Jennifer Schulz, CEO of Experian, North America kicked off Experian’s annual Vision conference Tuesday morning pointing to data, analytics, technology and collective curiosity as the drivers for change and a more impactful tomorrow to more than 700 attendees. Keynote speaker: Jennifer Bailey Jennifer Bailey, Vice President of Apple Pay and Apple Wallet, spoke about the customer experience “ethos.” She explained how Apple takes a long-term view and values the single most important performance metric as customer experience. She said creating a seamless customer experience comes down to making things simple and understandable, and asking, “Are we solving a customer problem?” and “How are we making it easier for customers to enjoy and liver their lives. Bailey, who said of all apps she uses the weather app the most, also talked about innovation, and that both intent and making mistakes are important parts of the process. Apple’s products are known for their user-friendliness, and design is part of that. She encouraged the audience to give design teams room to create without bottom line pressures and not to be afraid to take well-considered risks. Keynote Speaker: Gary Cohn Gary Cohn, Vice Chairman of IBM, talked about the current economic climate, and while it’s a natural viewpoint to look to the past for guidance, the current environment is unlike any before. Cohn discussed regulatory compliance in the banking industry and prioritizing safety and soundness. While AI is topical and in numerous headlines recently, Cohn reminded the conference goers that AI isn’t new. He said what is new and important is that you can now teach models to find the information needed rather than having to feed all the information yourself. He believes AI is not the end of employment, but rather helps boost productivity, efficiency, and job satisfaction and provides organizations more data. As for advice for the audience, Cohn shared opportunities are in the uncomfortable zones and you have to be willing to fail in order to succeed. Session highlights – Day 1 The conference hall was buzzing with conversations, discussions and thought leadership. Overall themes that were frequently part of the conversation included seamless customer experiences, agility in face of economic changes and leveraging AI/ML into strategies. Fraud automation and preventing commercial fraud More businesses are opening than ever before and lenders and service providers need a way to determine risk from businesses who are less than a year old. There is no one-size-fits-all approach to fraud. A layered solution assesses risk and applies the correct friction to resolve the risk and pass or refer the applicant. Identity Today’s consumer wants a personalized experience and is privacy conscious. Additionally, regulators are also pushing for greater privacy. Clean rooms allow you and a partner to add data to a safe space and learn more about consumers without exposing data. The right data improves acquisition rates, identity verification and allows you to anticipate customer needs. Advanced scoring Data, models and strategy are the levers institutions are using to leverage responsible analytics to meet their objectives like safely growing existing portfolios, managing the “right” level of risk, and providing a seamless digital experience. However, the total value of a decisioning system is almost always constrained by its most rudimentary component. The panel of experts discussed their uses and goals for leveraging models and customer experience was at the top of their priorities. Recession preparedness Delinquency is on the rise and lending offers made continue to drop. Changes in the economic climate require frequent monitoring of portfolio and decisions, benchmarking against peers, updating credit models and decision strategies, and stress testing portfolio and models. Trends in credit risk management While AI at the hands of everyone is topical today, it ranked lowest on the list of trends attendees believed were impacting their business. At the top of the list? The growing demand for simpler, faster and seamless experiences. More insights from Vision to come. Follow @ExperianVision and @ExperianInsights to see more of the action.

To reach customers in our modern, diverse communications landscape, it's not enough to send out one-size-fits-all marketing messages. Today's consumers value and continue to do business with organizations that put them first. For financial institutions, this means providing personalized experiences that enable your customers to feel seen and your marketing dollars to go further. How can you achieve this? The answer is simple: a customer-driven credit marketing strategy. What is customer-driven marketing? Customer-driven marketing is a strategy that focuses on putting consumers first, rather than products. It means thinking about the needs, wants and motivations of the prospects you're trying to reach and centering your marketing campaigns and messages around that audience. When done well, this comprehensive approach extends beyond the marketing team to all members of a company. The benefits of customer-driven credit marketing One benefit of this type of personalized credit marketing is that you can target customers with a potentially higher lifetime value. By focusing your marketing efforts on the right prospects, you'll ensure that budgets are being spent wisely and that you're not wasting valuable marketing dollars communicating with consumers who either won't respond or aren't a fit for your business. Customer-driven marketing enables you to identify and reach the most profitable, highly responsive prospects in the most efficient way, while also engaging with current customers to optimize retention rates. When you create marketing programs that are customer-driven, you're not just selling; you're building relationships. Rather than being simply a service provider, you become a trusted financial partner and advisor. This kind of data-driven customer experience can help you onboard more customers and retain them for longer, translating to better results when it comes to your bottom line. Customer-driven marketing: How to get started Customer-driven marketing is less funnel, more spiral. You research, test, refine and repeat, all while taking into account customer feedback and campaign results. It starts with defining your target audience and creating customer personas. As you do this, think about all the factors that are involved in your target customers’ path to purchase, from general awareness and growing need to the final motivation that pushes them to commit. You'll also want to consider what their pain points may be and the barriers that may prevent them from buying. Next, develop a marketing strategy that aligns with your target customers' needs and outlines how and where you'll reach them. It may also be helpful to gather and respond to customer feedback to ensure the value propositions in your campaigns are aligned with customer expectations. These insights can help you refine your messaging, resulting in increased response and retention rates. Use the right data to extend relevant credit offers When you send credit offers, you want to ensure they're reaching the right prospects at the right time. You also want to make sure these credit offers are relevant to the consumers that receive them. That's where quality data comes in. By optimizing your data-driven customer segmentation, you can develop timely and personalized credit offers to boost response rates. For example, you might have a target audience of consumers who are both creditworthy and looking for a new vehicle. Segmenting this audience into smaller groups by demographic, life stage, financial and other factors helps you create credit marketing campaigns that speak to each type of customer as an individual, not just a number. Meet consumers on their preferred channels Nowadays, consumer behavior is more fragmented than ever. This is relevant not just from a demographic point of view, but from the perspective of purchasing behavior. Customer-driven marketing helps you interact with prospects as individuals so that the value propositions they encounter are a true fit for their life situation. For instance, different age groups tend to spend time on different platforms. But why they're on those channels at any particular time matters too. Messaging aimed at prospects in their leisure time should be different from messaging they'll encounter when actively researching potential purchases. Keep up with your customers This is one answer to the question of how to improve customer retention as well. Research demonstrates that it's more cost-effective to keep a customer than to acquire a new one. When you tailor retention efforts with a well-thought-out customer-driven marketing strategy, you're likely to boost retention rates, which in many cases lead to better profits over time. Importance of a customer-driven marketing strategy Putting consumers at the center of credit marketing strategies — and at the center of your business as a whole — is the foundation for personalized experiences that can ultimately increase response rates and customer satisfaction. For more on how your organization can develop an effective customer-driven marketing strategy, learn about our credit marketing solutions.

A funnel describes marketing and sales opportunities because it is the widest at the top and narrowest at the bottom. This is an accurate representation because only a fraction of consumers who enter a sales funnel will become buyers. At the top of the funnel, you find consumers exploring and learning about purchase options. These consumers respond to awareness-based marketing regarding vehicle features or comparisons. They are not typically focused on pricing but rather just learning about options. In the middle of the funnel is where you find customers getting closer to a vehicle purchase. They are evaluating their options, including new versus used, and exploring specific units on consumer sites. These consumers have moved beyond general market awareness and vehicle feature interest and into evaluating what vehicle features meet their needs and what price range and financing options may best suit their budget. During this time, marketing and sales contacts with specific incentives or vehicles of interest-based marketing are effective. Nearing the lower funnel As you near the lower funnel, you will find consumers who are initiating the process with the intent to purchase. These consumers are visiting consumer shopping sites for used vehicle research as well as dealer websites. Used vehicle consumers are visiting Vehicle Detail Pages (VDPs) and viewing vehicle history reports. These lower funnel consumers are exploring trade-in values and trying to put together their vehicle sale and purchase plan. There are many ways lower funnel opportunities interact with the automotive ecosystem. With improvements in digital retailing even when just one small part of the sales process is initiated prior to the consumer visiting the brick-and-mortar store, dealers have an opportunity to capture these lower-funnel consumers. Some effective examples include quick “sell your trade” links or prequalification links on web pages that allow consumers to obtain trade values/trade offers and, in some cases, to get full prequalification for loans. Often these digital retailing features are able to track and communicate to dealers about these lower funnel and fully engaged consumers. Take advantage of lower funnel leads with digital retailing tools As online digital retailing steps become more commonplace, dealers will find themselves leveraging these leads for sales. Utilizing effective, consumer-friendly, and secure functions that allow consumers to access or work through the components of a sale will maximize engagement. Keeping consumers tied to your website during the process can keep them working with your dealership processes. To learn how Experian Automotive can help you gain lower funnel opportunities, contact Mike Costanzo.

In previous posts, I’ve explored the potential ramifications of the end of the Public Health Emergency (PHE) and how it will impact agency plans such as Medicaid eligibility redeterminations. Many states may have already prepared a risk-based approach to address the unwinding process. States need to balance these plans with onboarding new applicants and maintaining the service levels required by the Centers for Medicare & Medicaid Services (CMS). Regardless of the approach, states should look for efficiency in all aspects of the redeterminations process, including aligning pending work with other program recertifications and maximizing the use of available information and tools. What does the end of the PHE mean for state agencies? From the end of the PHE, state agencies will have 12 months to initiate all citizen eligibility renewals and a total of 14 months to complete them. States may begin the unwinding process 60 days prior to the month in which the PHE ends. Many states have already begun Medicaid eligibility redeterminations in an effort to meet this deadline. CMS has provided extensive guidance in their Planning for Resumption document, which state agencies can refer to for full details. Building a proper redeterminations plan Redeterminations plans should verify citizen information with all available information, including residency, age, income, and deceased status. These plans should also support the assessment of identity risk and have the ability to ensure continuous outreach with accurate mailing addresses, phone numbers for calls and texts, email addresses, and assessments of returned mail. CMS guidance encourages states to verify eligibility requirements by mail, email, and other communications channels while minimizing the amount of time and documentation required of beneficiaries. The benefit of standing up this structure? More effective day-forward solutions that can help agencies assess any new and ongoing benefits requests and maintain accurate eligibility lists.   How can Experian help? Experian® has a range of products designed to help organizations verify contact information, such as phone numbers and mailing addresses, as well as income and employment. Our exclusive income and employment data can be leveraged incrementally in non-automated verification methods so that individuals not found by other services can be processed quickly via batch processing — minimizing any impact to beneficiaries while improving overall program performance. Our address verification tools provide improved outreach to beneficiaries with the best and most accurate mailing addresses, leveraging the National Change of Address (NCOA) database, as well as phone number information. The phone number information includes a mobile phone indicator, enabling text message outreach. Additionally, Experian can provide email address provisioning to verify or provide email addresses, which creates another path for contact. All of this helps agencies develop better redeterminations plans to manage the end of the PHE, and better process future benefits requests. To learn more about how we can help, visit us or request a call.  

The science of turning historical data into actionable insights is far from magic. And while organizations have successfully used predictive analytics for years, we're in the midst of a transformation. New tools, vast amounts of data, enhanced computing power and decreasing implementation costs are making predictive analytics increasingly accessible. And business leaders from varying industries and functions can now use the outcomes to make strategic decisions and manage risk. What is predictive analytics? Predictive analytics is a type of data analytics that uses statistical modeling and machine learning techniques to make predictions based on historical data. Organizations can use predictive analytics to predict risks, needs and outcomes. You might use predictive analytics to make an immediate decision. For example, whether or not to approve a new credit application based on a credit score — the output from a predictive credit risk model. But organizations can also use predictive analytics to make long-term decisions, such as how much inventory to order or staff to hire based on expected demand. How can predictive business analytics help a business succeed? Businesses can use predictive analytics in different parts of their organizations to answer common and critical questions. These include forecasting market trends, inventory and staffing needs, sales and risk. With a wide range of potential applications, it’s no surprise that organizations across industries and functions are using predictive analytics to inform their decisions. Here are a few examples of how predictive analytics can be helpful: Financial services: Financial institutions can use predictive analytics to assess credit risk, detect fraudulent applicants or transactions, cross-sell customers and limit losses during recovery. Healthcare: Using data from health records and medical devices, predictive models can predict patient outcomes or identify patients who need critical care. Manufacturing: An organization can use models to predict when machines need to be turned off or repaired to improve their longevity and avoid accidents. Retail: Brick-and-mortar retailers might use predictive analytics when deciding where to expand, what to cross-sell loyalty program members and how to improve pricing. Hospitality: A large hospitality group might predict future reservations to help determine how much staff they need to hire or schedule. Advanced techniques in predictive modeling for financial services Emerging technologies, particularly AI and machine learning (ML), are revolutionizing predictive modeling in the financial sector by providing more accurate, faster and more nuanced insights. Taking a closer look at financial services, consider how an organization might use predictive credit analytics and credit risk scores across the customer lifecycle. Marketing: Segment consumers to run targeted marketing campaigns and send prescreened credit offers to the people who are most likely to respond. AI models can analyze customer data to offer personalized offers and product recommendations. Underwriting: AI technologies enable real-time data analysis, which is critical for underwriting. The outputs from credit risk models can help you to quickly approve, deny or send applications for manual review. Explainable machine learning models may be able to expand automation and outperform predictive models built with older techniques by 10 to 15 percent.1 Fraud detection models can also raise red flags based on suspicious information or behaviors. Account management: Manage portfolios and improve customer retention, experience and lifetime value. The outputs can help you determine when you should adjust credit lines and interest rates or extend offers to existing customers. AI can automate complex decision-making processes by learning from historical data, reducing the need for human intervention and minimizing human error. Collections: Optimize and automate collections based on models' predictions about consumers' propensity to pay and expected recovery amounts. ML models, which are capable of processing vast amounts of unstructured data, can uncover complex patterns that traditional models might miss. Although some businesses can use unsupervised or “black box" models, regulations may limit how financial institutions can use predictive analytics to make lending decisions. Fortunately, there are ways to use advanced analytics, including AI and ML, to improve performance with fully compliant and explainable credit risk models and scores. WHITE PAPER: Getting AI-driven decisioning right in financial services Developing predictive analytics models Going from historical data to actionable analytics insights can be a long journey. And if you're making major decisions based on a model's predictions, you need to be confident that there aren’t any missteps along the way. Internal and external data scientists can oversee the process of developing, testing and implementing predictive analytics models: Define your goal: Determine the predictions you want to make or problems you want to solve given the constraints you must act within. Collect data: Identify internal and external data sources that house information that could be potentially relevant to your goal. Prepare the data: Clean the data to prepare it for analysis by removing errors or outliers and determining if more data will be helpful. Develop and validate models: Create predictive models based on your data, desired outcomes and regulatory requirements. Deciding which tools and techniques to use during model development is part of the art that goes into the science of predictive analytics. You can then validate models to confirm that they accurately predict outcomes. Deploy the models: Once a model is validated, deploy it into a live environment to start making predictions. Depending on your IT environment, business leaders may be able to easily access the outputs using a dashboard, app or website. Monitor results: Test and monitor the model to ensure it's continually meeting performance expectations. You may need to regularly retrain or redevelop models using training data that better reflects current conditions. Depending on your goals and resources, you may want to start with off-the-shelf predictive models that can offer immediate insights. But if your resources and experience allow, custom models may offer more insights. CASE STUDY: Experian worked with one of the largest retail credit card issuers to develop a custom acquisition model. The client's goal was to quickly replace their outdated custom model while complying with their model governance requirements. By using proprietary attribute sets and a patented advanced model development process, Experian built a model that offered 10 percent performance improvements across segments. Predictive modeling techniques Data scientists can use different modeling techniques when building predictive models, including: Regression analysis: A traditional approach that identifies the most important relationships between two or more variables. Decision trees: Tree-like diagrams  show potential choices and their outcomes. Gradient-boosted trees: Builds on the output from individual decision trees to train more predictive trees by identifying and correcting errors. Random forest: Uses multiple decision trees that are built in parallel on slightly different subsets of the training data. Each tree will give an output, and the forest can analyze all of these outputs to determine the most likely result. Neural networks: Designed to mimic how the brain works to find underlying relationships between data points through repeated tests and pattern recognition. Support vector machines: A type of machine learning algorithm that can classify data into different groups and make predictions based on shared characteristics. Experienced data scientists may know which techniques will work well for specific business needs. However, developing and comparing several models using different techniques can help determine the best fit. Implementation challenges and solutions in predictive analytics Integrating predictive analytics into existing systems presents several challenges that range from technical hurdles to external scrutiny. Here are some common obstacles and practical solutions: Data integration and quality: Existing systems often comprise disparate data sources, including legacy systems that do not easily interact. Extracting high-quality data from these varied sources is a challenge due to inconsistent data formats and quality. Implementing robust data management practices, such as data warehousing and data governance frameworks, ensure data quality and consistency. The use  of APIs can facilitate seamless data integration. Scalability: Predictive business analytics models that perform well in a controlled test environment may not scale effectively across the entire organization. They can suffer from performance issues when deployed on a larger scale due to increased data volumes and transaction rates. Invest in scalable infrastructure, such as cloud-based platforms that can dynamically adjust resources based on demand. Regulatory compliance: Financial institutions are heavily regulated, and any analytics tool must comply with existing laws — such as the Fair Credit Reporting Act in the U.S. — which govern data privacy and model transparency. Including explainable AI capabilities helps to ensure transparency and compliance in your predictive models. Compliance protocols should be regularly reviewed to align with both internal audits and external regulations. Expertise: Predictive analytics requires specialized knowledge in data science, machine learning and analytics. Develop in-house expertise through training and development programs or consider partnerships with analytics firms to bridge the gap. By addressing these challenges with thoughtful strategies, organizations can effectively integrate predictive analytics into their systems to enhance decision-making and gain a competitive advantage. From prediction to prescription While prediction analytics focuses on predicting what may happen, prescription analytics focuses on what you should do next. When combined, you can use the results to optimize decisions throughout your organization. But it all starts with good data and prediction models. Learn more about Experian's predictive modeling solutions. 1Experian (2020). Machine Learning Decisions in Milliseconds *This article includes content created by an AI language model and is intended to provide general information.

Despite economic uncertainty, new-customer acquisition remains a high priority in the banking industry, especially with increasing competition from fintech and big tech companies. For traditional banks, standing out in this saturated market doesn’t just involve enhancing their processes — it requires investing in the future of their business: Generation Z. Explore what Gen Z wants from financial technology and how to win them over in 2023 and beyond: Accelerate your digital transformation As digital natives, many Gen Zers prefer interacting with their peers and businesses online. In fact, more than 70% of Gen Zers would consider switching to a financial services provider with better digital offerings and capabilities.1 With a credit prescreen solution that harnesses the power of digital engagement, you can extend and represent firm credit offers through your online and mobile banking platforms, allowing for greater campaign reach and more personalized digital interactions. READ: Case study: Drive loan growth with digital prescreen Streamline your customer onboarding process With 70% of Gen Z and millennials having already opened an account online, it’s imperative that financial institutions offer a digital onboarding experience that’s quick, intuitive, and seamless. However, 44% of Gen Z and millennials state that their digital customer experience has been merely average, noting that the biggest gaps exist in onboarding and account opening.2 To improve the onboarding process, consider leveraging a flexible decisioning platform that accepts applications from multiple channels and automates data collection and identity verification. This way, you can reduce manual activity, drive faster decisions, and provide a frictionless digital customer experience. WATCH: OneAZ Credit Union saw a 25% decrease in manual reviews after implementing an integrated decisioning system Provide educational tools and resources Many Gen Zers feel uncertain and anxious about their financial futures, with their top concern being the cost of living. One way to empower this cohort is by offering credit education tools like step-by-step guides, score simulators, and credit alerts. These resources enable Gen Z to better understand their credit and how certain choices can impact their score. As a result, they can establish healthy financial habits, monitor their progress, and gain more control of their financial lives. By helping Gen Z achieve financial wellness, you can establish trust and long-lasting relationships, ultimately leading to higher customer retention and increased revenue for your business. To learn how Experian can help you engage the next generation of consumers, check out our credit marketing solutions. Learn more 1Addressing banking’s key business challenges in 2023.

The rise of the digital channel lead to a rise in new types of fraud – like cryptocurrency and buy now, pay later scams.  While the scams themselves are new, they’re based on tried-and-true schemes like account takeover and synthetic identity fraud that organizations have been working to thwart for years, once again driving home the need for a robust fraud solution.   While the digital channel is extremely attractive to many consumers due to convenience, it represents a balancing act for organizations – especially those with outdated fraud programs who are at increased risk for fraud. As organizations look for ways to keep themselves and the consumers they serve safe, many turn to fraud risk mitigation. What are fraud risk management strategies? Fraud risk management is the process of identifying, understanding, and responding to fraud risks. Proper fraud risk management strategies involve creating a program that detects and prevents fraudulent activity and reduces the risks associated with fraud. Many fraud risk management strategies are built on five principles: Fraud Risk AssessmentFraud Risk GovernanceFraud PreventionFraud DetectionMonitoring and Reporting By understanding these principles, you can build an effective strategy that meets consumer expectations and protects your business. Fraud risk assessment Fraud protection begins with an understanding of your organization’s vulnerabilities. Review your top risk areas and consider the potential losses you could face. Then look at what controls you currently have in place and how you can dial those up or down to impact both risk and customer experience. Fraud risk governance Fraud risk governance generally takes the form of a program encompassing the structure of rules, practices, and processes that surround fraud risk management. This program should include the fraud risk assessment, the roles and responsibilities of various departments, procedures for fraud events, and the plan for on-going monitoring. Fraud prevention “An ounce of prevention is worth a pound of cure.” This adage certainly rings true when it comes to fraud risk management. Having the right controls and procedures in place can help organizations stop a multitude of fraud types before they even get a foot in the door. Account takeover fraud prevention is an ideal example of how organizations can keep themselves and consumers safe. Fraud detection The only way to stop 100% of fraud is to stop 100% of interactions. Since that’s not a sustainable way to run a business, it’s important to have tools in place to detect fraud that’s already entered your ecosystem so you can stop it before damage occurs. These tools should monitor your systems to look for anomalies and risky behaviors and have a way to flag and report suspicious activity. Monitoring and reporting Once your fraud detection system is in place, you need active monitoring and reporting set up. Some fraud detection tools may include automatic next steps for suspicious activity such as step-up authentication or another risk mitigation technique. In other cases, you’ll need to get a person involved. In these cases it’s critical to have documented procedure and routing in place to ensure that potential fraud is assessed and addressed in a timely fashion. How to implement fraud risk management By adhering to the principles above, you can gain a holistic view of your current risk level, determine where you want your risk level to be, and what changes you’ll need to make to get there. While you might already have some of the necessary tools in place, the right next step is usually finding a trusted partner who can help you review your current state and help you use the right fraud prevention services that fit your risk tolerance and customer experience goals. To learn more about how Experian can help you leverage fraud prevention solutions, visit us or request a call. Learn more

 With nearly seven billion credit card and personal loan acquisition mailers sent out last year, consumers are persistently targeted with pre-approved offers, making it critical for credit unions to deliver the right offer to the right person, at the right time. How WSECU is enhancing the lending experience As the second-largest credit union in the state of Washington, Washington State Employees Credit Union (WSECU) wanted to digitalize their credit decisioning and prequalification process through their new online banking platform, while also providing members with their individual, real-time credit score. WSECU implemented an instant credit decisioning solution delivered via Experian’s Decisioning as a ServiceSM environment, an integrated decisioning system that provides clients with access to data, attributes, scores and analytics to improve decisioning across the customer life cycle. Streamlined processes lead to upsurge in revenue growth   Within three months of leveraging Experian’s solution, WSECU saw more members beginning their lending journey through a digital channel than ever before, leading to a 25% increase in loan and credit applications. Additionally, member satisfaction increased with 90% of members finding the simplified process to be more efficient and requiring “low effort.” Read our case study for more insight on using our digital credit solutions to: Prequalify members in real-time at point of contact Match members to the right loan products Increase qualification, approval and take rates Lower operational and manual review costs Read case study

BNPL is a misunderstood form of credit. In fact, many consumers are unaware that it is credit at all and view it simply as a mode of payment. This guide debunks common BNPL myths to explain what BNPL data will mean for lenders and consumers. In the past year, Experian collected more than 130 million buy now, pay later (BNPL) records from four major BNPL fintech lenders and conducted the most comprehensive analysis of BNPL data available today. The results provided valuable insights on: Who are the consumers using BNPL loans? What is the nature of their current mainstream credit relationships? What do their current BNPL behaviors look like? BNPL myth-busting: Who’s using it, how much are they spending and how risky are BNPL loans? Since BNPL launched in the United States in the 2010s, BNPL has exploded into the consciousness of online shoppers, especially during the COVID-19 pandemic.  According to Forrester, Millennials are the biggest adopters of BNPL at 18%, followed by their younger counterparts in Gen Z at 11%.1 But looking at statistics like these without additional analysis could be problematic.  The dramatic growth of leading BNPL fintechs such as Klarna, Affirm and Afterpay has demonstrated how strongly these services resonate with consumers and retailers. The growth of BNPL has attracted the attention of established lenders interested in capitalizing on the popularity of these services (while also looking to minimize its impact on their existing services, such as credit cards or personal loans). Meanwhile, the Consumer Financial Protection Bureau (CFPB) has urged caution about potential risks, calling for more consistent consumer protections market-wide and transparency into consumer debt accumulation and overextension across lenders. The underlying assumptions debated are that BNPL is used: Predominantly by young people with limited incomes and credit history To pay for frequent, low-value purchases using a cheap and readily available source of credit As a result, it is often seen as a riskier form of lending. But are these assumptions correct? Using data from more than 130 million BNPL transactions from four leading BNPL fintech lenders, we’ve obtained a more detailed and comprehensive understanding of BNPL users and their defining features. Our findings look somewhat different to the popular stereotypes. Myth 1: BNPL is used only for low-value purchases According to our analysis, most BNPL purchases, 95 percent are for items costing $300 or less.2 Some of it is low-value, but not all. In fact, we found that the average purchase using BNPL was similar to that of a credit card, at $132.Average transaction sizes have increased 10 percent year-over-year, and we now see BNPL purchases for goods costing well over $1,000. We also see that consumers take out an average of 5 BNPL loans in a year and 23 percent of them have loans with more than one BNPL provider at a time. Myth 2: BNPL is simply an easier payment method Consumers see BNPL as a simple, quick and convenient way to pay. But, as shoppers receive goods for which payment is deferred, it’s also a form of credit. However, unlike short-term high-interest loans, BNPL credit comes at zero cost to the borrower, with some, but not all BNPL fintech providers charging late payment fees – fueling many borrowers’ sense that it’s an easy way to pay, rather than a loan. Myth 3: Only Gen Z shoppers aged 25 and below are using BNPL Younger shoppers are slightly more represented in the data transactions, but our analysis shows consumers of all ages use BNPL. BNPL is going mainstream, and its appeal is widening. The average age of BNPL consumers is 36 years old, with an average credit history of 9 years.2 The ease of use of these services at the checkout means they have a broad appeal. Over half of U.S. adults have reported using a BNPL service at least once. Despite Millennials and Gen Z having used BNPL financing the most, Gen Xers are not too far behind in usage, with 52% having used it.2 We anticipate that in the use of BNPL will continue to grow as more customers become more familiar with the benefits, and the diversification of products continues. Understanding the opportunities this growth presents to both consumers and lenders is critical to protecting their interests. And helping to facilitate access to credit, enabling responsible spending, while also limiting risks and providing services that consumers can afford is also critical. Download the full guide for additional myths we’re exposing We will take a deep dive into what our early data analysis suggests about the market and the BNPL myths our analysis is exposing. Additionally, we will examine: Why BNPL data matters to providers and lenders How BNPL data can improve visibility of consumers’ creditworthiness Ways in which transparency of BNPL data could benefit consumers 1The Buy Now, Pay Later (BNPL) Opportunity,” Forrester Report, April 29, 2022.2Experian data and analytics derived from 130M+ BNPL transactions

It's easy to ignore a phone call—especially from an unknown number—or delete an email without looking past the subject line. Even physical letters get thrown out without being opened. But nearly everyone will quickly open and read a text. Surveys have repeatedly found text message open rates can range from around 90 to 98 percent. And now, debt collectors that are serious about streamlining operations and connecting with consumers via their preferred channel can integrate text messaging into their process. Learn more Using text messages in debt collection It's been a couple of years since the Consumer Financial Protection Bureau (CFPB) revised Regulation F, which implements the Fair Debt Collection Practices Act (FDCPA). The ruling was effective starting November 2021 and confirmed that debt collectors could use emails, text messages and other digital communication channels. Businesses in many other industries have been communicating with customers by text for years. At a high level, the changes to Regulation F allow debt collectors to add new outreach methods to their debt collection tools. However, even with the go-ahead to communicate via text, strategy and compliance must be top of mind. WATCH: Webinar: Keeping pace with collections compliance changes The move to digital debt collections Incorporating text messaging could be part of a larger shift toward digitizing operations. Some debt collection agencies are also using artificial intelligence, big data and automation to help verify consumers' contact information, assist call center agents and follow up with consumers. As the Experian 2022 Global Insights Report reports, 81 percent of consumers think more highly of brands if they have a positive online experience with that brand that involves multiple digital touchpoints. And over half of consumers trust organizations that use AI.1 Your website or mobile app is an important starting point. And digital tools, such as chatbots that can answer common questions and virtual negotiators offering payment plans, could be part of that experience. Your automated and manual text message outreach could also be increasingly important in the coming years. The benefits of debt collection text messages A text message strategy can be part of an omnichannel approach, and it offers debt collectors a few distinct benefits: Get direct access to consumers who will likely see and read your messages. Allow consumers to respond and ask questions via a channel that may be easier or more comfortable for them than a phone call. Start a two-way dialogue and build rapport. Save time by texting multiple consumers simultaneously and automating responses to common questions. However, collection agencies also need to beware of the potential drawbacks. Consumers might see your texts as a nuisance if you frequently send messages or if you're messaging people who truly can't afford a payment right now. Many consumers are also rightly wary of scammers texting them and asking them to click on a link. You'll want to carefully think through your messaging strategy. Starting by getting consent to send a text message while you're on the phone or when the consumer fills out a form online—and then immediately sending a text with an opt-in—can help overcome this potential barrier. How to leverage debt collection text messages Sending payment requests via text to consumers who have a high propensity to repay, and including a link to self-service payment portals, could offer a quick and easy win. However, it may be best to think through how you'll use text messaging to optimize your outreach rather than replace other communication channels. WATCH: Webinar: Adapting to the new collections landscape Perhaps you've spoken directly with someone and helped them set up a payment plan. You could now use automated texts to remind them of upcoming payment due dates and thank them for their payments. It's a simple way to test the water without sending debt collection-related messages that may fall under stricter regulatory requirements. Staying compliant while texting As part of a highly regulated industry, debt collection agencies must consider compliance. And it's especially important to consider when trying new technology that directly interacts with consumers. Laws and rulings may change, and it's important to consult your counsel before making any decisions or implementing a text message strategy. However, at a high level, the Regulation F requires debt collectors to: Prioritize capturing consent.You must obtain direct consent from a consumer or indirect consent from an original creditor that got the consumer's consent. The initial communication before sending a text or email must be written. Debt collectors that use specific procedures for obtaining consent may receive safe harbor protections against inadvertent disclosures to third parties. Make opting out easy. You must send consumers a clear and conspicuous opt-out notice and offer them a reasonable and simple method to opt out of text messaging or other electronic communications. Debt collectors must identify when they receive an opt-out request, even if the request doesn't follow their specific instructions. For example, if a consumer sends “end," you may need to recognize that as an opt-out even if your opt-out instructions tell them to send “stop." Continue complying with FDCPA harassment guidelines. There's no specific federal limit on how often you can text consumers. However, you'll still need to comply with the FDCPA's general rules regarding harassment and contacting consumers at convenient times. In general, you may want to send texts between 8 a.m. and 9 p.m. local time (for the consumer), unless they request a different time. Limiting how many texts you send can also improve consumers' experiences and may lead to better long-term results. Reconfirm consent every 60 days. Even if consumers don't opt out, the implied or expressed consent you received could only be valid for 60 days. To continue texting a consumer, you may need to have them reconfirm their consent or use a complete and accurate database to confirm that their phone number was not reassigned.2 You may also be subject to more stringent state or local laws. For instance, Washington State laws might prohibit debt collectors from sending more than two texts in a day.3 And Washington, D.C. forbids debt collectors from initiating communications with consumers via written or electronic communications (including text messages) during and for at least 60 days following a public health emergency. READ: A Digital Debt Collection Future: Maximizing Collections and Staying Compliant Partnering with Experian Experian offers access to vast data sources, skip tracing tools for collections and advanced analytical capabilities that help debt collectors move into the digital age. From optimizing outreach with the AI-driven PowerCurve® Collection to verifying real-time phone ownership using Phone Number ID™ with Contact Monitor™, you can integrate the latest technology while remaining compliant. You can then decide the best ways to use text messages, or other electronic communication methods, to make profitable decisions and maximize recovery rates. Learn more about Experian's debt collection solutions. ¹Experian. (April 2022). Experian 2022 Global Insights Report ²Consumer Financial Protection Bureau. (2023). 1006.6 Communications in connection with debt collection. ³Washington State Legislator. (2023). RCW 19.16.250 Prohibited practices

Dealers are always looking for reasons to connect with consumers. From back-to-school or graduation specials to holiday offers, dealers leverage seasonal and routine aspects of daily life to connect with consumers. Tax season offers a unique annual opportunity to position your vehicles and dealership for purchase by a consumer expecting a tax refund. In many cases, even consumers not receiving a hefty tax refund will be receptive to the tax time message. With the right strategy, message, and audience, you can market to consumers who are a few thousand dollars richer! Consider a tax refund match program Even if you are not in a position to offer consumers extraordinary sales offers, you may be able to create some special dealership-level seasonal offers that take your tax refund message to the next level. For example, offering a Tax Refund match program that offers consumers a discount off a vehicle matching the tax refund applied as a down payment would surely make your dealership stand out! Target consumers with service incentives What about consumers who did not expect refunds or have already spent them? Perhaps offering service incentives such as offering free tax filing software with the purchase of a prepaid service plan would be appealing. Or simply incentivize consumers to receive a discount coupon book during tax season to lighten the burden tax season brings.Tax season often sets the stage for the spring and summer vehicle sales season. Setting the stage by offering service incentives and tax refund matching programs creates rapport with your consumers that you can build upon. Start developing more effective marketing strategies The Experian Marketing Engine (EME) gives dealers and agencies the ability to build effective marketing plans by providing comprehensive market analysis along with powerful audience list creation. Tax time is just one of many messages dealers can deploy utilizing EME's solutions. At Experian Automotive, we leverage our world-class data set to give our dealer and agency clients unparalleled information to market effectively. If you find this topic interesting, you should read one of our others blogs, How to Effectively Use Audiences for Traditional and Online Marketing. 

Experian Automotive has just released electric vehicle data insights in our 2022 Electric Vehicle Year In Review infographic.

There’s an undeniable link between economic and fraud trends. During times of economic stress, fraudsters engage in activities specifically designed to target strained consumers and businesses. By layering risk management and fraud prevention tools, your organization can manage focus on growing safely. Download infographic Review your fraud strategy  

A data-driven customer experience certainly has a nice ring, but can your organization deliver on the promise? What we're really getting at is whether you can provide convenience and personalization throughout the customer journey. Using data to personalize the customer journey About half of consumers say personalization is the most important aspect of their online experience. Forward-thinking lenders know this and are working to implement digital transformations, with 87 percent of business leaders stating that digital acceleration has made them more reliant on quality data and insights. For many organizations, lack of data isn't the issue — it's collecting, cleaning and organizing this data. This is especially difficult if your departments are siloed or if you're looking to incorporate external data. What's more, you would need the capabilities to analyze and execute the data if you want to gain meaningful insights and results. LEARN: Infographic: Automated Loan Underwriting Journey Taking a closer look at two important parts of the customer journey, here's how the right data can help you deliver an exceptional user experience. Prescreening To grow your business, you want to identify creditworthy consumers who are likely to respond to your credit offers. Conversely, it's important to avoid engaging consumers who aren't seeking credit or may not meet your credit criteria. Some of the external data points you can incorporate into a digital prescreening strategy are: Core demographics: Identify your best customers based on core demographics, such as location, marital status, family size, education and household income. Lifestyle and financial preferences: Understand how consumers spend their time and money. Home and auto loan use: Gain insight into whether someone rents or owns a home, or if they'll likely buy a new or used vehicle in the upcoming months. Optimized credit marketing strategies can also use standard (and custom) attributes and scores, enabling you to segment your list and create more personalized offers. And by combining credit and marketing data, you can gain a more complete picture of consumers to better understand their preferred channels and meet them where they are. CASE STUDY: Clear Mountain Bank used Digital Prescreen with Micronotes to extend pre-approved offers to consumers who met their predetermined criteria. The refinance marketing campaign generated over $1 million in incremental loans in just two months and saved customers an average of $1,615. Originations Once your precise targeting strategy drives qualified consumers to your application, your data-driven experience can offer a low-friction and highly automated originations process. Alternative credit data: Using traditional and alternative credit data* (or expanded FCRA-regulated data), including consumer-permissioned data, allows you to expand your lending universe, offer more favorable terms to a wider pool of applicants and automate approvals without taking on additional risk. Behavioral and device data: Leveraging behavioral and device data, along with database verifications, enables you to passively authenticate applicants and minimize friction. Linked and digital applications: Offering a fully digital and intuitive experience will appeal to many consumers. In fact, 81 percent of consumers think more highly of brands after a positive digital experience that included multiple touchpoints. And if you automate verifications and prefill applications, you can further create a seamless customer experience. READ: White paper: Getting AI-driven decisioning right in financial services Personalization depends on persistent identification The vast majority (91 percent) of businesses think that improving their digital customer journey is very important. And rightly so: By personalizing digital interactions, financial institutions can identify the right prospects, develop better-targeted marketing campaigns and stay competitive in a crowded market. DOWNLOAD: A 5-Step Checklist for Identifying Credit-Active Prospect To do this, you need an identity management platform that enables you to create a single view of your customer based on data streams from multiple sources and platforms. From marketing to account management, you can use this persistent identity to inform your decisions. This way, you can ensure you're delivering relevant interactions and offers to consumers no matter where they are. WATCH: Webinar: Omnichannel Marketing - Think Outside the Mailbox Personalization offers a win-win Although they want personalization, only 33 percent of consumers have high confidence in a business' ability to recognize them repeatedly.4 To meet consumer expectations and remain competitive, you must deliver digital experiences that are relevant, seamless, and cohesive. Experian Consumer View helps you make a good first impression with consumer insights based on credit bureau and modeled data. Enrich your internal data, and use segmentation solutions to further refine your target population and create offers that resonate and appeal. You can then quickly deliver customized and highly targeted campaigns across 190 media destinations. From there, the Experian PowerCurve® Originations Essentials, an automated decisioning engine, can incorporate multiple external and internal data sources to optimize your strategy. *Disclaimer: When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

Experian’s State of the Automotive Finance Market Report: Q4 2022 found that the year-over-year (YOY) average new loan amount increased 4.04%, a smaller growth rate compared to 12.46% YOY in Q4 2021