Data & Analytics

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.

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.

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.

 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

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.

Many organizations commit to diversity, equity, and inclusion (DEI) policies and practices to build a more diverse and just workplace. Organizations that  live by these values ensure they're reflected in the products and services they offer, and in how they attract and interact with customers. For financial institutions, there could be a direct link between their DEI efforts and financial inclusion, which can open up growth opportunities. Defining DEI and financial inclusion DEI and financial inclusion aren't new concepts, but it's still important to understand how organizations are using these terms and how you might define a successful outcome. What is DEI? DEI policies help promote and support individuals and groups regardless of their backgrounds or differences. In the Experian 2022 Diversity, Equity and Inclusion Report, we define these terms more specifically as: Diversity: The presence of differences that may include thought, style, sexual orientation, gender identity/expression, race, ethnicity, dis(ability), culture, and experience. Equity: Promoting justice, impartiality, and fairness within the procedures, processes, and distribution of resources by institutions or systems. Inclusion: An outcome to ensure those who self-identify as diverse feel and are welcomed. You meet your inclusion outcomes when you, your institution, and your programs are inviting to all. We also recognize the importance of belonging, or “a sense of fitting in or feeling you are an important member of a group." A company's DEI strategy might include internal efforts, such as implementing hiring and promotion practices to create a more diverse workforce, and supporting employee resource groups to foster a more inclusive culture. Companies can also set specific and trackable goals, such as Experian's commitment to increase its representation of women in senior leadership roles to 40 percent by 2024.1 But DEI efforts can expand beyond internal workforce metrics. For example, you might review how the products or services you sell — and the messaging around those offerings — affect different groups. Or consider whether the vendors, suppliers, nonprofits, communities, and customers you work with reflect your DEI strategy. What is financial inclusion? Financial inclusion is less specific to a company or organization. Instead, it describes the strategic approach and efforts that allow people to affordably and readily access financial products, services, and systems. Financial institutions can promote financial inclusion in different ways. A bank can change the requirements or fees for one of its accounts to better align with the needs of people who are currently unbanked. Or it can offer a solution to help people who are credit invisible or unscoreable by conventional scoring models establish their credit files for the first time. For example, Mission Asset Fund, a San Francisco-based nonprofit, organizes credit-building lending circles that have historical roots in savings programs from around the world. Participants can use them to build credit without paying any interest or fees. In particular, the organization focuses on helping immigrants establish and improve their credit in the U.S. Financial institutions are also using non-traditional data scoring to lend to applicants that conventional scoring models can't score. By incorporating alternative credit data1 (also known as expanded FCRA-regulated data) into their marketing and underwriting, lenders can expand their lending universe without taking on additional risk. READ MORE: Experian's Improving Financial Health Report 2022 has many examples of internal products and external partnerships that help promote financial literacy and inclusion. DEI and financial inclusion can complement each other Although DEI and financial inclusion involve different strategies, there's an undeniable connection that should ultimately be tied to a business's overall goal and mission. The groups who are historically underrepresented and underpaid in the workforce also tend to be marginalized by the established financial system. For example, on average, Black and Hispanic/Latino workers earn 76 percent and 73 percent, respectively, as much as white workers.2 And 27 percent of Black and 26 percent of Hispanic/Latino consumers are either credit invisible or unscoreable, compared to only 16 percent of white consumers.3 Financial institutions that work to address the inequities within their organizations and promote financial inclusion may find that these efforts complement each other. During a webinar in 2022 discussing how financial growth opportunities can also benefit underserved communities, Experian asked participants what they thought was the greatest business advantage of executing financial inclusion in their financial institution or business. The majority of respondents (78 percent) chose building trust and retention with customers and communities — undoubtedly an important outcome. But the second most popular choice (14 percent) was enhancing their brand and commitment to DEI, highlighting how these efforts can be interconnected.4 By building a more diverse workforce, organizations can also bring on talent that better relate to and understand consumers who weren't previously part of the company's target market. If the company culture supports a range of ideas, this can unlock new ways to propel the business forward. In turn, employees can be more engaged and excited about their work. Find partners that can help you succeed Setting measurable outcomes for your DEI and financial inclusion efforts and tracking your progress can be an important part of implementing successful programs. But you can also leverage partnerships to further define and achieve your goals. Experian launched Inclusion ForwardTM with these partnerships in mind. Building on our commitment to DEI and financial inclusion, we offer various tools to help consumers build and understand their credit and to help financial institutions reach underserved communities. Products like Experian GoTM and Experian BoostTM help consumers establish their credit file and add positive utility, rent, and streaming service payments to their Experian credit report. Lenders can benefit from access to various non-traditional credit data and expanded FCRA-regulated scoring models, including Experian's Lift PremiumTM, which can score 96 percent of U.S. adults. Whether you've established your strategy and need help with implementation or are at the starting stages, Experian can help you promote DEI and enhance your financial inclusion efforts. Learn more about driving financial inclusion to bring change  1Experian (2022). 2022 Diversity, Equity and Inclusion Report 2U.S. Department of Labor (N/A). Earnings Disparities by Race and Ethnicity 3Oliver Wyman (2022). Financial Inclusion and Access to Credit 4Experian (2022). Three Ways to Uncover Financial Growth Opportunities that Benefit Underserved Communities.

Machine learning (ML) is a powerful tool that can consume vast amounts of data to uncover patterns, learn from past behaviors, and predict future outcomes. By leveraging ML-powered credit risk models, lenders can better determine the likelihood that a consumer will default on a loan or credit obligation, allowing them to score applicants more accurately. When applied to credit decisioning, lenders can achieve a 25 percent reduction in exposure to risky customers and a 35 percent decrease in non-performing loans.1 While ML-driven models enable lenders to target the right audience and control credit losses, many organizations face challenges in developing and deploying these models. Some still rely on traditional lending models with limitations preventing them from making fast and accurate decisions, including slow reaction times, fewer data sources, and less predictive performance. With a trusted and experienced partner, financial institutions can create and deploy highly predictive ML models that optimize their credit decisioning. Case study: Increase customer acquisition with improved predictive performance Looking to meet growth goals without increasing risk, a consumer goods retailer sought out a modern and flexible solution that could help expand its finance product options. This meant replacing existing ML models with a custom model that offers greater transparency and predictive power. The retailer partnered with Experian to develop a transparent and explainable ML model. Based on the model’s improved predictive performance, transparency, and ability to derive adverse action reasons for declines, the retailer increased sales and application approval rates while reducing credit risk. Read the case study Learn about our custom modeling capabilities 1 Experian (2020). The Art of Decisioning in Uncertain Times

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™

As economic conditions shift and consumer behavior fluctuates, first- and third-party debt collectors must adapt to continually maintain effective debt collection strategies. In this article, we explore collections best practices that can empower collectors to improve operational efficiency, better prioritize accounts and enhance customer interactions, all while ensuring compliance with changing regulations. Best practices for improving your collection efforts 1. Implement a data-driven collection strategy Many collectors are already using artificial intelligence (AI) and machine learning (ML) to gain a more complete view of their consumers, segment accounts and create data-driven prioritization strategies. The data-backed approach is clearly a trend that's going to stick. But access to better (i.e., more robust and hygienic) data and debt collection analytics will distinguish the top performers.You can use traditional credit data, alternative credit data, third-party data and advanced analytics to more precisely segment consumers based on their behavior and financial situation — and to determine their propensity to pay. Supplementary data sources can also help with verifying consumers' current contact information and improving your right-party contact rates.Cloud-based platforms and access to various data sources give debt collectors real-time insights. Quickly identifying consumers who may be stretched thin or trending in the wrong direction allows you to proactively reach out with an appropriate pre-collection plan.And for consumers who are already delinquent, the more precise segmentation and tracking can help you determine the best contact channels, times and personalized treatments. For instance, you could optimize outreach based on specific account details (rather than general time-based metrics) and offer payment plans that the customer can likely afford. 2. Use technology to maximize your resources Data-driven prioritization strategies can help you determine who to contact, how to contact them and the treatment options you offer. But you may need to invest in technology to efficiently execute these findings. Although budgets may be limited, the investment in debt recovery tools can be important for handling rising account volumes without increasing headcount. Some opportunities include: Automate processes and outreach: Look for opportunities to automate tasks, particularly monotonous tasks, to reduce errors and free up your agents' time to focus on more valuable work. You could also use automated messages, texts, chatbots and virtual negotiators with consumers who will likely respond well to these types of outreaches. Establish self-service platforms: Create self-service platforms that give consumers the ability to choose how and when to make a payment. This can be especially effective when you can accurately segment consumers based on the likelihood that they'll self-cure and then automate your outreach to that segment. Keep consumer data up to date: Have systems in place that will automatically verify and update consumers' contact information, preferences and previous collection attempts. Reprioritize old accounts based on significant changes: Tools like Experian's Collection Triggers℠ allow you to monitor accounts and automatically get alerted when consumers experience a significant change, such as a new job, that could prompt you to put their account back into your queue. 3. Prioritize customer experience In some ways, debt collectors today often work like marketers by embracing digital debt collection and a customer-first philosophy to improve the consumers' experiences. Your investment in technology goes together with this approach. You'll be able to better predict and track consumers' preferences and offer self-cure options for people who don't want to speak directly with an agent. You also may need to review your regular onboarding and training programs. Teaching your call center agents to use empathy-based communication techniques and work as a partner with consumers to find a viable payment plan can take time. But the approach can help you build trust and improve customer lifetime value. 4. Continue to carefully monitor regulatory requirements Keeping up with regulatory requirements is a perennial necessity for collectors, and you'll need to consider how to stay compliant while adding new communications channels and storing consumer data. For example, make sure there are “clear and conspicuous" opt-out notices in your electronic communications and that your systems can track which channels consumers opt out of and their electronic addresses.1In some cases, the customer-first approach may help minimize regulatory risks, as you'll be training agents to listen to consumers and act in their interest. Similarly, data-driven optimizations can help you increase collections with fewer contacts.WATCH: Explore credit union collection trends and successful account management strategies. Partner with a top provider to achieve success Experian has partnered with many debt collectors to help them overcome challenges and increase recovery rates. There are multiple solutions available that you can use to improve your workflow: TrueTrace™ and TrueTrace Live™: Leverage access to the consumer credit database that has information on over 245 million consumers, and additional alternative databases, to maintain current addresses and phone numbers. PriorityScore for Collections ℠ Know which accounts you should focus on with over 60 industry-specific debt recovery scores. You can choose to prioritize based on likelihood to pay or expected recovery amount. Collection Triggers℠: Daily customer monitoring can tell you when it's time to approach a consumer based on life events, such as new employment or recent credit inquiries. Phone Number ID™ with Contact Monitor™: Increase right-party contact rates and avoid Telephone Consumer Protection Act (TCPA) violations with real-time phone ownership and type monitoring from over 5,000 local exchange carriers. Experian's PowerCurve® Collections and Experian® Optimize solutions also make AI-driven automated systems accessible to debt collectors that previously couldn't afford such advanced capabilities. Building on Experian's access to many sources of credit and non-credit data, these solutions can help you design debt collection strategies, predict consumer behavior and automate decisioning.Learn more about Experian's debt collection solutions. Learn more This article includes content created by an AI language model and is intended to provide general information.

"Out with the old and in with the new" is often used when talking about a fresh start or change we make in life, such as getting a new job, breaking bad habits or making room in our closets for a new wardrobe. But the saying doesn't exactly hold true in terms of business growth. While acquiring new customers is critical, increasing customer retention rates by just 5% can increase profits by up to 95%.1 So, what can your organization do to improve customer retention? Here are three quick tips: Stay informed Keeping up with your customers’ changing interests, behaviors and life events enables you to identify retention opportunities and create personalized credit marketing campaigns. Are they new homeowners? Or likely to purchase a vehicle within the next five months? With a comprehensive consumer database, like Experian’s ConsumerView®, you can gain granular insights into who your customers are, what they do and even what they will potentially do. To further stay informed, you can also leverage Retention TriggersSM, which alert you of your customers changing credit needs, including when they shop for new credit, open a new trade or list their property. This way, you can respond with immediate and relevant retention offers. Be more than a business – be human Gen Z's spending power is projected to reach $12 trillion by 2030, and with 67% looking for a trusted source of personal finance information,2 financial institutions have an opportunity to build lifetime loyalty now by serving as their trusted financial partners and advisors. To do this, you can offer credit education tools and programs that empower your Gen Z customers to make smarter financial decisions. By providing them with educational resources, your younger 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 also 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 re-presenting credit offers. This is important as companies with omnichannel customer engagement strategies retain on average 89% of their customers compared to 33% of retention rates for companies with weak omnichannel strategies. 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. To learn more about how your organization can develop an effective customer retention strategy, explore our marketing solutions. Increase customer retention today 1How investing in cardholder retention drives portfolio growth, Visa. 2Experian survey, 2023.

Believe it or not, 2023 is underway, and the new year could prove to be a challenging one for apartment operators in certain ways. In 2021 and into the beginning of 2022, demand for apartment rentals approached record levels, which shrunk vacancy rates and increased monthly rents. The rest of the year remained stagnant while other regions saw some decline, but inflation and other economic factors have many apartment communities confronted with labor shortages, and other challenges which can certainly make leasing and operating properties difficult. Against that backdrop, here are some of the technologies and solutions operators should consider for optimizing their success and efficiencies in 2023 and beyond. Tools that allow prospective residents to have a fully digital and contactless leasing experience — During the pandemic, many operators rushed to implement virtual tours, onsite self-guided tours and other solutions that allowed prospects to apply for and finalize their leases remotely. Prospective renters have undoubtedly grown fond of navigating the leasing process from their homes and taking self-guided tours when onsite, and the demand for digital solutions will surely continue even after COVID distancing is no longer a factor. Therefore, apartment owners and operators should think of these capabilities as long-term investments and always seek ways to optimize the digital leasing experience they provide. Along those lines, forward-thinking operators are employing solutions that allow them to embed credit functionality into their websites and mobile apps using modern, RESTful APIs like the Experian ConnectSM API. Not only does it enhance the information included in a lease application with credit report data, but it also allows prospective renters to easily apply for more than one property at once, enhancing their experience at the same time. Automated lease application form fill — By using information entered by a lease applicant (such as first name, last name, postal code and the last four digits of a Social Security number), this technology uses information from credit files to automatically fill other data fields in a lease application. This tool reduces the effort required by prospective renters to complete the application process, resulting in a better user experience, faster completions, greater accuracy and reduced application abandonment. Automated verification of income, assets, and employment — These solutions eliminate the need for associates to manually verify these components of a lease application. Manual verification is both time-consuming and prone to human error. In addition, automated tools eliminate the opportunity for applicants to supply falsified supporting documentation. The best part about verification is the variety of options available; leasing managers can pick and choose verification options that meet their needs. Renter Risk Score™ and custom-built scores and models applying RentBureau data — These options offer a score designed expressly to predict the likelihood that an applicant will pay rent. Renter risk score can be purchased with preset score logic, or for high-volume decisions, a model can be built calibrated for your specific leasing decisioning needs. A rental payment history report — The RentBureau Consumer Profile tool can provide detailed insight into a lease applicant's history of meeting their lease obligations, which is invaluable information during the lease application process. Having a tool to report rental payment histories to credit bureaus can be a powerful financial amenity. By reporting these payments, operators can help residents build credit histories and improve financial well-being. Such an amenity can attract and retain residents and provide them with a powerful incentive to pay rent on time and in full. In the end, tools that seek to manage risk and create improved experiences for prospective renters have a multitude of benefits. They create meaningful efficiencies for onsite staff by greatly reducing the time, resources and paperwork required to process applications and verify applicant information. This gives overextended associates more time to handle their many other responsibilities. Beyond just efficiency savings, these technologies and solutions also can help operators avoid the complications and loss of income that result from evictions. In fact, the National Association of Realtors estimates that average eviction costs $7,685. Managing risk and providing the best possible customer experience should always be top of mind for rental housing operators. And with the solutions outlined above, they can effectively accomplish those goals in 2023 and beyond.

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