis. Businesses rose to the occasion and there’s opportunity ahead. Our latest report reveals that consumer expectations for digital experiences continue to rise. For example, even as consumers enjoy the ease of online banking and shopping, security is top-of-mind. In response, businesses are renewing their focus on preventing and mitigating account takeover fraud, transactional fraud, and digital takeaway fraud (e.g. buy online and pick up in-store). And they’re looking for solutions they can use throughout the digital customer journey, not just account opening. Consumers are also looking for greater customer support across digital channels. For example, when a customer is engaging with a business digitally, access to customer service is essential. It’s also an area where many businesses are falling short. However, businesses have made redefining the customer journey a priority and they're investing in capabilities, such as artificial intelligence and automation, to deliver on customer expectations. Consumers and businesses have embraced the digital channel— and the promise it offers is only growing. Now as we move toward a new, post-pandemic era, organizations that re-imagine the customer journey and create digital experiences that place customers at the center stand to win. find out what businesses are using to help improve the customer journey across digital channels, as they prepare for post-Covid customer engagements.

In a world drastically and constantly changing, industries and technologies are being transformed at a rate never before experienced. Recently, I had the opportunity to speak with Roy Schulte, Distinguished VP Analyst at Gartner, about trends in the evolving world of big data, advanced analytics, and decision intelligence — trends that are powered by advancements in cloud technology, machine learning, and real-time streaming. Below, I will share a summary of key highlights. The growth of decision management More businesses are implementing decision management. They want to make more automated decisions to accelerate outcomes while improving applicability. In tandem, decisions are becoming more complicated with regulators increasingly expecting decisions to be explainable and with audit trails built-in. Equally, the combination of machine learning and decision management has placed greater focus on the importance of avoiding bias. Bringing all of this together promises continuous decision improvement; updating models and strategies in days or even hours rather than weeks and months. Essential in responding to the rapidly changing environments, none more so than the impact of Covid-19. As businesses are implementing decision management, they are putting the new systems into the cloud. Based on a Gartner survey in mid-2020, 67% of respondents said their digital business platform will be cloud-native application architecture. It’s the primary criteria for the architecture of many of these new systems. Migrating to the cloud The reason decision management is going to the cloud is the same reason other areas of business are taking this step. Organizations are highly motivated not to run their own systems. There is no competitive advantage to doing so. They want to entrust it to others so they can focus on what the business does best. The migration will continue gradually to the cloud, with a current acceleration based on Covid-19. In a recent Gartner survey, 65% of respondents said the pandemic accelerated their plans and funding for doing digital business. Most models and strategies will be built in the cloud, and the actual runtime decisions will be distributed with some on the cloud, some on-premise in a data center, and some out in the edge in a mobile device. Real-time streaming In the past, traditional business information was done on static, snaps of data from the past. Today, much of this is going real-time, depending on the kind of decision that is being made. For example, when a customer is visiting a website, there are mere seconds to generate the next best offer. This is a real-time decision. However, some decisions do not require real-time, such as the strategic decision to acquire another company or not. That is why it’s important to align the decision speed and cadence with the actual business problem. If real-time data will be used, such as for an e-commerce situation, some of it must be streaming data. With the increase in factors taken into account when making decisions, you need data that is connected, contextual and continuous. The data must come for your entire ecosystem, not a single department, but across your company, business partners, your customers, or market data. Streaming examples for e-commerce might include location, what the person has been doing on the web recently (clickstreams), and records of contact with your business such as calls and emails. A real-time lookup is involved with inventory and external factors like credit rating through an API, and customer data will include historical and real-time. With these factors, real-time decisions for e-commerce will be more effective, with higher yield rates and lower fraud rates. Machine learning in model building and execution Machine learning (ML) is making predictions, not decisions. When a prediction is made and a score is provided, a rule must be applied to determine outcomes based on the score. If considering rules or analytics, the truth is that in most cases, both are needed. The goal of ML in decision management is to have applications that are easier to develop, faster to develop, and lead to more accurate outcomes. To achieve this objective, a process covering all stages of a decision cycle is needed — Observe: Getting connected, contextual, continuous intelligence. Governance is key at this step to know where data is coming from and that it will be used in authorized ways. ML models and strategies must avoid bias and alternative data sources and lending criteria should be considered to expand the business without incurring increased risk. Orient: The next step is putting the data in context. When dealing with models at scale, it’s important to be able to track outcomes through tools such as performance dashboards. Eventually, this will lead to the hyper-personalization of models. Decide: Once models are built, strategy, rule authoring, and approvals are needed. Workflow and collaboration mechanisms help manage the process and accelerate the pace of developing new decisions. Act: Next comes the deployment of models. Using logic to make decisions across multiple applications accelerates deployment, often referred to as centralized management or reuse of decision factors. Feedback: Finally, continuous logging of decisions and effects of decisions. Tracking provides the ability to audit past decisions, explain what was done, and accurately post hoc remediate. Ongoing feedback also enables continuous decision improvement at an accelerated pace. The future of decision management includes decision intelligence In summary, there are five considerations for the future of decision management — A systematic approach to decision making, including a lot more automation and decision intelligence, is clearly on its way. Migration to the cloud is well underway with acceleration thanks to Covid-19. Equilibrium will be reached where some decisions are made at run time at other locations, but most of the development of decision-making, modeling, and strategies will be based on cloud platforms. Data science ML vendors have not focused on decisions. Some may come to realize the reason you do analytics is decisions and broaden the scope of what they do, or they may stay focused and instead partner with other vendors to enable end-to-end decision making. For certain kinds of logic, graph databases and graph analytics can be very powerful. Likely this will become a big part of decision intelligence going forward. Finally, there is huge untapped potential in optimization technology to improve decisions either at development time or even at run time by applying optimization techniques. This could lead to achieving the full vision of artificial intelligence. Related stories Insights in Action Podcast: Identifying the core capabilities your business needs to get MLOps right New Tech Talks Daily Podcast: Machine learning and AI in business — investment trends pre- and post-pandemic In digital transformation, small wins lead to big outcomes

As the world witnessed, the Covid-19 pandemic led to a swift and dramatic digital explosion. As lockdowns began, our day-to-day quickly shifted to a virtual environment. Now, on the back of this widespread response, businesses are forced to rethink their customer engagement model. And, with new digital-first customer journeys, there must be a shift to recognize customers in a predominantly digital way as well. The concept of identity – even digital identity – must evolve. Digitally observable information Recently, I spoke with Juniper Research about this imperative. After analyzing the global digital identity market, they’ve offered insights on current dynamics and trends shaping its future in their Digital Identity Report 2020-2025. Importantly, as we progress digital identities, we must consider more than what a user might typically provide about themselves. We must include digitally observable information, which forms part of a consumer's digital identity. This data includes their device (what they use), and behavioral insights (how they use the device or interact with an app or website). It even includes the specific context of their efforts (what they are doing), such as signing up for an account, moving money, making a payment, virtual window shopping, etc. Related story: View digital identity market trends infographic Intelligent data processing Of course, pulling these kinds of observations together in a meaningful and useful way requires intelligent data processing. This need leads to the use of technologies such as advanced analytics and machine learning to help make sense of the broad streams of data. The double benefit of understanding how to use this aggregated data is that, given the transparent and passive nature of observing data of this nature, it can be used without requiring the consumer to "do" anything other than going about their business. So, businesses can achieve multiple benefits by adopting a forward-looking stance to identity, including reduced risk of fraud, improved customer experience, and stronger consumer/business relationships, which ultimately leads to increased top-line growth. Consumer privacy preferences Finally, to maintain consumer trust as we progress, it's important to acknowledge consumer privacy preferences. Given consumers' concerns around privacy and security, this is an important element within the path forward. Businesses that are transparent around the use of data have been shown to garner greater consumer trust than those that don't offer that transparency. So, any reimagining of digital identity must also have "privacy by design" as a foundation to the approach – not only to meet growing regulatory demands – but, more importantly, to manage consumer expectations. “[It’s] estimated that in 2024 over $43 billion will be lost due to online payment fraud. As we carry on into an unknown future, disrupted by the pandemic, this interwoven nature of identity-security-privacy will play a vital part in making sure our internet, workplace, government services, and banking are safe havens.” -Digital Identity Report 2020-2025, Juniper Research Learn more about: Importance of the evolution of digital identities, including the ability to manage and access the growing volume of online accounts. Advancement of the identity space occurring through the simplified transmission of information via APIs, but the challenge remaining to ensure data is valid, authentic, and from an authorized person. Government attempts at digital identities have faced many challenges, but these use cases continue to progress the development of the digital identity landscape. Benefits to fraud management through the adoption of digital identities can be tremendous – decreasing risk by decoupling identities from transactions, making them more secure from both ends. Usability is king – a good customer experience underlying the use of digital identities will be critical to adoption, and therefore success. Maturation of identity offerings is currently occurring and what’s likely to be successful includes solutions that simplify identity services and those that rely on broader ecosystems. Remote working changes the enterprise approach, with the adoption of Zero Trust Architectures and relevant supporting technologies continuing to emerge to create a safe, yet flexible working environment. The digital identity competitive landscape is evaluated, including vendor analysis and Juniper’s leaderboard. Related stories: Fraud trends during a very pandemic holiday Digital Identity and Blockchain: What lenders need to know Why consumer trust in the digital experience is so important in a pandemic era

The rise of digital decisioning software enables organizations to scale a similar level of personalization, offering customers what they need at the exact right time. And organizations that do it well dramatically improve the customer experience and drive loyalty and revenue in the process. But realizing this promise takes the right tools. The most effective decisioning platforms include a powerful combination of data, analytics, and technology. Equally important, the software must allow non-technical users to update and change strategies to better meet customer and organization needs without burdening IT. Good versus great From a credit risk perspective, there's a vast difference between good decisions and really great decisions. For many organizations, the current status quo still involves decisions made in silos, with business groups sitting in different locations (now even more so, given the prevalence of work-from-home). Creating usable predictive models and then putting them to use can take weeks or even months. What's more, changing the model often requires that business users make requests of perpetually overloaded IT teams. To be sure, the process eventually yields decisions. However, from the customer's POV, they may be slightly irrelevant or feel less than personal. On the business side, the model may lack essential data from across the organization or not yet include critical factors in a rapidly changing landscape. A great decision, on the other hand, benefits the customer and organization alike. Robust analytics enable the decisioning process to reflect the most relevant customer data, from websites they've recently browsed to purchases they've made. The decisions, as a result, reflect that thoughtfulness. They're immediately useful and relevant to customers, putting forth guidance and products when customers need it most. Exponential organizational value Improved customer experience is a key objective for many organizations. Digital decisioning can help further that goal while also providing returns in multiple other ways. For instance, an advanced digital decisioning platform enables organizations to pivot quickly in the face of crisis. Organizations can add new data sources and explore new models in rapid fashion, tailoring them to immediate demands. In doing so, they not only improve predictive power, but they also produce better decisions. The process allows companies to discover and launch new products, reach new markets, and surface early signs of trouble within customer segments. This past year, we witnessed first-hand how organizations leveraged digital decisioning to deftly navigate a challenging environment. For instance, one of our customers, a large bank, used the software to run simulations of new strategies it was considering in response to the pandemic. In doing so, the bank gleaned a better understanding of how the plan would impact its portfolio. The company was also able to identify areas of overlapping services and take proactive measures to eliminate duplication and reduce expenses at a critical time. The cumulative result of improved digital decisioning is an increased ability for companies to differentiate themselves from the crowd. This is true across industries and verticals, from innovating consumer financing for automotive companies to helping healthcare organizations better manage patient debt. That secret sauce Like the friend that really gets you, a great decisioning platform is invaluable. But what makes a platform rise to the top? As noted above, the ability to incorporate and integrate lots of high-quality data is essential. Timely customer data helps identify customer trends and fuels more accurate predictions of future behavior. Platforms should also take regulatory obligations, business constraints, and changing risk factors into account. Solutions that leverage advanced analytics can then transform an ever-growing body of data into decision insights. The software should capture the data used in making those thousands or millions of decisions and make it available real-time to business users, creating a continuous feedback loop. The latter ensures that businesses can stay relevant and nimble. Notably, leading digital decisioning platforms also prioritize the business user along with IT expertise. At a moment that demands quick responses and near real-time solutions to customer needs, business users also need the ability to design, build, test, and deploy strategies. The democratization of the software ensures that the organizations can experience a digital decisioning platform's full potential. In this new era, the organizations that deliver value across the customer journey will be the ones that thrive. Digital decisioning empowers organizations to manage costs and risk while keeping the focus on the customer. They can do this even as they grow, building healthier, more responsive companies with customers at the core of every decision. Related stories: Cloud-based decision management is a must for re-imagining the customer journey Impact of technology on changing business operations Digitally managing your at-risk customers most impacted by Covid-19

In this episode of Insights in Action, we talk about currently available technology used in machine learning, including APIs, SaaS and IaaS. Businesses of all sizes can leverage these methods to build the right cloud ecosystem and accelerate their AI operations. Experian Global Decision Analytics experts Mark Spiteri, SVP of Software Engineering, and Srikanth Geedipalli, SVP of Analytics & AI Products, share practical advice to help businesses identify core capabilities needed to get MLOps right. Get useful insights about: Ways for businesses to leverage AI and machine learning to drive greater impact and ultimately improve the lives of consumers How to use AI and advanced analytics to help manage your business in the current marketplace Setting business goals and strategic plans to operationalize and deploy your machine learning models at scale The 4Cs of a successful MLOps framework Real-life examples of businesses set for success with MLOps Listen now: Related stories: New Tech Talks Daily Podcast: Machine learning and AI in business — investment trends pre- and post-pandemic Impact of technology on changing business operations Game changers: Women in artificial intelligence  

In this Tech Talks Daily podcast, Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI, speaks with podcaster Neil C. Hughes. Santhanam discusses trends based on data from our recent Global Insights Report, which found that nearly 70% of businesses have used either machine learning or AI in business management and almost 60% of businesses are increasing their budget for analytics and customer creditworthiness in the next 12 months. Here are highlights from this 21-minute podcast: Covid-19 disruption has become a catalyst for breaking largely mindset-based transformation barriers, leading to unprecedented digital disruption and adoption of advanced technologies Experian global research confirms a fundamental change in the way businesses and consumers think about digital adoption and experiences Businesses will continue to increase budgets to grow data science resources to align their intent with their capacity Consumers have high expectations and little patience through their digital engagements, with 1/3 of customers only willing to wait 30 secs or less before abandoning an online transaction The greatest success from analytics and AI in business is realized when teams are focused and agile in their approach Listen now: Get more insights from these podcasts featuring Shri Santhanam: New Podcast from AI in Business: The evolution of the data business in the age of AI What is the right approach to AI and analytics for your business? Four fundamental considerations Forbes Podcast: Looking to Data, Analytics and AI to plan the way forward