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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:

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:

The pandemic accelerated the number of digital interactions in finance. Typical methods of managing finances, connecting with lenders, and buying goods and services were much harder due to lockdown measures, so consumers went digital, including large numbers of non-digital natives. As the demand for online banking and services has intensified – moving from a necessity to a preference for many - pressure on businesses is twofold. They must rapidly build new and better models to onboard customers and create a more dynamic customer journey. In many markets, doing so is the biggest competitive differentiator right now. Creating a dynamic digital journey and understanding the customer With Millennial customers becoming a bigger influence in the space, organizations were always going to have to plan for a slicker and quicker digital customer experience to keep up with expectations. The pandemic simply accelerated this, forcing businesses to rapidly react. In fact, although 9 in 10 businesses have a digital customer journey strategy, 49% of those businesses only put this in place as Covid-19 began according to research in our Global Decisioning Report 2021. This did help them improve in some areas, including access to quicker customer service responses online. But without the right technology in place, it is not surprising that 55% of customers surveyed said they expect more from their digital experiences. Such a rapid shift has exposed weaknesses around agility, leaving traditional institutions trailing Fintech competitors further down the digital transformation road. However, whilst Fintechs have the benefits of agility, traditional, established lenders have large amounts of customer data from which they can target and tailor existing customer journeys more effectively. Improve the digital onboarding process Optimizing the digital experience for new customers from the beginning encourages usage and, ultimately, loyalty. A stress-free and fast onboarding process is an expectation for the younger generation but can also capture the ‘new to digital’ group migrating online. Bio-metric recognition technology, instant document verification, and auto-filling customer data are far more appealing than entering hundreds of data points, and can boost efficiency and reduce friction. The problem is businesses rightly want to make sure they can remove any bad actors to reduce risk and prevent fraud. The key is doing so without disrupting the genuine, low risk customers. Building better models to onboard customers Covid continues to shift population demographics due to factors such as job losses, furlough schemes and migration of workers to alternative sectors. There is also the realization of pent-up demand for property and vehicles, in particular - among those fortunate enough to be less impacted - such as those able to save more as they work from home. This has led to a change in the demand for finance with a need to tailor experiences to specific customer requirements. As the number of credit needs grow, lenders must have a structure in place that allows them to scale and handle the increased volume. New models must also be introduced to allow organizations to access extensive data insights and ensure they are reflecting the ‘new normal’. As businesses move away from sampling towards models that are based on full populations there must be a marriage of technology with data. Data is ultimately captured for the benefit of the lenders, helping them to gauge risk and tackle fraud. But a blended, multi-layered approach in which customers are only asked for the information specific to their individual circumstances – at the appropriate time – can provide a positive and tailored onboarding process. Having solutions in place that combine risk-based authentication, identity proofing, credit risk decisioning and fraud detection into a single platform ensures all checks can be carried out in one place with minimal disruption to the onboarding journey. Putting businesses in first place Online experience and credit and fraud risk management need to be more closely entwined. As the demand for a simple and fast experience intensifies, a digital-first approach that puts businesses ahead of the game must involve embracing the right technology that supports the entire customer journey. Download a copy of the eBook here.   Stay in the know with our latest research and insights:

Innovation in fraud detection and prevention is key in today's ever-evolving digital landscape. Juniper Research, a research firm that specializes in identifying and appraising new high growth market sectors, recognized organizations and platforms that drive innovation and growth in the banking, fraud and security, and retail and payments through their Future Digital Awards. The firm awarded Experian as the Platinum Winner for Fraud Detection and Prevention Platform (CrossCore™) and the Gold Winner for the Artificial Intelligence Platform (Ascend Intelligence Services™). Keeping more consumers safe According to this year's Global Identity and Fraud Report, more than half of businesses will continue to invest in fraud prevention solutions over the coming year to combat several types of fraud: new account opening fraud, account takeover fraud, and other types of identity fraud, with at least 57 percent of businesses report higher losses from account opening and account takeover fraud. Identity-related fraud has evolved towards more automation, in the form of scripted attacks and bot attacks, as well as more sophisticated phishing attacks. The speed at which fraudsters adapt to new technology and behavior has always been a problem, and with sudden and unpredictable change, reacting quickly with new fraud strategies has never been more important for businesses looking for ways to safeguard digital transactions. CrossCore™, launched in 2016, is used globally to connect identity and fraud capabilities. The system combines robust risk-based authentication, identity proofing and fraud detection into a single, state-of-the-art cloud platform to make real-time risk decisions throughout the customer lifecycle. Typically, businesses need to move through validation, contract and then integration in order to combat fraud – making for a long, tedious and expensive process. CrossCore pre-qualifies fraud and intelligence services so that businesses can choose how they want their transactions to be processed and which fraud and identity services they want to use. The platform is designed to help businesses instantly identify good customers, catch fraud and enhance the customer experience. Juniper Research’s Future Digital Awards for Fintech & Payments recognized Experian’s CrossCore as the Platinum Winner for the Fraud Detection and Prevention Platform. The recognition comes at a time CrossCore and AIS platforms are helping businesses all over the world combat fraud and maintain a safe digital experience for their customers. This recognition underscores the commitment to using advanced capabilities in data, analytics and technology to bring innovative fraud solutions to the market, enabling businesses outpace fraud while making it safer for consumers to engage with them digitally. Providing better digital service The acceleration to digital has caused financial institutions to quickly evolve and improve their processes including reducing time for loan approvals, access to more financial produce and new innovative payment methods. What is most important is that businesses focus on more on advanced technologies for lending. Launched in January 2021, AIS provides financial institutions and other lenders with AI solutions delivered rapidly and digitally, resulting in better business outcomes at every stage of the customer lifecycle. AIS is a one-stop-shop of building, documenting, deploying, monitoring, and retraining analytics, all on the same AI platform. The system allows businesses to process data with extreme speed and efficiency in a streamlined approach to detect and monitor identity models and strategies. Juniper Research’s Future Digital Awards for FinTech & Payments also recognized Ascend Intelligence Services™ (AIS) as the Gold Winner for the AI Platform. By creating accessible AI solutions for our business clients, people engage with their favorite financial brands in a more meaningful way across the customer lifecycle, truly democratizing advanced analytics. Learn more about Ascend Intelligence Services and CrossCore.   Stay in the know with our latest research and insights:

Why digital acceleration has created more opportunities for deepfake fraud tactics like voice cloning and what businesses can do about it Digital acceleration has placed information and services in the hands of the masses, connecting individuals on a global level like never-before, and in turn making them increasingly dependent on devices in their daily lives. The argument for technology as an equalizer in society is a strong one. Most people have a voice and a platform, producing millions of virtual interactions and recordings every day. But in this digital world of relative anonymity, it is difficult to know who is really on the other side of the connection. This uncertainty gives fraudsters an opening to threaten both businesses and consumers directly, especially in the realm of deepfakes. What is a deepfake? Deepfakes are artificially created images, video and audio designed to emulate real human characteristics. Deepfakes use a form of artificial intelligence (AI) called deep learning. A deep learning algorithm can teach itself how to solve problems using large sets of data, swapping out voices and faces where they appear in audio and video. This technology can deliver extraordinary outcomes across accessibility, criminal forensics, and entertainment, but it also allows a way in for cybercriminals that hasn’t existed until now. Deepfake fraud tactics A principal tactic among deepfake fraud is voice cloning – the practice of taking sample snippets of recorded speech from a person and then leveraging AI to understand speech patterns from those samples. Based on those learnings, the modeler can then use AI to apply the cloned voice to new contexts, generating speech that was never spoken by the actual voice owner. For businesses, deepfake tactics such as voice cloning means access to points of vulnerability in authentication processes that can put organizations at risk. Fraudsters may successfully bypass biometric systems to access areas that would otherwise be restricted. For government leaders, it can mean the proliferation of misinformation – a growing area of concern with huge repercussions. For consumers, the risk of falling victim to scams involving access to personal information or funds is particularly high when it comes to voice cloning. How to prevent deepfake fraud 1. Vigilance: Stay on top of sensitive personal information that could be targeted. Fraudsters are always at work, relentlessly seeking out opportunities to take advantage of any loophole or weak spot. Pay close attention to suspicious voice messages or calls that may sound like someone familiar yet feel slightly off. In an era of remote work, it is important to question interactions that can impact business vulnerabilities – could it be a phishing or complex social engineering scam? 2. Machine learning and advanced analytics: Deepfake fraud is an emerging threat, which leverages the development and evolution of the technology that fuels it. The flip side is that businesses can in fact use the same technology against the fraudsters, fighting fire with fire by deploying deepfake detection and analysis. 3. Layered fraud prevention strategy: Leveraging machine learning and advanced analytics to fight deepfake fraud can only be effective within a layered strategy of defense, and most importantly, at the first line of defense. Ensuring that the only people accessing the points of vulnerability are genuine means using identification checks such as verification, device ID and intelligence, behavioral analytics, and document verification simultaneously to counter how fraudsters may deploy or distribute deepfakes within the ecosystem. As with many types of fraud, staying one step ahead of the fraudsters is critical. The technology and the tactics continually evolve, which may make the countermeasures on the table right now obsolete, however the fundamentals of sound risk management, with the right layered approach, and a flexible and dynamic solution set, can mitigate these emerging threats.   Stay in the know with our latest research and insights:

Fraud threats continue to rise across the globe as consumers are spending record amounts of time online due to the pandemic. At the same time, emerging threats of fraud are growing, as fraudsters are taking advantage of the globally shifting economic conditions. Fraud prevention remains a top concern for both consumers and businesses alike. Anticipating future fraud risk is critical and companies are adopting more complex technology systems to ensure consumers’ financial safety. To provide a safe and convenient experience, businesses need to take a customer-first approach when evaluating the latest technology and solutions available to them. To ensure they are providing secure online experiences, businesses are turning to verification strategies using data technology and other detection methods. In fact, according to this year’s Global Identity and Fraud Report, customer recognition security strategies have become the new norm for businesses with 82 percent of companies saying they now have one in place, a 26 percent increase since the start of the pandemic. An independent research firm headquartered in Germany, KuppingerCole Analysts, released a report, Leadership Compass: Fraud Reduction Intelligence Platforms, that provides an overview of the market segment, vendor service functionality, prevention measures and innovative solutions to fraud. The report cites Experian as an overall leader, product leader, innovation leader, market leader and technology leader in fraud reduction intelligence platforms. Experian is also credited for taking a client-oriented upgrade approach and delivering other cutting-edge features while maintaining compatibility with our older platform releases. We also scored a strong positive for interoperability, usability, deployment, innovativeness, market position, financial strength and ecosystem; and a positive in security and functionality. We pride ourselves in our digital identity protection services and consumer safety, taking proactive approaches to fraud prevention and providing businesses with the necessary tools to identify risks of fraud. The report discusses fraud prevention measures and innovative solutions to fraud. According to the report, cybercrime costs will reach $10.5 trillion by 2025. The report evaluated 15 different data security and fraud prevention platforms and ranked their products, innovation, market positioning and technology in their report. All of Experian’s fraud detection and prevention services are available through our CrossCore® partner ecosystem. By combining advanced analytics, rich data assets, identity insights and fraud prevention capabilities, businesses can connect any new or existing tools and systems in one place, whether it be Experian’s, Experian’s partners or its own. With its built-in strategy design and enhanced workflow, fraud and compliance teams have more control to quickly adjust strategies based on evolving threats and business needs, which helps to improve efficiency and reduce operational costs. Learn more about the CrossCore platform.   Stay in the know with our latest research and insights:

How elite leaders train analytics teams to unearth and convey the highest quality data insights and better manage risk. It's surprising how much of an art the effective use and analysis of qualitative data in the business world truly is. Too often, data scientists are tasked with turning raw data into insights without ever actually being taught the true art of identifying and reporting the most meaningful insights that address the problems at hand. Instead, data teams often produce reams of summarized information without drawing any useful conclusions – falling short of discovering deeper truths hidden within. I've been fortunate to work for, with, and manage data scientists of various titles, abilities, and personalities over the years. I've found that the true "artists" in this profession can combine technical proficiency, tactical communications with an affinity for the science, and excellent detective skills. Objectivity in Data Analysis As Arthur Conan Doyle wrote in Sherlock Holmes says, "I never guess. It is a capital mistake to theorize before one has correct data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts." As data scientists, we're often sent down a singular path to analyze data to support a narrative. Data is inherently objective; analyzing with subjective intent typically leads to ineffective results when put into practice. However, with the proper guidance, probing questions, and some detective work, scientists can uncover deeper insights leading to effective outcomes in the form of actionable intelligence and forecasts. Early in my career, I was tasked by a business partner to pull data that demonstrated higher customer satisfaction scores for a customer call center. Requests like this – "just get me the data" – are (unfortunately) common. In this case, however, he was open to discussing the "why" behind his ask. As a result, this incident proved a learning opportunity for me on how to satisfy a requirement while simultaneously producing information explicitly valuable to the organization. I've often had to find workable paths through figurative minefields with mandates such as "just get me the data" or "make the numbers work." During this scenario, I diligently asked ancillary questions to build into the data modeling outside the required parameters. I intended to generate value beyond the pre-conceived conclusion I was tasked with finding data for. The resulting report yielded compelling insights, actionable intelligence, and a clear forecasting plan. In this example, it was found that clients had higher satisfaction scores for reasons other than what we initially thought and had nothing to do with the seven million dollars my business partner spent on branding, training, etc. The solution was simple: move a training location. Tactical communication skills were necessary in this scenario as I had to tell my business partner where the efficiency gains were actually coming from and where future budgets could be more effective. Doing so was the catalyst behind an alternative business strategy and focus, resulting in a much more significant impact on our customer relationships. Asking the Right Questions The true purpose of analytics is to discover, interpret, and communicate meaningful patterns in data and the connective tissue between. Most importantly, it exists to aid in effective decision-making within an organization. Under that premise, I teach my teams to be communicative, especially during planning stages and consistently ask questions of the data throughout the analytical process. It's always imperative to identify the specific addressable problems our clients are trying to solve while frequently conversing with them to understand what actions and/or decisions the analysis is meant to inform. This strategy produces more profound results and focuses on solving a problem – not endlessly cycling through various cuts of the same data. As a result, the team will be primed to evaluate results objectively and be ready to dig beyond surface-level data, capturing vital insights hidden deep within. Using the Right Tools Nobody does arithmetic by hand anymore. A data scientist's best friend should be sophisticated model development software that leverages AI and Machine Learning. The efficiency they provide enables us to focus on areas where human intelligence is best applied, such as interpreting model performance within the context of how that model will be used. Elite leaders know how to leverage the right tools to maximize speed and efficiency. Ignoring the sheer processing power of cloud computing and other advancements places your organization at a distinct competitive disadvantage in performance and accuracy. I shudder when thinking about the dark days when it would take six to nine months to develop a new model. It reminds me of watching NASA mathematicians do advance calculations with slide rules in movies like Apollo 13 and Hidden Figures. Strategy optimization is a perfect example; how do I ensure that my portfolio is holistically delivering the highest value within risk constraints? I could grow my portfolio endlessly, but that likely means taking on too much back-end risk. Instead, mathematical optimization can be used to determine the right balance between growth, return, and risk. To do this successfully requires a vast amount of processing power. Gradient boosting, a Machine Learning technique that helps build far more accurate models, is another excellent example of what's possible with modern technology. Some of the operations we perform daily were literally not possible 10-15 years ago as we did not have access to such processing power. Thus, we're able to solve problems not previously solvable. What has also changed is our ability to process volumes of data and highly complicated, multi-tiered models, with extreme speed and efficiency. Organizations don't need to take all of this on, as companies like Experian effectively provide data science services where AI/ML solutions are delivered rapidly and digitally. A well-equipped, efficient, curious, and well-trained data team whose data analysis consistently helps corporate leaders make informed decisions is true art. The answers they provide to challenging business questions is their magnum opus. Read about topics related to this article Stay in the know with our latest research and insights:

In this opinion piece on CEO World, David Britton, VP of Industry Solutions, Global ID & Fraud, discusses why, in today's increasingly digital world, it is much easier for fraudsters to operate on a global scale. As commerce and financial services ramped up their online offerings due to the pandemic, it enabled criminals to take advantage of people in vulnerable situations. There has been a significant shift away from previously prevalent fraud schemes such as account takeover, account opening and card-not-present, towards the direct manipulation of individuals to get to their personal information and payment details. "Not only have they been taking over the world, but fraudsters have been taking advantage of the growing digital environment, and as recent research from Experian found, 55% of consumers say security is the most important factor in their digital experience. It is important for individuals to know what to do to ensure that their information is secure and to have technology to utilize in order to fight against this issue. For both personal and businesses, there are ways to combat the scandals of fraudsters." Business fraud prevention With a focus on ransomware and email compromise, there are many things businesses can do to minimise vulnerability to fraud. A layered approach to defence is key, along with device intelligence and strong employee training. Personal financial fraud Although there is a common misconception that credit card details pose the biggest fraud opportunity, identity theft is by far the one to watch for consumers today. Fraudsters can use personal information for credit or payments. "Businesses must invest in new technologies in order to give people the added security they desire when accessing their accounts. In fact, according to our most recent Global Identity & Fraud Report, consumers no longer believe passwords are the most secure method for authentication. Since the pandemic, consumers have an increasing level of comfort and preference for physical and behavior-based – or invisible – methods of security." Read the full article Stay in the know with our latest insights: