Innovation

Public and private organizations worldwide are embarking on ambitious digital identity initiatives, from the tiny country of Estonia to efforts that encompass much of Africa and India. At the core, the broad goal is often the same: Use blockchain or equivalent technology to provide individuals with a unique digital identifier. That digital identity then enables seamless, secure access to services—governmental, financial, or otherwise. However, as you delve into the details of each program, there remain more differences than similarities. Organizations may have different drivers for pursuing digital identities and varied approaches. And in these early days of digital identity development, there’s not yet a single plan for aligning initiatives across the public and private sector or even within the financial services industry. So how do organizations evaluate where to invest and when to act, when efforts are progressing and changing in real-time? The impetus now is to understand the fundamentals of digital identity programs and then evaluate what your organization stands to gain—or potentially lose. Get that sorted, and you’ll be ready to make smart digital identity decisions at the right time for your company and customers. The fundamentals of blockchain Much of the digital identity conversation centers around the notion of blockchain-based digital identity programs and their benefits to consumers or citizens. Broadly, these programs enable individuals to have a digital identity profile, which is tied to a basket of attributes and stored on a blockchain. Those attributes are verified when the identity is established. Consumers then use their digital ID, for example, to access their financial applications. And organizations can verify the person via their digital identity token. Such programs provide privacy for consumers; they also promise to accelerate and secure all sorts of processes from applying for loans to paying taxes. That’s because, with a digital identity, consumers don’t need to re-submit documents or provide personal information to various businesses and entities. Instead, they can allow institutions to access their digital identity for proof of who they are. The potential for such programs is already exciting, and we’ve likely just scratched the surface of what’s possible. Still, most of the discussions leave out a critical component. That is: how will programs establish a digital identity in the first place? As financial institutions assess the digital identity landscape, digging into how programs ensure that the right information makes it into the system is paramount. As the saying goes, it’s garbage in, garbage out. Regardless of how innovative the technology is, a consumer’s digital identity is only as ​trustworthy as the information that created it. The digital identity trade-offs The security of digital identities is very compelling—especially as cybercriminals become increasingly sophisticated. Businesses can easily authenticate customers, and consumers have more control over their information, which is an issue of growing importance. A recently released Experian study shows that consumers are most concerned about protecting their financial data over other types of information. As privacy and security assurances become part of the financial service value proposition, digital identity programs will likely be a differentiator for companies. That said, doubling down on digital identity can initially seem at odds with another dual technology priority: Taking advantage of data to provide hyper-personalized financial products and services. By tokenizing identity information, organizations may need to forgo some of the data that enables that personal, customized approach. In the long run, I believe companies will find creative ways to balance privacy with personalization needs. For instance, customers may rely on digital identities to navigate their financial networks and then opt to provide additional information about themselves in return for better, more personalized service. Financial institutions will need to weigh some similar factors when leveraging digital identity programs to improve customer experience. Digital identity programs promise to remove the friction caused by customer recognition and authentication. Again, the organization may give up some data collection to enable that seamless experience. But in the long run, companies will likely find that the related improvements and revenue opportunities gained more than makeup for any sacrificed information. ​At the same time, against a backdrop of an increasing number of stolen identity records, the idea that a digital identity program can help reduce the excessive proliferation of sensitive personal data is a significant benefit. The road ahead Financial institutions should prepare for the pending digital identity journey—even if they haven’t yet embarked. There are still multiple issues that the industry, consumers, and regulators will have to settle. For instance, there’s the question of adoption and how long it will take for businesses and consumers to use digital identity programs regularly. As we’ve discussed before, consumer trust ​and availability will remain a considerable component in driving that adoption. What’s more, we’ll likely see regulations follow digital identity efforts as specific initiatives gain steam and popularity. The rules may accelerate adoption or, conversely, increase the investment expense on behalf of financial service firms. For these reasons, financial institutions need to be involved early and voice their concerns often to ensure that regulations serve consumers without adversely affecting the business. In the meantime, businesses should remain aware that digital identity is a fragmented market, which may ultimately settle into an “ecosystem of ecosystems” across programs. It will be critical for enterprises to plan accordingly if they want to become early adopters. Or, at the very least, companies with a more moderate strategy should wait until a leading program emerges before making a significant investment. Digital identities represent a dramatic shift in how consumers navigate their online world and how companies continue to meet their online expectations and needs. Keep these developments on your radar, and you’ll be prepared to make smart digital identity decisions and investments. Related stories: Infographic: Global Identity & Fraud Trends, February 2020 The impact of Covid-19 on Consumers and Businesses, July 2020 The impact of Covid-19 on Consumers and Businesses, Oct 2020

In this episode of Insights in Action, we talk with Derek Garriock, Design & Innovation Director at Experian and David Bernard, SVP of Global Marketing & Strategy at Experian Decision Analytics, about the future of banking and trends and opportunities arising in the post-Covid-19 crisis era. The future of banking is being shaped, in part, by people's response to Covid-19 There is adaptation to the current crisis, but even as we start to progressively get out of lockdown in a number of countries, banks have realized there are a number of deeper things around their use of analytics, the fine-tuning of their scorecards, lending strategies and risk strategies that have to be redone. Also, there’s the general, longer-term trend towards moving some of their banking structure to the cloud, making sure that their decision strategies are fit for purpose, that they are flexible enough, building attributes into the system. So, there are a number of programs that are continuing and sometimes accelerating. David Bernard, SVP, Global Strategy & Marketing, Experian Decision Analytics Questions answered include: Are we already on the path to a different way of banking? Speed, convenience, and choice have gained a different meaning, accelerating digitalization efforts and demands virtually overnight. What are the current areas of focus for the industry based on experiences with financial institutions globally? Has this Covid-19 crisis further challenged the status quo in the industry and what is the anticipated impact between traditional financial services and fintech challengers? What are the pillars of a successful modern banking infrastructure, and what promising technologies can help meet new market dynamics? Related content: The role of the virtual assistant: What businesses can do to ensure consumer demand is met while taking care of customer experience Maximizing impact from AI investment: 4 pillars of holistic AI Be mindful of these 3 Strategies when engaging customers digitally

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

Due to Covid-19 , the focus on analytics and artificial intelligence (AI) has significantly increased. However, while companies have made significant investments in AI, many are struggling to show a tangible impact in return. One executive commented, “We have data science teams and a data lab where advance techniques like neural networks, GANs, etc. are successfully being used. However, less than 10% of our actual operational decisions and products are powered by AI and machine learning (ML). I would like us to be driving greater measurable impact and Covid-19 is exposing some of our execution gaps.” And, he’s not alone. Despite the investment, the true impact is elusive, and many businesses are not getting the desired effect from their efforts. Achieving the results needed to justify continuous investment will take a holistic approach. So, what can companies do to achieve this impact? The four pillars of holistic AI: performance, scaling, adoption and trust Achieving impact from AI requires taking a more holistic approach across four pillars — beyond just the delight of the data scientist producing a better performing model. 1. AI performance — outperforming the status quo and quantifying the impact This pillar is where most data scientists and companies tend to focus first, for example using modern AI techniques to create an underwriting model that performs better than traditional models. The so-called ‘data science moment of truth,’ where the data scientist declares that he has built a model which outperforms the status quo by 10%. However, it’s important to note model performance alone is not sufficient. We should look beyond the model to understand business performance. What quantifiable business impact does the 10% improvement deliver? How many more credit approvals? How much lower will the charge-off rate be? This reasoning provides the important business context around what the incremental performance means. 2. AI scaling — having the right technical infrastructure to operate models at scale This area is often ignored. The risk with data science teams is they can see their job as being completed with creating a better performing model. However, that’s just the beginning. The next important step is to operationally deploy the model and setup the operational infrastructure around it to make decisions at scale. If it is an underwriting model, is it deployed in the right decisioning systems? Does it have the right business rules around it? Will it be sufficiently responsive for real-time decision making, or will users have to wait? Will there be alerts and monitoring to ensure that the model doesn’t degrade? Are there clearly defined, transparent and explainable business strategies, and technology infrastructure and governance to ensure all stakeholders are aware? Is the regulatory governance around this model in place? Does the complexity in the model allow it to scale? Too often we see data scientists and data labs create great models that can’t scale and are impractical in an operating environment. One banking executive shared how her team had developed 5 machine learning models with better performance, but were in ‘cold storage’ verse in use, because they didn’t have the ability to scale and operationally deploy them effectively. 3. AI adoption — ensuring you have the right decisioning framework to help translate business decisions to business impact With better performing predictive models and the right technology, we now need to present the information in a way that is ‘human-consumable’ and ‘human-friendly.’ At one bank, we found they built a customer churn ML model for their front lines, but no one was using it. Why? They didn’t have the contextual information needed to talk to the customer — and the sales force didn’t have faith in it — so didn’t adopt it. Subsequently, they built a model with a simpler methodology and more information available at their fingertips — where decisions could be made. This was immediately adopted. This pillar is where the importance of decisioning tools is highlighted. The workflow and contextual information to allow a decision to be orchestrated and made is critical in driving AI adoption. 4. AI trust – having governance, guardrails and the appropriate explainability mechanisms in place to ensure models are compliant, fair and unbiased This final pillar is probably the most important for the future of AI — getting humans to trust it. In recent times we have seen numerous examples like the Apple Card, where the underlying principles and models have been called into question. For scalable AI impact, we need an entire ecosystem of people who can trust AI. To achieve this effect, you need to consistently apply the right principles over time. You also need the right decisions to be explained — like adverse action calls. Explainability capabilities help manage communication and understanding of advanced analytics, contributing to established AI trust. And, when fairness and bias issues come up, you need to provide good answers as to why decisions were made. AI is poised to fundamentally change the way we do business, and studies show that $3 to 5 trillion in global value annually, up to $15 trillion by 2030, is likely to be created. We believe the four pillars highlighted above will be key to accelerating the journey to driving positive results and capturing this value. At Experian, we are making investments to drive impact for our clients by delivering against these four pillars. Related articles: What is the right approach to AI and analytics for your business? Four fundamental considerationsHow rapidly changing environments are accelerating the Need for AI What’s new in online payment fraud Part 2: How AI and evolving regulation are driving change

Insights from Harry Singh, SVP, Global Decisioning, and Hristo Zahariev, Global Product Manager. Due to the global pandemic, one of the key challenges facing many consumers today is the ability to obtain support either from their credit provider or from government. This is manifesting itself in two ways – consumers facing very short-term financial difficulty, which might mean a payment holiday for a few months, or longer-term structural issues such as unemployment, which requires a very different set of treatments and outcomes. But what can businesses do to ensure consumer demand is met while taking care of customer experience? We look at the importance of digital channels within the decisioning environment, and how investment using AI can not only lead to consumer satisfaction now but also a sound business strategy for the future, regardless of how unpredictable that future may be.    How the industry can respond to consumers during this time of need A recent study from March this year looked at businesses that are not yet fully digital in terms of how they handle their consumer interactions, and how they can reach out to consumers to help them during the Covid-19 crisis. With call centers and operational centers closed, and anything between five and 50,000 applications a week coming into banks across the world since the pandemic began, businesses have inevitably been struggling with demand. Based on existing operational models examined within the study, if businesses were to manually manage these applications, they would need to double in size in terms of full-time employees, and follow-up interactions post approval may still not be met. Managing demand and staying compliant, while enabling consumers to successfully interreact without waiting hours to get through is the challenge faced by many businesses. It’s a balancing act that is both an opportunity and a risk and should be treated as such. Helping consumers in a way that is digital, while allowing for self-serve, is fundamental in meeting these new levels of demand - and doing so in a way that doesn’t feel demeaning to the consumer is where true differentiation begins. During a stressful time for consumers, it’s important that businesses step up to the challenge of demystifying their interactions, removing embarrassment around finances while also retaining an element of human engagement. Thanks to AI and a layered, cloud-first approach to decisioning, contacting pre-qualified consumers for both forbearance and hardship can now be done through a business’s banking application or their website, using artificially intelligent virtual assistants that can be deployed in a multitude of different digital channels. The consumer perspective: we need more than a chatbot Chatbots are very effective and useful in many ways, but when an interaction gets complex or there's something of a regulated or more subjective nature, it becomes difficult for that chatbot to provide the kind of service consumers are looking for. The answer lies in continuous learning, which moves away from the decision tree structure of a traditional chatbot and into the realms of natural language processing. The new age of virtual assistant remembers interactions and then learns from them, has short-term and long-term conversation goals, and recognizes small talk. The result feels a lot more empathetic and allows for always-on and real-time consumer interaction. How businesses can develop their strategies not only for today, but going forward Bringing together digital capabilities, analytical insights, and data to understand the affordability of a consumer is critical. Using demographic and geographic data, businesses need those insights, regardless of whether we are in a growth environment, a benign environment, or as we're seeing right now, a recession of macro-economic downturn. Businesses choosing to invest now to address their operational and strategic challenges are not just responding to Covid-19, they are looking beyond and into strategic requirements of the future. Financial difficulty may be more acute right now, but it has always existed and always will, for various reasons.  

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 a recent piece for the Forbes Technology Council, Businesses Need to Modernize Their Approach For Delivering Digital Experiences, I shared how the current rapidly changing environment has greatly accelerated the shift from offline to digital interactions. As businesses experience a need for heightened governance and controls, they must look towards technologies such as artificial intelligence (AI) and machine learning, coupled with access to data in real-time, to move forward. According to the report Experian commissioned Forrester Consulting to conduct, 53% of businesses struggle to make consistent customer decisions. Additionally, only 29% of businesses believe they do a good job of connecting analytics to action. When applying AI and machine learning to customer experiences, there are some concerns that businesses must keep in mind. The first is legal implications and privacy protections, which must always be a priority. The second is to combine analytics models with real-time decisions so that predictions can be harnessed and put into action in real-time. As more and more businesses shift to fully digital experiences, they must learn how to apply their vast amounts of data to models that can help inform the newly remote customer experience. If interested in the topic of businesses’ modernized approach to digital experiences, you can find the full article here.

The decisioning landscape is changing rapidly. In parallel to this, digital continues to redefine the customer experience with a big focus on removing friction from the customer journey. Mounting expectations around online customer experience mean that we are seeing a digital transformation both in terms of consumer interaction, and what the businesses are processing in the background. The front and back end are no longer mutually exclusive, and the driving force behind this transformation is digital, and it’s enabled by the cloud. How the pandemic has shifted priorities Before the Covid-19 pandemic took hold, businesses were well on their way to recognizing this. Digitizing more workflows while incorporating a truly customer-centric view was the goal of 2020. A Gartner report shows that in January, priorities for CIOs centered around Cloud and DevOps. This push to shorten the development lifecycle by combining software development and IT operations into a single discipline, alongside demand for Robotic Process Automation, using bots to focus on automating high volume repetitive tasks, were top of the list for businesses. By April, these priorities had changed. Businesses quickly shifted their focus to the pandemic, and with that, the need to enable remote or home working. But Cloud remains firmly within the top three. We look at why cloud-first decisioning remains critical to digital transformation, now more than ever. Why Cloud-first is even more important now Managing cash flow: When a CIO is in the cost optimization mode and trying to conserve cash, scaling back on the use of existing Cloud technology can afford immediate cost savings. Cloud cost for infrastructure of the service, or platform of the service, and even some software of the service is often tied to the business. The less usage, the more savings. When a CIO needs to implement new technologies in 2020, Cloud can offer the most cash flow optimized needs to do so. Less cash is spent upfront to acquire Cloud technology than to buy data center systems or licensed software. Business agility: Cloud technology makes it much easier to keep systems up to date and secure, alongside feature enhancements and new releases. The Cloud minimizes lengthy and costly delivery projects with solutions that can be deployed in weeks, not months and years. Customer journey: Many established market leaders are running digital transformation programs that re-orientate their business away from functional and product silos to focus on customer journeys enabled by Cloud services. Keeping it simple: Simplification is crucial. Simplifying the IT environment with Cloud services that eliminate the need to manage hardware and other infrastructure. Using Cloud-native architecture to support auto-scaling, zero downtime for upgrade. Security is paramount: The challenge to identify and fight fraud by analyzing behavior during the data capture process is ever-present. Software needs to evolve all the time to adapt to threats, and it needs to continuously update with new features to help businesses remain competitive. Businesses need to protect consumer digital accounts from Account Takeover threats while balancing consumer convenience. Cloud-first impacts all layers, from consumer interactions to data sourcing and processing, from fraud detection to identity verification, and at the heart of areas like credit and decisioning. Integrated decisioning, and decisioning that is governed and can be clearly explained to both the auditor and to the regulator is the goal of every business, and it is enabled by the cloud.

I recently had the opportunity to talk to Christian Hubbs and Muhammed Shuaibi from Artificially Intelligent Podcast about the value AI and analytics generate for businesses. We reviewed how a growing number of businesses are seeing a lot of value added in terms of problem-solving when they bring in more sophisticated machine learning models and technology.  The conversation quickly pivoted towards how to determine the analytics and AI that better suit your business needs, as well as understanding what is required to operationalize those promising models. Think of performance, scalability, adoption and trust before embarking on your AI journey Ensuring that AI is right for your business requires a holistic approach, which is fundamentally based on four components:   AI Performance – selecting and framing problems, with a view to demonstrate that what you build outperform traditional methods.  AI Scalability - what starts as an experiment conducted by data scientists needs to be turned into a scalable system that truly impacts the business.   AI Adoption – ensuring that your AI and analytics are embraced and used by consumers and businesses and, ultimately, change the way they make decisions.  AI Trust – explaining decisions in a transparent way so the models and systems you build can be trusted, explainable and stand the test and scrutiny of regulators. Leveraging an outcome-based approach to solve COVID-19 related business challenges At Experian, we are applying this holistic approach to identify and address the most pressing concerns our clients are dealing within the context of COVID-19. The first is helping our clients understand what’s currently happening with different customer segments. We’re creating tools that bring together a series of early warnings and indicators and portraying how different customer segments are seeing various patterns in credit. We’re also identifying those most affected or needing concessions around lending, and understanding what banks are doing in terms of forbearance. Our priority is identifying these needs and quickly get the relevant AI and analytical solutions to our clients.  We are expecting to see a later urge in the industry to recalibrate existing models and to expand the type and volume of decisions they can make. Updating and monitoring them will be also a big area of focus over the next couple of years.  Listen to the podcast

Businesses can leverage technological advances in process optimisation, automation, data analysis and cognitive science to put customers first and truly understand and address their needs.

In this podcast episode of Insights in Action we talk to David Britton, VP of Global Identity & Fraud at Experian Decision Analytics, about how businesses worldwide are driving towards a more consumer-centric approach in both their operations and structure.

Shri Santhanam, Executive Vice President and General Manager of Global Analytics and AI, speaks to Forbes' Peter High on his Technovation podcast about Experian's Analytics and AI solutions. During times of crisis, innovation accelerates. What was once considered innovation, suddenly falls into the realms of the necessary, with businesses seeking quick, smart solutions to emerging challenges. Although this conversation took place before COVID19 reached the levels of a global pandemic, Santhanam discusses how advanced analytics and AI can be a game-changer for businesses. Key topics include how businesses need to bring together data, tech and analytics to formulate best in class products and services using AI in the form of examples such as Experian Boost. What it takes to run the global analytics and AI function at Experian. How high-profile consulting positions within previous businesses have placed Santhanam in an ideal position to problem-solve. And what he considers to be the two stand-out developments in the analytics and AI space. Listen to the podcast, or read the interview.