Innovation

The ecosystem of credit lending platforms and technologies has rapidly grown in the past year. The top business priority emerging from the pandemic has been to prioritise investments in new artificial intelligence and machine learning models for smarter customer decisions. According to our latest report, business confidence in AI is growing: 81% up from 77% last year. Three reasons why data-centric AI models lead to more accurate and inclusive decisions More observations to better represent the population Easy o update with the most current data Enriched and expanded data sets for a complete view of the customer 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.

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:

Getting the most out of your AI investment Work backward from impact - give yourself room to experiment Hire the best data talent and partner with the right provider Take a holistic approach - don't just focus on performance AI allows businesses to process sheer volumes of data and multi-tiered models with extreme speed and efficiency. But, scaling AI to meet shifting business demand can be challenging. Experian's Ascend Intelligence Services expertly partners with organizations to build custom, scalable AI and ML solutions to meet those requirements. Listen to Shri Santhanam advise on how to scale AI

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:

A recent industry-leading analyst report looking at loan origination solutions found that lenders are experiencing high volumes of new loan applications, but many are struggling to process them. This alongside increased consumer demand for improved digital experience, and a shifting credit landscape means lenders are trying transform both to keep operating costs down and meet the needs of a changing market. This tracks closely to findings from our Global Decisioning Report 2021. We look at what is changing, and how the Now Tech: Loan Origination Solutions report advises lenders to move forward. Consumers went online, and have high expectations of the digital experience The pandemic shut down banking and retail locations around the world. Amidst the lockdown, consumers turned online to manage finances, connect with lenders, and buy essential goods and services. The crisis especially accelerated digital adoption for older consumers and created a new digital imperative for lenders wanting to meet customers’ evolving needs. The rise of self-service and new payment methods There was also an increase in the already growing demand for digital self-service in terms of applying for credit and seeking out repayment support. Consumers expect to be able to apply for credit when and where they need it, often using a mobile-friendly device. In return for convenience and security, consumers report that they’re more willing to provide additional personal data. Timely, meaningful credit and repayment offers, convenient interactions, and improved communication with lenders make the exchange worth it. The convenience of digital channels is also creating the opportunity for new payment methods, such as subscription models and Buy Now Pay Later (BNPL). Both are occurring across a range of products and services, from cars to clothes to beauty essentials. Our Global Decisioning Report found that 27% of consumers reported purchasing products using BNPL programs. Traditional lenders will need to consider the needs that the emerging BNPL market meets. This includes making purchases easier for consumers by providing increased payment flexibility. APIs, security, integration, and explainable AI According to the Now Tech report, lenders should look for solutions that allow access to data via APIs for credit decisioning, have strong data security and privacy practices, integrate with third-party technology products and services, and leverage explainable AI for underwriting. Allowing lenders to acquire customers digitally is key, and loan origination solutions provide a digital portal that can be accessed across devices and which supports real-time customer input, document uploads, data aggregation and analysis, and digital signatures. Want to read the full 2021 Global Decisioning Report?

Financial institutions have long been dependent on technology for business operations, resulting in a long history of tech additions, upgrades and vendors. Changes made to legacy IT systems can not only impact customers, but in many cases, the economy too. Often these systems feel safe and familiar, so it can be a difficult choice to make a change. However, over the last year the pandemic has highlighted the need for agility within the market. Responding to changing customer needs in an increasingly digital environment is number one priority. What do we mean by legacy tech? The term legacy tech has a lot of negative connotations. It refers to a set of computer systems, software and technologies that can no longer be maintained or easily updated. The system could be out of support or in extended support. Integration becomes a challenge because different technologies have accumulated over the lifespan of the business, and the associated support levers around it are all different. There is also the challenge of finding the skills to maintain these systems – in-house or outsourced from providers. Maintenance costs can be high – security and resilience test costs will add to this, while performance will drop with the increasing need for work-arounds. Upgrades can be complex, expensive or even impossible on legacy systems, generating extra costs. Financial institutions create their own legacy systems when they start integrating various data sets from different sources. It can happen when the business grows to new locations, new lines of product, extended consumer services, while using different tech from different vendors. Cloud as an enabler for business transformation From the moment code is written and deployed, it becomes legacy. Cloud integration allows for daily code releases and automated upgrades meaning that businesses are constantly adjusting and responding to client needs, regulation and strategic changes. They can instead focus on their business model and innovation, staying relevant and up to date. Budget is directed towards improvements and innovation instead of maintaining the legacy tech. It brings an interesting level of agility, with the ability to respond to the market much more quickly and effectively. How cloud can benefit the customer Cloud-based services have allowed banks to revolutionize onboarding processes and timescales. Processes like KYC (Know Your Customer) can be carried out by partners for a fast and efficient experience. Throughout the lifecycle of a customer, banks can leverage third parties for every part of the journey and ultimately improve customer experience. Beyond the onboarding process, the entire customer lifecycle, from originations to collections, can be transformed by removing friction and using AI to create interest, and ML to make decisions for quick results. Experian has partnered with Open Banking Expo TV to produce a series on Cloud-based solutions. Sign up to watch. Related content

Shri Santhanam, EVP and Global Head of Analytics and AI, talks to Ganesh Padmanabhan from Stories in AI about why he hopes the changing world of lending will lead to better financial inclusion. "The whole digital revolution in lending means that financial institutions are scrambling to make the process much more seamless, reduce time for approvals, let consumers have access to different financial products, and have innovative products like buy now pay later. But underneath it all, you have to get more nuanced and more sophisticated about the methodologies that you use for lending. And this is where AI and ML come in." Expect to hear discussions about the future of finance, how to drive impact by leveraging data analytics and AI, frameworks for setting up and institutionalizing an AI center of competence for a large organization, and how to scale data science efforts through hiring, promoting from within, and setting up the right structure and processes to make it happen. "Experian for over 100 years now has leveraged the power of data. We’ve been a very powerful data company. We’ve used that data to improve the lives of consumers and improve how businesses make decisions. Fundamentally, we’ve had a set of pioneers who before Big Data tech was introduced to the world, figured out that having a data marketplace or collecting high quality data on consumer lending will be of value, and that’s been the core of our business. That dynamic is changing. We see a lot of value migrating what we call up the stack. So from purely data to actually the decisions that are made with the data, to products and services in the data."   Related content

Credit providers have long relied on data to lend insights into how their customers are faring—and help predict what's to come. The pandemic, however, introduced unexpected anomalies that have made understanding the actual credit landscape far more challenging. For example, while government assistance programs have enabled customers to stay up-to-date on their payments, they've also made it harder to discern the true financial impacts of the crisis. Our recent research gives voice to these challenges. We surveyed businesses around the world three times from July 2020 through January 2021 for our annual Global Decisioning Report. The results reveal that business confidence in credit risk analytics models has declined over the pandemic, dropping by nine percentage points for Tier 1 lenders, and 15 percentage points for Tier 3 lenders. As we look ahead, credit providers need ways to improve confidence in their analytics models so that that they can make smarter, faster decisions on behalf of their customers and businesses. This is where synthetic and alternative data are beginning to make a real difference. The rise of AI and machine learning solutions has opened the door for lenders to leverage this data. Understanding how to put it to use—and why it's imperative to do so—will help lenders navigate the end of this crisis and prepare them for any economic volatility in the future. The data differentiator  Before we dive into how lenders can best utilize alternative and synthetic data, let's quickly define what we're discussing. Credit providers have traditionally used credit bureau data to assess their portfolio risk and inform credit decisions. But as noted, in times of crisis, supplementing that data with additional context can significantly improve its effectiveness. Alternative data does just that. Alternative data refers to primarily unstructured data from non-traditional sources. For example, social media data can help paint a more complete picture of customer behavior. And location data can provide information about customer geography, such as opportunities for travel-related purchases. Synthetic data complements alternative data but is not the same. Synthetic data is new data created by altering existing data. So a lender might change the profile of its customer base and then use that dataset in analytics models to better understand what the future may hold. Both types of data work together, with alternative data providing a more complete customer view and synthetic data allowing lenders to account for additional variables and offset their risk accordingly. New data in action  Confidence in analytics models may have dropped during the crisis. But lenders aren't resting on their laurels. Instead, nearly half of businesses report that they are dedicating resources to enhance their analytics efforts. Those that include alternative and synthetic data in their improved models have the opportunity to leverage the information in multiple ways. Some of the most exciting applications of alternative and synthetic data include: Anticipating purchasing behavior New data sources, especially from social media, help lenders understand what's happening in their customers' lives and how that may translate into purchases. For example, a customer who has recently moved into a new home may be considering purchasing furniture or home décor. Or customers who are celebrating life milestones such as birthdays or graduations may be buying gifts or spending on events. Predicting credit risk In this realm, synthetic data can be beneficial. Lenders can use synthetic data to understand how credit profiles may change in specific circumstances, such as modeling a higher unemployment rate or dramatic income shifts. They can then use analytics models to determine the related impact on customer affordability. Improving fraud detection With an improved customer view, fraud prevention teams can more easily identify unusual patterns in customer behavior and spending. For instance, does a customer's current location (per location data) match their most recent transactions? Or has the number of contacts on their phone dramatically changed (it may not be their phone)? Enhancing pricing Both types of data are useful in improving pricing models across company portfolios and at a personal level. The additional context can help everyone from lenders to insurers to banks assess customer needs and provide products that meet them—at prices that make sense. What's more, machine learning automates that pricing, allowing companies to scale personalization across the organization. Improving marketing In the same vein, new sources of data can also give marketing efforts a boost. The ability to access more real-time information about customer behaviors uncovers opportunities to provide them with credit, insurance, and other lending products that may prove immediately helpful. Data can also help identify new markets entirely or highlight rising needs that may demand the development of additional products or services. The past year was an anomaly in so many ways. However, as we ease out of the crisis, financial service companies have the opportunity to strengthen their data models—and leverage new types of data to reduce their risk and provide improved decisioning no matter what the future holds.

In a recent DataTalk interview, I had the chance to reflect on and discuss how we define digital identity these days. The big digital shift we have been immersed in since the coronavirus pandemic started has certainly changed the way we create, relate to, and protect our identities online. One of the most interesting aspects of this change is that the majority of people don't think about how they're being represented online; there's a lot of information that represents us that we don't typically take ownership over. We don't tend to think about that, but it's absolutely vital to the whole process. In this regard, this year’s Global Identity & Fraud Report shows that 8 in 10 businesses now have a customer recognition strategy in place, up 26% since the start of the pandemic. Many companies also developed digital strategies as they strove to improve their online experience and provide security and fraud prevention measures when customers needed it most. That certainly marks an inflection point, as for 55% of consumers globally, security is the most important element of their online experiences. Covid-19 has changed the definition of digital identity The covid-19 pandemic has impacted the way people rely on technology for their day-to-day interactions, from shopping to banking to digital identification. It’s particularly interesting seeing how for people that weren't really engaged online before, that weren't big believers in the whole idea of buying goods and services online, the risk of walking into a store during the pandemic outweighed their fears of shopping online. That translated into about 20% of the population moving their shopping online in the last twelve months, per Experian’s 2021 Global Identity & Fraud Report. Looking ahead, the expectation is that 46% of consumers worldwide purchase and do more things online, even when physical stores and venues are safe to go back in again, meaning that people’s digital footprint is growing faster than ever. In this context, we could define digital identity as how we represent ourselves in a digital environment and how do people recognize us. For example, in the same way that years ago a good way to identify someone was looking up that person’s address and phone number, as landlines become a thing of the past, it’s possible to validate someone’s identity online using data gathered from mobile phones. The majority of people wouldn’t share those with anyone else, so their mobile phone becomes a really strong representation of their identity in the digital world. Today, opening our mobile phones with our thumbprint or via facial recognition feels very normal (that’s already part of our digital identity). Something similar happens with voice biometrics, IP addresses and device information, sources for identity data that are gaining prevalence in the digital-first world. All this identity data that is generated in the background starts to add up and creates uniqueness, helping people get recognized digitally. Related Content The race to Digital Identification, a DataTalk with Eric Haller What is digital identity and why should we care  What are consumers the most concerned about when it comes to digital iterations

As part of our Women Making Waves in Technology special podcast series, I had the chance to talk to Jayme Beck, Vice President of Global Marketing & Customer Experience at Experian Decision Analytics about how innovation translates into tech marketing and customer experience. In this 20-minute conversation, Jayme shares her passion for connecting with people and reminds us of the importance of looking for that human element, as behind every screen, behind every algorithm, decision, email, there is a person. Innovation comes in all sizes, also in marketing This podcast also explores topics such as managing innovation from a marketing standpoint, especially nowadays that everyone is talking about omni-channel experiences. In this day and age, it’s all about embracing a growth mindset and bearing in mind that innovation comes in all sizes. The pandemic has resurfaced the importance of communication, and while communications practice and discipline isn’t something that comes naturally to everybody, this is a great time for communications and marketing professionals to grip their place in the innovation cycle. For Jayme, this starts with letting go of the idea of perfection and overthinking. Adopting a ‘try & learn’ mindset is the next step. “I like to revisit a talk from this baseball coach I know, where he encourages everyone to let go of the idea of perfect and put one foot in front of the other, and see where that takes us to.” Discover other stories from the Women Making Waves in Tech podcast series Access all episodes of Insights in Action on Soundcloud, Spotify, Google Podcasts

Digital interactions have been the norm for some time, and have recently accelerated due to the pandemic, surfacing digital identity as a fundamental focal point for businesses. A shift from analog identity to digital is happening organically due to digital demand, but the necessity of safeguards from businesses and policymakers, and an understanding of what it means to have a digital identity from consumers, make this a complex and increasingly important topic. "Sometimes it's easier to think about analog and how that might relate to digital. Our analog life, our identity, is pretty straightforward. We're all used to showing a driver's license, an identity card, or a passport. It's been authenticated by somebody that we trust like the government. It's something that we use as an entry point to open up our first checking account at the bank or when we go to the doctor's office and we provide insurance and our identity. In the digital world, it's very similar. If you think about that provenance or that trusted source of identity, it often starts with some things that we don't even see. That digital footprint is growing and expanding into a lot of pieces of information that most of us aren't familiar with or think about. That data becomes the gateway to how we access goods and services in a digital environment." Eric Haller In this CXO Talk, Michael Krigsman asks Eric Haller, Executive Vice President and Group Head of Experian DataLabs, what it means to have a digital identity and why it's important. They take a look at the following topics: What is digital identity? How do you establish digital identity? How to improve digital identity? How can we protect against AI-generated deep fakes? What is the intersection of digital identity and GPT-3? Blockchain and digital identity Societal implications of digital identity Advice to businesses and policymakers on digital identity