Innovation

The pandemic may have accelerated digital transformation across the world of financial services , but behind the scenes, banks and lenders still face a significant tech debt, and many organizations are committed to continuing the innovation. That's for good reason. Today's consumers increasingly expect a digital-first customer experience. The days of visiting a local bank branch to access financial services and products are fading away. Fintechs have risen to the occasion, transforming the market and meeting the growing digital demand. For traditional banks and lenders, waiting to innovate is no longer an option—it's a must to remain competitive. So what comes next? Here's a look at the technology trends that stand to impact and transform financial services as we advance. 1. The rapid rise of low-code/no-code solutions According to a recent survey from TechRepublic1, nearly half of companies are already using low-code/no-code solutions (LCNC). The same report also notes that among companies not using LCNC solutions, one in five plans to begin within the year. The driving force behind this trend is the global shortage of digital skills, from software development to data analytics to information security. The pool of technical talent has long been smaller than the demand, and the Great Resignation has only exacerbated the problem. For instance, 75% of software developers2 report they're currently looking for other jobs. Amidst this ongoing talent shortage, there's another stressor—the need to deploy technology products to market faster and faster. LCNC solutions answer these challenges by making doing so easier and quicker. The technology democratizes software development, allowing business users—or citizen developers—in different functions to design and deploy applications. With the skills gap likely to continue, the interest in LCNC solutions will too. LCNC solutions enable financial institutions to keep pace with technology changes and meet the digital demand, even with limited technical resources. 2. Leveraging data will require adding value—and engendering trust Financial service organizations have used advanced data analytics to provide consumers with more personalized products. And consumers have been on board as long as they see the benefit. For example, a 2021 consumer survey by Experian showed that 42% of consumers would share personal data, and 56% would share contact information, if it improves their experience. However, this research speaks to growing tension between consumers and financial service providers. The first want more personalized services, but they are also more selective about which companies they share data with. Consider a recent McKinsey study that revealed that 44% of consumers don't fully trust digital services3. As we advance, organizations that want to build and keep consumer trust will need to be thoughtful about the data they ask for and increasingly transparent about how they plan to use it. 3. Doubling down on AI but looking for ROI in the process AI has proven helpful in multiple ways, from powering recommendation engines and chatbots within the retail world to improving fraud analysis and prevention in the banking industry. But there's still so much more organizations can do, especially with the AI they already have. Financial service and fintech companies have funneled massive resources into AI solutions. However, only 20% of AI models4 are ever used in widespread deployment. What’s more, the current average return on AI investments hovers around 1%. This year, expect to see more organizations examining the ROI of AI-powered technology and looking to get more from the investments they've made. Technology partners can help by identifying additional opportunities for AI models to drive customer engagement, validate credit scoring, and protect businesses against fraud. 4. Banking-as-a-Service will yield even more choices and more competition There have long been high barriers that protect traditional financial service organizations from much new competition. But the advent of open APIs and Banking-as-a-Service (BaaS) is knocking these barriers down, yielding a considerable influx of startups that provide banking-like services. And this wave of new fintech has captured consumer interest. Consumers have shown that they’re willing to try financial service products from an array of providers; they're not married to sticking with traditional banks. In fact, 27% of global consumers5 have relationships with neobanks, and 40% report using financial apps6 outside of their primary banking app. However, the gold rush towards BaaS will yield a few winners and a lot of losers. The question for the near-term is who will survive in this crowded market. Consumers will also begin to figure out what makes sense in terms of how many financial organizations they want to connect with and when to say enough is enough. 5. Embedded finance is the new black in retail In a similar theme, the influx of embedded finance products into retail experiences continues to gain traction. There's only more to come. Multiple leading retailers, both longstanding and new D2C brands, have incorporated Buy Now Pay Later (BNPL) payment options into their checkout process, and shoppers are rapidly adopting these new payment methods. One-third of consumers report they've used BNPL before7. Though the payment method still lags far behind other forms of credit, awareness of BNPL and other embedded finance solutions is rising, especially among younger consumers. Looking forward, expect to see embedded finance make inroads not only with more retailers but also across other industries such as hospitality or entertainment. These pressing tech trends are reshaping financial services. In the process, they're bringing new solutions to consumers and new opportunities to banks and non-traditional lenders. Organizations that keep pace with these trends will lay the foundation for their next generation of customers as well as the future of their business. More 2022 trends and predictions Stay in the know with our latest research and insights: 1.TechRepublic Survey: Low-code and no-code platform usage increases 2.Stack Overflow: The Great Resignation is here. What does that mean for developers? 3.McKinsey: Are you losing your digital customers? 4.ESI ThoughtLab: Driving ROI through AI 5.EY: How can banks transform for a new generation of customers? 6.Axway: Consumers are starting to sense an open banking transformation 7.PYMNTS.com: No slowdown in sight for surging BNPL as consumers want it, retailers need it

Steve Wagner, Managing Director, Global Decision Analytics on Redesigning the future of consumer lending with data and analytics. Find Steve Wagner's interview in Raconteur's Future of data report to discover what businesses need to do to succeed in an increasingly digital world. “The good thing is that technology and data now allow businesses to put the customer journey at the heart of what they’re doing. With the advanced technologies available today, businesses can access relevant data and deliver on customer expectations in their moment of need. Whether it’s access to a loan or mortgage, or to consolidate debts, a real-time view of the consumer is possible.” Read the full article and find out about: Why the digital customer experience, enabled by both data and analytics, is the new battleground for many industries. Consumers reporting they were online 25% more in 2021 compared to a year before. Online retail sales saw four years of growth in just 12 months during the Covid pandemic. Demand for frictionless journeys through biometrics or multimodal authentication mean customers can see the value exchange in sharing personal data. Behavioural biometrics is the next frontier in tackling fraud and providing a seamless customer journey. Technology is allowing us to analyse far more data sources in real time, providing a comprehensive picture of an individual. Open Banking and the democratisation of data are part of the progressive change around data. Importance of extracting the insight lenders and fintech providers need to implement the best customer journey and make the best decisions. Businesses can make credit-risk decisions using automation and advanced analytics. This will lead to more opportunities for credit and better financial inclusion. Harnessing the power of 'insight everywhere' for better knowledge bases. "The application of advanced analytics, artificial intelligence and machine learning is allowing businesses to tailor their services to an audience of one - at scale." Stay in the know with our latest research and insights:

*Stats from Experian Global Insights Research Read related content The evolution of data: Unlocking the potential of data to transform our world Be more open: Results of the 2021 Open Banking survey - Experian Academy Full text: The future of consumer lending in a digital economy With the advanced technologies available today, businesses can access relevant data and deliver on customer expectations in their moment of need. As more people go online and use digital channels, your business must do more to create a seamless and secure experience. Online activity has increased by 25% globally Online retail sales saw 4 years of growth in 12 months Now online, consumers have high expectations for digital experience without sacrificing security, convenience, and privacy. 64% of consumers have abandoned an online transaction in the past 12 months Consumers, regardless of age, now prefer online banking and payments over in-person transactions The future of credit and fraud risk management is integrating data and technology seamlessly to put the customer at the centre of it all. 74% of businesses are adopting AI (2021), up from 69% the year before Businesses can embrace customer-centricity at scale through: Behavioural biometrics within a layered strategy of defence to make it easier to tackle fraud and maintain a seamless customer journey Open source data so businesses of all sizes can build a view of potential customers, minimise credit risk, and bring more people into mainstream financial services Advanced analytics, AI, and machine learning for real-time underwriting, fraud detection and a truly personalised service “The market is now driven by consumer demand for digital services. Those companies that are able to tailor the digital customer journey – so it reflects the best-in-class consumer experience – are the ones that will win.” – Steve Wagner, Managing Director of Global Decision Analytics

Dr Mark D. Spiteri writes on the Forbes Technology Council about how Experian has embraced DevOps culture to not only improve internal IT processes, but also to reshape the mindset of product development teams. What is DevOps? DevOps is the hybrid of development operations - a combination of software development and IT operations that shortens the product lifecycle and delivers a higher quality operational performance, benefiting the company and customer alike. The shift towards a service-centric culture As tech businesses move away from on-premises, product-centric culture, they are seeking alternatives that enable a service-centric approach. DevOps helps to do this by expanding upon agile and lean software development principles that ultimately lead to a cultural shift towards SaaS. The goal is to improve efficiency and accelerate the distribution of product enhancements, but it's all in the integration of these new ways of working. "It’s not a question if DevOps can help your company upgrade its product cycle; it’s a question of how well you can implement it into your organization." Foundations of DevOps People: Small, autonomous teams with a focus on collaboration and achieving system-orientated outcomes. Processes: End-to-end agile, lean practices for rapid IT service delivery. Technology: Automation tools that make the complete flow and pipeline of development and testing repeatable and reliable. "Improving the DevOps process can make a sea of change across every part of your product’s lifecycle, and what’s most fascinating is that the most important elements do not require a huge IT investment." Read the full article   Stay in the know with our latest research and insights:

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