All posts by Jesse Hoggard

There’s a common saying in the fraud prevention industry: where there’s opportunity, fraudsters are quick to follow. Recent advances in technology are providing ample new opportunities for cybercriminals to exploit. One of the most prevalent techniques being observed today is password spraying. From email to financial and health records, consumers and businesses are being impacted by this pervasive form of fraud. Password spraying attacks often fly under the radar of traditional security measures, presenting a unique and growing threat to businesses and individuals.  What is password spraying?  Also known as credential guessing, password spraying involves an attacker applying a list of commonly used passwords against a list of accounts in order to guess the correct password. When password spraying first emerged, an individual might hand key passwords to try to gain access to a user’s account or a business’s management system.   Credential stuffing is a similar type of fraud attack in which an attacker gains access to a victim’s credentials in one system (e.g., their email, etc.) and then attempts to apply those known credentials via a script/bot to a large number of sites in order to gain access to other sites where the victim might be using the same credentials. Both are brute-force attack vectors that eventually result in account takeover (ATO), compromising sensitive data that is subsequently used to scam, blackmail, or defraud the victim.  As password spraying and other types of fraud evolved, fraud rings would leverage “click farms” or “fraud farms” where hundreds of workers would leverage mobile devices or laptops to try different passwords in order to perpetrate fraud attacks on a larger scale. As technology has advanced, bot attacks fueled by generative AI (Gen AI) have taken the place of humans in the fraud ring. Now, instead of hand-keying passwords into systems, workers at fraud farms are able to deploy hundreds or thousands of bots that can work exponentially faster.  The rise and evolution of bots  Bots are not necessarily new to the digital experience — think of the chatbot on a company’s support page that helps you find an answer more quickly. These automated software applications carry out repetitive instructions mimicking human behavior. While they can be helpful, they can also be leveraged by fraudsters, to automate fraud on a brute-force attack, often going undetected resulting in substantial losses.   Generation 4 bots are the latest evolution of these malicious programs, and they’re notoriously hard to detect. Because of their slow, methodical, and deliberate human-like behavior, they easily bypass network-level controls such as firewalls and popular network-layer security.  Stopping Gen4 bots  For any company with a digital presence or that leverages digital networks as part of doing business, the threat from Gen AI enabled fraud is paramount. The traditional stack for fighting fraud including firewalls, CAPTCHA and block lists are not enough in the face of Gen4 bots. Companies at the forefront of fighting fraud are leveraging behavioral analytics to identify and mitigate Gen AI-powered fraud. And many have turned to industry leader, Neuro ID, which is now part of Experian.  Watch our on-demand webinar: The fraud bot future-shock: How to spot & stop next-gen attacks  Behavioral analytics is a key component of passive and continuous authentication and has become table stakes in the fraud prevention space. By measuring how a user interacts with a form field (e.g., a website, mobile app, etc.) our behavioral analytics solutions can determine if the user is: a potential fraudster, a bot, or a genuine user familiar with the PII entered. Because it’s available at any digital engagement, behavioral data is often the most consistent signal available throughout the customer lifecycle and across geographies. It allows risky users to be rejected or put through more rigorous authentication, while trustworthy users get a better experience, protecting businesses and consumers from Gen AI-enabled fraud.  As cyber threats evolve, so must our defenses. Password spraying exemplifies the sophisticated methods and technologies attackers now employ to scale their fraud efforts and gain access to sensitive information. To fight next-generation fraud, organizations must employ next-generation technologies and techniques to better defend themselves against this and other types of cyberattacks.  Experian’s approach embodies a paradigm shift where fraud detection increases efficiency and accuracy without sacrificing customer experience. We can help protect your company from bot attacks, fraudulent accounts and other malicious attempts to access your sensitive data. Learn more about behavioral analytics and our other fraud prevention solutions.  Learn more

In today's fast-paced financial landscape, financial institutions must stay ahead of the curve when it comes to account opening and onboarding. Digital account opening, empowering a prospective client to securely and efficiently open a new account, is key to how banks, credit unions and other financial institutions grow their business and expand their portfolio. Regardless of the time, money and other resources a financial institution invests in marketing to the right target prospect and tailoring an attractive offer, it’s worthless if that prospective customer can’t complete the process due to a poor account opening experience. Unhappy customers vote with their feet. A recent Experian study found that of the more 2,000 consumers surveyed who’d opened a new account in the last six months, 37% took their business elsewhere due to a negative account opening experience.   The choice of a reliable partner can make all the difference to your account opening and onboarding experience. The right partner must provide your financial institution with access to the freshest credit data; advanced analytics, scores and models to empower you to say yes to the right customers that meet your lending criteria; and industry-leading decision engines that make the best decisions and enable you to provide a seamless customer experience.  Moreover, the right partner will also help you in maintaining high levels of security without compromising user experience, all while adhering to regulatory compliance.  Recently, Liminal, a leading advisory and market intelligence firm specializing in the digital identity, cybersecurity, and fintech markets, released its highly anticipated Link™ Index Report for Account Opening in Financial Services, which evaluates solution providers in the financial sector, in the areas of compliance and fraud prevention for account opening. The report recognized Experian as a market leader for compliance and fraud prevention capabilities and market execution. Experian’s identity verification and fraud prevention solutions, including CrossCore® and Precise ID®, received the highest score out of the 32 companies highlighted in the report. It found that Experian was recognized by 94% of buyers and 89% identified Experian as a market leader.   “We’re thrilled to be named the top market leader in compliance and fraud prevention capabilities and execution by Liminal’s Link Index Report,” said Kathleen Peters, Chief Innovation Officer for Experian’s Decision Analytics business in North America. “We’re continually innovating to deliver the most effective identity verification and fraud prevention solutions to our clients so they can grow their business, mitigate risk and provide a seamless customer experience.”  You can access the full report here. To learn more about Experian’s award-winning fraud solutions, visit our identity fraud hub.  Download Liminal Link Index Report

Today’s digital-first world is more interconnected than ever. Financial transactions take place across borders and through various channels, leaving financial institutions and their customers at increasing risk from evolving threats like identity theft, fraud and others from sophisticated crime rings. And consumers are feeling that pressure. A recent Experian study found that over half of consumers feel like they are more of a target for online fraud than a year ago. Likewise, more than 40% of businesses reported increased fraud losses in recent years. It’s not only critical that organizations ensure the security and trustworthiness of digital transactions and online account activity to reduce risk and losses but what consumers expect. In the same Experian study, more than 85% of consumers said they expect businesses to respond to their fraud concerns, an expectation that has increased over the last several years.   Businesses and financial institutions most successful at mitigating fraud and reducing risk have adopted a layered, interconnected approach to identity confirmation and fraud prevention. One vital tool in this process is identity document verification. This crucial step not only safeguards the integrity of financial systems but also protects individuals and organizations from fraud, money laundering and other illicit activities. In this blog, we will delve into the significance of identity document verification in financial services and explore how it strengthens the overall security landscape.  Preventing identity theft and fraud Identity document verification plays a vital role in thwarting identity theft and fraudulent activities. By verifying the authenticity of identification documents, financial institutions can ensure that the individuals accessing their services are who they claim to be. Sophisticated verification processes, including biometric identification and document validation, help detect counterfeit documents, stolen identities and impersonation attempts. By mitigating these risks, financial institutions can protect their customers from unauthorized access to accounts, fraudulent transactions and potential financial ruin. Compliance with regulatory requirements Financial institutions operate in an environment governed by stringent regulatory frameworks designed to combat money laundering, terrorist financing and other financial crimes. Identity document verification is a key component of these regulatory requirements. By conducting thorough verification checks, financial service providers can adhere to Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. Compliance safeguards the institution's reputation and helps combat illicit financial activities that can have far-reaching consequences for national security and stability. Mitigating risk and enhancing trust Effective identity document verification mitigates risks associated with financial services. By verifying the identity of customers, financial institutions can reduce the likelihood of fraudulent activities, such as account takeovers, unauthorized transactions and loan fraud. This verification process bolsters the overall security of the financial system and creates a more trustworthy environment for stakeholders. Trust is fundamental in establishing long-lasting customer relationships and attracting new clients to financial institutions.   Facilitating digital onboarding and seamless customer experience As financial services embrace digital transformation, identity document verification becomes essential for smooth onboarding processes. Automated identity verification solutions enable customers to open accounts and access services remotely, eliminating the need for in-person visits or cumbersome paperwork. By streamlining the customer experience and minimizing the time and effort required for account setup, financial institutions can attract tech-savvy individuals and enhance customer satisfaction.  Combating money laundering and terrorist financing Proper document verification is a key component of combating money laundering and terrorist financing activities. By verifying customer identities, financial institutions can establish the source of funds and detect suspicious transactions that may be linked to illicit activities. This proactive approach helps protect the integrity of the financial system, supports national security efforts, and contributes to the global fight against organized crime and terrorism. Identity document verification is a vital component in the layered, interconnected approach to mitigating and preventing fraud in modern financial services. By leveraging advanced technologies and robust verification processes, financial institutions can ensure the authenticity of customer identities, comply with regulatory requirements, mitigate risk and enhance trust.  As financial services continue evolving in an increasingly digital landscape, identity document verification will remain a crucial tool for safeguarding the security and integrity of the global financial system.  For more information on how Experian can help you reduce fraud while delivering a seamless customer experience, visit our fraud management solutions hub.   Learn more

Between social unrest across the globe, the lingering pandemic, and the digital transformation brought on by the health crisis, the fraud landscape has expanded dramatically for businesses and consumers alike. According to Experian’s latest global identity and fraud report, 93% of U.S. companies have mid-to-high concern for fraud, and 81% say that their worries about fraud have increased over the past 12 months. Monitoring unused or dormant accounts for fraud is often a warning directed at consumers. However, it’s now advice an increasing number of businesses are wishing they’d followed, as growing synthetic identity (SID) fraud is fueling a dramatic increase in losses—SID related charge-offs ballooned to $20 billion in 2021 alone, according to the Federal Reserve Bank of Boston. The threat of SIDs SIDs are made to look like an actual consumer, combining both real and fake data to form a new composite identity. They typically evolve using a combination of tactics that include: Identifying and creating relationships with businesses that have a high tolerance for identity discrepancies. These include businesses whose products expose the business to low fraud risk and/or products offered to market segments where identity verification is expected to be challenging. Either of these enable an SID to be planted among consumer data sources. Attaching the SID to existing accounts and relationships that belong to other consumers. Often these existing accounts were established by collusive criminals or by using other SIDs, but there are also ways for legitimate consumers to collect ‘rent’ in exchange for adding other consumers to existing accounts. Either approach improves the SID’s appearance of credit worthiness. Progressively building the SID’s independent ability to access larger and larger amounts of credit until they spend quickly and default on all obligations, leaving no one for the victimized businesses to pursue. “They’re difficult to identify because of the combination of real and fake data and because there’s no actual victim reporting an identity theft. As a result, businesses typically have trouble separating SID losses from credit losses,” said Chris Ryan, Experian’s go-to-market lead for fraud and identity. “SID fraud isn’t committed haphazardly.  It’s carefully planned and executed—and it adapts to policy changes. Some businesses change their underwriting policy or focus on early-lifecycle account activity like purchases, payments, and requests for additional credit to reduce SID losses that occur immediately after an account is opened. SIDs can adapt to this. If six months of responsible account behavior earns a credit line increase or the ability to spend large amounts in a single billing cycle, the perpetrators are willing to wait,” Ryan said. “It’s something businesses and lenders need to be on guard for, especially with the fast-paced holiday shopping season ahead,” he said. Addressing SIDs Solving the increasingly complex problem of SID fraud requires a thoughtful approach. The institutions seeing success at preventing multi-faceted fraud are using a layered approach to identifying and mitigating fraud. Here are three steps lenders can take today to prevent SID fraud across your portfolio: Use data and analytics that extend beyond credit to evaluate identities and their histories more completely. Apply those analytics across the lifecycle from marketing and origination to portfolio management recognizing that SID risk is not restricted to a single lifecycle stage. Have a rigorous verification process that escalates to document verification or the Social Security Administrations Electronic Consent Based SSN Verification (eCBSV) process For more information on how you can leverage a multi-layered approach to fraud in your business, visit our fraud and identity solutions hub or request a call to discuss customizing a solution for your company.

The digital innovation that has come out from the pandemic across businesses of all kinds – and the resulting improvement to customer experience – has been welcomed by consumers and the financial services industry.   However, it created a challenge for those institutions, namely credit unions, who thrived on and were famous for an excellent in-person customer experience. But rather than viewing it as a threat, the savviest credit unions began to look at some of the cloud-based tools this pandemic-induced digital wave brought with it.   To continue to deliver personalized, secure and fast decisions to their members, credit unions are now adopting cloud-based decisioning and fraud prevention platforms. These systems, like Experian PowerCurve, have helped credit unions and financial institutions alike overcome a dependency on manual processes and other potential resource constraints. In doing so, they can to deliver an excellent online customer experience that can handle high volumes of members from a variety of backgrounds, which reinforces the brand promises of trust and personalized customer service.    But it’s not just members who are benefiting from an improved lending experience. Credit unions may want to follow OneAZ Credit Union, who has seen a 26% increase in booking rates after implementing PowerCurve, in addition to a 25% reduction in manual reviews.  With 21 branches and a full-service digital team, OneAZ prides itself on a world-class member experience while helping members exceed their financial goals. They partnered with Experian® to implement an advanced decisioning system that would increase efficiency and further improve the member experience.  “The speed at which we can return a decision and our better understanding of future performance has really propelled us in being able to better serve our members,” said John Schooner, VP Credit Risk Management for OneAZ.  To read the full case study, click here. And to find more information on how Experian can improve your lending experience through automated decisioning tools,  you can read more about PowerCurve here.   

Fintechs have been an enormously disruptive force of change in financial services over the past 10 years. From digital payments, lending, insurance, digital banks, to personal finance and many other subsectors in between, fintechs have rapidly transformed everything from business and operating models to customer expectations. It’s this innovative drive that is celebrated and fostered each year at LendIt Fintech - a conference that brings together the fintech and financial services community to connect and reimagine the future of finance. And there may not be another year on record that called for the reimagining of finance more than 2020. Last year, the financial services industry – from consumers, fintechs and other subdivisions across the globe – endured many changes and challenges due to the COVID-19 pandemic. But it also brought accelerated innovations; and with them, increased customer expectations and a focus on financial equity and inclusion. As consumer credit scores and demand for credit continue to rise, fintechs have an opportunity to re-examine what credit looks like in a post-COVID lending environment, and explore opportunities for growth in 2021. Experian’s Chief Product Officer Greg Wright tackled this topic at the recent Lendit Fintech conference, alongside Ibo Dusi of Happy Money, Myles Reaz of Upgrade and the Garry Reeder with the American Fintech Council. Watch the full panel discussion in the video below and hear more about: How panelists define data, alternative data and how it factors in their lending How alternative data can help drive financial inclusion and get to a ‘yes’ more often with consumers Using data to make the consumer experience more frictionless and seamless For more information about how Experian can help fintech organizations of all sizes reach their business and lending goals, visit our fintech solutions page. Explore Experian's Fintech Solutions

Experian is proud to announce, for the second year in a row, we have been named to the global Fintech Leaders list, placing in the top 20 for 2021. The list and adjoining report are released annually by international research organization, the Center for Financial Professionals (CeFPro). In addition to placing 19th on the list, Experian also placed in the Credit Risk category. The Center for Financial Professionals’ Fintech Leaders 2021 Report is one of the most rigorous programs that rank fintech industry leaders. The report’s coverage includes evaluating top fintech companies, solution providers, and vendors. The results are usually based on gathered surveys from end-users, practitioners, and subject matter experts. CeFPro’s report comes from the group’s market analysis and original research, which are backed by an advisory board that consists of 60 international industry professionals. Andreas Simou, CeFPro’s Managing Director, shared that the CeFPro board and voting members recognized Experian within the fintech survey as leaders for their data, decisioning and analytical capabilities. Simou said, "Experian cements its place on the Fintech Leaders List, and has once again been very highly regarded, as a leading player within credit risk, most notably for their subject-matter expertise and excelling within the areas of data management and modelling,” he said. “We are honored to once again be recognized as a Fintech Leader by CeFPro and the global Fintech marketplace,” said Jon Bailey, Vice President for Fintech at Experian. “We are committed to supporting the Fintech community and we will continue to invest and innovate to help our clients solve problems, create opportunities, and promote financial inclusion,” Bailey said.

With 2020 firmly behind us and multiple COVID-19 vaccines being dispersed across the globe, many of us are entering 2021 with a bit of, dare we say it, optimism. But with consumer spending and consumer confidence dipping at the end of the year, along with an inversely proportional spike in coronavirus cases, it’s apparent there’s still some uncertainty to come. This leaves businesses and consumers alike, along with fintechs and their peer financial institutions, wondering when the world’s largest economy will truly rebound.   But based on the most recent numbers available from Experian, fintechs have many reasons to be bullish. In this unprecedented year, marked by a global pandemic and a number of economic and personal challenges for both businesses and consumers, Americans are maintaining healthy credit profiles and responsible spending habits. While growth expectedly slowed towards the end of the year, Q4 of 2020 saw solid job gains in the US labor market, with 883,000 jobs added through November and the US unemployment rate falling to 6.7%. Promisingly, one of the sectors hit hardest by the pandemic, the leisure and hospitality industry added back the most jobs of all sectors in October: 271,000. Additionally, US home sales hit a 14-year high fueled by record low mortgage rates. And finally, consumer sentiment rose to the highest level (81.4%) since March 2020. Not only are these promising signs of continued recovery, they illustrate there are ample market opportunities now for fintechs and other financial institutions.   “It’s been encouraging to see many of our fintech partners getting back to their pre-COVID marketing levels,” said Experian Account Executive for Fintech Neil Conway. “Perhaps more promising, these fintechs are telling me that not only are response rates up but so is the credit quality of those applicants,” he said.  More plainly, if your company isn’t in the market now, you’re missing out. Here are the four steps fintechs should take to reenter the lending marketing intelligently, while mitigating as much risk as possible.   Re-do Your Portfolio Review Periodic portfolio reviews are standard practice for financial institutions. But the health crisis has posted unique challenges that necessitate increased focus on the health and performance of your credit portfolio. If you haven’t done so already, doing an analysis of your current lending portfolio is imperative to ensure you are minimizing risk and maximizing profitability. It’s important to understand if your portfolio is overexposed to customers in a particularly hard-hit industry, i.e. entertainment, or bars and restaurants. At the account level there may be opportunities to reevaluate customers based on a different risk appetite or credit criteria and a portfolio review will help identify which of your customers could benefit from second chance opportunities they may not have otherwise been able to receive. Retool Your Data, Analytics and Models As the pandemic has raged on, fintechs have realized many of the traditional data inputs that informed credit models and underwriting may not be giving the complete picture of a consumer. Essentially, a 720 in June 2020 may not mean the same as it does today and forbearance periods have made payment history and delinquency less predictive of future ability to pay. To stay competitive, fintechs must make sure they have access to the freshest, most predictive data. This means adding alternative data and attributes to your data-driven decisioning strategies as much as possible. Alternative data, like income and employment data, works to enhance your ability to see a consumer’s entire credit portfolio, which gives lenders the confidence to continue to lend – as well as the ability to track and monitor a consumer’s historical performance (which is a good indicator of whether or not a consumer has both the intention and ability to repay a loan). Re-Model Your Lending Criteria  One of the many things the global health crisis has affirmed is the ongoing need for the freshest, most predictable data inputs. But even with the right data, analytics can still be tedious, prolonging deployment when time is of the essence. Traditional models are too slow to develop and deploy, and they underperform during sudden economic upheavals. To stay ahead in times recovery or growth, fintechs need high-quality analytics models, running on large and varied data sets that they can deploy quickly and decisively. Unlike many banks and traditional financial institutions, fintechs are positioned to nimbly take advantage of market opportunities. Once your models are performing well, they should be deployed into the market to actualize on credit-worthy current and future borrowers. Advertising/Prescreening for Intentional Acquisition As fintechs look to re-enter the market or ramp up their prescreen volumes to pre-COVID levels, it’s imperative to reach the right prospects, with the right offer, based on where and how they’re browsing. More consumers than ever are relying on their phones for browsing and mobile banking, but aligning messaging and offers across devices and platforms is still important. Here’s where data-driven advertising becomes imperative to create a more relevant experience for consumers, while protecting privacy.   As 2021 rolls forward, there will be ample chance for fintechs to capitalize on new market opportunities. Through up-to-date analysis of your portfolio, ensuring you have the freshest, predictive data, adjusting your lending criteria and tweaking your approach to advertising and prescreen, you can be ready for the opportunities brought on by the economic recovery. How is your fintech gearing up to re-enter the market? Learn more

Experian recently announced the new members named to its Fintech Advisory Board. The board and its members provide Experian with valuable insights and key perspectives into the unique and quickly evolving needs of the fintech industry. “For years Experian has been committed to partnering with innovators in the fintech industry to bring better opportunities to businesses and consumers alike,” said Experian North American CEO Craig Boundy. “We appreciate the thought leadership we get from our Fintech Advisory Board members and the challenge and the push that comes along with it,” he said. The board met virtually last month, welcoming representatives from across the fintech ecosystem representing payments, personal and secured loan lenders, credit card issuers, investors and others. “This was my first board meeting with Experian, and I’m very pleased to see the investment Experian has put into being the best of the three major bureaus in having the best technology to enable us to turnaround our models more quickly, and better data and alternative data sources like Boost,” said one of the new executives appointed to the board. “We are delighted to gather this group of innovators together to ensure we are consistently meeting the needs of our fintech partners,” said Experian Vice President Jon Bailey, who oversees the fintech vertical.  “Now more than ever it’s important that we work alongside them in shaping the industry and helping them meet their goals for the future,” he said. Experian’s fintech vertical provides leading-edge solutions and data across the credit lifecycle specifically designed to impact Fintech and marketplace lending companies and their customers. For more information on Experian’s fintech services or the advisory board, click here.

The financial services industry is not always synonymous with innovation and forward-thinking. While there are some exceptions with top-10 banks and some savvy regionals, as a whole, the sector tends to fall on the latter half of the diffusion of innovation curve, usually slotting in the late majority or laggard phase. Conversely, the opposite is true for fintechs who have been an enormously disruptive force of change in financial services over the past 10 years.   For many businesses, the pandemic has created uncertainty and an inability to conduct or generate business. However, the silver lining with COVID-19 might just be that it’s driving digital innovation across industries. Andreesen Horowitz, a venture capital firm, estimates businesses of all kinds are experiencing at least two years’ worth of digitization compressed into the last six months. And while they have been significantly impacted, for fintechs who were already pushing the envelope and challenging existing business models, COVID-19 suddenly accelerated financial services innovation into overdrive. Here are three challenges fintechs are answering in the wake of the COVID-19 health crisis. Digital Banking   The first lockdowns flipped the digital switch in financial services. Seemingly overnight, banking moved digital. In April, new mobile banking registrations increased 200%, while mobile banking traffic rose 85%. Likewise, Deloitte reported online banking activity has increased 35% since the pandemic started. Being mobile-first or digital-only has allowed many fintechs to win in offering presentment, activation, underwriting, and a contextual digital interface, all capabilities that will only become more relevant as the pandemic stretches on. At Square, direct deposit volumes grew by three times from March to April, up to $1.3 billion; Chime saw record signups. Continued social distancing will only serve to accelerate customers’ use of mobile and online platforms to manage their finances.  Contactless Payments  Similar to digital banking as a whole, the health crisis has accelerated the necessity for contactless payments. Whereas convenience and a seamless customer experience may have been drivers for payments innovation in the past, now, many customers may view it as a life or death health concern. Phones, wearables and even connected vehicles are empowering customers to participate in commerce while avoiding handling cash or coming in contact with an infected surface. Through their adoption of IOT-powered contactless payments, fintechs are accelerating this area of financial services to keep customers safe.  Financial Inclusion and Speeding Economic Relief  Any disaster disproportionally affects the underbanked and those living at the poverty line, and COVID-19 is no different. While it will undoubtedly contribute to an increase in unbanked households, the pandemic may also provide an opportunity to innovate through this problem. Financial inclusion was already a focus for many fintechs, who’ve made it their mission to bring equity by offering basic financial services in a transparent way. Unencumbered by legacy systems and business models, fintechs are well positioned to work across the financial ecosystem, from financial services, retail and government to efficiently and more quickly distribute benefits to at-risk groups and impacted businesses.   From their ability to quickly ingest new and novel data sources, to a focus on using a digital-first approach to delight customers, fintechs will continue to harness their strengths to disrupt financial services, even during the pandemic. How is your fintech driving innovation and customer experience during the health crisis?   Learn more

For financial marketers, long gone are the days of branded coffee mugs, teddy bears and the occasional print ad. Financial marketers are charged with customizing messaging and offerings at a customer level, increasing conversion rates, and moving beyond digital while keeping an eye on traditional channels. Additionally, financial marketing teams are having to do it all with less; according to CMO Survey, marketing budgets have remained stagnant for the last 6 years. Accordingly, competing in today’s world requires transforming your organization to address rapidly increasing complexity while containing costs.  Here are four tactics leading-edge firms are using to respond to changes in the market and better serve customers. More data, fewer problems Financial institutions ingest a mind-boggling variety of data, transaction details, transaction history, credit scores, customer preferences, etc. It can be difficult to know where to start or what to do with what is often terabytes of data. But the savviest teams are mining their unique data, along with bureau data, and other alternative and third-party data for rich decision making that drives differentiation. Getting analytical In financial institutions, advanced analytics has traditionally lived with lenders, underwriters, risk and fraud, departments, etc. But marketers too can find the value in the volume, velocity and variety of new data sources available to financial institutions. Using advanced analytics allows the most forward-thinking financial marketers to better target customers, personalize experiences, respond in near-real-time or even predict actions, and measure the impact of marketing investments. Customized quality time with customers Thanks to the likes of Google and Amazon, consumers have become accustomed to individualized interactions with firms they utilize. And this desire is just as present when it comes to their financial institution. But banks, credit unions and fintechs have been historically slow to respond. According to a recent Capgemini study, 70% of US consumers feel like their financial institution doesn’t understand their needs. The most dynamic financial marketing teams tailor quality experiences that increase consumer engagement and long-term relationships. All the channels, all the time The financial marketer’s job doesn’t stop at creating bespoke experiences for customers. Firms are also having to leverage an omnichannel approach to reach these clients, across an ever-growing number of channels and touchpoints. If that wasn’t enough, campaign cycles are shortening to match consumers changing demands and need for instant gratification—again, thanks Amazon. But the best teams determine which media or interaction resonates most effectively with clients, whether face-to-face, via an app, chatbot, or social media and have conversations across all of them seamlessly. It’s clear, financial firms must transform their approach to address increasing market complexity without increasing costs. Financial marketers are saddled with stagnant marketing budgets, proliferating media channels and shorter campaign cycles, with an expectation to continue delivering results. It’s a very tall order, especially if your financial institution is not leveraging data, analytics and insights as the differentiators they could be. CMOs and their marketing teams must invest in new technologies, strategies and data sources that best reflect the expectations of their customers. How is your bank or credit union responding to these financial marketing challenges? Watch our 2020 Credit Marketing Trends On-Demand Webinar  

The challenges facing today’s marketers seem to be mounting and they can feel more pronounced for financial institutions. From customizing messaging and offerings at an individual customer level, increasing conversion rates, moving beyond digital while keeping an eye on traditional channels, and more, financial marketers are having to modernize their approach to customer acquisition. The most forward-thinking financial firms are turning to customer acquisition engines to help them best build, test and optimize their custom channel targeting strategies faster than ever before. But what functionality is right for your company? Here are 5 capabilities you should look for in a modern customer acquisition engine. Advanced Segmentation It’s without question that targeting and segmentation are vital to a successful financial marketing strategy. Make sure you select a tool that allows for advanced segmentation, ensuring the ability to uncover lookalike groups with similar attributes or behaviors and then customize messages or offerings accordingly. With the right customer acquisition engine, you should be able to build filters for targeted segments using a range of data including demographic, past behavior, loyalty or transaction history, offer response and then repurpose these segments across future campaigns. Campaign Design With the right campaign design, your team has the ability to greatly affect customer engagement. The right customer acquisition engine will allow your team to design a specific, optimized customer journey and content for each of the segments you create. When you’re ready to apply your credit criteria to the audience to generate a pre-screen, the best tools will allow you to view the size of your list adjusted in real-time. Make sure to look for an acquisition engine that can do all of this easily with a drag and drop user experience for faster and efficient campaign design. Rapid Deployment Once you finalize your audience for each channel or offer, the clock starts ticking. From bureau processing, data aggregation, targeting and deployment, the data that many firms are currently using for prospecting can be at least 60-days. When searching for a modern customer acquisition engine, make sure you choose a tool that gives you the option to fetch the freshest data (24-48 hours) before you deploy. If you’re sending the campaign to an outside firm to execute, timing is even more important. You’ll also want a system that can encrypt and decrypt lists to send to preferred partners to execute your marketing campaign. Support Whether you have an entire marketing department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best customer acquisition solution for your company will have a robust onboarding and support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. The best customer acquisition tool should be able to take your data and get you up and running in less than 30 days. Data, Data and more Data Any customer acquisition engine is only as good as the data you put into it. It should, of course, be able to include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a customer acquisition engine, pick a system that gives your company access to the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data. The optimum solutions can be fueled by the analytical power of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a marketing and technology partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%.

It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?

Big Data, once thought to be overhyped consultant-speak, is now a term and business model so ubiquitous it underpins billions of dollars in revenue across nearly every industry. Similarly, the advanced analytics derived from big data are key to staying relevant in an everchanging global economy and to consumers with expanding expectations. But for many financial institutions, using big data and advanced analytics seemed to only be in reach for big banks with large, advanced data teams. With the expansion of the Experian Ascend Technology PlatformTM, the conversation is changing. Financial institutions of all sizes can now leverage advanced analytics, artificial intelligence and machine learning with new configurations in the award-winning platform. In a release earlier this week, Experian announced new tools and configurations in the Ascend Analytical SandboxTM to fit teams of every size and skill level. Now fintechs, banks and credit unions of every size can have access to Experian’s one-stop source for advanced analytics, business intelligence and ultimately, better decisions. The secure hybrid-cloud environment allows users to combine their own data sets with Experian’s exclusive data assets, including credit, alternative, commercial, auto and more. From there, users can build and test models across different stages of the lending cycle, including originations, prescreen, account management and collections, and seamlessly put their models into production. Experian’s Ascend Analytical Sandbox also allows users to benchmark their portfolios against the industry, identify credit trends and explore new product opportunities. All the insights gathered through the Ascend Analytical Sandbox can be viewed and shared through interactive dashboards and customizable reports that can be pulled in near real time. Additional use cases include: Reject inferencing – refine models, scorecards and strategies by analyzing trades opened by previous applicants who were rejected or approved but did not move forward Prescreen campaigns – design prescreen campaigns, evaluate results and improve strategies Cross-sell – identify cross-sell opportunities for existing customers and identify how they may be working with other lenders Collections strategies, stress testing and loss forecasting – build stronger models to identify customers that have ability and willingness to pay debts, stress test and forecast loss Peer benchmarking and industry trends – compare current portfolio against peers and the industry Recession planning – identify areas to adjust your portfolio to prepare for an economic downturn OneMain Financial, a large provider of personal installment loans serving 10 million total customers across more than 1,700 branches, turned to Experian to improve its risk modeling and credit portfolio management capabilities with the Ascend Analytical Sandbox. Since using the solution, the company has seen significant improvements in reject inferencing – a process that is traditionally expensive, manually-intensive and time consuming. According to OneMain Financial, the Ascend Analytical Sandbox has shortened the process to less than two weeks from up to 180 days. "Experian's Ascend Technology Platform and Analytical Sandbox is an industry gamechanger," said Michael Kortering, OneMain Financial's Senior Managing Director and Head of Model Development. "We're completing analyses that just weren't possible before and we're getting decisions to our clients faster, without compromising risk.” For more information on Ascend Analytical Sandbox SX – the latest solution for financial institutions of all sizes – or other enterprise-wide capabilities of the Experian Ascend Technology Platform, click here.

You’ve Got Mail! Probably a lot of it. Birthday cards from Mom, a graduation announcement from your third cousin’s kid whose name you can’t remember and a postcard from your dentist reminding you you’re overdue for a cleaning. Adding to your pile, are the nearly 850 pieces of unsolicited mail Americans receive annually, according to Reader’s Digest. Many of these are pre-approval offers or invitations to apply for credit cards or personal loans. While many of these offers are getting to the right mailbox, they’re hitting a changing consumer at the wrong time. The digital revolution, along with the proliferation and availability of technology, has empowered consumers. They now not only have access to an abundance of choices but also a litany of new tools and channels, which results in them making faster, sometimes subconscious, decisions. Three Months Too Late The need to consistently stay in front of customers and prospects with the right message at the right time has caused a shortening of campaign cycles across industries. However, for some financial institutions, the customer acquisition process can take up to 120 days! While this timeframe is extreme, customer prospecting can still take around 45-60 days for most financial institutions and includes: Bureau processing: Regularly takes 10-15 days depending on the number of data sources and each time they are requested from a bureau. Data aggregation: Typically takes anywhere from 20-30 days. Targeting and selection: Generally, takes two to five days. Processing and campaign deployment: Usually takes anywhere from three days, if the firm handles it internally, or up to 10 days if an outside company handles the mailing. A Better Way That means for many firms, the data their customer acquisition campaigns are based off is at least 60 days old. Often, they are now dealing with a completely different consumer. With new card originations up 20% year-over-year in 2019 alone, it’s likely they’ve moved on, perhaps to one of your competitors. It’s time financial institutions make the move to a more modern form of prospecting and targeting that leverages the power of cloud technology, machine learning and artificial intelligence to accelerate and improve the marketing process. Financial marketing systems of the future will allow for advanced segmentation and targeting, dynamic campaign design and immediate deployment all based on the freshest data (no more than 24-48 hours old). These systems will allow firms to do ongoing analytics and modeling so their campaign testing and learning results can immediately influence next cycle decisions. Your customers are changing, isn’t it time the way you market to them changes as well?