As holiday shoppers flood online to finish up last-minute gift-buying, there's a high chance that they're paying attention to not just product prices or shipping times but also the security of their transactions. In 2020, with many stores still closed down due to the pandemic, digital sales over the holidays increased by 20%. Though we're still awaiting figures from this year, all signs point toward an increase in digital transactions that's here to stay. But as online transactions have ramped up, so have consumer concerns about the safety of their online activities. The recent Global Insights Report showed that 42% of consumers are more worried now about online safety than they were last year. The concern is understandable—as more people head online, we've seen a record number of breaches. However, now more than ever, businesses need to integrate security into their customer experience, taking a layered approach that provides added protection without additional hassle. Heading into the new year, those that can show they prioritize security as part of the customer experience—and not adjacent to it—will earn the trust and business of a rapidly expanding online customer base. More activity, more risk We've been tracking consumer and business activity online over the course of the pandemic. Our most recent research, drawn from surveys done in October, reveals a 25% increase in digital transactions worldwide since the beginning of the pandemic. It's a figure that's remained constant, even as covid-related restrictions wane and people venture back out to physical stores and banks. This massive digital shift happened in response to a crisis. Businesses such as financial services, restaurants, medical organizations, and retailers suddenly experienced a flood of online business and digital demand. Their option: Respond or be left behind. But as the dust settles, the enormity of the shift and how fast consumers normalized digital behavior is quite astounding. Someone who may have never considered online grocery delivery now uses it regularly. People who habitually visited their bank branch may now bank on their mobile devices. The examples are infinite. Consumers that made the online shift did so initially for physical safety reasons. They didn't want to be close to crowds or strangers because of the virus. Online felt safer. But now that digital transactions are part of many people's daily activities, consumers are awakening to the risks of online transactions. Many may have already experienced a breached account or received a notice that their data was compromised. Indeed, we saw a significant increase in attacks over the year across industries. Ransomware attacks alone are on track to reach 700 million by the end of 2021, a 1,300% increase from the year before. Best practices for better online security in 2022 More consumers are transacting digitally, and that's good news—businesses can expand their reach, grow their revenues, and introduce new digital products. But the question is: How can you leverage the growth while still keeping customers safe—and importantly, not impeding, their online experience? The answer rests part in mentality and part in action. Let's start with the first. Understandably, security guidance in the past often split the onus of safety between the business and customer. Who hasn't reminded customers that they need good password hygiene, device security, and personal data practices, or they may put themselves at risk. Indeed, customers paid attention; they ranked security as their number one priority. But the days of relying on customer actions are over. Businesses that gain customer trust in the future will be those that empower customers to improve their security while actively working to ensure that even if customers fail—their systems do not. You can achieve this by: 1. Beginning everything with a security mindset Businesses need to make security part of their growth strategy. That way, when they do experience planned — or unplanned — surges in activity, their security systems scale to meet them. Coordinating security across functional teams in the event of anticipated demand increases is another smart way to keep customers safe as your business grows. For instance, if marketing is planning a major campaign to spur online purchases, then IT and security need to know about it ahead of time. 2. Developing a multi-layered security strategy There is no magic bullet for preventing cyberattacks, account takeovers, or data breaches. But you can create hurdles for bad actors at every single turn. Combining device recognition, document and identify verification, and behavioral identification makes it that much harder for cybercriminals to impersonate your customers. Our research shows that customers are increasingly willing to provide more personal information to businesses if it means increasing their online security. They're eager to double-down if you are. 3. Utilizing vendors that keep you competitive The security space is evolving rapidly, and it's difficult for individual businesses to mind their own digital operations and keep pace with cybersecurity trends. Fortunately, high-quality vendors can do that for you, providing updated systems, education on new threats, and access to emerging technologies that keep your company and customers safe. The added benefit of these best practices is that they improve the customer experience along the way. Our research shows that customer loyalty to specific online brands is dipping—61% say they're interacting with the same companies online, which is a decrease of 6 percentage points from the previous year. Add in supply chains issues that are impacting inventory, and consumers are primed to find alternatives to their favorite online businesses. But the problems we’ve faced during the pandemic don’t have to define our digital future. Combine security with a quality experience in 2022, and you can attract and retain online customers that come for your product or service and stay because they feel safe. Stay in the know with our latest research and insights:

Historically, identity graphs were used to drive marketing for businesses, allowing marketers to understand and target their audience with relevant content. But in recent years, identity graphs have emerged as a useful tactic to help businesses detect and prevent fraud due to the magnitude of data they collate and analyse. As fraud continues to evolve, businesses need to get creative and resourceful when it comes to fighting online fraud to keep pace with the fraudsters. Identity graphs allow businesses to map multiple data points to create individual customer profiles while highlighting connections across all customer profiles in their current portfolio. Download our latest Global Identity and Fraud Report How do identity graphs work? Identity graphs are databases that create a consolidated unique customer profile. Information is collected from different platforms, both online and offline, and merged into a single view. This process of gathering and merging information is known as identity resolution. The primary goal of identity resolution is to create a real-time, holistic view of an individual. How identity graphs can be used across different types of fraud Account Takeover: Identity graphs make it simple to tell when the same individual is logging into multiple accounts or when all data associated with a particular user account suddenly changes. Identity graphs can screen customer accounts that are suspected of having been compromised by takeover attacks. Credit Card Fraud: Identity graphs collate data from both online and offline means. Having access to this data can be hugely beneficial in preventing counterfeit credit card transactions. Identity graphs will map common links between cardholders and data such as point of sale locations or historic transactional behaviour. Understanding these behaviours means identity graphs can uncover suspicious transactions, helping to expose compromised credit cards and prevent fraud. Referral Fraud: Many businesses offer reward incentives to their customers to help drive engagement. While good intended, businesses that offer referral rewards may expose vulnerabilities to referral fraud. In referral fraud attacks, fraudsters will take advantage of the offered rewards without ever meeting the conditional requirements. Identity graphs make it possible to uncover referral fraud, for example, highlighting multiple referrals from one household. Gaming Fraud: Fraudsters will make multiple online gambling accounts to take advantage of any sign-up offers the vendor may offer. Likewise, fraudsters will often use multiple accounts to bet against themselves, ensuring they always win. Identity graphs can help track and highlight these instances flagging relationships between the multiple accounts. Synthetic ID Theft: Recently fraudsters have been turning to synthetic IDs to commit fraud, as opposed to sourcing legitimate IDs as per traditional identity theft. Fraudsters will combine personal data from multiple victims to create a new, non-existent identity that they can then use during online transactions. These new personas, and the inconsistencies they contain, can be easier spotted when identity graphs are applied. Anti-Money Laundering (AML): When fraudsters illegally obtain funds, they will recruit individuals to pass these funds from one source to another, making their origin hard to trace. Identity graphs can help organisations track financial transactions, providing a clear image of the journey the funds have taken, all the way from origin to destination. Innovative ways identity graphs are helping to detect and prevent fraud Cross-device Identification: Identifying customers through PII and digital data, through both deterministic and probabilistic matching, allows organisations to better identify the same user across multiple devices. This allows them to be treated as a single entity, highlighting suspicious anomalies in behaviours. Real-time: Our digital world is notoriously fast paced, and not known for standing still. Identity graphs operate by collating data and updating the associated customer profiles in real-time. Ensuring we always make decisions on accurate and up-to-date customer information is crucial for both regulatory and risk reasons. Fraud Rings: Identity graphs collect and link a vast magnitude of data. Examining each data point in tabular form can be a laborious task for investigators and spotting suspicious connections can prove difficult. When connections are presented within a graph, they can easily present powerful insights that can uncover fraud rings that could otherwise be missed. Stay in the know with our latest research and insights:

Did you miss these November 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. Online retailers work to turn pandemic buyers into loyal customers Digital Commerce 360 cites that only 73% of U.S. consumers say they're loyal to the brands they shopped with before the pandemic, down from 79% last year, according to Experian's latest wave of Global Insights research. So what does this mean for businesses? Donna DePasquale on Using Tech to Modernize Financial Services In this podcast, Donna DePasquale, EVP Global Decisioning Software, talks to eWeek about how the use of data analytics has evolved in the financial sector, the challenges involved, where we are at now, and what the future might look like. Was that for real? Delving into the deepfake reality Digital Journal spoke to David Britton, VP of Industry Solutions, on deepfake learning benefits and risks, focusing on how bad actors can deceive or manipulate consumers and businesses - and what they can both do to mitigate the dangers. Experian Finds 25 Percent Increase in Online Activity Since Covid-19 Business Information Industry Association looks at Experian's latest research and why the pandemic-accelerated increase in digital transactions is here to stay and how businesses must continue to transform their operations as they head into 2022. Stay in the know with our latest research and insights:

What increasing expectations of the digital customer experience mean for your business and technology investment Economic recovery and waning customer loyalty are creating new opportunities 59% of businesses globally say they’re mostly or completely recovered from the pandemic 61% of customers engaging with the same companies they did a year ago, down 6% in twelve months Data, analytics and decisioning technologies help provide customers with a secure and convenient digital experience Consumers are prioritising security, privacy and convenience when engaging online 75% of consumers feel the most secure using physical biometrics Scalable software solutions give companies of all sizes the ability to better manage risk and digitally transform the customer experience 50% of businesses are exploring new data sources 7 in 10 businesses say they’re frequently discussing the use of advanced analytics and AI, to better determine consumer credit risk and collections 76% of businesses are improving or rebuilding their analytics models “Dwindling customer loyalty along with heightened customer expectations and increased competition could mean potential revenue loss or gain. Businesses must find integrated credit and fraud solutions to improve digital engagement and customer acquisition.” Steve Wagner, Global Managing Director, Decision Analytics, Experian We surveyed 12,000 consumers and 3,600 businesses across 10 countries as part of a longitudinal study that started in June 2020 Read the full report to find out where businesses are focusing their investments

In this eSpeak podcast, eWeek’s James Maguire talks to Donna DePasquale, EVP of Global Decisioning Software, about the use of technology in financial services, and how it can satisfy the ever-increasing demand for real-time intelligence. Listen to the podcast to hear Donna DePasquale discussing: Data and decisioning challenges involved with helping financial institutions reduce risk Helping lenders make better decisions about their customers by providing simplified and streamlined services. Consumers have more choice than they’ve ever had before when it comes to credit, this, along with high expectations for their online experience, is driving businesses to invest in digital transformation and automation solutions. Growing diversity among populations in terms of spending means financial services are working to provide more personalised, real-time, meaningful experiences. Consumers want secure and convenient experiences online without compromise. Evolution of data technology Businesses can now deploy new types of analytics and new types of data services in order to serve customers. Digital transformation allows automation and insights to work together improving credit risk analysis and assessment, smoothing out the customer journey throughout the lifecycle. Access to new data types and advanced analytics. AI and analytics is not a static process, it’s a dynamic process. AI and machine learning allow for constant updates and enhancements to strategy. Future of data analytics and the credit markets Financial inclusion is a very important to the future of data analytics, especially when thinking about those growing economies around the world. We believe that all consumers deserve fair and affordable access to credit, and using alternative data sources to improve credit profiles will directly impact this. Customer experience and credit risk analysis should coexist seamlessly – asking clients to do less without sacrificing the security, convenience, relevance, and privacy of consumer experiences. Stay in the know with our latest research and insights:

How is Covid-19 impacting digital consumer behaviour and business strategy? To find out, we surveyed 12,000 companies and 3,600 businesses across 10 countries as part of a longitudinal study that started in June 2020. Watch the video for an overview of the results or download the full report. Stay in the know with our latest research and insights: This is what we discovered: Heightened consumer expectations is paving the way for digital innovation. 59% of businesses are mostly or completely recovered from the pandemic. And 47% of consumers are somewhat or completely recovered. As economic stability returns and spending resumes. Consumers are most concerned with online security and convenience. Businesses are leveraging advanced decisioning technology to simultaneously meet security and convenience expectations. Innovative decisioning technologies across fraud and credit are improving the customer experience and levelling the playing field. With 42% of consumers happy to share personal information and adoption of AI increasing significantly across businesses – from 69% in 2020 to 74% in 2021. AI, machine learning, and advanced analytics are helping businesses of all sizes to improve: Digital decisioning Credit risk management Fraud prevention and more. Digital investment has become a differentiator - in the race to improve digital customer experience there is no standing still. Those lagging behind can lose customers and opportunities. That’s why businesses across the globe are prioritising digital engagement and digital acquisition. With 76% improving analytics models and over 60% planning to increase fraud detection and credit risk analytics budgets. Since the start of the pandemic, there has been a 25% increase in digital transactions globally. Online activity and high consumer expectations are here to stay. By adopting digital solutions that separate them from the competition, businesses can thrive in 2022. Watch the video for an overview of the results or download the full report.

It’s no secret that the pandemic created a level of economic uncertainty that makes it incredibly tricky for lenders to understand their risk on a customer-by-customer basis, and therefore its impact on decision management. It’s no wonder they’re uncertain; the customers themselves are just as unsure. According to the Global Decisioning Report 2021, one out of every three consumers worldwide are still concerned about their finances even as the second anniversary of the COVID-19 outbreak approaches. While some consumers were able to easily work from home during the pandemic, others suffered job losses, cut wages, or increased expenses due to lost childcare or having to care for a loved one. As the impact of the pandemic continues to be felt – especially as government support programs begin to conclude – financial institutions will have to figure out how to navigate the uneven recovery. By leveraging advanced data and analytics, financial institutions can better understand their risk and improve their decision management. In turn, many financial institutions are creating predictive models to target their best customers and reduce exposure to unnecessary risk. However, a model is not always the end-all, be-all solution for reducing risk. Here’s why: a model requires of the right data in order to work effectively. If there isn't a data sample over a long enough time frame, the risk of creating blind spots that can leave businesses on the hook for unexpected losses can be high. Also, there will always be the need for a strategy even with a custom model. A global financial institution likely has more than enough data to create accurate, powerful custom models. However, financial institutions like local or regional credit unions or fintechs simply don't have enough customer data points to power a model. In addition, many outsourced model developers lack the specific financial industry domain expertise required to tweak their models in a way that accounts for the nuances of regulations and credit data. Finally, the pandemic continues to change the economic picture for customers by the minute, which can make a model designed for today outdated tomorrow. When a strategy makes more sense For many financial institutions, it can make more sense to focus on building out a decision management strategy instead of leveraging custom models. While a model can provide a score, it can’t tell you what to do with it. By focusing on a decision management strategy, you can leverage other information and attributes about different customer segments to inform actions and decisions. In an ideal world, of course, the choice wouldn't exist between a model and a strategy. Each has an important role to play, and each makes the work of the other more effective. However, strategy is often the smart place to start when beginning an analytics journey. The benefits of starting with strategy include: Adaptability: A strategy is much easier to change than a model. While models often have rigorous governance standards, a strategy can be adapted with relatively little compliance impact. This helps businesses adapt to changes in goals, vision, or shifts in the marketplace in a bid to attract the ideal customer. In a world that changes by the day, the ability to adjust risk tolerance on the fly is crucial. Speed: A custom model can take weeks or even months to build, test, deploy, and optimize. As a result, this can put businesses behind in analytics transformation while leaving them unnecessarily exposed to risk. On the other hand, a strategy can be developed and deployed in a relatively rapid manner, and then adapted on an ongoing basis to reflect the realities on the ground. Consistency: A strategy helps drive improvement across operations by allowing team members to ‘sing from the same songbook,’. In smaller organizations where work is still done manually by a handful of people, a strategy allows for automated processes like underwriting so businesses can scale decisioning. Strategy or model? Three questions to consider Do you need a strategy or a model? Again, in an ideal world the answer is ‘both’ due to the unique role each plays, but in the real world it depends on the institution. Here are three questions to ask in order to determine where to focus time and resources: “How different are the people I am lending to than the national average?” If the institution is lending to segments that look just like everyone else, leveraging existing third-party data sources will allow the use of generic models. In this case, the focus would be on using those generic models to power the strategy. However, for businesses that serve a niche population, a national average might skew results; in this case, it may make more sense to build a custom model. “What is my sample size?” Take a close look at the number of applications coming in each month, quarter, or year. In addition, compare it to periods dating back years to understand growth rates. This will indicate the if the data inflow required exists to power a custom model. Don’t forget to analyze how many of those applications eventually become delinquent; because some smaller financial institutions have conservative policies, they may have low delinquency rates. While this is good for the institution’s bottom line, it can make it difficult to build a model that will be able to detect future delinquencies. Therefore, even a large application sample size might not have enough variance to create an accurate custom model. “What are my long-term future goals?” This is the most difficult question to sometimes answer, as many financial institutions remain focused on navigating today’s challenges. As market conditions change, goals naturally adapt. That said, some goals might require custom models in order to effectively achieve the business vision. For example, if the plan is to enter new markets, create new partnerships, or offer new products that are different than what has been done in the past, a custom model could provide a more accurate understanding of potential risk. Our research also shows that nearly half of businesses report that they are dedicating resources to enhancing their analytics, with one-third of businesses planning on rebuilding their models from scratch. Rapid changes in consumer needs and desires means there’s less confidence in consumer risk management analytics models that are based on yesterday’s customer understanding. By focusing on a decisioning strategy, businesses can be empowered to effectively leverage analytics today to take action while creating a steppingstone for more sophisticated model-based analytics tomorrow. Stay in the know with our latest research and insights:

Businesses with priorities to acquire and retain customer loyalty should be prioritizing technology investments that improve the digital customer experience as well as prevent fraud and better manage consumer credit risk.  In our latest survey of consumers globally, we found that the increase in online activity between June and October 2020 has sustained itself for the past year with little sign of digital fatigue. Consumers report that they’re online 25% more today than they were just a year ago. Many lenders and retailers have transformed their operations and met consumers’ needs for accessing goods and services online throughout the pandemic; however, customer expectations for their digital experience may be outpacing those efforts.  Our same study found that customer loyalty toward businesses during the pandemic was at an all time high, but now starting to slip. 61% of consumers reported continuing to engage with the same companies they did a year ago, down 6% in twelve months. Consumers cite security, privacy, and convenience as their top priorities for engaging online. As companies adopt more digital processes and automation to deliver on the real-time financial transactions of their customers, they’re looking to access advanced capabilities for more accurate fraud prevention and credit risk management. Globally, the adoption of artificial intelligence in credit risk decisions is trending up, and 60% of businesses intend on increasing their analytics budgets. Similarly, 65% of companies are increasing their fraud prevention Scalable solutions are creating opportunities for businesses of all sizes to compete for the digital customer. What this means to a mid-size bank, credit union, building society, Fintech and neo-bank is greater accessibility to cloud-based credit risk decision management software. Decades of decisioning best practices coupled with leading edge analytics and technology can help more companies achieve their growth ambitions by attracting, acquiring, and engaging more customers. In fact, confidence in on-demand, cloud-based decisioning has grown to 81%, up from 72% in the past twelve months. Access more insights from our latest research here Other key insights: Consumers report that they are online 25% more now than they were just a year ago 42% of consumers have increased concern for the safety banking and shopping transactions 61% of consumers say they’re transacting with the same businesses, down 6% from last year Consumers rank their priorities online: security #1, privacy #2, convenience #3 Business adoption of advanced analytics has increased over last year – AI is up from 69% to 74% Confidence in on-demand, cloud-based credit risk decisioning is trending up from 72% to 81% Businesses globally say improving digital engagement and customer acquisition is their top priority 75% of consumers feel the most secure using physical biometrics #1 Digital investment is decisioning software, followed by AI and digital enablement for staff Businesses plan to increase budgets for fraud prevention (65%) and consumer credit analytics (60%) In our latest research, we surveyed 3,000 consumers and 900 businesses across Australia, Brazil, Germany, India, Italy, Japan, Singapore, Spain, United Kingdom, and United States. This report is part of a longitudinal study and published series that started in June 2020 through October 2021 exploring the major shifts in consumer behavior and business strategy throughout Covid-19.   Stay in the know with our latest research and insights:

Did you miss these October 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. Best practices to detect and mitigate deepfake attacks David Britton, VP of Industry Solutions, writes for Search Security on how deepfake technology enables fraudsters to distort reality and commit financial crimes. Learn about how the technology works and what best practices to deploy to mitigate deepfake attacks. View deepfake infographic Consumers prefer biometrics to passwords, think less of brands with bad authentication Biometric Update looks at recent research from the CMO Council which found that a far greater number of consumers would choose to use biometrics for authentication ahead of passwords. This supports findings from the Global Identity and Fraud report from earlier this year. This article features an overview of authentication education resources for businesses to better understand where the industry is headed. Managing the Impact of Disinformation Via Deepfakes The Business Information Industry Association looks at why rapidly evolving technology platforms pave the way for even more creative approaches to fraudulent activity. With a focus on deepfakes, this piece looks at what businesses should do to minimise the impact by identifying areas of infiltration and creating a layered strategy of defence. Stories from around the world TDavivienda responde tras robo a cuentas que afectó a Jessica de La Peña CyberArk, Cybersecurity Awareness Month Stay in the know with our latest insights:

What is a deepfake? Fraudsters can distort reality by manipulating existing imagery to replace someone’s likeness. How does AI deepfake technology work? Artificial neural networks are computer systems that recognise patterns in data. A deepfake can be created by feeding hundreds of thousands of images into the artificial neural network, which tarins the data to identify and reconstruct face patterns. Adoption of more advanced AI means less images and videos are needed allowing fraudsters to use these tools at scale. How to detect a deepfake Jerky movement. Shifts in lighting from one frame to the next. Shifts in skin tone. Strange blinking or no blinking at all. Poor lip synch with the subject's speech. What businesses can do Use emerging authentication technology in video. Deploy AI and machine learning to detect deepfakes. Apply a layered fraud defence strategy to better identify deepfakes.

One of the most exciting things about financial services innovation is our growing ability to deliver personalized customer experiences. For example, consider a customer who enters a shopping center during the holiday season. By leveraging decisioning software, lenders can proactively offer that customer more credit—in real-time. The person has the financial ability to get what they need and doesn't have to experience a rejected transaction based on previous credit availability. What's behind such personalized offers? They are powered by the latest data—information that goes far beyond traditional credit ratings and references. For the holiday shopper, that may include geolocalization and behavior data that project a customer's likelihood of reaching a credit limit while shopping. The information empowers lenders to provide that personalized experience at the exact right time. But to make that possible, the data must be interoperable across systems, analytical and operational environments, and third-party data providers. Looking ahead, the financial service companies that enable this interoperability will be able to innovate faster, compete better, and scale their personalization to ultimately win more business. Why interoperability matters Our most recent Global Decisioning Research Report denotes consumers' evolving expectations and the increasingly vital role data and analytics play in meeting their needs. Financial service companies must leverage data to understand customer circumstances better, changing risk profiles and emerging credit needs, especially as we move out of the pandemic. Indeed the right data can help lenders support customers across their entire journey. But utilizing data to improve the customer experience is not as straightforward as it seems. The amount and diversity of the data available are huge. And the data required to power personalized products and experiences are not always readily accessible, well-formed, or high quality. As a result, data integration projects often take longer and cost more than many financial service companies anticipate. Legacy systems add to the complexity and expense. The evolving open standards for data interoperability are helping alleviate some of these challenges. But companies still need to determine which standards and platforms to use. Selecting the right ones can accelerate innovation and prevent expensive stops, starts, and detours down the road. Cultivating a healthy ecosystem The good news is that these challenges are surmountable. The first step is to understand where your organization is in its data interoperability journey. Then you can create a strategy that makes data-based innovation easier, faster, and more cost-effective. For example, consider: Prioritizing industry-leading open standards for interoperability. Requiring CSV and JSON data formats is smart; both are currently ubiquitous across the industry. Using standard APIs to share data. For example, Rest APIs using Swagger provide a description of the API, the data and facilitate the discoverability and use of the API. Exploring API aggregation services and marketplace platforms. These make it easy for developers to add services and for your organization to put them to use. Leveraging low-code data integration tooling. This helps you remove data silos and empower staff to navigate older, traditional data integration methods until they evolve to use open standards. These actions can make a significant impact on your company's ability to take advantage of various data sources now, as well as set your organization up for the future. Data meets decisioning Selecting the right decisioning software is a crucial way to facilitate the steps noted above. As you consider decisioning solutions, look for products that allow you to publish and consume data using open APIs and simple visual drag and drop approaches. In addition, evaluate the core data management capabilities of potential solutions, and prioritize those that can natively also support semi-structured data. For instance, applications that allow you to leverage frequently changing data sources ensure that when a source evolves, only the specific areas loading the data are impacted—not the wider solution. Lastly, as mentioned above, solutions that provide lightweight, low-code middleware allow you to leverage third-party data no matter where you’re at in your interoperability journey. Those new sources of data will inform and enhance your customer's experience.   Stay in the know with our latest research and insights:

Did you miss these September 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. Lending in a Two-Lane Economy Harry Singh, Senior VP, Global Decisioning, features on this CU Management podcast, discussing ways in which Credit Unions can best serve their customers with loans and other products within what Experian's latest research refers to as the two-lane economy. The deepfake-scape: How to fight fraud in the digital age This Biometric Update article by David Britton, VP of Industry Solutions, looks at why deepfakes are a big risk to businesses and consumers, and how fighting fire with fire in the form of artificial intelligence and machine learning can be the best form of defence for organizations. Focus on Data, Advanced Analytics and Decisioning Creates a Winning Strategy for Experian Global Banking and Finance announce that Experian has been ranked number 11 in the IDC FinTech Rankings Top 100 which highlights the top 100 global providers of financial technology, with the piece referring to Experian as a “rising star.” The Rise Of Voice Cloning And DeepFakes In The Disinformation Wars Forbes's Jennifer Kite-Powell uncovers that although deepfake fraud is dominant in social media, it is quickly moving into business sectors. Kite-Powell talks to David Britton, VP of Industry Solutions, about what businesses can do to counteract deepfake fraud tactics like voice-cloning. Shri Santhanam talks AI in lending On this Fintech One to One podcast from Lendit FinTech News, Shri Santhanam, Global Head of Advanced Analytics and AI, talks about how lenders in the FinTech space should be using AI and machine learning, and what key trends he has encountered through the years, and what we might expect to see in the future. Stay in the know with our latest insights: