With fraud expected to surge amid uncertain economic conditions, fraudsters are preparing new deception techniques to outsmart businesses and deceive consumers. To help businesses prepare for the coming fraud threats, we created the 2023 Future of Fraud Forecast. Here are the fraud trends we expect to see over the coming year: Fake texts from the boss: Given the prevalence of remote work, there’ll be a sharp rise in employer text fraud where the “boss” texts the employee to buy gift cards, then asks the employee to email the gift card numbers and codes. Beware of fake job postings and mule schemes: With changing economic conditions, fraudsters will create fake remote job postings, specifically designed to lure consumers into applying for the job and providing private details like a social security number or date of birth on a fake employment application. Frankenstein shoppers spell trouble for retailers: Fraudsters can create online shopper profiles using synthetic identities so that the fake shopper’s legitimacy is created to outsmart retailers’ fraud controls. Social media shopping fraud: Social commerce currently has very few identity verification and fraud detection controls in place, making the retailers that sell on these platforms easy targets for fraudulent purchases. Peer-to-peer payment problems: Fraudsters love peer-to-peer payment methods because they’re an instantaneous and irreversible way to move money, enabling fraudsters to get cash with less work and more profit “As fraudsters become more sophisticated and opportunistic, businesses need to proactively integrate the latest technology, data and advanced analytics to mitigate the growing fraud risk,” said Kathleen Peters, Chief Innovation Officer at Experian Decision Analytics in North America. “Experian is committed to continually innovating and bringing solutions to market that help protect consumers and enable businesses to detect and prevent current and future fraud.” To learn more about how to protect your business and customers from rising fraud trends, download the Future of Fraud Forecast and check out Experian’s fraud prevention solutions. Future of Fraud Forecast Press Release

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

Alternative credit scoring has become mainstream. Lenders that use alternative credit scores can find opportunities to expand their lending universe without taking on additional risk and more accurately assess the credit risk of traditionally scoreable consumers. Obtaining a more holistic consumer view can help lenders improve automation and efficiency throughout the customer lifecycle. What is alternative credit scoring? Alternative credit scoring models incorporate alternative credit data* that isn't typically found on consumer credit reports. These scores aren't necessarily trying to predict alternative outcomes. The goal is the same — to understand the likelihood that a borrower will miss payments in the future. What's different is the information (and sometimes the analytical techniques) that inform these predictions.Traditional credit scoring models solely consider information found in consumer credit reports. There's a lot of information there — Experian's consumer credit database has data on over 245 million consumers. But although traditional consumer data can be insightful, it doesn't necessarily give lenders a complete picture of consumers' creditworthiness. Alternative credit scores draw from additional data sources, including: Alternative financial services: Credit data from alternative financial services (AFS) can tell you about consumers' experiences with small-dollar installment loans, single-payment loans, point-of-sale financing, auto title loans and rent-to-own agreements. Buy Now Pay Later: Buy Now Pay Later (BNPL) borrowing is popular with consumers across the scoring spectrum, and lenders can use access to open BNPL loans to better assess consumers' current capacity. Rental payments: Landlords, property managers, collection companies, rent payment services and consumer-permissioned data can give lenders access to consumers' rent payment history. Full-file public records: Credit reports generally only include bankruptcy records from the previous seven to ten years. However, lenders with access to full-file public records can also learn about consumers' property deeds, address history, and professional and occupational licenses. READ: Take a deep dive into Experian's State of Alternative Credit Data report to learn more about the different types of alternative credit data and uses across the loan lifecycle. With open banking, consumers can now easily and securely share access to their banking and brokerage account data — and they're increasingly comfortable doing so. In fact, 70% would likely share their banking data for better loan rates, financial tools or personalized spending insights.Tools like Experian Boost allow consumers to add certain types of positive payment information to their Experian credit reports, including rent, utility and select streaming service payments. Some traditional scores consider these additional data points, and users have seen their FICO Score 8 from Experian boosted by an average of 13 points.1 Experian Go also allows credit invisible consumers to establish a credit report with consumer-permissioned alternative data.  The benefits of using alternative credit data The primary benefit for lenders is access to new borrowers. Alternative credit scores help lenders accurately score more consumers — identifying creditworthy borrowers who might otherwise be automatically denied because they don't qualify for traditional credit scores. The increased access to credit may also align with lenders' financial inclusion goals.Lenders may additionally benefit from a more precise understanding of consumers who are scoreable. When integrated into a credit decisioning platform, the alternative scores could allow lenders to increase automation (and consumers' experiences) without taking on more credit risk. The future of alternative credit scoring Alternative credit scoring might not be an alternative for much longer, and the future looks bright for lenders who can take advantage of increased access to data, advanced analytics and computing power.Continued investment in alternative data sources and machine learning could help bring more consumers into the credit system — breaking barriers and decreasing the cost of basic lending products for millions. At the same time, lenders can further customize offers and automate their operations throughout the customer lifecycle. Partnering with Experian Small and medium-sized lenders may lack the budget or expertise to unlock the potential of alternative data on their own. Instead, lenders can turn to off-the-shelf alternative models that can offer immediate performance lifts without a heavy IT investment.Experian's Lift PlusTM score draws on industry- leading mainstream credit data and FCRA-regulated alternative credit data to provide additional consumer behavior insights. It can score 49% of mainstream credit-invisible consumers and for thin file consumers with a new trade, a 29% lift in scoreable accounts. Learn more about our alternative credit data scoring solutions. Learn more * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.1Experian (2023). Experian Boost

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 the last decade, electric vehicle registrations have increased by 3,600%, and the demand for alternative fuel vehicles continues to soar. Manufacturers are rapidly expanding alternative fuel operations to keep up with the demand from consumers that has expanded across all generations. Target in-market EV consumers Today’s automotive marketers understand that finding targeted consumer audiences is critical to a successful marketing strategy. With more electric vehicle model options available and improved infrastructure driving popularity, we’re seeing automotive marketers wanting to target in-market EV consumers as well as current alternative fuel vehicle owners. Applying data-driven insights to find targeted consumer audiences is critical to today’s marketing strategies. For example, as of Q2 2022, 23.5% of plug-in hybrid owners that returned to market, migrated to an electric vehicle As a marketer, would it be helpful to select In-Market Likely Segment Switchers as your target audience for your marketing campaign? Or Hybrid owners as a whole? Experian Automotive has a variety of alternative fuel owner audiences and in-market consumer audiences to help marketers target the right consumer with the right message on the right channel. The Experian Marketing Engine Syndicated Auto Audience portfolio includes 70+ audiences focused on likely buyers and owners of Electric Vehicle (EV) and Plug-In Hybrid (PHEV) vehicles. Of Experian’s 750+ syndicated auto audiences, we offer a subset of over 25 audiences focused on individual EV/PHEV vehicle models. How to find EV audiences on your preferred platform Experian electric vehicle audiences are available in the Auto Audience area of your preferred platform.  Simply navigate to Experian Automotive’s Audiences to find Electric Vehicle related audiences, as well as all of Experian’s Auto Audiences.  To learn more about Auto Audiences for Electric Vehicles, contact our Subject Matter Expert, Gary Meteer.

For a credit prescreen marketing campaign to be successful, financial institutions must first define their target audience. But just because you’ve identified your ideal customers, it doesn’t mean that every individual within that group has the same needs, interests or behaviors. As such, you’ll need to use data-driven customer segmentation to create messages and offers that truly resonate. Customer segmentation example Customer segmentation is the practice of dividing your target audience into smaller sub-groups based on shared characteristics, behaviors or preferences. This allows you to develop highly targeted marketing campaigns and engage with individual groups in more relevant and meaningful ways. What role does data play in customer segmentation? When it comes to segmenting customers, there isn’t a one-size-fits-all approach that works perfectly for all campaigns and markets. However, regardless of the campaign, you’ll need accurate and relevant data to inform your segmenting strategy. Let’s walk through a customer segmentation example. Say you want to launch a credit marketing campaign that targets creditworthy consumers in the market for a new mortgage. Some of the most influential data points to consider when segmenting include: Demographics Demographic data allows you to get to know your customers as individuals in terms of age, gender, education, occupation and marital status. If you want to create a segment that consists of only middle-aged consumers, leveraging demographic data makes it easier to identify these individuals, refine your messaging and predict their future buying behaviors. Life stage Life event data, such as new parents and new homeowners, helps you connect with consumers who have experienced a major life event. Because you’re targeting consumers in the market for a new mortgage, using fresh and accurate life stage data can help you create an engaging, event-based marketing campaign relevant to their timeline. Financial Financial data segments go beyond income and estimate the way consumers spend their money. With deeper insights into customers’ financial behaviors, you can more accurately assess creditworthiness and make smarter lending decisions. Transactional Transactional data segments group your customers according to their unique buying habits. By getting to know why they purchase your products or their frequency of spend, you can gain a better understanding of who your most engaged customers are, segment further and find opportunities for cross-sell and upsell. Why is data-driven customer segmentation critical for your business? With data-driven customer segmentation, you can develop relevant marketing campaigns and messages that speak to specific audiences, enabling you to demonstrate your value propositions more clearly and deliver personalized customer experiences. Additionally, because customer segmentation enables you to tailor your marketing efforts to those most likely to respond, you can achieve higher conversions while cutting down on marketing spend and resources. Ready to get started? While data-driven customer segmentation may seem overwhelming, Experian can help fill your marketing gaps with custom-based data, audiences and solutions. Armed with a better understanding of your consumers’ patterns and journeys, you can start targeting them more effectively. Create highly targeted credit marketing campaigns

From chatbots to image generators, artificial intelligence (AI) has captured consumers' attention and spurred joy — and sometimes a little fear. It's not too different in the business world. There are amazing opportunities and lenders are increasingly turning to AI-driven lending decision engines and processes. But there are also open questions about how AI can work within existing regulatory requirements, how new regulations will impact its use and how to implement advanced analytics in a way that increases equitable inclusion rather than further embedding disparities. How are lenders using AI today? Many financial institutions have implemented — or at least tested — AI-driven tools throughout the customer lifecycle to: Target the right consumers: With tools like Ascend Intelligence ServicesTM Target (AIS Target), lenders can better identify consumers who match their credit criteria and send right-sized offers, which enables them to maximize their acceptance rates. Detect and prevent fraud: Fraud detection tools have used AI and machine learning techniques to detect and prevent fraud for years. These systems may be even more important as new fraud risks emerge, from tried-and-true methods to generative AI (GenAI) fraud. Assess creditworthiness: ML-based models can incorporate a range of internal and external data points to more precisely evaluate creditworthiness. When combined with traditional and alternative credit data*, some lenders can even see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model. Manage portfolios: Lenders can also use a more complete picture of their current customers to make better decisions. For example, AI-driven models can help lenders set initial credit limits and suggest when a change could help them increase wallet share or reduce risk. Lenders can also use AI to help determine which up- and cross-selling offers to present and when (and how) to reach out. Improve collections: Models can be built to ease debt collection processes, such as choosing where to assign accounts, which accounts to prioritize and how to contact the consumer. Additionally, businesses can implement AI-powered tools to increase their organizations' productivity and agility. GenAI solutions like Experian Assistant accelerate the modeling lifecycle by providing immediate responses to questions, enhancing model transparency and parsing through multiple model iterations quickly, resulting in streamlined workflows, improved data visibility and reduced expenses. WATCH: Explore best practices for building, fine-tuning and deploying robust machine learning models for credit risk. The benefits of AI in lending Although lenders can use machine learning models in many ways, the primary drivers for adoption in underwriting include: Improving credit risk assessment Faster development and deployment cycles for new or recalibrated models Unlocking the possibilities within large datasets Keeping up with competing lenders Some of the use cases for machine learning solutions have a direct impact on the bottom line — improving credit risk assessment can decrease charge-offs. Others are less direct but still meaningful. For instance, machine learning models might increase efficiency and allow further automation. This takes the pressure off your underwriting team, even when application volume is extremely high, and results in faster decisions for applicants, which can improve your customer experience. Incorporating large data sets into their decisions also allows lenders to expand their lending universe without taking on additional risk. For example, they may now be able to offer risk-appropriate credit lines to consumers that traditional scoring models can't score. And machine learning solutions can increase customer lifetime value when they're incorporated throughout the customer lifecycle by stopping fraud, improving retention, increasing up- or cross-selling and streamlining collections. Hurdles to adoption of machine learning in lending There are clear benefits and interest in machine learning and analytics, but adoption can be difficult, especially within credit underwriting. A recent Forrester Consulting study commissioned by Experian found that the top pain points for technology decision makers in financial services were reported to be automation and availability of data. Explainability comes down to transparency and trust. Financial institutions have to trust that machine learning models will continue to outperform traditional models to make them a worthwhile investment. The models also have to be transparent and explainable for financial institutions to meet regulatory fair lending requirements. A lack of resources and expertise could hinder model development and deployment. It can take a long time to build and deploy a custom model, and there's a lot of overhead to cover during the process. Large lenders might have in-house credit modeling teams that can take on the workload, but they also face barriers when integrating new models into legacy systems. Small- and mid-sized institutions may be more nimble, but they rarely have the in-house expertise to build or deploy models on their own. The models also have to be trained on appropriate data sets. Similar to model building and deployment, organizations might not have the human or financial resources to clean and organize internal data. And although vendors offer access to a lot of external data, sometimes sorting through and using the data requires a large commitment. How Experian is shaping the future of AI in lending Lenders are finding new ways to use AI throughout the customer lifecycle and with varying types of financial products. However, while the cost to create custom machine learning models is dropping, the complexities and unknowns are still too great for some lenders to manage. But that's changing. Experian built the Ascend Intelligence Services™ to help smaller and mid-market lenders access the most advanced analytics tools. The managed service platform can significantly reduce the cost and deployment time for lenders who want to incorporate AI-driven strategies and machine learning models into their lending process. The end-to-end managed analytics service gives lenders access to Experian's vast data sets and can incorporate internal data to build and seamlessly deploy custom machine learning models. The platform can also continually monitor and retrain models to increase lift, and there's no “black box" to obscure how the model works. Everything is fully explainable, and the platform bakes regulatory constraints into the data curation and model development to ensure lenders stay compliant. Learn more * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

Strategic automotive marketing and measurement are getting more complicated with the increase in consumer channels and devices. This makes it harder for marketers to obtain a complete measurement picture. Measurement terminology is also evolving. Here's a look at some of today’s key definitions to familiarize you with the nuances and challenges it may already bring to your analytics. What is the open web? The open web is the web as a whole or the public side of the web with all the millions of sites that do not require a subscription or fee to use them. For example, in our industry, this would be an auto manufacturer’s website, a dealership’s website, or an online consumer shopping portal where you list your vehicles for sale – all of these are on the open web. These sites use open-source standards to deliver content to consumers without a separate app or company acting as gatekeepers. However, tracking approaches on the open web will shift as cookies will eventually disappear. What is a walled garden? A walled garden is a closed platform or ecosystem (e.g., Amazon, Apple, Facebook) wherein the platform provider controls the content, applications, and/or media and restricts access as it sees fit. The publisher offers consumer privacy and rich first-party data to advertisers, but the measurement is limited to activity within the ‘walls’ of the garden.  From an advertising perspective, buyers can only access these platforms through their own buying tools; they do not give access to any independent platforms. The publisher (the Walled Garden) handles all the buying, serving, tracking, and reporting within their ecosystem.  So, let’s say you are an automotive consumer checking out vehicles. If you’re reading your Facebook feed on your phone and you see an advertisement for a vehicle or a dealership, that OEM or dealership is advertising in a walled garden – in this case, the walled garden is Facebook. The challenge to an advertiser is that they can only measure activity that occurred within that ecosystem using the walled garden’s platform and measurement tools. What is a hedged garden? The “hedged garden” is a new industry concept. A hedged garden is when a network of publishers work together to activate first-party data sets in a privacy-compliant way across many partners at scale. These publishers run their businesses with large amounts of first-party consumer data. They often do not own or operate complete buying stacks. For example, companies like Target and Walmart let advertisers employ their data on shoppers for ad targeting, but brands can use their own buying tools. Other examples of a hedged garden might include Connected TV platforms such as Vizio’s or Samsung’s in-house ad businesses. If you’re sitting on your couch watching your Vizio-connected TV and you see an advertisement for a dealership or a manufacturer, they are advertised within that hedged garden.  As an advertiser, the advantage is that you can use their buying tool when targeting shoppers for your advertising. How to fill in the gaps the walled garden may leave open The walled garden can challenge marketers who desire cross-channel activation and measurement. If you're a marketer working within a walled garden, we can work with the data you have to give you a complete picture of your audience’s digital journey. Our experience and vast databases, including vehicle, credit, and customer insights, allow us to continue building strong partnerships within the fast-growing (Hedged Garden) ecosystem. We can help. Our Subject Matter Expert, Laurel Malhotra will be happy to answer any questions you may have. Contact her today.

According to Experian’s Automotive Market Trends Report: Q3 2022, new vehicle registrations were down 16.4%, going from 12.2 million through Q3 2021 to 10.2 million this quarter. Used vehicles experienced a 12.6% decline, coming in at 29.8 million through Q3 2022, from 34.1 million the previous year.

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

Financial wellness is defined by the United States Consumer Financial Protection Bureau as “a state of being in which you can fully meet your current and future financial obligations while feeling secure in your financial future and making choices that allow you to enjoy life,” as cited by Annuity.org.[1] This is a sense of security that most people strive for, but many have trouble achieving. When you provide financial wellness services to your customers, your likelihood of acquiring and retaining better customers who make smarter choices, borrow more money, and accumulate more wealth may increase. Increasing the number of these financially stable customers is crucial for business success. So how can financial wellness offerings create better business opportunities? 1. Build customer loyalty Loyal customers are key to the success of your business. Long-standing customers tend to spend more, try more new products, and provide more useful feedback than newer customers. By investing in the financial well-being of your customers, you could establish trust while creating longer-lasting relationships with the people you do business with. This could ultimately lead to higher customer retention and an increase in revenue for your business.  2. Help customers manage their financial stress Financial stress can have serious negative consequences if left unchecked. 88% of Americans see room for improvement in their overall financial wellness, and 71% say they are likely to set financial goals in 2023.[2] For this reason, it’s important to provide valuable financial information and resources to your customers as well as reassurance that they are not alone. Financial services such as credit alerts and identity monitoring can empower your customers to take a more proactive approach to reducing their stress and achieving financial wellness. 3. Encourage good customer habits Financial well-being is not attained overnight. For customers to feel confident with their finances, they need to practice good habits on a regular basis and see meaningful progress as a result of their efforts. Friendly reminders and encouragement for sticking to a solid financial plan are effective ways to keep your customers in good standing, and they also portray your business as a trusted resource for best practices. Tools like credit score trackers and financial calculators can offer valuable insights to your customers as they strive to maintain healthy financial habits. Providing financial wellness services for your customers could have a positive impact on your business and your bottom line. When your customers show loyalty to your business, feel less stress, and maintain good habits, they may be more likely to continue doing business with you and potentially refer your products and services to friends and family. By helping your customers achieve financial well-being, you are more likely to set your customers and your business on a path to success. Learn more about our financial wellness services [1] Annuity.org. 2022. Financial Wellness. [2] Lincoln Financial Group. 2022. Most Americans See Room to Improve Their Overall Financial Wellness in 2023, Says New Lincoln Financial Group Study.

With an abundance of loan options in today’s market, retaining customers can be challenging for banks and credit unions, especially small or regional institutions. And as more consumers look for personalization and digital tools in their banking experience, the likelihood of switching to institutions that can meet these demands is increasing.1 According to a recent Experian survey, 78% of consumers have conducted personal banking activities online in the last three months. However, 58% of consumers don’t feel that businesses completely meet their expectations for a digital online experience. To remain competitive in today's market, organizations must enhance their prescreen efforts by accelerating their digital transformation. Prescreen in today's economic environment While establishing a strong digital strategy is crucial to meeting the demands of today’s consumers, economic conditions are continuing to change, causing many financial institutions to either tighten their marketing budgets or hold off on their prescreen efforts completely. Fortunately, lenders can still drive growth during a changing economy without having to make huge cuts to their marketing budgets. How? The answer lies in digital prescreen. Case study: Uncover hidden growth opportunities Wanting to grow their business and existing relationships, Clear Mountain Bank looked for a solution that could help them engage customers with money-saving product offers while delivering a best-in-class digital banking experience. Leveraging Digital Prescreen with Micronotes, the bank was able to identify and present dollarized savings to customers who held higher-priced loans with other lenders. What’s more, the bank extended these offers through personalized conversations within their online and mobile banking platforms, resulting in improved digital engagement and increased customer satisfaction. By delivering competitive prescreen offers digitally, Clear Mountain Bank generated more than $1 million in incremental loans and provided customers with an average of $1,615 in cost savings within the first two months of deployment. “Digital Prescreen with Micronotes supplied the infrastructure to create higher-quality, personalized offers, as well as the delivery and reporting. They made prescreen marketing a reality for us.” – Robert Flockvich, Director of Community Outreach and Retail Lending at Clear Mountain Bank To learn more about how you can grow your portfolio and customer relationships, read the full case study or visit us. Download the case study Visit us 1The Keys to Solving Banking’s Customer Loyalty & Retention Problems, The Financial Brand, 2022.

The only thing constant is change. And as 2022 wraps up and businesses and consumers look toward 2023, the need for insights and data is at an all-time high to help forge the path ahead. With recent slowing economic growth, and uncertain macroeconomic and geopolitical climates, leading organizations are turning to credit, market, and economic trends, to help shape and inform future strategies. The challenge? With so many sources of information, it can be overwhelming to determine which information is relevant.  Experian Edge, our new thought leadership hub, compiles proprietary Experian data, and economic, credit and market trends in a single, easy-to-consume place. Covering the automotive, financial services, healthcare, retail and small business sectors, Experian Edge helps businesses navigate tomorrow with today’s insights.   Featured Publication: 2022 Experian Edge Chartbook  The data stories told during 2022 - particularly credit and economic trends - run the full gamut. From economic growth and the labor market, to consumer health and inflation, there is no shortage of insights to glean. The inaugural 2022 Experian Edge Chartbook compiles those key insights giving a comprehensive look at economic and credit trends and what they could mean for 2023. Download 2022 Experian Edge Chartbook Want more insights? Examples of what else you’ll find on Experian Edge include:  State of the Automotive Finance Market Report: Exclusive quarterly report on the latest trends and analysis of the U.S. automotive finance market. State of Alternative Credit Data Report: A deep dive into the uses of alternative data in consumer and small business lending.  State of Claims: 200 executive healthcare professionals shed light on the current claims environment.  Holiday Retail Guide 2022: Learn what types of behaviors you can expect to see from consumers this holiday shopping season. Beyond the Trends Report: Quarterly insights and commentary on economic conditions and future small business performance. Visit and bookmark Experian Edge for the latest intel you need to propel your business forward.  Visit Experian Edge

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

Reflections, New Predictions, and What to Expect by 2033.  Where We’ve Been: A Cybersecurity Recap It’s been a decade since Experian released its first forecast. At the time, hacker activity was heating up, and breach "fatigue" was setting in. The report highlighted the budding threat of healthcare incidents, started a conversation about the connection between the cloud, big data, and big international breaches, and was one of the first—if not the first preparedness and response organization to sound the alarm on the cyber insurance surge. Fast forward to 2023: Clever cybercriminals have not slowed, and data breaches are busier and livelier than ever, with cyberattacks costing organizations $2.9 million every minute1, with major businesses suffering losses of $25 per minute.2 Hold on to your keyboard if you’re wondering where the cybercriminals could go next. The Tenth Annual Experian Data Breach Industry Forecast findings offer a road map into the future. findings offer a road map into the future. Literally. It outlines how modern technology, cyber resilience, and cyber recovery will play a role in the next generation of attacks. With six predictions instead of five, this year’s report also candidly reflects on what we got right and where we missed the mark over the last nine years while homing in on what 2023 and 2033 could bring. Nearly 70% of business leaders feel their cybersecurity risks are increasing, and only 5% of companies2 data is probably protected.3 Where We Are: Reality. It’s Not Quite What It Seems With more than 80% of U.S.4 adults expressing some concern about the metaverse and deepfake-enabled attacks up 53% from 2021,5 2023 could see cyberattacks move into unprecedented and unchartered territory. Will keyboards and screens become easy gateways to widespread attacks in seen and unsuspected ways for corporate entities and consumers alike? What about the continued rise of remote work? Will its staying power reveal vulnerabilities? As technology evolves, so too can scams and increased risk. Are you prepared? Globally, cybercrime is on track to cost $10.5 trillion annually by 2025.6 Where We’re Headed: Today and 10 Years From Now The Tenth Annual Data Breach Industry Forecast isn’t a crystal ball, but it’s close. With now ten reports issued and over 18 years of experience servicing, researching, and tracking data breaches, I’ve encountered almost everything in the what-if world of preparedness drills and real-world live incident responses. I’ll end with this fact. Only time will tell what happens next. Until then, if you’re a CISO, cyber risk insurer, CFO, General Counsel, or other professional responsible for or connected to cybersecurity preparedness and response, I recommend you review the Tenth Annual Experian Data Breach Industry Forecast. Your company’s future could depend on it. Read the 2023 Experian Data Breach Industry Forecast 1-2 https://businessinsights.bitdefender.com/what-are-the-biggest-cyber-threats-of-the-future 3 https://www.accenture.com/_acnmedia/PDF-96/Accenture-2019-Cost-of-Cybercrime-Study-Final.pdf#zoom=50 4-5 https://www.varonis.com/ 6 Cybersecurity Ventures, Cybercrime Magazine