Historically, identity graphs were used to drive marketing for businesses, allowing marketers to understand and target their audience with relevant content. But in recent years, identity graphs have emerged as a useful tactic to help businesses detect and prevent fraud due to the magnitude of data they collate and analyse. As fraud continues to evolve, businesses need to get creative and resourceful when it comes to fighting online fraud to keep pace with the fraudsters. Identity graphs allow businesses to map multiple data points to create individual customer profiles while highlighting connections across all customer profiles in their current portfolio. Download our latest Global Identity and Fraud Report How do identity graphs work? Identity graphs are databases that create a consolidated unique customer profile. Information is collected from different platforms, both online and offline, and merged into a single view. This process of gathering and merging information is known as identity resolution. The primary goal of identity resolution is to create a real-time, holistic view of an individual. How identity graphs can be used across different types of fraud Account Takeover: Identity graphs make it simple to tell when the same individual is logging into multiple accounts or when all data associated with a particular user account suddenly changes. Identity graphs can screen customer accounts that are suspected of having been compromised by takeover attacks. Credit Card Fraud: Identity graphs collate data from both online and offline means. Having access to this data can be hugely beneficial in preventing counterfeit credit card transactions. Identity graphs will map common links between cardholders and data such as point of sale locations or historic transactional behaviour. Understanding these behaviours means identity graphs can uncover suspicious transactions, helping to expose compromised credit cards and prevent fraud. Referral Fraud: Many businesses offer reward incentives to their customers to help drive engagement. While good intended, businesses that offer referral rewards may expose vulnerabilities to referral fraud. In referral fraud attacks, fraudsters will take advantage of the offered rewards without ever meeting the conditional requirements. Identity graphs make it possible to uncover referral fraud, for example, highlighting multiple referrals from one household. Gaming Fraud: Fraudsters will make multiple online gambling accounts to take advantage of any sign-up offers the vendor may offer. Likewise, fraudsters will often use multiple accounts to bet against themselves, ensuring they always win. Identity graphs can help track and highlight these instances flagging relationships between the multiple accounts. Synthetic ID Theft: Recently fraudsters have been turning to synthetic IDs to commit fraud, as opposed to sourcing legitimate IDs as per traditional identity theft. Fraudsters will combine personal data from multiple victims to create a new, non-existent identity that they can then use during online transactions. These new personas, and the inconsistencies they contain, can be easier spotted when identity graphs are applied. Anti-Money Laundering (AML): When fraudsters illegally obtain funds, they will recruit individuals to pass these funds from one source to another, making their origin hard to trace. Identity graphs can help organisations track financial transactions, providing a clear image of the journey the funds have taken, all the way from origin to destination. Innovative ways identity graphs are helping to detect and prevent fraud Cross-device Identification: Identifying customers through PII and digital data, through both deterministic and probabilistic matching, allows organisations to better identify the same user across multiple devices. This allows them to be treated as a single entity, highlighting suspicious anomalies in behaviours. Real-time: Our digital world is notoriously fast paced, and not known for standing still. Identity graphs operate by collating data and updating the associated customer profiles in real-time. Ensuring we always make decisions on accurate and up-to-date customer information is crucial for both regulatory and risk reasons. Fraud Rings: Identity graphs collect and link a vast magnitude of data. Examining each data point in tabular form can be a laborious task for investigators and spotting suspicious connections can prove difficult. When connections are presented within a graph, they can easily present powerful insights that can uncover fraud rings that could otherwise be missed. Stay in the know with our latest research and insights:
Did you miss these November business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Online retailers work to turn pandemic buyers into loyal customers Digital Commerce 360 cites that only 73% of U.S. consumers say they're loyal to the brands they shopped with before the pandemic, down from 79% last year, according to Experian's latest wave of Global Insights research. So what does this mean for businesses? Donna DePasquale on Using Tech to Modernize Financial Services In this podcast, Donna DePasquale, EVP Global Decisioning Software, talks to eWeek about how the use of data analytics has evolved in the financial sector, the challenges involved, where we are at now, and what the future might look like. Was that for real? Delving into the deepfake reality Digital Journal spoke to David Britton, VP of Industry Solutions, on deepfake learning benefits and risks, focusing on how bad actors can deceive or manipulate consumers and businesses - and what they can both do to mitigate the dangers. Experian Finds 25 Percent Increase in Online Activity Since Covid-19 Business Information Industry Association looks at Experian's latest research and why the pandemic-accelerated increase in digital transactions is here to stay and how businesses must continue to transform their operations as they head into 2022. Stay in the know with our latest research and insights:
What increasing expectations of the digital customer experience mean for your business and technology investment Economic recovery and waning customer loyalty are creating new opportunities 59% of businesses globally say they’re mostly or completely recovered from the pandemic 61% of customers engaging with the same companies they did a year ago, down 6% in twelve months Data, analytics and decisioning technologies help provide customers with a secure and convenient digital experience Consumers are prioritising security, privacy and convenience when engaging online 75% of consumers feel the most secure using physical biometrics Scalable software solutions give companies of all sizes the ability to better manage risk and digitally transform the customer experience 50% of businesses are exploring new data sources 7 in 10 businesses say they’re frequently discussing the use of advanced analytics and AI, to better determine consumer credit risk and collections 76% of businesses are improving or rebuilding their analytics models “Dwindling customer loyalty along with heightened customer expectations and increased competition could mean potential revenue loss or gain. Businesses must find integrated credit and fraud solutions to improve digital engagement and customer acquisition.” Steve Wagner, Global Managing Director, Decision Analytics, Experian We surveyed 12,000 consumers and 3,600 businesses across 10 countries as part of a longitudinal study that started in June 2020 Read the full report to find out where businesses are focusing their investments
In this eSpeak podcast, eWeek’s James Maguire talks to Donna DePasquale, EVP of Global Decisioning Software, about the use of technology in financial services, and how it can satisfy the ever-increasing demand for real-time intelligence. Listen to the podcast to hear Donna DePasquale discussing: Data and decisioning challenges involved with helping financial institutions reduce risk Helping lenders make better decisions about their customers by providing simplified and streamlined services. Consumers have more choice than they’ve ever had before when it comes to credit, this, along with high expectations for their online experience, is driving businesses to invest in digital transformation and automation solutions. Growing diversity among populations in terms of spending means financial services are working to provide more personalised, real-time, meaningful experiences. Consumers want secure and convenient experiences online without compromise. Evolution of data technology Businesses can now deploy new types of analytics and new types of data services in order to serve customers. Digital transformation allows automation and insights to work together improving credit risk analysis and assessment, smoothing out the customer journey throughout the lifecycle. Access to new data types and advanced analytics. AI and analytics is not a static process, it’s a dynamic process. AI and machine learning allow for constant updates and enhancements to strategy. Future of data analytics and the credit markets Financial inclusion is a very important to the future of data analytics, especially when thinking about those growing economies around the world. We believe that all consumers deserve fair and affordable access to credit, and using alternative data sources to improve credit profiles will directly impact this. Customer experience and credit risk analysis should coexist seamlessly – asking clients to do less without sacrificing the security, convenience, relevance, and privacy of consumer experiences. Stay in the know with our latest research and insights:
It’s no secret that the pandemic created a level of economic uncertainty that makes it incredibly tricky for lenders to understand their risk on a customer-by-customer basis, and therefore its impact on decision management. It’s no wonder they’re uncertain; the customers themselves are just as unsure. According to the Global Decisioning Report 2021, one out of every three consumers worldwide are still concerned about their finances even as the second anniversary of the COVID-19 outbreak approaches. While some consumers were able to easily work from home during the pandemic, others suffered job losses, cut wages, or increased expenses due to lost childcare or having to care for a loved one. As the impact of the pandemic continues to be felt – especially as government support programs begin to conclude – financial institutions will have to figure out how to navigate the uneven recovery. By leveraging advanced data and analytics, financial institutions can better understand their risk and improve their decision management. In turn, many financial institutions are creating predictive models to target their best customers and reduce exposure to unnecessary risk. However, a model is not always the end-all, be-all solution for reducing risk. Here’s why: a model requires of the right data in order to work effectively. If there isn't a data sample over a long enough time frame, the risk of creating blind spots that can leave businesses on the hook for unexpected losses can be high. Also, there will always be the need for a strategy even with a custom model. A global financial institution likely has more than enough data to create accurate, powerful custom models. However, financial institutions like local or regional credit unions or fintechs simply don't have enough customer data points to power a model. In addition, many outsourced model developers lack the specific financial industry domain expertise required to tweak their models in a way that accounts for the nuances of regulations and credit data. Finally, the pandemic continues to change the economic picture for customers by the minute, which can make a model designed for today outdated tomorrow. When a strategy makes more sense For many financial institutions, it can make more sense to focus on building out a decision management strategy instead of leveraging custom models. While a model can provide a score, it can’t tell you what to do with it. By focusing on a decision management strategy, you can leverage other information and attributes about different customer segments to inform actions and decisions. In an ideal world, of course, the choice wouldn't exist between a model and a strategy. Each has an important role to play, and each makes the work of the other more effective. However, strategy is often the smart place to start when beginning an analytics journey. The benefits of starting with strategy include: Adaptability: A strategy is much easier to change than a model. While models often have rigorous governance standards, a strategy can be adapted with relatively little compliance impact. This helps businesses adapt to changes in goals, vision, or shifts in the marketplace in a bid to attract the ideal customer. In a world that changes by the day, the ability to adjust risk tolerance on the fly is crucial. Speed: A custom model can take weeks or even months to build, test, deploy, and optimize. As a result, this can put businesses behind in analytics transformation while leaving them unnecessarily exposed to risk. On the other hand, a strategy can be developed and deployed in a relatively rapid manner, and then adapted on an ongoing basis to reflect the realities on the ground. Consistency: A strategy helps drive improvement across operations by allowing team members to ‘sing from the same songbook,’. In smaller organizations where work is still done manually by a handful of people, a strategy allows for automated processes like underwriting so businesses can scale decisioning. Strategy or model? Three questions to consider Do you need a strategy or a model? Again, in an ideal world the answer is ‘both’ due to the unique role each plays, but in the real world it depends on the institution. Here are three questions to ask in order to determine where to focus time and resources: “How different are the people I am lending to than the national average?” If the institution is lending to segments that look just like everyone else, leveraging existing third-party data sources will allow the use of generic models. In this case, the focus would be on using those generic models to power the strategy. However, for businesses that serve a niche population, a national average might skew results; in this case, it may make more sense to build a custom model. “What is my sample size?” Take a close look at the number of applications coming in each month, quarter, or year. In addition, compare it to periods dating back years to understand growth rates. This will indicate the if the data inflow required exists to power a custom model. Don’t forget to analyze how many of those applications eventually become delinquent; because some smaller financial institutions have conservative policies, they may have low delinquency rates. While this is good for the institution’s bottom line, it can make it difficult to build a model that will be able to detect future delinquencies. Therefore, even a large application sample size might not have enough variance to create an accurate custom model. “What are my long-term future goals?” This is the most difficult question to sometimes answer, as many financial institutions remain focused on navigating today’s challenges. As market conditions change, goals naturally adapt. That said, some goals might require custom models in order to effectively achieve the business vision. For example, if the plan is to enter new markets, create new partnerships, or offer new products that are different than what has been done in the past, a custom model could provide a more accurate understanding of potential risk. Our research also shows that nearly half of businesses report that they are dedicating resources to enhancing their analytics, with one-third of businesses planning on rebuilding their models from scratch. Rapid changes in consumer needs and desires means there’s less confidence in consumer risk management analytics models that are based on yesterday’s customer understanding. By focusing on a decisioning strategy, businesses can be empowered to effectively leverage analytics today to take action while creating a steppingstone for more sophisticated model-based analytics tomorrow. Stay in the know with our latest research and insights:
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.
The pandemic accelerated the number of digital interactions in finance. Typical methods of managing finances, connecting with lenders, and buying goods and services were much harder due to lockdown measures, so consumers went digital, including large numbers of non-digital natives. As the demand for online banking and services has intensified – moving from a necessity to a preference for many - pressure on businesses is twofold. They must rapidly build new and better models to onboard customers and create a more dynamic customer journey. In many markets, doing so is the biggest competitive differentiator right now. Creating a dynamic digital journey and understanding the customer With Millennial customers becoming a bigger influence in the space, organizations were always going to have to plan for a slicker and quicker digital customer experience to keep up with expectations. The pandemic simply accelerated this, forcing businesses to rapidly react. In fact, although 9 in 10 businesses have a digital customer journey strategy, 49% of those businesses only put this in place as Covid-19 began according to research in our Global Decisioning Report 2021. This did help them improve in some areas, including access to quicker customer service responses online. But without the right technology in place, it is not surprising that 55% of customers surveyed said they expect more from their digital experiences. Such a rapid shift has exposed weaknesses around agility, leaving traditional institutions trailing Fintech competitors further down the digital transformation road. However, whilst Fintechs have the benefits of agility, traditional, established lenders have large amounts of customer data from which they can target and tailor existing customer journeys more effectively. Improve the digital onboarding process Optimizing the digital experience for new customers from the beginning encourages usage and, ultimately, loyalty. A stress-free and fast onboarding process is an expectation for the younger generation but can also capture the ‘new to digital’ group migrating online. Bio-metric recognition technology, instant document verification, and auto-filling customer data are far more appealing than entering hundreds of data points, and can boost efficiency and reduce friction. The problem is businesses rightly want to make sure they can remove any bad actors to reduce risk and prevent fraud. The key is doing so without disrupting the genuine, low risk customers. Building better models to onboard customers Covid continues to shift population demographics due to factors such as job losses, furlough schemes and migration of workers to alternative sectors. There is also the realization of pent-up demand for property and vehicles, in particular - among those fortunate enough to be less impacted - such as those able to save more as they work from home. This has led to a change in the demand for finance with a need to tailor experiences to specific customer requirements. As the number of credit needs grow, lenders must have a structure in place that allows them to scale and handle the increased volume. New models must also be introduced to allow organizations to access extensive data insights and ensure they are reflecting the ‘new normal’. As businesses move away from sampling towards models that are based on full populations there must be a marriage of technology with data. Data is ultimately captured for the benefit of the lenders, helping them to gauge risk and tackle fraud. But a blended, multi-layered approach in which customers are only asked for the information specific to their individual circumstances – at the appropriate time – can provide a positive and tailored onboarding process. Having solutions in place that combine risk-based authentication, identity proofing, credit risk decisioning and fraud detection into a single platform ensures all checks can be carried out in one place with minimal disruption to the onboarding journey. Putting businesses in first place Online experience and credit and fraud risk management need to be more closely entwined. As the demand for a simple and fast experience intensifies, a digital-first approach that puts businesses ahead of the game must involve embracing the right technology that supports the entire customer journey. Download a copy of the eBook here. Stay in the know with our latest research and insights:
Innovation in fraud detection and prevention is key in today's ever-evolving digital landscape. Juniper Research, a research firm that specializes in identifying and appraising new high growth market sectors, recognized organizations and platforms that drive innovation and growth in the banking, fraud and security, and retail and payments through their Future Digital Awards. The firm awarded Experian as the Platinum Winner for Fraud Detection and Prevention Platform (CrossCore™) and the Gold Winner for the Artificial Intelligence Platform (Ascend Intelligence Services™). Keeping more consumers safe According to this year's Global Identity and Fraud Report, more than half of businesses will continue to invest in fraud prevention solutions over the coming year to combat several types of fraud: new account opening fraud, account takeover fraud, and other types of identity fraud, with at least 57 percent of businesses report higher losses from account opening and account takeover fraud. Identity-related fraud has evolved towards more automation, in the form of scripted attacks and bot attacks, as well as more sophisticated phishing attacks. The speed at which fraudsters adapt to new technology and behavior has always been a problem, and with sudden and unpredictable change, reacting quickly with new fraud strategies has never been more important for businesses looking for ways to safeguard digital transactions. CrossCore™, launched in 2016, is used globally to connect identity and fraud capabilities. The system combines robust risk-based authentication, identity proofing and fraud detection into a single, state-of-the-art cloud platform to make real-time risk decisions throughout the customer lifecycle. Typically, businesses need to move through validation, contract and then integration in order to combat fraud – making for a long, tedious and expensive process. CrossCore pre-qualifies fraud and intelligence services so that businesses can choose how they want their transactions to be processed and which fraud and identity services they want to use. The platform is designed to help businesses instantly identify good customers, catch fraud and enhance the customer experience. Juniper Research’s Future Digital Awards for Fintech & Payments recognized Experian’s CrossCore as the Platinum Winner for the Fraud Detection and Prevention Platform. The recognition comes at a time CrossCore and AIS platforms are helping businesses all over the world combat fraud and maintain a safe digital experience for their customers. This recognition underscores the commitment to using advanced capabilities in data, analytics and technology to bring innovative fraud solutions to the market, enabling businesses outpace fraud while making it safer for consumers to engage with them digitally. Providing better digital service The acceleration to digital has caused financial institutions to quickly evolve and improve their processes including reducing time for loan approvals, access to more financial produce and new innovative payment methods. What is most important is that businesses focus on more on advanced technologies for lending. Launched in January 2021, AIS provides financial institutions and other lenders with AI solutions delivered rapidly and digitally, resulting in better business outcomes at every stage of the customer lifecycle. AIS is a one-stop-shop of building, documenting, deploying, monitoring, and retraining analytics, all on the same AI platform. The system allows businesses to process data with extreme speed and efficiency in a streamlined approach to detect and monitor identity models and strategies. Juniper Research’s Future Digital Awards for FinTech & Payments also recognized Ascend Intelligence Services™ (AIS) as the Gold Winner for the AI Platform. By creating accessible AI solutions for our business clients, people engage with their favorite financial brands in a more meaningful way across the customer lifecycle, truly democratizing advanced analytics. Learn more about Ascend Intelligence Services and CrossCore. Stay in the know with our latest research and insights:
Why digital acceleration has created more opportunities for deepfake fraud tactics like voice cloning and what businesses can do about it Digital acceleration has placed information and services in the hands of the masses, connecting individuals on a global level like never-before, and in turn making them increasingly dependent on devices in their daily lives. The argument for technology as an equalizer in society is a strong one. Most people have a voice and a platform, producing millions of virtual interactions and recordings every day. But in this digital world of relative anonymity, it is difficult to know who is really on the other side of the connection. This uncertainty gives fraudsters an opening to threaten both businesses and consumers directly, especially in the realm of deepfakes. What is a deepfake? Deepfakes are artificially created images, video and audio designed to emulate real human characteristics. Deepfakes use a form of artificial intelligence (AI) called deep learning. A deep learning algorithm can teach itself how to solve problems using large sets of data, swapping out voices and faces where they appear in audio and video. This technology can deliver extraordinary outcomes across accessibility, criminal forensics, and entertainment, but it also allows a way in for cybercriminals that hasn’t existed until now. Deepfake fraud tactics A principal tactic among deepfake fraud is voice cloning – the practice of taking sample snippets of recorded speech from a person and then leveraging AI to understand speech patterns from those samples. Based on those learnings, the modeler can then use AI to apply the cloned voice to new contexts, generating speech that was never spoken by the actual voice owner. For businesses, deepfake tactics such as voice cloning means access to points of vulnerability in authentication processes that can put organizations at risk. Fraudsters may successfully bypass biometric systems to access areas that would otherwise be restricted. For government leaders, it can mean the proliferation of misinformation – a growing area of concern with huge repercussions. For consumers, the risk of falling victim to scams involving access to personal information or funds is particularly high when it comes to voice cloning. How to prevent deepfake fraud 1. Vigilance: Stay on top of sensitive personal information that could be targeted. Fraudsters are always at work, relentlessly seeking out opportunities to take advantage of any loophole or weak spot. Pay close attention to suspicious voice messages or calls that may sound like someone familiar yet feel slightly off. In an era of remote work, it is important to question interactions that can impact business vulnerabilities – could it be a phishing or complex social engineering scam? 2. Machine learning and advanced analytics: Deepfake fraud is an emerging threat, which leverages the development and evolution of the technology that fuels it. The flip side is that businesses can in fact use the same technology against the fraudsters, fighting fire with fire by deploying deepfake detection and analysis. 3. Layered fraud prevention strategy: Leveraging machine learning and advanced analytics to fight deepfake fraud can only be effective within a layered strategy of defense, and most importantly, at the first line of defense. Ensuring that the only people accessing the points of vulnerability are genuine means using identification checks such as verification, device ID and intelligence, behavioral analytics, and document verification simultaneously to counter how fraudsters may deploy or distribute deepfakes within the ecosystem. As with many types of fraud, staying one step ahead of the fraudsters is critical. The technology and the tactics continually evolve, which may make the countermeasures on the table right now obsolete, however the fundamentals of sound risk management, with the right layered approach, and a flexible and dynamic solution set, can mitigate these emerging threats. Stay in the know with our latest research and insights:
Fraud threats continue to rise across the globe as consumers are spending record amounts of time online due to the pandemic. At the same time, emerging threats of fraud are growing, as fraudsters are taking advantage of the globally shifting economic conditions. Fraud prevention remains a top concern for both consumers and businesses alike. Anticipating future fraud risk is critical and companies are adopting more complex technology systems to ensure consumers’ financial safety. To provide a safe and convenient experience, businesses need to take a customer-first approach when evaluating the latest technology and solutions available to them. To ensure they are providing secure online experiences, businesses are turning to verification strategies using data technology and other detection methods. In fact, according to this year’s Global Identity and Fraud Report, customer recognition security strategies have become the new norm for businesses with 82 percent of companies saying they now have one in place, a 26 percent increase since the start of the pandemic. An independent research firm headquartered in Germany, KuppingerCole Analysts, released a report, Leadership Compass: Fraud Reduction Intelligence Platforms, that provides an overview of the market segment, vendor service functionality, prevention measures and innovative solutions to fraud. The report cites Experian as an overall leader, product leader, innovation leader, market leader and technology leader in fraud reduction intelligence platforms. Experian is also credited for taking a client-oriented upgrade approach and delivering other cutting-edge features while maintaining compatibility with our older platform releases. We also scored a strong positive for interoperability, usability, deployment, innovativeness, market position, financial strength and ecosystem; and a positive in security and functionality. We pride ourselves in our digital identity protection services and consumer safety, taking proactive approaches to fraud prevention and providing businesses with the necessary tools to identify risks of fraud. The report discusses fraud prevention measures and innovative solutions to fraud. According to the report, cybercrime costs will reach $10.5 trillion by 2025. The report evaluated 15 different data security and fraud prevention platforms and ranked their products, innovation, market positioning and technology in their report. All of Experian’s fraud detection and prevention services are available through our CrossCore® partner ecosystem. By combining advanced analytics, rich data assets, identity insights and fraud prevention capabilities, businesses can connect any new or existing tools and systems in one place, whether it be Experian’s, Experian’s partners or its own. With its built-in strategy design and enhanced workflow, fraud and compliance teams have more control to quickly adjust strategies based on evolving threats and business needs, which helps to improve efficiency and reduce operational costs. Learn more about the CrossCore platform. Stay in the know with our latest research and insights:
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