Credit Lending

The unsecured personal loan, one of the most popular products in the financial space, has seen ebbs and flows over the last several years due to many factors, including economic volatility, the global pandemic, changing consumer behaviors and expectations, and more. Experian data scientists and analysts took a deep dive into data between 2018 and 2022 to uncover and analyze trends in this important industry segment. Additionally, they recommend fintech lending solutions to help fintechs stay ahead of ever-changing market conditions and discover new opportunities. This analysis shows that digital loans accounted for 45 percent of the market in 2022. While this is down from 52% in 2021, digital loan market share continues to grow. The analysis also provides a detailed look into who the digital borrower is and how they compare to traditional borrowers. As we look to the rest of 2023 and beyond, fintechs must be armed with the best digital lending technology, tools, and data to fuel profitable growth while mitigating as much risk as possible. Download our fintech trends report for a full analysis on origination volume trends, delinquency trends, and consumer behavior insights. Download now

Every data-driven organization needs to turn raw data into insights and, potentially, foresight. There was a time when lack of data was a hindrance, but that's often no longer the case. Many organizations are overwhelmed with too much data and lack clarity on how to best organize or use it. Modern business intelligence platforms can help. And financial institutions can use business intelligence analytics to optimize their decisioning and uncover safe growth opportunities. What is business intelligence? Business intelligence is an overarching term for the platforms and processes that organizations use to collect, store, analyze and display data and information. The ability to go from raw data to useful insights and foresight presents organizations with a powerful advantage, and can help them greatly improve their operations and efficiencies. Let’s pause and break down the below terms before expanding on business intelligence. Data: The raw information, such as customers' credit scores. Many organizations collect so much data that keeping it all can be an expensive challenge. Access to new types of data, such as alternative credit data, can assist with decisioning — but additional data points are only helpful if you have the resources or expertise to process and analyze them.Information: Once you process and organize data points, you can display the resulting information in reports, dashboards, and other visualizations that are easier to understand. Therefore, turning raw data into information. For example, the information you acquire might dictate that customers with credit scores over 720 prefer one of your products twice as much as your other products.Insight: The information tells you what happened, but you must analyze it to find useful and actionable insights. There could be several reasons customers within a specific score band prefer one product over another, and insights offer context and help you decide what to do next. In addition, you could also see what happened to the customers who were not approved.Foresight: You can also use information and insights to make predictions about what can happen or how to act in the future given different scenarios. For example, how your customers' preferences will likely change if you offer new terms, introduce a new product or there's a large economic shift. Business intelligence isn't new — but it is changing. Traditionally, business intelligence heavily relied on IT teams to sift through the data and generate reports, dashboards and other visualizations. Business leaders could ask questions and wait for the IT team to answer the queries and present the results. Modern business intelligence platforms make that process much easier and offer analytical insights. Now even non-technical business leaders can quickly answer questions with cloud-based and self-service tools. Business intelligence vs. business intelligence analytics Business intelligence can refer to the overall systems in place that collect, store, organize and visualize your data. Business intelligence tends to focus on turning data into presentable information and descriptive analytics — telling you what happened and how it happened. Business intelligence analytics is a subset of business intelligence that focuses on diagnostics, predictive and prescriptive analytics. In other words, why something happened, what could happen in the future, and what you should do. Essentially, the insights and foresight that are listed above. How can modern business intelligence benefit lenders? A business intelligence strategy and advanced analytics and modeling can help lenders precisely target customers, improve product offerings, streamline originations, manage portfolios and increase recovery rates. More specifically, business intelligence can help lenders uncover various trends and insights, such as: Changes in consumers' financial health and credit behavior.How customers' credit scores migrate over time.The risk performance of various portfolios.How product offerings and terms compare to competitors.Which loans are they losing to peers?Which credit attributes are most predictive for their target market? Understanding what's working well today is imperative. But your competitors aren't standing still. You also need to monitor trends and forecast the impact — good or bad — of various changes. WATCH: Webinar: Using Business Intelligence to Unlock Better Lending Decisions Using business intelligence to safely grow your portfolio Let's take a deeper dive into how business intelligence could help you grow your portfolio without taking on additional risk. It's an appealing goal that could be addressed in different ways depending on the underlying issue and business objective. For example, you might be losing loans to peers because of an acquisition strategy that's resulting in declining good customers. Or, perhaps your competitors' products are more appealing to your target customers. Business intelligence can show you how many applications you received, approved, and booked — and how many approved or declined applicants accepted a competitor's offer. You can segment and analyze the results based on the applicant’s credit scores, income, debt-to-income, loan amounts, loan terms, loan performance and other metrics. An in-depth analysis might highlight meaningful insights. For example, you might find that you disproportionately lost longer-term loans to competitors. Perhaps matching your competitors' long-term loan offerings could help you book more loans. READ: White paper: Getting AI-driven decisioning right in financial services Experian's business intelligence analytics solutions Lenders can use modern business intelligence platforms to better understand their customers, products, competitors, trends, and the potential impact of shifting economic circumstances or consumer behavior. Experian's Ascend Intelligence Services™ suite of solutions can help you turn data points into actionable insights. Ascend Intelligence Services™ Acquire Model: Create custom machine learning models that can incorporate internal, bureau and alternative credit data to more accurately assess risk and increase your lending universe.Ascend Intelligence Services™ Acquire Strategy: Get a more granular view of applicants that can help you improve segmentation and increase automation.Ascend Intelligence Services™ Pulse: A model and strategy health dashboard that can help you proactively identify and remediate issues and nimbly react to market changes.Ascend Intelligence Services™ Limit: Set and manage credit limits during account opening and when managing accounts to increase revenue and mitigate risk.Ascend Intelligence Services™ Foresight: A modern business intelligence platform that offers easy-to-use tools that help business leaders make better-informed decisions. Businesses can also leverage Experian's industry-leading data assets and expertise with various types of project-based and ongoing engagements. Learn more about how you can implement or benefit from business intelligence analytics.

On average, the typical global consumer owns three or more connected devices.1 80% of consumers bounce between devices, while 31% who turned to digital channels for their last purchase used multiple devices along the way.2 Considering these trends, many lenders are leveraging multiple channels in addition to direct mail, including email and mobile applications, to maximize their credit marketing efforts. The challenge, however, is effectively engaging consumers without becoming overbearing or inconsistent. In this article, we explore what identity resolution for credit marketing is and how the right identity tools can enable financial institutions to create more cohesive and personalized customer interactions. What is identity resolution? Identity resolution connects unique identifiers across touchpoints to build a unified identity for an individual, household, or business. This requires an identity graph, a proprietary database that collects, stitches, and stores identifiers from digital and offline sources. As a result, organizations can create a persistent, high-definition customer view, allowing for more consistent and meaningful brand experiences. What are the types of identity resolution? There are two common approaches to identity resolution: probabilistic ID matching and deterministic ID matching. Probabilistic ID matching uses multiple algorithms and data sets to match identity profiles that are most likely the same customer. Data points used in probabilistic models include IP addresses and device types. Deterministic ID matching uses first-party data that customers have produced, enabling you to merge new data with customer records and identify matches among existing identifiers. Examples of this type of data include phone numbers and email addresses. What role does identity resolution play in credit marketing? Maintaining a comprehensive customer view is crucial to credit marketing — the insights gained allow lenders to determine who they should engage and the type of offer or messaging that would resonate most. But there are many factors that can prevent financial institutions from doing this effectively: poor data quality, consumers bouncing between multiple devices, and so on. Seven out of 10 consumers find it important that companies they interact with online identify them across visits. Identity resolution for credit marketing solves these issues by matching and linking customer data from disparate sources back to a single profile. This enables lenders to: Create highly targeted campaigns. If your data is incomplete or inaccurate, you may waste your marketing spend by engaging the wrong audience or sending out irrelevant credit offers. An identity resolution solution that leverages expansive, regularly updated data gives you access to high-definition views of individuals, resulting in more personalization and greater campaign engagement. Deliver seamless, omnichannel experiences. To further improve your credit marketing efforts, you’ll need to keep up with consumers not only as their needs or preferences change, but also as they move across channels and devices. Instead of creating multiple identity profiles for the same person, identity resolution can recognize an individual across touchpoints, allowing you to create consistent offers and cohesive experiences. Picking the right marketing identity resolution solution While the type of identity resolution for marketing solution can vary depending on your business’s goals and challenges, Experian can help you get started. To learn more, visit us today. 1 Global number of devices and connections per capita 2018-2023, Statista. 2 Cross Device Marketing - Statistics and Trends, Go-Globe.

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.

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.

 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

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.

Recent statistics certainly illustrate why many renters are feeling anxious lately. More than 40% of renter households in the U.S. — that’s 19 million households — spent more than 30% of their total income on housing costs during the 2017–2021 period, according to the U.S. Census Bureau’s new American Community Survey (ACS). Households that spend more than 30% of their income on housing costs — including rent or mortgage payments, utilities, and other fees — are considered “housing cost burdened” by the U.S. Department of Housing and Urban Development. Digging a little deeper, nearly 8% of the nation’s 3,143 counties had a median housing cost ratio for renters above 30% during the five-year period, according to ACS, and nearly a third of all U.S. renters lived in these counties. Unsurprisingly, 60% of Americans say they’re “very concerned” about the cost of housing, according to the Pew Research Center. The financial plight of renters today underscores the importance of incorporating renter payment history into screening efforts. It also indicates why reporting positive rent payments to credit bureaus can be such a powerful amenity. Rental data: The key to optimizing the screening process Simply put, a screening process that includes an applicant’s rental payment history provides a more comprehensive understanding of their risk profile and likelihood of paying rent on time and in full. That’s especially critical in an environment when paying rent can be something of a financial burden for many. Wouldn’t an apartment manager want to make a leasing decision by taking into consideration every possible bit of relevant data, especially the most relevant data available — rental payment history? Credit scores are often at the heart of an operator’s screening process. A credit score can give a very general sense of the risk posed by a prospect, but it doesn't provide crystal-clear insight into the likelihood of an applicant paying their rent on time and in full. Even people who are financially responsible and diligent about paying their rent can find themselves with less-than-ideal credit scores. Maybe they were injured in an accident, came down with a serious illness or lost their job, and then suffered a host of financial consequences that harmed their credit score. It can't be assumed people who have been through these situations won't pay their rent on time. At the same time, especially given the burden rent payments pose for many renters, reporting positive payments to credit bureaus can serve as an effective way to attract residents. Unfortunately, unlike homeowners, apartment residents traditionally have not seen a positive impact on their credit reports for making their rent payments on time and in full, even though these payments can very large and usually make up their largest monthly expense. Rental reporting According to the Credit Builders Alliance (CBA), renters are seven times more likely to be credit invisible — meaning they lack enough credit history to generate a credit score — when compared to homeowners. But by reporting their on-time rent payments to credit bureaus, apartment communities can help renters build their credit histories, which can make it easier for them to do things such as secure a car loan or credit card — and to do so at favorable interest rates. Additionally, rent reporting gives residents a strong incentive to pay their rent on time and in full. And it can provide apartment communities with a competitive advantage since this financial amenity is not widespread throughout the rental-housing industry. The data is clear: this is a challenging time for many renters. But by making rental payment histories part of their screening, operators can minimize their risk. And by reporting positive rental payments, they can attract residents and help them build a better financial future. To learn more about Experian’s largest rental payment database and how to start reporting with us, visit us online. Experian RentBureau™

In a dynamic, consumer-driven market, speed and agility are essential to providing seamless customer experiences. However, many financial institutions are still relying on legacy processes and systems to acquire new customers, leading to slow decision-making and significant customer dropout. Experian surveyed over 6,000 consumers and 1,800 businesses worldwide to gain insights into the latest digital consumer trends and key business priorities. Here are some findings to consider if you’re looking to refine your customer acquisition strategy: 40% of businesses consider investing in more digital and automated operations a priority. From application processing to identity verification, many lenders are still performing customer onboarding tasks manually. To increase efficiency and digital acquisition, forward-thinking businesses are focusing on flexible, data-driven technologies that enable centralized, automated, and scalable decision-making. 58% of consumers don’t feel that businesses completely meet their digital online experience. With today’s consumers expecting instant responses, lenders must ensure they’re providing quick and seamless credit application experiences. A nimble decisioning platform can help by providing lenders with greater visibility into consumers through automated data connectivity, allowing them to drive faster, more informed decisions digitally. For more consumer and business trends, download our infographic and check out our customer acquisition solution to learn how to optimize your customer acquisition strategy. Access infographic Power your customer acquisition process

With the new year comes new goals, new accomplishments and new opportunities. And while new things are often associated with growth and success, nurturing what you already have should be just as important. The same goes for customer retention — although many financial institutions mainly focus on expanding their customer base, statistics show that a 5% increase in customer retention can lead to a company’s profits growing by 25% to 95% over time.1 What’s more, acquiring a new customer can cost five to seven times more than retaining an old one.2 What can your organization do to improve your customer retention efforts? Let’s first dive into recent consumer behavior trends. Consumer behaviors are changing High prices hit consumers, but service spending continues. Consumers are still seeing short-term price pressures. While spending on goods decreased by 0.9% in December, service spending remained flat. Consumers are starting to pull back. As economic uncertainty persists and excess savings from the pandemic dwindle further, consumers are saving more. Consumers aren’t completely satisfied when interacting with businesses digitally. 58% of consumers don’t feel that businesses completely meet their expectations for a digital online experience. With these trends in mind, how can your organization improve customer retention in 2023? Here are three tips to help you get started: Stay informed. Keeping up with your customers’ changing interests, behaviors, and life events enables you to identify cross-sell opportunities and create relevant credit marketing campaigns. With a large and comprehensive consumer database, like Experian’s ConsumerView®, you can better understand your customers, including the types of products they like to purchase and if they’re likely to buy a new or used vehicle in the next six months. To further enhance your customer retention efforts, you can also leverage Prospect TriggersSM, which allow you to stay alert whenever a customer is actively shopping for credit and extend preapproved credit offers to customers within hours or minutes, helping increase response rates. Be more than a business – be human. As consumers save more, financial institutions can build lifetime loyalty by serving as trusted financial partners and advisors. To do this, organizations can launch credit education programs and services that empower their customers to make smarter financial decisions. Helping consumers take control of their finances is especially important in today’s changing economy providing them with educational tools and resources, customers will learn how to strengthen their financial profiles while continuing to trust and lean on your organization for their credit needs. Think outside the mailbox. While direct mail is still an effective way to reach consumers, forward-thinking lenders are now meeting their customers online. To ensure you’re getting in front of your customers where they spend most of their time, consider leveraging digital channels, such as email or mobile applications when presenting and representing credit offers. This way, you can better connect with your customers and stay competitive. Importance of customer retention Rather than centering most of your growth initiatives around customer acquisition, your organization should focus on holding on to your most profitable customers, especially now with consumer behaviors changing and an abundance of credit options in the market. To learn more about how your organization can develop an effective customer retention strategy, explore our customer loyalty solutions. Improve customer retention today 1 Customer Retention Versus Customer Acquisition, Forbes, December 2022.

How businesses respond to economic uncertainty can determine whether they get ahead or fall behind. To better prepare for the coming months, you must remain up to date on the latest economic developments to better understand and evolve with changing consumer needs. With insight into critical macroeconomic and consumer trends, you can proactively manage your portfolio, enhance your decisioning and seize new opportunities. Grab a cup of coffee and join Experian's Shawn Rife, Client Executive, and Josee Farmer, Economic Analyst, during our fireside chat on February 16 @ 1 P.M. ET/10 A.M. PT. Our expert speakers will provide a view of the latest economic and market trends, their impact on consumers, and how financial institutions can survive and thrive. Highlights include: Macroeconomic and consumer credit trends Economic implications on consumer behavior How financial institutions can adapt Register now

Putting customers at the center of your credit marketing strategy is key to achieving higher response rates and building long-term relationships. To do this, financial institutions need fresh and accurate consumer data to inform their decisions. Atlas Credit was looking to achieve higher response rates on its credit marketing campaigns by engaging consumers with timely and personalized offers. The company implemented Experian’s Ascend Marketing, a customer marketing and acquisition engine that provides marketers with accurate and comprehensive consumer credit data to build and deploy intelligent marketing campaigns. With deeper insights into their consumers, Atlas Credit created timely and customized credit offers, resulting in a 185% increase in loan originations within the first year of implementation. Additionally, the company was able to effectively manage and monitor its targeting strategies in one place, leading to improved operational efficiency and lower acquisition costs. To learn more about creating better-targeted marketing campaigns and enhancing your strategies, read the full case study. Download the case study Learn more

Alternative credit scoring has become mainstream — and for good reasons. These scoring models could help nearly 50 million consumers who don't meet the criteria for a traditional credit score and often find themselves excluded from popular financial products.1Lenders that use alternative credit scores can find opportunities to expand their lending universe without taking on additional risk and more accurately assess the credit risk of traditionally scoreable consumers. Obtaining a more holistic consumer view can help lenders improve automation and efficiency throughout the customer lifecycle. What is alternative credit scoring? Alternative credit scoring models incorporate alternative credit data* that isn't typically found on consumer credit reports. These scores aren't necessarily trying to predict alternative outcomes. The goal is the same — to understand the likelihood that a borrower will miss payments in the future. What's different is the information (and sometimes the analytical techniques) that inform these predictions.Traditional credit scoring models solely consider information found in consumer credit reports. There's a lot of information there — Experian's consumer credit database has data on over 245 million consumers. But although traditional consumer data can be insightful, it doesn't necessarily give lenders a complete picture of consumers' creditworthiness. Alternative credit scores draw from additional data sources, including: Alternative financial services: Credit data from alternative financial services (AFS) can tell you about consumers' experiences with small-dollar installment loans, single-payment loans, point-of-sale financing, auto title loans and rent-to-own agreements. Buy Now Pay Later: Buy Now Pay Later (BNPL) borrowing is popular with consumers across the scoring spectrum, and lenders can use access to open BNPL loans to better assess consumers' current capacity. Rental payments: Landlords, property managers, collection companies, rent payment services and consumer-permissioned data can give lenders access to consumers' rent payment history. Full-file public records: Credit reports generally only include bankruptcy records from the previous seven to ten years. However, lenders with access to full-file public records can also learn about consumers' property deeds, address history, and professional and occupational licenses. Learn more in the 2022 State of Alternative Credit Data Report Consumers also now have options for easily and securely sharing access to their banking and brokerage account data — and they're increasingly comfortable doing so. Tools like Experian Boost allow consumers to add certain types of positive payment information to their Experian credit reports, including rent, utility and select streaming service payments. Some traditional scores consider these additional data points, and users have seen their FICO Score 8 from Experian boosted by an average of 13 points.2Experian Go also allows credit invisible consumers to establish a credit report with consumer-permissioned alternative data. Lenders that gain direct access to consumer-permissioned data may be able to use it to power their custom credit scores and decisioning. Along with payment information that they can glean, the transaction-level data can help lenders understand consumers' income and spending patterns in real-time. The benefits of using alternative credit data The primary benefit for lenders is access to new borrowers. Alternative credit scores help lenders accurately score more consumers — identifying creditworthy borrowers who might otherwise be automatically denied because they don't qualify for traditional credit scores. The increased access to credit may also align with lenders' financial inclusion goals.Lenders may additionally benefit from a more precise understanding of consumers who are scoreable. When integrated into a credit decisioning platform, the alternative scores could allow lenders to increase automation (and consumers' experiences) without taking on more credit risk. The future of alternative credit scoring Alternative credit scoring might not be an alternative for much longer, and the future looks bright for lenders who can take advantage of increased access to data, advanced analytics and computing power.Continued investment in alternative data sources and machine learning could help bring more consumers into the credit system — breaking barriers and decreasing the cost of basic lending products for millions. At the same time, lenders can further customize offers and automate their operations throughout the customer lifecycle. Learn more in our latest webinar Partnering with Experian Small and medium-sized lenders may lack the budget or expertise to unlock the potential of alternative data on their own. Instead, lenders can turn to off-the-shelf alternative models that can offer immediate performance lifts without a heavy IT investment.Experian's Lift PremiumTM score uses alternative data. The scoring model's unique decision tree modeling approach can offer up to a 10 percent lift compared to traditional models — including up to a two percent lift on thick-file consumers. And it can score 96 percent of U.S. adults, which is 15 percent more than traditional scores.3 Third-party score developers, including Experian, can help lenders create and validate custom and specialty models that incorporate alternative credit data and lenders' internal data sets. And Experian's thousands of unique credit attributes can help partners spot trends and valuable insights that give them an edge over the competition. Learn more about our alternative credit data scoring solutions * 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 (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.1Oliver Wyman (2022). Financial Inclusion and Access to Credit [White Paper]2Experian (2023). Experian Boost3Experian (2023). Experian Lift Premium