Tag: Advanced analytics

At Experian, we have a long history of driving positive change in society. For example, our founder Si Ramo was one of the original thinkers around a cashless society much like the one that we are headed to today.   Indeed, everything we do today as a company is built upon a foundation of data and information technology and anchored on the mission of giving consumers access to the financial products and financial information that they deserve. All of that while enabling consumers to protect their identities and to help businesses achieve their outcomes.   Experian Decision Analytics’ mission is taking the complexity out of decision-making, enabling businesses to drive meaningful outcomes for consumers in moments that matter. We achieve that by making sense of that data, by applying advanced analytics and technology in ways that help businesses better serve their customers.   Cloud-based technology helps make smarter, quicker customer decisions  We are committed to extending our expertise and capabilities to businesses of all sizes so they can take advantage of a range of simple, affordable, and configurable solutions. That means that what was once only available to very large businesses is available to all, and that’s better for consumers.    For us, helping businesses serve the needs of more customer segments, with confidence, is paramount. Leveraging decisioning software, rich data sets, advanced analytics, and cloud-based technology, we empower customers to make great risk-based decisions quickly, easily, and safely. That translates into innovation at scale, a lower total cost of ownership for clients, and greater access to the most effective fraud protection methods.  We do this to help businesses lend more effectively, minimize and detect fraud losses, and comply with regulatory and privacy requirements. But, more importantly, we do this to help them deliver great experiences to their customers. Ultimately, all of that work helps society to flourish.   More resources on digital transformation and automated decision management:  Fair and explainable artificial intelligence is accelerating industry transformation How digital transformation is defining a new way to do business Automating Fairness: Using Analytics to Help Consumers in Pandemic Era

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

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.

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

Get the latest from our global experts with these top December headlines, including meeting the demand for digital, increasing consumer expectations, women leading artificial intelligence, and protecting against fraudsters over the holiday shopping season. Investment priorities to meet consumer demand for digital banking In this BAI Banking article, Chris Fletcher, SVP Decision Management & Cloud Services, explores the investment required of financial institutions to transform their use of data and analytics and deliver on credit risk strategies. What’s the proper path for better payments? In context to consumers’ digital expectations post-Covid-19, Progressive Grocer considers the future of payments in food retail and beyond – with contactless payment options already rolling out at a large drug store chain. Wisdom from the women leading the AI industry, with Laura Stoddart of Experian Authority Magazine speaks with Laura Stoddart, Data Scientist, about her career path, her experiences working on ethical AI and using emerging datasets to evaluate risk as well as her thoughts on the future of this industry. #TradeTalks: Increasing consumer demands and expectations Steve Wagner, Global Managing Director of Decision Analytics, joins Nasdaq’s Jill Malandrino to discuss recent research findings on increasing consumer demands and digital expectations, and ongoing considerations for a post-Covid-19 world. A holiday season like no other: What to know to guard your company against fraud Itay Levy, Forbes Councils member and CEO and Co-Founder of Identiq, provides his perspective on the increased preference for online shopping and the need to strike the right balance between customer experience and efforts to mitigate fraud. Stay in the know with our latest insights:

Check out the 10 most popular stories of 2020 that will help to kick-start your 2021. It includes a look back on how trends evolved throughout the past year, which trends will be durable in the new year, and what the global pandemic has taught us about creating meaningful relationships between consumers and businesses. Top 10 list of the best stories in 2020: 10. Model recalibration drives impactful results during constant change Banks have managed through stressed scenarios in the past but none have ever had to predict customer behavior in a pandemic. General indicators of risk or stress didn’t reveal enough about what was going on in customer portfolios. Active model calibration in our current situation had a measurable effect on approvals and expected losses but executives still needed to regain control over disrupted models. Read full article 9. Digitally managing your at-risk customers most impacted by Covid-19 Lenders felt a tremendous amount of pressure this year trying to help reduce the impact of the financial burden Covid-19 put on consumers by supporting payment forgiveness and deferment programs. This made it difficult, though, to understand changes in the credit profile of a previous solvent customer and mobilize their operations teams to service these good but at-risk customers. Read full article 8. The rising need for identity verification Consumers turned to digital when mass closures of businesses prevented in-person transactions. Even as some businesses re-opened with precautions in place, many consumers still felt it was safer to do business online emphasizing the importance of security and identity verification. But while some level of friction invokes a sense of security, too much or unnecessary friction had an adverse effect. Read full article 7. Proactively restructuring debt to help improve customer affordability At the beginning of the year, no one could accurately predict how the world would be impacted by Covid-19 or how long it would last. Customer affordability models shifted into unknown territory and businesses tried to figure out how to assess customer risk in this new context. Lenders relied on the customer data and insights available to them and needed them to work harder at anticipating changes. Read full article 6. Be mindful of these 3 strategies when engaging customers digitally The road to digital was already being paved when the pandemic started but consumers and businesses were pushed there to engage en masse this year. There were practical challenges that needed to be addressed in the short-term, like managing call volume with a remote workforce. But more importantly, it put the spotlight on massive areas in need of modernization, such as the management of liquidity and risk. Read full article 5. Banking trends and opportunities in the post-Covid-19 crisis era This year was marked by adaptation, resilience, and reflection – which can be said for our personal lives – but in the context of the banking industry, it created an opportunity to change or accelerate priorities. Moving operations to the cloud, making sure decision strategies are fit-for-purpose, and applying analytics in a more useful way are some of the stickier trends we’ll likely see continue in 2021. Read full article 4. Why consumer trust in the digital experience is so important in a pandemic era Despite the uncertainty of this past year, one thing remained certain – cultivating customer trust is critical to brand loyalty. Digital customer trust, however, required businesses to consider several specific factors that inform and build trust. Digital adoption was mistakenly considered the most important of those yet being treated fairly, customer recognition, and fraud prevention were stronger signals. Read full article 3. Game changers: Women in Artificial Intelligence Artificial intelligence offers a lot of value, especially when used to better support customers’ financial needs. As more businesses processed huge amounts of data with advanced analytics and AI this year, human oversight was key to ensure transparency and explainability. This “human element” was the inspiration for an article mini-series featuring five women who are making a real difference using AI innovation. Read full article 2. Digital transformation through cloud-first decisioning The credit and fraud risk decision management landscape changed this year – including how the customer journey is being redefined. Mounting consumer expectations for a better digital experience meant the front and back end of a business’ operations were no longer mutually exclusive. Cloud-based applications was the reset needed to move away from functional and product silos to focus on the customer. Read full article 1. Covid-19 as a gateway to fraud Fraudsters are opportunistic which exposed another ugly side of the pandemic throughout the year. As people and businesses moved to digital to engage with one another, criminals exposed weaknesses in the tools, processes, and systems used to protect those interactions. Investment in fraud prevention was already on the rise but steadily increased throughout the year as new fraud trends emerged. Read full article

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

Consumer demand has shaped the way businesses worldwide have adjusted or intend to adjust operations and investments throughout the pandemic. Businesses that have struggled to meet the new expectations of consumers will need to meet ever-changing conditions with careful investment and data-driven analyses. Experian’s latest Global Insights Report shows two-thirds of consumers globally have remained loyal to their favorite brands during the pandemic. Brand loyalty was found to be the highest in India, at 80%, and lowest in France, with a bit more than half, at 57%. However, loyalty may not be a given going forward. Competitive differentiation is founded on how you engage customers, at every interaction. Our research shows that loyalty is intrinsically linked to trust, security and convenience. Payment system providers, such as PayPal, have retained the top spot for customer loyalty for three years in a row, but there continues to be movement among the remaining top five industries. This fluctuation, indicative of consumer preferences and behaviors, is fueled by the varying speed at which businesses globally are transforming front- and back-end systems. Particularly, this holds true for the pace of digitization of credit risk and fraud risk operations.   Leverage data to retain loyalty Consumers have higher expectations than ever before, and businesses need to meet or exceed these expectations by adapting to correlate with the dynamic nature of the customer journey throughout the continuing pandemic. The report also found that 60% of people have higher expectations of their digital experience than before Covid-19, increasing the need for businesses to make sure that they are leveraging data to benefit their customers, providing secure and convenient digital experiences. Although most customers have shifted to digital and prefer the conveniences of online, mobile and contactless transactions, concerns over data security remain. In response, businesses need to carefully navigate customer experiences to ease apprehension. A great example is the trust, and therefore loyalty, that can be established by using customer data for identity authentication.  Customers gain protection while enjoying a hassle-free experience that is non-threatening and transparent. Some businesses recognize these needs, with 40% reporting they are doing a better job communicating how customer data is used to enhance the customer experience, protect consumer information and personalize products and services. Integrating data, analytics and technology Our survey also found that only 24% of businesses are deliberately making changes to their digital customer journey. However, many of them have intentions of making changes and are increasing their budgets in order to do so. Three of the top five solutions businesses are using to help improve the customer journey are designed for driving insights into faster customer decisions. Of these top five solutions, the use of AI to improve customer decisions ranks first amongst banks, payment providers, and retailers ranks first. Companies who are, or plan to, accelerate the implementation of AI can make faster, smarter data-driven decisions to better serve consumers. The key to better serving customers lies in a business’s ability to integrate data and decisioning technology to deliver fast and relevant products and services. In fact, the study found that one in three consumers are only willing to wait 30 seconds or less before abandoning an online transaction, including accessing their bank accounts. With such a short window to keep the customer engaged, faster decision making is imperative to not only retaining a customer’s loyalty on a long-term basis but getting them to commit to a transaction once. Businesses, particularly retailers and financial services who implement the necessary technologies will help move economies from sustainability mode towards a future of growth but cannot do so without continued consumer demand. While customer loyalty does remain, it is up to businesses to adapt and accommodate to retain, and potentially increase the impact of these adjustments. Regardless of where they’re transacting, consumers expect a secure, convenient experience—and they’ll quickly abandon transactions if they’re let down. So, businesses must keep their focus on transformation. Discover more insights from our longitudinal study of the impact of Covid-19 on businesses and consumers.