Tag: AI

Online fraud has increased at unprecedented levels over the past two and half years, with numerous reports coming from all corners of the world to confirm that. From benefits and unemployment fraud to authorised push payment fraud, and more advanced scams such as synthetic identity fraud and deepfake fraud, cybercrime has been on the rise. Understandably, the increase in criminal activity has had a significant impact on financial services businesses, and it is little wonder that this has been reflected in our recent study: • 48% of businesses reported that fraud is a high concern, and 90% reported fraud as a mid-to-high concern • 70% of businesses said their concern about fraud has increased since last year • 80% of businesses said that fraud is often or always discussed within their organisations High levels of fraud have also raised consumer concern, and their expectations of the protection businesses should offer them. Nearly three-quarters of consumers said that they expect businesses to take the necessary security steps to protect them online. However, only 23% of respondents were very confident that companies were taking steps to secure them online. Businesses need to take additional steps to meet consumer demand, while also protecting their reputation and revenue streams. Businesses are investing in fraud prevention, so why isn’t it working? As a result of the rise in fraud during the pandemic, there has been an increase in spending related to fraud prevention tools and technology, with 89% of businesses surveyed in our latest research indicating that investment in fraud detection software is important to them. However, there is a risk that institutions could take a siloed approach, and funds could be spent on point solutions that solve one or two problems without adding the needed flexibility to fight multiple attack patterns. This gives fraudsters the opportunity to exploit these gaps. Orchestration and automation drive fraudsters away Criminals constantly evolve. They are not new to technology and have multiple attack patterns that they can rely on. They also share information between themselves at a higher rate and pace when compared with financial institutions, banks, and merchants. Fraudsters can learn how to bypass one or two features in an organisation’s fraud prevention strategy if they recognise weak spots or a vulnerability that they can take advantage of. However, when multiple fraud prevention tools and capabilities work harmoniously against them, the chances are higher that they will eventually be blocked or forced to move to a weaker place where they can exploit another system. Synchronizing multiple solutions together is the key to excellent fraud orchestration Fraud orchestration platforms give businesses the chance to layer multiple solutions together. However, taking a layered approach is not only about piling multiple point solutions but also about synchronizing them to achieve the best output possible. Every solution looks at different signals and has its own way of scoring the events, which is why they need to be governed into a workflow to achieve the desired results. This means that institutions can control and optimize the order in which various solutions or capabilities are called, as the output of one solution could result in a different check for a subsequent one or even the need to trigger another solution altogether. It also gives companies the ability to preserve their user journeys while answering different risks presented to them. Some businesses are seeking to build trust with customers but want to stay invisible to remove friction from their digital customer experience. This is where capabilities such as device intelligence, behavioural biometrics, or fraud data sharing could be added as an additional layer in the fraud prevention strategy. Those additional solutions may only be called 30 per cent of the time when there is a real need for an additional check. Excellent orchestration means that organisations can rely on multiple solutions while only calling the services they need, exactly when they need them. Building trust through a secure but convenient customer experience. Machine Learning should be the final layer to rule them all The results from our research revealed the top initiatives that businesses are leveraging to improve the digital customer journey with the top two being: • Improving customer decisioning with AI • New AI models to improve decisioning While our April 2022 Global Insight Report showed that consumers are becoming more comfortable with AI, with 59% saying they trust organisations that use AI. Fraud orchestration platforms allow companies to deploy unified decisioning by leveraging machine learning (ML) on top of multiple fraud prevention tools. This means they can rely on one cohesive output instead of looking at separate, sometimes contradictory results across various platforms and making subjective decisions. ML can also offer explainability by pointing out the attributes that contributed the most to a particular suggestion or decision. These could be attributes coming from a few different tools instead of one. This also means that operational teams, like fraud investigators, have a single view of activity, resulting in operational efficiency - removing the need to log in to different tools and look at multiple screens, views, and scores, while also enabling faster decisions. Stay in the know with our latest research and insights:

Did you miss these March business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Experian partners with Black Opal to bring credit options to US immigrants PYMNTS.com covers the partnership between Experian and Black Opal to boost consumer credit access to immigrants in the US. Using Crosscore and PowerCurve, Block Opal will be able to make real-time credit decisions while also managing using the platform’s tools to better manage identity verification and fraud prevention. Fraud shifting as online activity increases In this CUNA article, Brock Fritz explores Experian's Future of Fraud Index for 2022, with Experian's Chief Innovation Officer, Kathleen Peters, offering up solutions for businesses looking to mitigate the effects of more online fraud. How AI is modernizing online transactions Donna DePasquale, EVP of Global Decisioning Software, writes in Dataversity about the importance of automation and insights as objectives driving modernization through AI for businesses, and what they should focus on in order to increase customer acquisition. Online payment fraud Online payment fraud will reach 206 Billion by 2025. David Britton, Experian VP Industry Solutions Global Identity and Fraud is interviewed by David Cogan, host of the Heroes Show and founder of Eliances entrepreneur community. 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. Lenders now find themselves in an increasingly competitive market with new players emerging on the scene. More companies now have access to advanced analytics and automation capabilities, and this is helping businesses improve the accuracy and inclusivity of consumer lending decisions – a giant step toward achieving their growth ambitions. Our recent research shows that one of the top priorities for businesses has been to invest in new artificial intelligence and machine learning models for smarter customer decisions. But how effective is building new AI models without considering the data? What is data-centric AI? Building AI models on fixed data has already become an outdated approach. But by coupling data with the best model, better outcomes can be achieved. The concept of data-centric AI was coined by leading thinker in the AI space, Andrew Ng. Ng believed that models in production are only as good as the point-in-time data used to build them. As businesses continue to receive new data, this data needs to feed back into the model if it’s going to continue delivering the best results. This continuous loop of enriching the model with new data can be applied across use cases. The value of data-centric AI models for acquiring new customers By using the latest available data, rather than from 6-12 months ago or longer when the model was originally developed, data-centric AI models can: • More rapidly account for changes in the economy and consumer finances • Reach under-represented populations and provide greater access to credit • Take advantage of newly available types of information from data providers The value of data-centric AI in existing frameworks More observations AI is often limited by the data that was used to create the model. By using a more fluid open-source alternative, different data sets can be inputted to get more observations based on different characteristics and findings. For example, if a business wants to acquire a new type of customer, traditional AI would require a new model with new data sets to be in order to target this new customer. With data-centric AI, businesses can use an existing model and simply expand the data, thus allowing the model to work far more efficiently and target a new consumer base. It is a shared view that businesses should not build models with just their own data, because those data sources are too limited. At the very least, businesses want to combine data with a peer sample. However, an even better way is to use hybrid data sets in order to get the most observations. Data-centric AI makes that process easy without the need to create different models to see different outcomes. Up-to-date data The world is in a state of flux—populations change, people change. This means that the data pools AI models draw on may be compromised, no longer relevant, or have new meaning over time. It’s important to keep AI data sets recent and up to date, and not assume that the models used two years ago still apply today. For AI models to operate efficiently they need current, relevant data. Having a data-centric approach and sweeping through collected observations is essential for any business relying on their AI solutions. Businesses must have processes to understand and test their data to be sure the values are still adding up to what they should be. Being disciplined about data hygiene, all the way back to the source, is a necessity. Enriched and expanded data With model-centric AI, businesses are limited by the data they start with. Data-centric AI makes it possible to expand on the current customer base, which already includes data on customer attributes, with new potential customers that might mimic characteristics of a business's current base. Expanded data can also play a role with financial inclusion and credit worthiness. Having a low credit score does not necessarily mean the consumer is a bad risk or that they shouldn't be allowed access to credit—sometimes, it could mean there is simply a lack of data. Expanding data to include varied sources and adding it to current models without changing their structure, enables businesses to provide credit for individuals who may not have originally been accepted. This new approach in AI is creating solutions that are far more inclusive than previously possible. Data has massively expanded and is constantly evolving. By using data combined with advanced analytics, such as AI, there will be more sophistication in the observations that come from the data. This will allow businesses to better decide what data they choose to rely on while ensuring accuracy. By using expanded data sources, the outcomes of models are changed, leading to more inclusive models better fit for decision making and improving performance. "Models in production are only as good as the point-in-time data used to build them." Andrew Ng Infographic: Why data-centric AI leads to more accurate and inclusive decisions Stay in the know with our latest research and insights:

During the week of International Womens' Day, we shine a spotlight on the women thought leaders across Global Decision Analytics.  In this Juniper Research interview, Kathleen Maley, VP of Analytics Product Management talks about the current state of data analytics, with the backdrop of Juniper Research's Future of Digital Awards and its recognition of AIS. Watch the video to discover: Current problems with data analytics Broad nature of activities of what is now defined as analytics Model development, model scoring, model regulatory control, model risk management and model deployment Where is data coming from - is it clean and do we understand it? Importance of humans in the development of algorithms Lack of data - where do we need to close gaps? How does looking at the past help with looking to the future - the importance of current/real-time data The expense of maintenance - tech stack - there are now alternatives Democratization of data - expanding credit access by using non-traditional sources of data Talent shortage of data scientists - low-code and no-code Extracting data value for businesses when data is ever-expanding Stay in the know with our latest research and insights:

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

Did you miss these January business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Next-gen AI analytic apps in credit In this Lendit Fintech webinar about the future of AI analytics in credit, Srikanth Geedipalli, SVP of Global analytics and AI, joins a panel of experts to explain how Experian deals with delinquencies and retains customers using a proactive approach. A successful DevOps strategy is more than just technology 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. 7 payments trends for 2022 as innovation climbs David Bernard, SVP Global Decision Analytics, talks to Payments Dive about cross-border services, BNPL and cybersecurity tools, and how there will be no shortage of innovation and competition in the payments industry as businesses and their regulators shape new digital tools. Deepfakes – the good, the Bad, and the ugly In this Forbes article, Eric Haller, VP & General Manager, Identity, Fraud & DataLabs, talks about how the creation of deepfakes can be thought of as the latest development in the ongoing battle between business and counterfeiting. 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:

Did you miss these December business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. How are companies responding to consumer behavior? Nasdaq Trade Talk's Jill Maladrino talks to Steve Wagner, Global Managing Director of Decision Analytics, about the increase in online activity over the course of the pandemic, how inflation can impact brand loyalty, and why businesses need to respond to consumer demand with better customer experience and fraud prevention. Q&A: Why the increased use of digital transactions is here to stay David Bernard, SVP of Strategy, Marketing and Digital, talks to Digital Journal about how businesses should be approaching the increase in digital transactions using advanced analytics and decisioning technologies to improve the digital customer experience and grow their businesses. How criminals are using synthetic identities for fraud Dark Reading's The Edge talks to David Britton, VP of Industry Solutions, about why businesses must improve their fraud detection and prevention protocols to detect synthetic identities and ensure that they are protecting their consumers' personal information. Latest retail trends: AI is on the up, consumer loyalty is heading down Digital Journal looks at Experian's latest research that uncovers how businesses are incorporating machine learning and artificial intelligence into everyday operations and investments in response to an upward trend in online activity and a downward trend in customer loyalty. Stay in the know with our latest research and insights:

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

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.