Tag: Advanced analytics

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

We may not always get what we want, but in many cases, if we feel that we were treated fairly, we’re satisfied. Our July 2020 global research reveals as much—in the survey, 52% of U.S. consumers that believed that organizations treated them fairly during the Covid-19 crisis said they’d give the company more of their business. Conversely, 76% of consumers who thought businesses treated them unfairly reported that they wouldn’t be returning customers. As we progress through the pandemic, fairness will become a critical component of the customer experience. Government support for workers and businesses in many countries is ending, and we’re likely only beginning to feel the real economic impacts. Financial institutions that prioritize fairness in their customer engagements—and leverage advanced analytics and automation to help—will likely retain more customers in the near-term and build relationships that last into the future. We’ve only just begun  For most of the West, the pandemic began in earnest in March. The economic consequences were quick to follow. In our global survey conducted in July, two times as many consumers reported that they were having difficulty paying their bills compared to before the Covid-19 crisis. As a response, 20% of consumers said they were cutting back their discretionary spending, and another 13% reported that they’d dipped into their savings to make ends meet. Around the world, those who were struggling reached out to financial institutions for help. A full 5% of global consumers enrolled in some form of financial assistance, including from savings and loan institutions, retail banks, insurance companies, and government programs. Hearteningly, more than half of these consumers said they’d had a positive experience. And as previously noted, a similar percentage felt they were treated fairly. That’s the silver lining of an exceptionally challenging year. However, for consumers, the struggle will likely continue. Much of the support that financial institutions have provided came via government aid or mandates that are close to expiring. For instance, in the U.S., the CARES act required lenders to offer homeowners six months of fee-free forbearance on loan payments. With that grace period coming to an end, one lender reports that only 10% of borrowers have exited forbearance into a modified payment plan. Government assistance is running out, but the fact that entire sectors such as travel and hospitality remain incapacitated should still cause concern. Over the next year, consumers will likely continue to face financial obstacles, but with a shrinking safety net. Streamlining fairness Amidst the continued uncertainty, organizations should continue to prioritize fairness. Advanced data analytics can help with that task, and the technology also promises to make it easier and faster. Consider that in the 2008-2009 financial crisis, assessing a customer’s ability to afford a loan or credit product was primarily a manual process. Organizations faced backlogs of customers needing help and were unable to respond in a timely manner. Today, financial institutions can use advanced analytics and machine learning to leverage data, with the specific aim of assisting customers in financial straits. For instance, it may not be feasible for banks to permit customers to remain forbearance for another six months. However, they can use data to quickly and accurately determine what payments customers can afford. The technology enables organizations to scale their financial workout or accommodation efforts, reducing the manual workload. Just as importantly, the analytics also provides data to back decisions, making the process more transparent. There’s a big difference between thinking you’re being fair and being able to prove it. With an analytics program, organizations can inform customers exactly what they’re being offered and why. Understanding the data empowers customers to make better decisions about whether they accept any aid. In some parts of the world, regulators are also requesting similar assurance that the banks have provided options in the customers’ best interest. A challenge—and opportunity Fairness and trust are closely connected. And when it comes to the customer experience, incorporating both yields happier, more loyal customers. I often think back to work I did with a banking organization earlier in my career. Our NPS scores regarding our collections, recovery, and fraud team were quite good. It’s easy to assume that customers in financial distress may be less than pleased to be dealing with creditors or lenders. But the dynamic shifts when you’re able to help them at the time they need it most. Now, thanks to data and advanced analytics, financial institutions can implement fairness at every turn—limiting the economic damage to customers, reducing their own risk, and enhancing their relationships along the way. Related articles: Global research study: The impact of Covid-19 on consumer behaviors and business strategies The role of the virtual assistant: Meet consumer demand for digital experience Digitally managing your at-risk customers most impacted by Covid-19

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

Recently we commissioned Forrester Research to look into senior executives’ perceptions on key business data challenges and the importance of achieving a holistic view of their customers. This research uncovered that nearly a third of business leaders worldwide say they don't have enough data to get the insights they need or that the quality of the data they have access to is poor. While getting the type, quality, and amount of data right is paramount to success in your endeavors to create actionable insights that take your business to the next level, data alone is not enough. To get value from data, there's a whole ecosystem that needs to be in place that enables the business to create, manage and maintain a holistic view of the customer, create analytically driven insights into those customers, and deploy them into production environments that drive optimal customer actions and journeys. Organizations also have the opportunity to explore new data assets from traditional sources or those dynamically created in a myriad of places across mobile devices and the Internet of Things. There must be systems and procedures in place to continuously improve and assess these new data sources, by bringing them into analytical processes where insights are derived and predictive models generated.  The critical task is then to seamlessly ingest and embed the data and models into production environments in a robust and compliant way. And that's got to be a continuous process. Otherwise, businesses will stagnate, and they will lose out to those competitors who are actively doing this. Addressing the lack of data your business needs to get actionable insights: Three practical steps Prior to even considering external or additional data sources, you need to get a solid understanding of the data you currently have access to within your organization, what value those data sets bring in and what are the gaps to be filled.   You should also review your internal processes and technology stack to understand if further IT investment is required to create a more effective ecosystem.  With the right tools and processes, you must be able to easily assess the uplift of new data sources in your analytics environment, as well as ingest those new data sources into production environments, to drive new models, run segmentation rules, and execute customer-centric actions. What are the three steps you need to take to get enough data to gain business insight you can take action on? Look at the quality of your internal data. We see a number of organizations that have powerful data in their own business but don't leverage it as well as they could. So matching data together, making sure that they've got a really, really strong view of that customer across all of their systems is really essential. And then having processes ongoing to make sure that they maintain that view whenever they touch the customer, whether it's through an online channel or face-to-face, so that they always know who that customer is, and they can match them to their existing relationship profile. Getting your internal data process correct is a foundational element to this whole piece. Understanding the value and role of new data. In terms of new data, it’s about understanding if that new data can actually add value to the business rather than plugging it into core systems straight away.  You need to work with the vendor or the source of that data to get hold of a dataset, match it to your customers, and run analytical processes to identify whether the data adds value. If it does, consider what models or segmentations could you create from that data that'll actually drive value in the business.Identify the software and architectures you have in place that allow you to connect to data and drive that data into a tool that can dynamically apply models and rules in a heavily regulated environment.  With the right toolset forming the bridge between your off-line analytics environment and your on-line production environment, you can leverage predictive data to continuously improve your customer-centric decisoning across the lifecycle for all of your portfolios.

Digital interactions between businesses and consumers are on the rise. The ability to authenticate and recognize customers provides a convenient and secure experience. However, the latest Global ID & Fraud Report shows a significant disparity in perception between businesses and consumers when it comes to recognition. View Infographic

In many respects, the explosion in the type and volume of customer data businesses gather to facilitate security, ensure a convenient, user-friendly approach to customer interactions, and personalize interactions is a double-edged sword. In an era when businesses are awash in data, customers' expectations regarding its use continue to grow. Nonetheless, when it comes time to recognize a consumer by utilizing the data, there is a disconnect between how confident businesses are in their ability to recognize the consumer and the consumer's confidence in businesses' ability to do the same. In our latest Global Identity and Fraud Report, where input from over 6,500 consumers and 650 businesses worldwide was gathered, 95% of businesses expressed confidence in their ability to recognize their customers whereas only 55% of consumers reported that they don't feel recognized by businesses. So why do businesses feel they are recognizing their customers better than customers think they are? At the heart of the problem, many businesses fail to appreciate the risks and shortcomings associated with weak or no identity verification and customer authentication tools, including their inability to prevent criminal activity or offer seamless processes that minimize customer friction. And while businesses possess the means of gathering data from customers through a multitude of identity verification and authentication touchpoints, they sometimes struggle to develop an overarching picture of individual customers, in conjunction with their needs during each phase of the customer lifecycle. This, in turn, results in a myopic view of the customer, despite the existence of extensive data. A never-ending torrent of data Due to the rapid increases in the number of connected devices, there is exponential growth occurring in the amount of data generated, with some estimates predicting an excess of 79.5 zettabytes (or 79.5 billion terabytes) of generated data by 2025. With these facts in mind, many companies experience the shortcomings of big data solutions and their ability to make sense of the unprecedented growth in consumer data at the fingerprints. This inability to provide actionable insight means that what started as promising data lakes now resemble data swamps, meaning that companies possess unfathomable amounts of data but struggle with how to put it to good use. The security implications for business and consumers While businesses rush to embrace digitization by gathering all manner of data from customers at every stage of their journey, vast amounts of data continue to be exposed. Furthermore, as stated earlier, when it comes to customer engagement, there are expectations that businesses must meet regarding security, convenience, and personalization, yet many businesses struggle to understand the interrelationship between these three elements. In specific terms, as a customer interacts with a company, they provide additional data, with each interaction. This helps paint a more accurate picture of their identity and behaviors. In turn, this increasingly detailed, data-driven portrait improves an organization's ability to recognize them in subsequent interactions. Moreover, with a more detailed understanding of the customer, the need for burdensome security processes lessens, resulting in less friction for the customer. In a nutshell, security, convenience, and personalization form individual legs of the same stool. Consequently, failing to consider this fact, leads to isolated security measures, peppered throughout the customer lifecycle. For example, while browsing online, a customer may receive recommendations regarding the products or services they may like. However, when they access their account profile during the same session, the company may force them to reauthenticate their access. Using this example, since the company had sufficient data to personalize the customer's experience, in theory, at least, they also possessed sufficient information about the customer and their identity to grant unfettered access to their profile. Was there a genuine need to reauthenticate the customer in this scenario? At the heart of that interaction lies the customer's identity, which forms the basis for any interactions. When disparate systems capture various elements of a customer's digital identity, a mechanism must exist to aggregate the elements, to minimize the friction customers experience when interacting with businesses at different points in the lifecycle. And while relatively sophisticated CRM systems exist to memorialize customer preferences, due to their inability to capture a holistic view of the customer's identity and subsequent activity during all touchpoint in the customer lifecycle, they often fall short as in their ability to deliver a cohesive, consistent and appealing approach when it comes to security. The power of layers and analytics When fractured infrastructures are in place, businesses often subject their customers to a complicated and disjointed approach to security and risk requests, while simultaneously bombarding them with attempts to up-sell or cross-sell products and services. So, while the goal of data gathering and analysis should in part facilitate convenience, that is far from the customer experience when interacting with certain businesses. Conversely, when customer identity and recognition involves layers of data gathered from across business units, coupled with advanced analytics and quality identity verification tools, businesses can present a more compelling, user-friendly approach that minimizes the stress placed on the customer while providing a positive customer experience. With this approach in mind, businesses can do a great deal to foster engagement which is secure and trusted by the customer. Our research determined that 86 percent of businesses state that advanced analytics is a strategic priority. Yet only 67 percent of businesses consider the use of advanced analytics, like artificial intelligence, to be important for fraud prevention, whereas only 57 percent deem advanced analytics as important for identifying customers. Even fewer respondents see a reason to adopt a hybrid approach involving machine learning involving both unsupervised and supervised models with business rule logic – 45 percent globally and with the United States and Japan as the outliers at 58 percent. However, when businesses pursue the adoption of more sophisticated authentication strategies and advanced fraud detection tools, they will improve their ability to identify and their customers, reducing their exposure to risk and ultimately leading to increased trust. Trust is the linchpin for any transaction and while it's easy to underestimate the importance of trust, given how difficult it is to measure and maintain, without it consumers and businesses will part ways. In a world with no shortage of data, with the right tools and methodology in place, businesses can mitigate various forms of risk, refine the customer experience, and foster the trust needed to support a mutually beneficial relationship between businesses and the customers they serve.

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

The speed at which the world is feeling the impact of Covid-19 is unparalleled. Because of this customer affordability has shifted into the unknown and businesses are trying to react quickly to assess customer risk in a brand-new context, albeit a temporary one. We look at the five key areas businesses should be considering when it comes to customer affordability. 1. Looking to insights The last financial crisis taught us that the first line of defense for many organizations, large and small, is to move straight into proactive debt restructuring to reduce the volume of customers who would otherwise fall immediately into debt collection. This crisis is no different, but identifying those in hardship, restructuring debt at speed, and in line with restricted policies are where businesses should be focusing to successfully tackle this. 2. Keeping regulators front of mind As a result of the last downturn, many financial regulators are placing a much higher weight of responsibility on lenders to make fair and transparent lending decisions when it comes to affordability. Not just when it comes to new lending, but also how they act and behave within collections. These rules are not going to be relaxed, so it’s important that businesses continue to prove that they remain compliant. 3. Predicting what’s to come Anticipating arrears before they happen, and at speed, is fundamental to managing the restructure of debt effectively. Especially where traditional data sources provide less predictive value. For businesses without advanced and automated debt restructure or collections-based program to begin with, this is an opportunity to develop something that will carry them through this time of crisis and beyond. 4. Harnessing analytics and AI Thinking predictively means getting the right analytical capabilities or models in place, ideally harnessing Machine Learning and AI to get the fastest and best results. For larger organizations, this will mean having the agility to rapidly update and deploy existing models, and for the less mature, it will mean building this from the ground up (but quickly). Businesses will undoubtedly see their analytics teams overstretched during this period, so now is the time to reduce the manual load and invest in these capabilities. 5. Automation for demand control Making sure customers can deal with organizations digitally will be critical to maintaining customer experience. It’s just as important to ensure that channels are integrated and automated in the backend. Businesses are looking to omni-channel digital solutions to help feed new demand through the funnel without having the added complication of a restricted workforce. It has never been more important to automate. More on Decision Analytics

The year 2020 will go down in history. That much is certain. Businesses are acting quickly to revise strategic and operational plans that seemed perfectly valid in January – now almost impossible to imagine, just a few months later.   However, predictions around fraud trends still stand. The opportunistic nature of hackers means that a global crisis can create the perfect breeding ground for fraudulent activity, and with users increasingly seeking solace and communication via digital means, businesses and consumers need to be even more vigilant.       Here’s what we found earlier in the year. Investment in fraud prevention is on the rise. According to our 2020 Global Identity & Fraud report, 84% of businesses say they are either investing more or maintaining the same budgets when it comes to identity-related fraud prevention. But with a complex digital landscape, rapid changes in consumer behavior, and customer experience playing a central role, how can businesses be sure that they are investing in the right place? We identified the top 5 global fraud trends to watch out for in 2020: 1. Authorized push (or wire transfer) payment fraud In the past 12 months, the most common fraud attack encountered by businesses were authorized push or wire transfer payment fraud (41%). Set to continue into 2020, authorized push payment fraud (or APP) is where victims are tricked into authorizing a payment from their own account to another account which is being controlled by a criminal. Fraudsters can socially engineer consumers or intercept communications, changing key information such as account details, leaving victims believing that they are authorizing a legitimate transaction when in fact they are making a payment into a criminal's account. Validation is crucial in tackling APP fraud Push payment fraud can be prevented with a validation exercise which carries out real-time checks, dramatically reducing the chances of payment fraud and error. It can be used to confirm that the beneficiary of a payment owns the bank account to which a payment needs to be sent to. As with many fraud prevention methods, one layer of verification is rarely enough so it's important that techniques like real-time validation sit within a wider fraud prevention and authentication strategy. 2. Account takeover fraud Next in line is account takeover fraud (37%), which is expected to significantly increase in light of the recent global pandemic. This is when a fraudster gains access to an account that doesn't belong to them and makes unauthorized transactions, sometimes changing key credentials of the account such as the rightful account owner's personal information or log-in details. This type of attack often involves phishing attempts to compromise customer data is much more likely in light of various government assistance programs due to the crisis. In recent years, fraudsters have done a great job of taking over bank login credentials, getting access to a user's account, then calling that account holder to inform them a fraudulent transfer is being attempted from their account. Since customers know that banks typically send SMS one-time-passwords for customers to verify transactions, the attackers use that layer against the account holder. Know Your Customer (KYC), Customer Identification Program (CIP), use of passwords and physical biometrics make up the top solutions currently used by businesses to detect and protect against fraud based on regulatory requirements. Although businesses seem confident in the ability of their existing solutions used to detect and protect against fraud, they are reporting 57% higher losses associated with account takeover fraud, so what's going wrong? Businesses must confidently engage customers using holistic and advanced, risk-based identity and device authentication, as well as targeted, knowledge-based authentication that allows good customers to move throughout the process and frustrate fraudsters. 3. Account opening fraud The third key fraud trend to watch out for in 2020 is account opening fraud. This takes place when criminals use stolen personal information to open new accounts for fraudulent activity such as borrowing money in another person's name. Identity verification is often the easiest control to bypass because so much identity data is compromised. Averting account opening risk requires strong identity authentication, proving that the person applying for the account (often digitally) is indeed the legitimate consumer. Acquiring legitimate customers from the beginning, whilst balancing a seamless customer experience is the challenge businesses face when it comes to account opening fraud. By improving the application process and identity-based authentication measures, businesses can decrease customer acquisition costs, reduce false positive rates, and save manual reviews for when they're really needed. 4. Transaction payment fraud Transactional payment fraud is any unauthorized transaction using stolen payment details or data. Fraudsters involved in this kind of criminal activity can range from small-scale amateurs to large-scale cyber-criminal rings. Criminals access stolen details in many ways, including phishing emails, and even direct contact with the victim. The key to combatting transactional payment fraud is the ability for businesses to quickly detect irregular activity, and then distinguish between legitimate and fraudulent transactions in real-time. In transactional fraud, strong fraud machine learning models and pattern and anomaly detection logic are key passive controls, with step-up challenge layers requiring customers to provide additional identity authentication when trying to complete high-risk activities or anomalous transfers. 5. Synthetic identity fraud (also known as fictitious identity fraud) One of the newest types of fraud, synthetic identity fraud uses a blend of fake information and real data to create brand new fake identities that expert-level criminals use to establish and build up an online credit history. Businesses can invest time and money in chasing people that turn out to not even exist. Synthetic identity fraud is an insight into the evolving world of fraud, and a reflection of how the criminal world reacts to sophisticated fraud prevention by becoming ever more sophisticated themselves. The role of advanced analytics The deployment of robust link analysis that monitors over time the use of identity elements such as name and Social Security/National Insurance, plus many other forms of personal information is paramount in tackling fraud. The ability to detect when identity elements look to be used inconsistently or at high velocities can be an indication of larger identity compromises or synthetics. Businesses should also utilize device intelligence to monitor common access points through which more organized fraud schemes may be occurring. In some instances, synthetic identity detection scores can also make up identity verification and fraud prevention layers, providing businesses with a separate synthetic identity score with each account opening event. This is because synthetic identity is difficult to detect with traditional verification controls or risk models. The good news is that the strategy to protect your customers and your business from these different trending types of fraud is similar - organizations need a strong layered series of defenses to both to recognise legitimate customers and to quickly pinpoint attackers if they want to combat fraudsters. New research available: The global impact of Covid-19 on businesses and consumers - September/October 2020  

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.