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

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

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

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

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

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

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

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

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

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

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

What Is Identity Proofing? Identity proofing, authentication and management are becoming increasingly complex and essential aspects of running a successful enterprise. Organizations need to get identity right if they want to comply with regulatory requirements and combat fraud. It's also becoming table stakes for making your customers feel safe and recognized. 63 percent of consumers expect businesses to recognize them online, and 48 percent say they're more trusting of businesses when they demonstrate signs of security. Identify proofing is the process organizations use to collect, validate and verify information about someone. There are two goals — to confirm that the identity is real (i.e., it's not a synthetic identity) and to confirm that the person presenting the identity is its true owner. The identity proofing process also relates to and may overlap with other aspects of identity management. Identity proofing vs identity authentication Identity proofing generally takes place during the acquisition or origination stages of the customer lifecycle — before someone creates an account or signs up for a service. Identity authentication is the ongoing process of re-checking someone's identity or verifying that they have the authorization to make a request, such as when they're logging into an account or trying to make a large transaction. How does identity proofing work? Identity proofing typically involves three steps: resolution, validation, and verification. Resolution: The goal of the first step is to accurately identify the single, unique individual that the identity represents. Resolution is relatively easy when detailed identity information is provided. In the real world, collecting detailed data conflicts with the need to provide a good customer experience. Resolution still has to occur, but organizations have to resolve identities with the minimum amount of information. Validation: The validation step involves verifying that the person's information and documentation are legitimate, accurate and up to date. It potentially involves requesting additional evidence based on the level of assurance you need. Verification: The final step confirms that the claimed identity actually belongs to the person submitting the information. It may involve comparing physical documents or biometric data and liveness tests, such as a comparison of the driver's license to a selfie that the person uploads. Different levels of identity proofing may require various combinations of these steps, with higher-risk scenarios calling for additional checks such as biometric or address verification. Service providers can implement a range of methods based on their specific needs, including document verification, database validation, or knowledge-based authentication. Building an effective identity proofing strategy By requiring identity proofing before account opening, organizations can help detect and deter identity fraud and other crimes. You can use different online identity verification methods to implement an effective digital identity proofing and management system. These may include: Document verification plus biometric data: The consumer uploads a copy of an identification document, such as a driver's license, and takes a selfie or records a live video of their face. Database validations: The proofing solution verifies the shared identifying information, such as a name, date of birth, address and Social Security number against trusted databases, including credit bureau and government agency data. Knowledge-based authentication (KBA): The consumer answers knowledge-based questions, such as account information, to confirm their identity. It can be a helpful additional step, but they offer a low level of assurance, partially because data breaches have exposed many people's personal information. In part, the processes you'll use may depend on business policies, associated risks and industry regulations, such as know your customer (KYC) and anti-money laundering (AML) requirements. But organizations also have to balance security and ease of use. Each additional check or requirement you add to the identity proofing flow can help detect and prevent fraud, but the added friction they bring to your onboarding process can also leave customers frustrated — and even lead to customers abandoning the process altogether. Finding the right amount of friction can require a layered, risk-based approach. And running different checks during identity proofing can help you gauge the risk involved. For example, comparing information about a device, such as its location and IP address, to the information on an application. Or sending a one-time password (OTP) to a mobile device and checking whether the phone number is registered to the applicant's name. With the proper systems in place, you can use high-risk signals to dynamically adjust the proofing flow and require additional identity documents and checks. At the same time, if you already have a high level of assurance about the person's identity, you can allow them to quickly move through a low-friction flow. Experian goes beyond identity proofing Experian builds on its decades of experience with identity management and access to multidimensional data sources to help organizations onboard, authenticate and manage customer identities. Our identity proofing solutions are compliant with National Institute of Standards and Technology (NIST) and enable agencies to confidently verify user identities prior to or during account opening, biometric enrollment or while signing up for services. Learn more   This article includes content created by an AI language model and is intended to provide general information.

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.

Generation Z, or people born between 1997 and 2012, make up about 27% of the American population[1] and have $360B in disposable income[2]. While they may be a young demographic now, Gen Z will soon represent a significant portion of buyers and borrowers in the United States, creating an enormous opportunity for financial institutions to start engaging with them now.  Here are three reasons why you should be marketing financial services to Gen Z. Improving financial wellness is a priority for Gen Z Gen Z is a pragmatic cohort of consumers, but they’re also uncertain and anxious about their financial future. The top concern amongst Gen Z is the cost of living. For these reasons, businesses have a unique opportunity to help those consumers feel less stressed and more confident by providing them with financial services. This can turn those consumers into loyal, long-standing customers.  Gen Z has the lowest credit score of any generation Gen Z ranks lowest in average and median VantageScore® credit score* compared to all other generations, including Gen Y (or millennials), Gen X, and baby boomers.[4] While this is partially due to Gen Z being younger than the others, it’s also a result of having shorter credit histories and fewer lines of credit. This presents a great chance for businesses to help Gen Z individuals establish responsible financial habits, such as opening a new line of credit to begin building a healthy credit history. Gen Z is actively seeking support now Consumers in the Gen Z age range recognize the importance of personal finance, but they also realize that they don’t have the knowledge needed to be successful. While people in Gen Z are still young (currently between the ages of about 11 and 26), many need guidance now for their financial wellness and many need help to keep their financial future secure. This means now is the perfect time to start building a lifetime relationship with them and become a trusted advisor by providing financial products and services to help them through their financial journey. There are about 72 million Americans in the Gen Z demographic[1]. A large percentage of this group may feel strongly about improving their financial wellness. With high levels of financial stress and generally low credit scores, many of them are looking for companies they can trust to help them build good credit and take control of their personal finances.  Since 2019, the number of consumers under the age of 30 enrolled in Experian Partner Solutions credit monitoring and identity protection services has doubled from 9% to 18%. Offering these financial tools to Gen Z is essential to building their trust and financial wellness, which can lead to an increase in future acquisition, retention, and revenue for your business. Click here to learn more *Calculated on the VantageScore® model. Your VantageScore® credit score from Experian® indicates your credit risk level and is not used by all lenders, so don’t be surprised if your lender uses a score that’s different from your VantageScore® credit score. Click here to learn more. [1] Insider Intelligence. 2023. Generation Z News: Latest characteristics, research, and facts. [2] Forbes. 2022. As Gen Z’s Buying Power Grows, Businesses Must Adapt Their Marketing. [3] Deloitte. Deloitte Gen Z and Millennial Survey 2022. Jan 2022. [4] Experian State of Credit Report. 2021.  [5] Greenlight Financial Technology, Inc. Survey finds Gen Z lacks knowledge and confidence in personal finance and investing. 2021. [6] NAPFA. NAPFA Survey on Americans’ sources for financial planning and retirement investing advice. 2021.

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