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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

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. 

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.

E-commerce digital transactions are rapidly increasing as online shopping becomes more convenient. In fact, e-commerce is projected to exceed 17% of all retail sales worldwide by 2027. As a result, opportunities for fraudsters to exploit businesses and consumers for monetary gain are reaching high levels. Businesses must be aware of the risks associated with card not present (CNP) fraud and take steps to protect themselves and their customers. What is card not present fraud? CNP fraud occurs when a criminal uses a stolen or compromised credit card to make a purchase online, over the phone, or through some other means where the card is not physically present at the time of the transaction. This type of fraud can be particularly difficult to detect and prevent, as it relies on the use of stolen card information rather than the physical card itself. CNP fraud can yield significant losses for businesses — these attacks are estimated to reach a staggering $28 billion in losses by 2026. Many have adopted various fraud prevention and identity resolution and verification tools to better manage risk and prevent fraud losses. Since much of the success or failure of e-commerce depends on how easy merchants make it for consumers to complete a transaction, incorporating CNP fraud prevention and identity verification tools in the checkout process should not come at the expense of completing transactions for legitimate customers. What do we mean by that? Let’s look at false declines. What is a false decline? False declines occur when legitimate transactions are mistakenly declined due to the business's fraud detection system incorrectly flagging the transaction as potentially fraudulent. This can not only be frustrating for cardholders, but also for merchants. Businesses may lose the sale and also be on the hook for any charges that result from the fraudulent activity. They can also result in damage to the business's reputation with customers. In either case, it is important for businesses to have measures in place to mitigate the risks of both. How can online businesses increase sales without compromising their fraud defense? One way to mitigate the risk of CNP fraud is to implement additional security measures at the time of transaction. This can include requiring additional verification information, such as a CVV code or a billing zip code to further authenticate the card holder’s identity. These measures can help to reduce the risk of CNP fraud by making it more difficult for fraudsters to complete a transaction. Machine learning algorithms can help analyze transaction data and identify patterns indicating fraudulent activity. These algorithms can be trained on historical data to learn what types of transactions are more likely to be fraudulent and then be used to flag potentially fraudulent transactions before it occurs. Businesses require data and technology that raise confidence in a shopper’s identity. Currently, the data merchants receive to approve transactions is not enough. A credit card owner verification solution like Experian Link fills this gap by enabling online businesses to augment their real-time decisions with data that links customer identity to the credit card being presented for payment to help verify the legitimacy of a transaction. Using Experian Link, businesses can link names, addresses and other identity markers to the customer’s credit card. The additional data enables better decisions, increased sales, decreased costs, a better buyer experience and better fraud detection. Get started with Experian Link™ - our frictionless credit card owner verification solution. Learn more

In recent blog posts, we’ve discussed growing in a down market and getting ahead with a proactive outreach and engagement strategy. In this article, we’ll focus on audience segmentation and multichannel marketing. As the market has shifted, effective cost management is a top priority. Lenders who get the most bang for their buck tend to use data to create their audience, segment and message. Best practice #1: audience segmentation It’s hard to beat the combination of credit and property data for mortgage lenders. Obtaining a holistic consumer view and property details (if they’re a homeowner), can help lenders determine the best mortgage product and refine their messaging. Many of our partners have great success leveraging a combination of property and credit insights to identify consumers for a home equity line of credit (HELOC) or new first mortgages. Let’s look at HELOC as an example. From a process perspective, we use property data to identify borrowers with properties that qualify for the lender’s HELOC program – sufficient equity, owner occupied, no tax liens, not listed for sale, a value below their upper lending bound, etc. Once the initial population is identified, we further segment their target population by adding key credit insights, such as current score and outstanding unsecured debt. This allows the lender to identify borrowers who qualify for their HELOC program and do specific outreach for either debt consolidation or remodel. By performing the equity and credit analytics with a single vendor, the lender can increase their speed to market.  The results? Lenders succeed by quickly reaching the right borrowers, with the right offer and message. Additionally, they don’t waste money on or disappoint applicants who don’t meet their program guidelines. Best practice #2: refining the message The next best practice I’d like to focus on is refining the message with relevant demographic and consumer behavior data. Experian studied the differences among consumers who recently purchased a home, those who recently secured a HELOC, and the general consumer population.   Look at these four categories from our Mosaic Group and consider how you would adjust your messaging if you really know your prospect? Might you incorporate different imaging for a Power Elite homeowner in your HELOC campaign than a Flourishing Family to whom you are marketing a first mortgage?   Or consider how different decision-making styles would impact the information you highlight in your outreach?  Look at the difference between HELOC borrowers and first mortgage borrowers in terms of their decision-making style. Different messaging will appeal to a consumer who is a brand loyalist versus someone who is a savvy researcher.  Best practice #3: omnichannel marketing strategy Finally, let’s focus on how best to reach the consumer. Not only is it important to meet consumers on their preferred channel, but a best practice is to execute an omnichannel strategy. We increasingly see lenders using emails in prescreen campaigns with invitations to apply, or ITAs, across multiple communication channels.  Look at the overall research for email, text, and direct mail. Increasingly, savvy marketers are asking us for emails in their prescreen campaigns, and it’s no surprise. Based on the research, a tailored email campaign can be very effective. Perhaps most surprising is the level of mortgage borrower engagement in streaming TV! This is just the tip of the iceberg in terms of how data can be sliced and diced to drive your omnichannel engagement strategy. In short, when executing a mortgage marketing campaign, it’s important to leverage available data for audience segmentation. Once your audience is identified, you’ll want to refine your message to resonate with each segment. Lastly, instituting a multichannel marketing strategy is key to ensuring you’re getting in front of your audience in the channel they’re most likely to engage. By adopting these best practices, you’ll reach the right borrower, with the right message, in the right channel, which, in-turn, will help boost the ROI of your marketing program.  To learn about Experian Mortgage solution offerings, click here. Learn more

Driving growth in a down mortgage market can be tricky. It’s a mad scramble to obtain quality mortgage leads that convert into profitable loans. At Experian Mortgage, we have a front row seat into the efficacy of different lead generation strategies, and what we know for certain, is that data matters in both the audience creation and outreach approach. I’ve compiled several best practices for identifying qualified prospects early in the homebuying journey and using analytics to focus your outreach on those most likely to convert. Best practice #1: credit-based triggers First, let’s focus on borrower-behavior triggers, as they’re key for getting ahead of the competition. I occasionally hear skepticism about tried-and-true credit-based prospect triggers, but many find them indispensable. Credit triggers alert you when borrowers apply for credit and when other indicators meet your specific lending criteria, including credit scores, score trends, credit limits, utilization and much more.  They’re effective – and not just for big lenders. Our clients leverage credit-based triggers to quickly pursue “hot leads,” and have reported higher response rates, lower acquisition costs and revenue growth. Best practice #2: property listing triggers Another borrower behavior to watch is listing a property for sale, which can be done using property listing triggers. You can use listing triggers to monitor current customers – and with Experian, you can prospect for new customers outside your portfolio. One of our clients instituted property listing triggers and immediately identified 40,000 homeowners in their footprint who had recently listed a property for sale. Experian research shows that a homeowner lists their property for sale, on average, 35 days before applying for a new mortgage. This means this lender had over a month to reach those consumers with a tailored message. Now that’s getting a jump on the competition! But what about those homeowners who list a property for sale but don’t move? We hear anecdotally about more homeowners putting their homes on the market to see what offers they can get. According to recent data, a higher percentage of listings fail to sell today than last year. While property listing remains one of the most predictive behaviors for purchase, there’s room to optimize. Whether your prospect came to you via a property or credit trigger, there’s an opportunity to improve your ROI by identifying trigger leads most likely to convert. Best practice #3: in-the-market models A key best practice in audience segmentation is to incorporate in-the-market models (ITMM). A good model is based on sophisticated analytics across hundreds of data elements and millions of loan applications. Additionally, a good model is tailored to your product. A consumer in the market to buy their first house will “look” very different than a consumer in the market for a Home Equity Line of Credit (HELOC). Experian clients are doing two impactful things with ITMM. First, they create their audience list by bundling ITMM with credit, income, and property data to identify qualified consumers likely to be in the market soon. Second, they optimize an existing marketing list. However, when it comes to a mortgage lead generation program, you can only optimize what you measure. Experian has been helping clients by analyzing their lost leads and lost loans. Several clients recently asked us to analyze their efficacy with marketing lists originating from digital mortgage lead aggregators (i.e., lists of consumers who sought information online about mortgages). I’ll focus here on the leads who did NOT originate a mortgage with our clients, but DID open a tradeline with someone else. My first observation is that prospects who opened a tradeline were significantly more likely to open a credit card than a mortgage. My second observation is when the prospect opened a mortgage loan with a different institution, 80% of the time that lender was a non-bank. This is higher than the current non-bank share of the market, which indicates non-banks are aggressive with their leads and poised to grow their share. Here’s where ITMM comes into play. By incorporating an ITMM specifically for your product – HELOC, purchase, refinance – you can focus attention on borrowers most likely to open a mortgage. In summary, instituting credit and property triggers is a critical best practice and will open the door to a plethora of prospects. If you want to level up your marketing strategy, incorporating an ITMM is key and will help you segment the trigger leads and home in on those that are most likely to convert. Be sure to check out the final blog post in this series, Lead Conversion Through Tailored Messaging and a Multichannel Mortgage Marketing Strategy. To learn about Experian Mortgage solution offerings, click here. Learn more

The collections landscape is changing due to shifting consumer behaviors, demands, regulations and an economy that’s in a constant state of flux. As the market evolves, the need for greater insight and analysis grows. Matthew Baltzer, Experian’s Senior Director of Product Marketing, discusses challenges facing the collections industry and how you can continue to build a profitable portfolio. For more information on enhancing your collections strategy, view our full Q&A video. Q: Which macroeconomic trends should debt collectors be the most aware of and why? A:While we are still seeing a reasonably healthy consumer, there are trends to monitor. The first would be employment, which continues to be strong. Laid-off individuals are typically able to move back into the labor force. Second, we're seeing strong consumer spending, with rates higher than in the past three years and high origination activity. A third is declining savings rates. During the pandemic, consumers stored away extra cash, which has since come to a halt. Part of that is likely due to inflation, but it could also point to signs of financial strain. Q: How could these trends impact debt collections strategies moving forward? A: At a portfolio level, they’re good news. The average consumer’s ability to pay has yet to degrade significantly. So, collectors should be able to continue collecting payments. However, six months from now, the impact of inflation and interest rates could take a toll, and settlement offers, or higher upfront payments, may be important tools to consider. Due to increasing interest rates, many households will send money to creditors, leaving less for everyday spending. Q: How has the average consumer been affected by inflation? A: As I mentioned, both consumer spending and overall debt are up. However, when it comes to spending, certain ‘categories’ are more impacted by inflation than others. Of course, home equity and mortgages are higher, which while important, is less impactful for debt collectors. In our recent webinar, ‘Economic Outlook and the Influence on Debt Collections,’ we highlighted the uneven impact inflation has on lower earners in categories such as rent, food and energy. Due to this, collectors may see a rise in delinquency rates, particularly in unsecured personal loans and potentially automotive loans. Q: How should consumers' response to inflation impact collections efforts? A: There may be an increase in opportunities in certain trades, such as utilities, automotive and unsecured personal loans. Are you positioned as an organization to target and serve those markets? For those in the industry, the real potential for an economic weakness should present an opportunity to evaluate your collection strategy. How will you adapt to a 20 to 30% increase in volume? What about working accounts with smaller balances, which we've seen more of since the last larger recession? Experian offers software and decisioning solutions that help debt collectors optimize their strategies for an improved return on investment. Q: What consumer specific data can help lenders better predict distressed consumers? A: As an originator, the first approach to consider should be leveraging new types of data that were not available during the last recession, such as trended, third-party and alternative credit data. Supplementary data can provide leading indicators that risk is increasing before a consumer goes delinquent and their accounts are past due. Additionally, advanced analytics scoring models can help you determine which accounts are more likely to be recoverable. Experian has a new scoring model that uses a complex blend of attributes to assess each trade's history and position in wallet to better predict the likelihood of that account self-curing and separate accounts that need the most attention from those that may need more time. Finally, with accurate consumer contact data, you can enhance your digital engagement strategy and reach the right person, at the right time, on the channel they prefer There’s no time like the present to equip yourself with a successful debt management strategy. With a more holistic consumer view, you can improve account prioritization, predictability and right-party contact rates. Learn more about our debt management solutions here. Watch on-demand webinar

Today’s mortgage market is challenging. Mortgage lenders and servicers will need to focus on product expansion to continue to grow their business. In a recent Q&A session, Susan Allen, Head of Product for Experian Mortgage, shared best practices for leveraging data for profitable growth.Q: At a high level, how can mortgage lenders and servicers grow their businesses?A: There are a lot of options to increase pipeline. One best practice we’re seeing now is to consider expanding both your product suite and your footprint. Very few lenders offer a comprehensive set of solutions in a national footprint. But demand is strong for solutions that go beyond traditional 30-year fixed-rate mortgages, including options to tap home equity. These types of products can help you grow your business by exposing you to new borrowers and broadening your relationships with clients. For example, we see several clients, even non-banks, venturing into credit cards and personal loans to meet their customers’ broader financial needs.Q: You mentioned demand for home equity solutions is strong. What should lenders consider when it comes to home equity loan growth strategies?A: The current record level of untapped equity makes home equity lines of credit (HELOCs) attractive for borrowers to use for debt consolidation, remodeling or to add to their rainy-day fund. For lenders to decide whether HELOCs would be profitable for their business, they should look broadly at data about borrowers, volumes and indicators of profitability, such as credit lines and utilization.Q: It’s one thing to talk about the HELOC market, but does Experian have any home equity data to show what’s happening in this space?A: Absolutely. We’re seeing several things when it comes to home equity data. First, HELOC volumes have doubled since January 2021, which indicates strong borrower interest. Second, we know that home prices are at record highs across the board, and we see this record of “tappable” equity translating into credit lines well over $100,000. What’s more, we’re seeing borrowers drawing down consistently at $37,000 on average, which is a healthy and profitable utilization rate. Lastly, greater than 90% of HELOC borrowers have a prime or super prime credit score. Our data shows HELOC borrowers have higher credit scores than new purchase borrowers. Additionally, conventional wisdom says that HELOCs are for seasoned homeowners, but according to the data, the younger generation of homeowners has tripled their HELOC originations. I’ve been in this industry for a long time, and to be honest, this shocked me. This makes it clear that it’s always important (especially for industry veterans) to constantly update our understanding of current market dynamics. Q: Wow, it sounds like expansion into home equity solutions is a no-brainer. What am I missing? A: HELOCs are a strong and growing market segment. But it’s not sufficient to look only at opportunity. We must also use the best data at our disposal to evaluate risk. With HELOC performance impacted by property values, recent concern over the stability of home prices is causing some lenders to pause. Clients tell us they would like to expand their HELOC offerings but aren’t sure when or where to start. Q: So, what’s the answer here?A: Data is key to taking the guesswork out of decisions. When it comes to HELOC expansion, lenders voice concern specifically about home price forecasts. Although it is notoriously hard to forecast home prices, you can use actual, current data to inform decisions about where and when to expand a home equity portfolio. For example, lenders can use listing data to gauge markets shifting from a “seller’s market” to a “buyer’s market.”Q: Susan, this has been a great discussion. Any final thoughts? A: As I’ve shared, great opportunities exist. With best-in-class data and analytics, lenders can find these opportunities and propel their businesses forward. Be sure to read the other blog posts in this series:Getting Ahead with a Proactive Mortgage Outreach and Engagement StrategyLead Conversion Through Tailored Messaging and a Multichannel Mortgage Marketing Strategy To learn about Experian Mortgage solution offerings, click here.

More than seven million Americans who are unbanked cite high account fees, insufficient funds to meet minimum balances and a lack of needed products and services as the main reasons for not having a checking or savings account.1 Credit unions understand that being unbanked comes at a steep cost and have turned their focus to developing products and strategies that prioritize financial inclusion — a movement to combat inequities in banking and better serve the financial needs of marginalized communities. In 2022, the House passed Expanding Financial Access for Underserved Communities Act to allow federal credit unions to add underserved areas to their fields of membership as a means of improving financial inclusion. “We believe diversity, inclusion, equity, belonging and accessibility has to be weaved into the strategic fabric of an organization [and its] culture," says Max Villaronga, President and Chief Executive Officer of Raiz Federal Credit Union. “When we don't participate in [diversity, equity and inclusion], we are complicit in essentially keeping people out of the banking system." For credit unions, driving financial inclusion starts with setting a vision that will leave a lasting legacy that includes fostering financial empowerment, closing the credit gap and building generational wealth among the communities they serve. Here's a roadmap for getting started. Best practices for engagement Establishing a set of best practices is the essential starting point for improving financial inclusion. The process begins with the mission statement and extends to all aspects of operations from hiring procedures to sponsorships and donations. Villaronga advocates three strategies for engagement: Engage the leadership team Conversations about financial inclusion need to start at the top. The C-suite must be willing to be honest about the barriers and willing to adopt changes that will make credit unions more inclusive. “[T]hese systemic barriers will exist until somebody deliberately moves them out of the way," Villaronga says. “The people who are feeling those barriers are not in the position to do the moving it's up to [CEOs and CFOs] to decide to do something to make a difference." Making a difference starts with choosing a leadership team that reflects the demographics of local communities. Case in point: At Raiz Federal Credit Union in El Paso, Texas, senior management and the board have LGBTQIA+ representation and include members from diverse racial and ethnic identities. The board of directors has also prioritized creating a pipeline that will attract more diverse talent to the board. “Many of [our board members] come from underserved backgrounds in our border community," Villaronga says. “This is a very personal journey for them because they can see themselves in the lives of the people we're serving." Build trust in underserved communities According to an FDIC Survey, “unbanked" U.S. households listed a lack of trust in financial institutions as a top reason for not having a bank account. And lack of access to a checking or savings account is most prominent among racial and ethnic minorities and low-income communities.2 Actions speak louder than words, according to Villaronga. Raiz Federal Credit Union uses diverse images in its advertising and provides information in both English and Spanish. The credit union was also awarded the Juntos Avanzamos (Together We Advance) designation from Inclusiv for its commitment to serving and empowering Hispanic communities by providing safe, affordable and relevant financial services. Villaronga believes that a designation like Juntos Avanzamos sends the message to the community that the credit union is committed to improving general financial literacy and pre-loan education, as well as reducing higher charge-offs and other barriers to accessing financial services that exist in lending and serving underserved communities. Dispel financial inclusion myths Among traditional financial institutions, myths about financial inclusion are widespread and include falsehoods that pricing products for marginalized communities are too challenging, reaching out is not profitable, and providing financial products to underserved markets is too risky. “Credit unions were really built to extend credit [and] were also originally established to serve consumers that were being ignored by the existing systems that were in place but those consumers are still being ignored today," Villaronga says. “Are those communities too risky to serve? Some companies are serving them [and] they would not be doing so if it was not profitable." Raiz Federal Credit Union offers several affordable loan products — from credit builder loans to citizenship loans and payday lender payoff loans along with credit cards — that allow members to build their credit scores and establish positive credit histories. Rather than pricing loans based on what the competition is charging, Villaronga calculates the fixed and variable costs, failure fraction and target return on assets to get a floor pricing per unit. The approach, he adds, allowed Raiz Federal Credit Union to report earnings of over 150 basis points in 2021 while maintaining a 12 percent capital ratio, proving that financial inclusion is good for the bottom line. “THE IDEA THAT YOU CANNOT [ACHIEVE FINANCIAL INCLUSION] IN A WAY THAT'S SAFE AND SOUND AND SATISFIES THE [NATIONAL CREDIT UNION ADMINISTRATION] IS TRULY A MYTH." - Max Villaronga, President and CEO, Raiz Federal Credit Union Partner for Success For credit unions, an important part of achieving financial inclusion goals is identifying partners that can help. Raiz Federal Credit Union set a goal to increase automated lending from 20 percent to 60 percent, but using a traditional loan origination program was insufficient to hit that target. A partnership with Experian allowed the credit union to access tools that allowed it to better identify non-traditional risks and opportunities, as well as develop more robust lending and optimized decision strategies. Experian launched Inclusion ForwardTM, an initiative to help boost financial inclusion and close the wealth gap, and support financial institutions by enhancing their inclusion approach by leveraging FCRA-regulated data sources (otherwise known as alternative data).3 In addition to providing a deeper view of unbanked and underbanked consumers and reducing friction and speed of decisioning through increased automation, Experian Lift PremiumTM uses income and employer data, social security and financial management insights — transaction behaviors that were historically credit invisible or unscorable — to help credit unions meet the needs of underserved markets and increase opportunities for inclusion. “This automation also allows us to reduce our fixed cost per unit — [and] it's a really big deal because this is not by little, but a lot," Villaronga says. “This lower cost to produce [a loan] allows us to improve our interest rates to underserved members, further creating an appealing value proposition that's in line with our financial inclusion strategy." Access our case study to learn more about how Experian can help grow your business with a frictionless digital prequalification experience. Access now 1Federal Reserve Bank of Cleveland (May 2022). Unbanked in America: A Review of Literature 2 Federal Deposit Insurance Corporation (December 2021). American Banks: Household use of Banking and Financial Services 3When 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

You walk into your home, flick the light switch, head to the fridge and grab a glass of cold water. Suddenly, you feel a chill and turn the thermostat up.  These habitual acts are basic, but fundamental to our lives. Unfortunately, not everyone has equal access to such luxuries. There is a substantial amount of people who are impacted by heavy energy burdens.   What is an energy burden?  An energy burden is the percentage of gross household income that goes towards energy costs. Two families can have similar energy bills, but different household incomes. Like many other industries, the utility sector is shifting its’ focus toward equitable outcomes and establishing and implementing effective efficiency programs.    Who do energy burdens impact?  Due to the energy burden, many communities of color have been historically underserved by energy efficiency and clean energy programs. The energy burden can also impact those who rent, have less efficient appliances or live in older homes.  According to the U.S. Census Bureau, as of August, 2022, 23.1% of U.S. adults lived in households that were unable to pay an energy bill in the last 12 months. Additionally, The American Council for Energy-Efficient Economy (ACEE) found that low-income Black, Hispanic and Native American households face dramatically higher energy burdens than average. How can Experian be a partner for energy equity?   As the “Consumer’s Bureau,” Experian is deeply committed to putting consumers’ best interests first as we make key decisions to support our clients. Like the energy industry, Experian wants to lessen the energy burden for underserved and low-income communities. This is a business of critical consequence, and we are focused on helping our clients accelerate progress and equity within the communities they serve. As we navigate along this inclusion journey together, we can assist with three core areas:  Measure and track: Understand geographies and audience segments containing the largest opportunities for inclusion within the communities you serve. Benchmark and track progress towards your internal diversity and inclusion goals. Determine who qualifies for energy efficiency programs by getting a more accurate view of the communities you serve. Include and reach: By incorporating supplementary data sources, we can help you identify and reach underserved consumers and small business owners who are often excluded from the traditional credit ecosystem. Inform and empower: Develop and educate vulnerable populations, offering the tools and support needed to advance their financial health journey. Enabling your consumers to obtain the assistance they need.  By leveraging our leading data assets, businesses can obtain a more holistic consumer view to drive better outcomes and opportunities while making smarter decisions and minimizing risk. With accurate data you can effectively prioritize field work, get correct assessment of household income, increase productivity of field personnel, and improve field collection rates. We care about doing the right thing and are here to ensure you meet your energy efficiency and equity goals. Together, we can make a positive impact on our communities and consumers.    To learn more about how Experian is helping the utility industry drive inclusion and bring equity to energy, visit us or request a call. Access the infographic Energy Burden Research. Aceee.org. (2022). Household Pulse Survey. Census.gov. (2022). Low-Income Households, Communities of Color Face High Energy. Aceee.org. (2022). Experian and Oliver Wyman find expanded data and advanced analytics can improve access to credit. Experian plc. (2022).

Last year, my wife and I decided to take advantage of Experian’s remote-work policy and move back to my hometown, so we could be closer to family and friends. As excited as we were, the idea of selling and buying a home during the market frenzy was a little intimidating. Surprisingly, finding a home wasn’t our challenge. We lucked out and found what we were looking for in the exact neighborhood we wanted. Our biggest challenge was timing. Our goal was to sell our current home and immediately move into the new one, with no overlap of payments or having to put our belongings in storage while we temporarily stayed with family (or in a short-term rental). Once we sold our home, we had exactly 30 days to close on our new home and move in. Since this wasn’t our first rodeo, I felt confident all would go smoothly. Things were on schedule until it came time to verify our income and employment. Who knew something so simple could be so hard? Let me share my experience with you (crossing my fingers you have a smoother experience in place for your borrowers): Pay statements — I was initially asked to provide pay statements for the previous two months. Simple enough for most borrowers, but it does require accessing your employer payroll system, downloading multiple pay statements and then either uploading them to your lender portal or emailing them to your loan officer (which no borrower should be asked to do). This took me less than 30 minutes to pull together. Verification report — After reviewing my pay statements, my lender told me they needed an official verification report on my current and previous employers. At the time, Experian had just acquired Corporate Cost Control (now part of Experian Employer Services), a company that offers verification-fulfillment services for employees, employers and verifiers. I told my lender I could provide the verification report via Corporate Cost Control and they agreed it would be sufficient. This took me several days to figure out. HR information — Just when I thought we were good, I received an email from my lender asking for one last thing — the HR contact information of my current and previous employers. Obtaining this information from Experian was easy, but I didn’t know where to start with my previous employer. I ended up texting some former colleagues to get the information I needed. This too took several days to figure out. Finally, I got the call from my lender saying everything checked out and I was good to proceed with the underwriting process. Whew! What I thought would take 30 minutes ended up taking a full week and threatened our ability to close on time. And not to mention was a massive headache for me personally. This isn’t how you want your borrowers to feel, which brings me to the title of this blog, it’s 2022, why is mortgage employment verification so painful in today’s digital age? Other industries have figured out how to remove pain and friction from their user experiences? Why is the mortgage industry lagging? Mortgage employment verification made easy If it’s lack of awareness, you should know there are tools that can automate verification decisions. Experian Verify™ is a perfect example where mortgage lenders can instantly verify a borrower’s income and employment information (both current and previous employers), without needing to ask the borrower to track down pay statements or HR contact information. You can literally verify information in seconds — not hours, days, or weeks.  And the service supports Day 1 Certainty® from Fannie Mae — giving you increased peace of mind the data is accurate and trusted. This not only improves the borrower experience but increases efficiency with your loan officers. Tools like Experian Verify are a win-win for you and your borrowers. So, what are you waiting for? Modernize your experience and give your borrowers (like me) the frictionless experience they deserve, and if we’re being honest, are starting to demand. Learn more about income and employment verification for mortgage    

Online transactions face a higher chance of being declined because face-to-face transactions come with a higher degree of confidence. Businesses who fail to address this problem run the risk of losing the customer permanently, damaging their reputation and bottom line. What can e-commerce marketplace merchants do to increase the approval rate of online payments without making fraud worse? Here are three tips: 1. Broaden access to data beyond what’s in the authorization stream. Merchants use a variety of solutions to prevent fraud and verify identities, but typically use very limited data to approve a transaction through the authorization stream between a merchant and issuer. The issuing bank often only compares the purchase data to the address listed on the card owner’s account, which can create discrepancies when a customer is trying to send an order to an alternate address from their primary home. That’s why it’s important for merchants to augment their decisioning with additional data sources to help inform the true customer risk profile. 2. Leverage capabilities that can assess risk for both the transaction and the individual behind it. Today, merchants leverage limited data including email address data, device information and other technologies in silos to augment their address verification capabilities. The challenge with these tools is that each judge the risk of a specific component of the transaction or the individual. Where integration is lacking, false positives are amplified. 3. Collaborate and share expertise and data across merchants and issuers. How can Experian help? Leveraging our multidimensional data, technical expertise and advanced analytics capabilities, we can help businesses frictionlessly authenticate valid customers, thus increasing revenue by increased approval rates, without increasing fraud or operating expenses. Only Experian Link™, our frictionless credit card owner verification solution can associate payment card with its owner. This solution combines Experian’s vast data assets – including over 500 million credit card account numbers on file in the U.S. across 250 million consumers – with our advanced analytics capabilities to match and assess the risk of the identity attributes presented to the merchant to the identity attributes contributed by the credit card’s issuer and to Experian’s network of credit and identity inquiries. The result: Experian Link’s patent-pending REST API simply and frictionlessly improves a merchant’s customer experience and helps increase revenue while reducing their fraud and operating expenses. Get started with Experian Link™ now. Experian Link

Experian recently attended Fintech Nexus USA, formally known as LendIt Fintech USA, the leading event for innovation in financial services. The event was held at the Javits Center in New York City on May 25-26. This year’s event housed over 4,000 attendees, 350 speakers and 225 sponsors. Experian was a proud platinum sponsor and participated in two expert sessions. Day one Gasan Awad, Product Management Vice President for Experian Fraud and Analytics, led the session, “Frictionless Fraud Prevention: Fintech’s Balancing Act.” Gasan was joined by Ibo Dusi, Chief Risk Officer for Revolut, and Ashish Gupta, Chief Risk Officer for LendingPoint, to discuss the growing fraud landscape. “ Fraud is not slowing down; it is getting more complex as customers continue to grow their online and digital usage.” Gasan Award There has been $56 billion in identity fraud losses since 2020, $13 billion stemmed from traditional identity fraud and $43 billion from identity fraud scams. 53% of consumers say security is the most important aspect of their online experience. During the session, our experts delved into important questions, including: What fraud and identity-proofing strategies should you consider to prevent sophisticated attacks and balance ease of interactions? How do you detect fraudsters without disrupting the customer experience? Want more insight? Access the discussion here. Learn more about how Experian supports fintechs by visiting our fintech resources page, and how we’re helping businesses of all types stay guarded against fraud with our fraud prevention solutions. Day two Greg Wright, Executive Vice President and Chief Product Officer for Experian, joined Afterpay, Sunbit and Jifiti in the session, “Reconciling Responsible Buy Now Pay Later (BNPL) with the Need for Access.”   BNPL industry fast facts: Last year in the U.S., 45 million Americans used BNPL. The number of U.S. users has grown 300% since 2018. Spending in the U.S. was $20.8B in 2021 and is forecasted to grow globally to $1T by 2025. Real-time data is critical for the BNPL industry. Greg provided insight into what Experian is doing to incorporate BNPL data into the lending ecosystem. Through The Buy Now Pay Later Bureau™, Experian plans to bring transparency to the BNPL and financial services industries. We are currently working with large BNPLs to support data furnishing of BNPL tradelines to the new bureau.     “We figured out a way to work with the BNPL clients to bring BNPL data into the lending ecosystem to where it does not have an immediate impact on your credit score just because you chose to use a BNPL option rather than a credit card,” said Greg Wright.      Typical lending risk models limit the accessibility of financing, but the nature of BNPL dictates that merchants and consumers need instant decision-making. Experian's response to the BNPL finance method is a consumer-friendly solution that supports end-to-end credit risk insights and point-of-sale financing solutions that do not fit into mainstream credit processes and aren’t adequately handled by traditional credit scores. This one-of-a-kind specialty bureau allows consumers to benefit from successful repayment behaviors and lenders of all types to drive more inclusive and responsible practices. Additionally, Experian has plans to make BNPL data visible on the core consumer credit profile. Ready to learn more? Access the discussion here. Discover how you can bring transparency to the industry with The Buy Now Pay Later Bureau and power innovative fintech lending solutions. Fintech resources The Buy Now Pay Later Bureau