The Experian Vision conference is an annual event hosted by the leader in global information services. Vision 2024, held in Scottsdale, Arizona, from May 20-23, gathered industry leaders, data experts, and business professionals to discuss the latest trends and innovations in data and analytics. Aligned with the theme of “Powering Opportunities,” Vision 2024 featured breakout sessions offering attendees valuable insights and strategies for using data to drive business growth and success. Here are the highlights from three of the sessions focused on housing topics. Two industry experts, Sam Khater, Chief Economist at Freddie Mac and Susan Allen, SVP of Product, Experian Housing, engaged in a lively and thought-provoking discussion. The program covered the current state of the mortgage market. Susan and Sam took turns presenting their findings, exchanging ideas, and sharing their perspectives about where lenders could see opportunity in the current challenging mortgage market. They identified these current challenges and opportunities for lenders and borrowers. The economy continues to expand at a solid growth rate. Consumer spending remains firm, and the labor market is tight. The healthy economy is causing inflation and interest rates to remain higher for longer. Home purchase demand is coming off cyclical lows, but home sales remain low with mortgage rates remain above 7%. Inventory is improving modestly, but it remains very low due to chronic undersupply. The dynamic of low home sales, and even lower supply will continue to pressure home prices to increase, especially given many borrowers are moving to more affordable markets more frequently than in the past. There are 46 million likely qualified non-homeowner consumers, of which 7 million appear ready for first time homeownership. Although affordability remains a significant challenge, there are geographic regions where aspiring first-time homeowners are finding better success. Lenders are pursuing data-driven, nuanced approaches to identify and successfully reach these consumers. Three recognized industry professionals headlined this panel discussion. Eric Czajka, VP of Governance and Oversight at Rocket Companies, Experian Housing’s Susan Allen, and Product Manager for Experian Housing, Angad Paintal, shared their insights with a review of recent innovations from Rocket, including specific Experian solutions that are supporting Rocket’s consumer engagement strategy. Lenders in attendance also learned the next steps they can take to win borrowers that ready to consider a refinance. Experian showcased what’s possible with the combination of multiple data sources in a user-friendly interface to help lenders prepare for a rate reduction, including the potential triggers for conventional refinance, VA refinance and FHA refinances. Each segment needs to move 50 basis points to make the possibility of a refinance reasonable for the borrower. Vision 2024 continued with a casual conversation between Newrez COO Joshua Bishop and Chris Travis, Software Sales Expert at Experian. Participants experienced a glimpse into recent developments in mortgage technology from the Newrez leader and how these advancements reflect the industry. The program featured an exchange of questions and answers centered around three crucial topics that have significant implications for housing industry growth and development. These include economic uncertainty (interest rates, refinances, and delinquency trends), government regulations and policies (Basel III, CFPB) and technology (big data and generative AI). The key takeaway from this session was that the mortgage industry is undergoing a tech revolution. Lenders and servicers are utilizing predictive models to assess risk and personalize communication, while generative AI streamlines document processing and provides a cleaner experience for internal and external users alike. Deep analytical tools provide a clearer picture of borrower finances and hardship resolutions. This technological embrace is transforming the mortgage process, making it faster, more efficient, and more accessible. Be part of the future at Vision 2025 Vision 2024 was a resounding success, bringing together our valued clients to share innovative ideas and forge new connections. We were thrilled by the thought-provoking discussions and the collaborative spirit that permeated the event. As we look ahead to next year's conference, we eagerly anticipate even more groundbreaking conversations and opportunities for growth. Don't miss out - secure your spot now and be part of the future at Vision 2025. Register now

As more consumers lean towards adaptable and efficient vehicles that fit their everyday lifestyle, it’s no surprise to see the nuanced shifts in consumer preferences over recent years. For instance, compact utility vehicles (CUVs) have resonated with those seeking versatility—emerging as the most registered new vehicle segment in the first quarter of 2024 at 51.1%, according to Experian’s Automotive Consumer Trends Report. When exploring the depths of CUV registrations, data showed Toyota led the market share for the non-luxury segment at 14.9% in Q1 2024. They were followed by Chevrolet (12.1%), Honda (11.4%), Subaru (10.4%), and Hyundai (10.0%). On the luxury side, Tesla accounted for 28.0% of the market share this quarter and Lexus trailed behind at 14.1%. Rounding out the top five were BMW (12.2%), Audi (8.6%), and Volvo (6.2%). CUV registration trends by generations It’s notable that different generations are drawn to CUVs for a multitude of personal preferences that align with their respective lifestyles. For example, Baby Boomers made up 32.3% of new retail registrations for CUVs and Gen X was close behind at 30.4% in Q1 2024. They were followed by Millennials (23.6%), Gen Z (7.9%), and the Silent Generation (5.4%). While some generations seek a vehicle that strikes a balance between practicality and comfort, others may prefer smaller and more maneuverable vehicles. Nonetheless, CUVs making up just over half of new retail registrations is something that should be watched closely. By leveraging multiple data points such as who is in the market for a CUV as well as the types of makes and models they’re interested in, professionals have the opportunity to strategize new ways to effectively reach shoppers. To learn more about CUVs, view the full report at Automotive Consumer Trends Report: Q1 2024. Or

Dealing with delinquent debt is a challenging yet crucial task, and when faced with economic uncertainties, the need for effective debt management and collections strategies becomes even more pressing. Thankfully, advanced analytics offers a promising solution. By leveraging data-driven insights, you can enhance operational efficiency, better prioritize accounts, and make more informed decisions. This article explores how advanced analytics can revolutionize debt collection and provides actionable strategies to implement treatment. Understanding advanced analytics in debt collection Advanced analytics involves using sophisticated techniques and tools to analyze complex datasets and extract valuable insights. In debt collection, advanced analytics can encompass various methodologies, including predictive modeling, machine learning (ML), data mining, and statistical analysis. Predictive modeling Predictive modeling leverages historical data to forecast future outcomes. By applying predictive models to debt collection, you can estimate each account's repayment likelihood. This helps prioritize your efforts toward accounts with a higher chance of recovery. Machine learning Machine learning algorithms can automatically identify patterns in large datasets, enabling more accurate predictions and classifications. For debt collectors, this means better segmenting delinquent accounts based on likelihood of repayment, risk, and customer behavior. Data mining Data mining involves exploring large datasets to unearth hidden patterns and correlations. In debt collection, data mining can reveal previously unnoticed trends and behaviors, allowing you to tailor your strategies accordingly. Statistical analysis Statistical methods help quantify relationships within data, providing a clearer picture of the factors influencing debt repayment and focusing on statistically significant repayment drivers, which aids in refining collection strategies. Benefits of advanced analytics in delinquent debt collection The benefits of employing advanced analytics in delinquent debt collection are multifaceted and valuable. By integrating these technologies, financial institutions can achieve greater efficiency, reduce operational costs, and improve recovery rates. Enhanced prioritization and decisioning With data and predictive analytics, you can gain a complete view of existing and potential customers to determine risk exposure and prioritize accounts effectively. By analyzing payment histories, credit scores, and other consumer behavior, you can enhance your collectoins prioritization strategies and focus on accounts more likely to  pay or settle. This ensures that resources are allocated efficiently, and decisions are informed, maximizing your return on investment. Watch: In our recent tech showcase, learn how to harness the power of our industry-leading collection decisioning and optimization capabilities.  Reduced costs Advanced analytics can significantly reduce operational costs by streamlining the collection process and targeting accounts with higher recovery potential. Automated processes and optimized resource allocation mean you can achieve more with less, ultimately increasing profitability. Better customer relationships With debt collection analytics, digital communication tools, artificial intelligence (AI), and ML processes, you can enhance your collections efforts to better engage with consumers and increase response rates. Adopting a more empathetic and customer-centric approach that embraces omnichannel collections can foster positive customer relationships. Implementing advanced analytics: A step-by-step guide Step 1: Data collection and integration The first step in implementing advanced analytics is to gather and integrate data from various sources. This includes payment histories, account information, demographic data, and external data such as credit scores. Ensuring data quality and consistency is crucial for accurate analysis. Step 2: Data analysis and modeling Once the data is collected, the next step is to apply advanced analytical techniques. This involves developing predictive models, training machine learning algorithms, and conducting statistical analyses to identify notable patterns and trends. Step 3: Strategy development Based on the insights gained from the analysis, you can develop targeted collection strategies. These may include segmenting accounts, prioritizing high-potential recoveries, and choosing the most effective communication methods. It’s essential to test and refine these strategies to ensure optimal performance continually. Step 4: Automation and implementation Implementing advanced analytics often involves automation. Workflow automation tools can streamline routine tasks, ensuring strategies are executed consistently and efficiently. Integrating these tools with existing debt collection systems can enhance overall effectiveness. Step 5: Monitoring and optimization Finally, continuously monitor the performance of your advanced analytics initiatives. Use key performance indicators (KPIs) to track success and identify areas for improvement. Regularly update models and strategies based on new data and evolving trends to maintain high recovery rates. Putting it all together Advanced analytics hold immense potential for transforming delinquent debt collection and can drive better return on investment. By leveraging predictive modeling, machine learning, data mining, and statistical analysis, financial institutions and debt collection agencies can perfect their collection best practices, prioritize accounts effectively, and make more informed decisions. Our debt collection analytics and recovery tools empower your organization to see the complete behavioral, demographic, and emerging view of customer portfolios through extensive data assets, advanced analytics, and platforms. As the financial landscape evolves, working with an expert to adopt advanced analytics will be critical for staying competitive and achieving sustainable success in debt collection. Learn more *This article includes content created by an AI language model and is intended to provide general information.

In this article...What is fair lending?Understanding machine learning modelsThe pitfalls: bias and fairness in ML modelsFairness metricsRegulatory frameworks and complianceHow Experian® can help As the financial sector continues to embrace technological innovations, machine learning models are becoming indispensable tools for credit decisioning. These models offer enhanced efficiency and predictive power, but they also introduce new challenges. These challenges particularly concern fairness and bias, as complex machine learning models can be difficult to explain. Understanding how to ensure fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations. What is fair lending? Fair lending is a cornerstone of ethical financial practices, prohibiting discrimination based on race, color, national origin, religion, sex, familial status, age, disability, or public assistance status during the lending process. This principle is enshrined in regulations such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA). Overall, fair lending is essential for promoting economic opportunity, preventing discrimination, and fostering financial inclusion. Key components of fair lending include: Equal treatment: Lenders must treat all applicants fairly and consistently throughout the lending process, regardless of their personal characteristics. This means evaluating applicants based on their creditworthiness and financial qualifications rather than discriminatory factors. Non-discrimination: Lenders are prohibited from discriminating against individuals or businesses on the basis of race, color, religion, national origin, sex, marital status, age, or other protected characteristics. Discriminatory practices include redlining (denying credit to applicants based on their location) and steering (channeling applicants into less favorable loan products based on discriminatory factors). Fair credit practices: Lenders must adhere to fair and transparent credit practices, such as providing clear information about loan terms and conditions, offering reasonable interest rates, and ensuring that borrowers have the ability to repay their loans. Compliance: Financial institutions are required to comply with fair lending laws and regulations, which are enforced by government agencies such as the Consumer Financial Protection Bureau (CFPB) in the United States. Compliance efforts include conducting fair lending risk assessments, monitoring lending practices for potential discrimination, and implementing policies and procedures to prevent unfair treatment. Model governance: Financial institutions should establish robust governance frameworks to oversee the development, implementation and monitoring of lending models and algorithms. This includes ensuring that models are fair, transparent, and free from biases that could lead to discriminatory outcomes. Data integrity and privacy: Lenders must ensure the accuracy, completeness, and integrity of the data used in lending decisions, including traditional credit and alternative credit data. They should also uphold borrowers’ privacy rights and adhere to data protection regulations when collecting, storing, and using personal information. Understanding machine learning models and their application in lending Machine learning in lending has revolutionized how financial institutions assess creditworthiness and manage risk. By analyzing vast amounts of data, machine learning models can identify patterns and trends that traditional methods might overlook, thereby enabling more accurate and efficient lending decisions. However, with these advancements come new challenges, particularly in the realms of model risk management and financial regulatory compliance. The complexity of machine learning models requires rigorous evaluation to ensure fair lending. Let’s explore why. The pitfalls: bias and fairness in machine learning lending models Despite their advantages, machine learning models can inadvertently introduce or perpetuate biases, especially when trained on historical data that reflects past prejudices. One of the primary concerns with machine learning models is their potential lack of transparency, often referred to as the "black box" problem. Model explainability aims to address this by providing clear and understandable explanations of how models make decisions. This transparency is crucial for building trust with consumers and regulators and for ensuring that lending practices are fair and non-discriminatory. Fairness metrics Key metrics used to evaluate fairness in models can include standardized mean difference (SMD), information value (IV), and disparate impact (DI). Each of these metrics offers insights into potential biases but also has limitations. Standardized mean difference (SMD). SMD quantifies the difference between two groups' score averages, divided by the pooled standard deviation. However, this metric may not fully capture the nuances of fairness when used in isolation. Information value (IV). IV compares distributions between control and protected groups across score bins. While useful, IV can sometimes mask deeper biases present in the data. Disparate impact (DI). DI, or the adverse impact ratio (AIR), measures the ratio of approval rates between protected and control classes. Although DI is widely used, it can oversimplify the complex interplay of factors influencing credit decisions. Regulatory frameworks and compliance in fair lending Ensuring compliance with fair lending regulations involves more than just implementing fairness metrics. It requires a comprehensive end-to-end approach, including regular audits, transparent reporting, and continuous monitoring and governance of machine learning models. Financial institutions must be vigilant in aligning their practices with regulatory standards to avoid legal repercussions and maintain ethical standards. Read more: Journey of a machine learning model How Experian® can help By remaining committed to regulatory compliance and fair lending practices, organizations can balance technological advancements with ethical responsibility. Partnering with Experian gives organizations a unique advantage in the rapidly evolving landscape of AI and machine learning in lending. As an industry leader, Experian offers state-of-the-art analytics and machine learning solutions that are designed to drive efficiency and accuracy in lending decisions while ensuring compliance with regulatory standards. Our expertise in model risk management and machine learning model governance empowers lenders to deploy robust and transparent models, mitigating potential biases and aligning with fair lending practices. When it comes to machine learning model explainability, Experian’s clear and proven methodology assesses the relative contribution and level of influence of each variable to the overall score — enabling organizations to demonstrate transparency and fair treatment to auditors, regulators, and customers. Interested in learning more about ensuring fair lending practices in your machine learning models?    Learn More This article includes content created by an AI language model and is intended to provide general information.

Experian's Automotive Consumer Trends Quarterly Report goes beyond understanding general car-buying trends. Each quarter, we delve deeper into a specific vehicle segment, analyzing the demographics (who's buying) and psychographics (why they're buying) of those consumers. New eBrochures help turn insights into action Although valuable, what can you do with this information? That's where The Trade Desk comes in. They leverage the insights from our report to create a comprehensive omnichannel strategy for reaching in-market car buyers. This strategy goes beyond demographics, revealing: Top web content preferences: Where are these consumers spending their online time? Frequented websites and apps: What digital platforms are most relevant to them? Top CTV and audio examples: Which streaming services and audio channels should be targeted? A Consolidated Snapshot The Trade Desk provides a clear picture of channel distribution, ensuring your advertising reaches the right audience across the most effective platforms. This combined approach empowers you to target car shoppers with laser precision, maximizing your advertising impact. Experian Automotive and The Trade Desk are committed to developing solutions that balance advertiser needs with consumer privacy. The Trade Desk’s clients can access Experian’s over 2,400 syndicated audiences across eight verticals, including over 750 automotive audiences by make, model, fuel type, price, vehicle age, and more. Accessing the insights: To view the latest Experian Automotive Consumer Trends Quarterly Report, visit us at: www.experian.com/automotive/auto-consumer-trends-form. You can review Experian and The Trade Desk’s collaborative eBrochures for the following vehicle segments:

In this article...What is a TOAD attack?How TOAD attacks happenEffective countermeasures Keeping TOADS at bay with Experian Imagine receiving a phone call informing you that your antivirus software license is about to expire. You decide to renew it over the phone, and before you know it, you have been “TOAD-ed”! What is a TOAD attack? Telephone-Oriented Attack Deliveries (TOADs) are an increasingly common threat to businesses worldwide. According to Proofpoint's 2024 State of the Phish Report, 10 million TOAD attacks are made every month, and 67% of businesses globally were affected by a TOAD attack in 2023. In the UK alone, businesses have lost over £500 million to these scams, while in the United States the reported monetary loss averaged $43,000 per incident, with some losses exceeding $1 million.TOADs involve cybercriminals using real phone numbers to impersonate legitimate callers, tricking victims into divulging sensitive information or making fraudulent transactions. This type of attack can result in substantial financial losses and reputational damage for businesses. How TOAD attacks happen TOAD attacks often involve callback phishing, where victims are tricked into calling fake call centers. Before they strike, scammers will gather a victim's credentials from various sources, such as past data breaches, social media profiles, and information bought on the dark web. They will then contact the individual through applications like WhatsApp or call their phone directly. Here is a common TOAD attack example: Initial contact: The victim receives an email from what appears to be a reputable company, like Amazon or PayPal. Fake invoice: The email contains a fake invoice for a large purchase, prompting the recipient to call a customer service number. Deception: A scammer, posing as a customer service agent, convinces the victim to download malware disguised as a support tool, granting the scammer access to the victim's computer and personal information. These techniques keep improving. One of the cleverer tricks of TOADs is to spoof a number or email so they contact you as someone you know. Vishing is a type of phishing that uses phone calls, fake numbers, voice changers, texts, and social engineering to obtain sensitive information from users. It mainly relies on voice to fool users. (Smishing is another type of phishing that uses texts to fool users, and it can be combined with phone calls depending on how the attacker works.) According to Rogers Communication website, an employee in Toronto, Canada got an email asking them to call Apple to change a password. They followed the instructions, and a “specialist” helped them do it. After receiving their password, the cyber criminals used the employee's account to send emails and deceive colleagues into approving a fake payment of $5,000. Artificial intelligence (AI) is also making it easier for TOAD phishing attacks to happen. A few months ago, a Hong Kong executive was fooled into sending HK$200m of his company's funds to cyber criminals who impersonated senior officials in a deepfake video meeting. Effective countermeasures To combat TOAD attacks, businesses must implement robust solutions. Employee training and awareness: Regular training sessions and vishing simulations help employees recognize and respond to TOAD attacks. Authentication and verification protocols: Implementing multi-factor authentication (MFA) and call-back verification procedures enhances security for sensitive transactions. Technology solutions: Bots and spoofing detection and voice biometric authentication technologies help verify the identity of callers and block fraudulent numbers. Monitoring and analytics: Advanced fraud detection and behavioral analytics identify anomalies and unusual activities indicative of TOAD attacks. Secure communication channels: Ensure consumers have access to verified customer service numbers and promote secure messaging apps. A strong strategy should also involve using advanced email security solutions with AI fraud detection and machine learning (ML) to effectively defend against TOAD threats. These can help identify and stop phishing emails. Regular security audits and updates are necessary to find and fix vulnerabilities, and an incident response plan should be prepared to deal with and reduce any breaches. By integrating technology, processes, and people into their strategy, organizations can develop a strong defense against TOAD attacks. Keeping TOADS at bay with Experian®  By working and exchanging information with other businesses and industry groups, you can gain useful knowledge about new or emerging threats and defense strategies. Governments and organizations like the Federal Communications Commission (FCC) have a shared duty to defend the private sector and public consumers from TOAD attacks, while many of the current rules and laws seem to lag behind what criminals are doing. By combining the best data with our automated ID verification processes, Experian® helps you protect your business and reputation. Our best-in-class solutions employ device recognition, behavioral biometrics, machine learning, and global fraud databases to spot and block suspicious activity before it becomes a problem. Learn more *This article includes content created by an AI language model and is intended to provide general information.

In the ever-evolving landscape of automotive marketing, insight and measurement are paramount. Recognizing this, Strategus and Experian Automotive have joined forces to help improve digital automotive marketing campaign measurement. The Challenge The case study unfolds against a backdrop familiar to many advertisers: a regional advertising agency representing an OEM sought to evaluate the performance of their CTV automotive marketing campaign. Their objectives were clear: Assess digital and TV campaign performance, attributing actual vehicle sales accurately. Pinpoint top-performing audience segments and offers, factoring in demographic and psychographic variations. Adapt marketing strategy based on real-time campaign performance metrics. The Solution Strategus took the helm, leveraging Experian’s OmniImpact for Automotive solution to navigate these goals. They crafted a programmatic campaign targeting previous buyers of a specific vehicle model within a targeted geographic region. Through CTV advertising, they heightened awareness among the targeted audience, ensuring maximum engagement. Joel Cox, co-founder and EVP of Innovation and Strategy at Strategus summarizes the value of this collaboration perfectly: “Strategus is thrilled to collaborate with Experian to offer this novel auto sales attribution solution. While we know the positive influence CTV advertising can play in driving auto sales, the measurement and attribution of those sales can be a murky and incongruent snapshot heavily reliant on assumption and incomplete first-party datasets. While Experian’s OmniImpact for Automotive campaign measurement solution has been available to the largest auto advertisers, this relationship between Strategus and Experian Automotive democratizes this comprehensive measurement solution to all auto-selling clients of Strategus, regardless of size or sales volume.” Click here to learn more about the specific results and how Strategus and Experian Automotive work together to optimize campaign focus. Conclusion In a digital age where data reigns supreme, collaborations like that between Strategus and Experian Automotive showcase the power of leveraging precise insights to drive campaign results and automotive sales. By combining innovative strategies with advanced analytics, advertisers can not only measure campaign performance accurately but also adapt in real-time to maximize impact. As automotive marketing continues to evolve, relationships such as these that prioritize precision and insight will undoubtedly shape the industry's future. Or

Over the past few years, we’ve seen in-market shoppers lean into the used vehicle space; however, with new vehicle inventory continuing to rebound, we’re starting to see a reversal of fortune. Data in the first quarter of 2024 shows how the resurgence of new vehicle inventory is reshaping the automotive landscape.

Mortgage lenders looking to attract first-time homebuyers must understand their needs, wants, and finances, especially as the economic environment and evolving generational trends shift. Understanding who this buyer segment is and what they buy unlocks growth potential for today’s attentive mortgage lenders. Financial diversity defines first-time homebuyers First-time homebuyers are searching for the attainable, which is not easy today. High interest rates, low housing inventory, and individual financial circumstances contribute to the hardships the housing market presents. Even with ups and downs and difficulties in the marketplace, first-time homebuyers continue to show their grit. Over two-thirds of first-time homebuyers have an annual household income over $90k, with 27% having household income over $180k. Additionally, Experian Housing research shows that 85% of first-time homebuyers have prime or super-prime credit scores. While credit and income play critical roles in evaluating borrower risk, they're not the only factors. The mortgage lending market is slowly leaning into the use of alternative credit data, such as rental payment information, to determine a borrower's creditworthiness. These changes are crucial in our industry's effort to support consumers on their journey towards homeownership. Financial realities impact property choices Experian Housing’s recent white paper looking at first-time purchasers shows over 85% buying single-family homes, with roughly 70% of these buyers belonging to Generation Y (Gen Y) and Generation Z (Gen Z). While starter homes suggest impermanence, Gen Y and Gen Z buying habits reflect their values and overall desire for stability. These motivated buyers that understand the economic woes, are adjusting and looking for options. More than three-fourths of first-time purchases are older homes, built before 2000. However, Experian’s same research showed sales of new construction homes (2021-2023) increased over the prior two years, particularly among first-time buyers. Builder credits and other incentives make new builds more appealing, and lenders leveraging their mortgage market expertise will be able to discuss options customized to the borrower, helping them make the decision best fitting their needs. Especially among younger generations, first-time homebuyers are considering different housing options in their path to homeownership. From multigenerational housing and co-owning a home with friends and family to smaller homes and moving further away for affordability reasons, options are on the table.1 Untapped potential for savvy lenders The modern mortgage landscape offers thoughtful lenders opportunities to drive growth. Diversity in the first-time homebuyer profiles means that lenders who distinguish themselves by tailoring their services to the borrower’s needs. This may include, but is certainly not limited to: Drawing on their knowledge of first-time homebuyer programs, grants and loans appropriate to the borrower. Improving their overall financial well-being with financial literacy education. Expertly guiding them through the complex lending process. Ensuring as swift and smooth a transaction as possible with timely and responsive communications. For more information about the lending possibilities for first-time homebuyers, download our white paper and visit us online. Download white paper Learn more 1 “Several Generations Under One Roof,” census.gov; “What to Know About Co-Buying A House,” myhome.freddiemac.com

In the previous episode of “The Chrisman Commentary” podcast, Joy Mina, Director of Product Commercialization at Experian, talked about the misconceptions associated with verifications and what organizations can do to enhance their strategies. In the latest episode, Experian's Ken Tromer and Jamie Norris discuss ways mortgage companies can optimize their business expenses and protect prospects. "The market has been asking for solutions to help with cost mitigation and lead protection for quite some time," said Jamie. "We've listened to the market and Power Profile Plus™ does just that." Listen to the full episode for all the details and learn more about Power Profile Plus™ for Mortgage. Listen to podcast Learn more

The morning kicked off with the buzz of innovation. Shri Santhanam, Financial Services and Data General Managers Platforms and Software, and leaders from Experian debuted the latest enhancements for the Experian Ascend Technology PlatformTM. The demo touted faster automation, seamless data integration, security and compliance, and simplified experience. Resiliency, security and gen AI capabilities are all core components and outputs of the new platform. Keynote speakers: Steffi Graf and Andre AgassiTennis icons Steffi Graf and Andre Agassi captivated the audience with stories of their memorable matches, how they met and their foray into pickleball. From notorious rivals to the pivotal moments that were part of their weeks at seeding in the number 1 position – 377 weeks and 101 weeks respectively. Keynote speaker: Jason SudeikisEmmy-award winning actor, comedian, writer and producer – and also known as the affable Ted Lasso – Jason Sudeikis had the crowd laughing reminiscing his favorite skits from his time at Saturday Night Live and outtakes from his numerous films. He talked about the impact of Ted Lasso and the origin of the “Believe” sign, as well as what’s ahead. Sessions – Day 2 highlights The conference hall was buzzing with conversations, discussions and thought leadership. Some themes definitely rose to the top — the increasing proliferation of fraud and how to combat it without diminishing the customer experience, leveraging AI and transformative technology in decisioning and how Experian is pioneering the GenAI era in finance and technology. Alternative dataAlternative data can be used to holistically measure a consumer's creditworthiness. Property data to banking insights to consumer-permissioned data and more can be a critical part of your strategies to segment, analyze and underwrite unbanked and new-to-credit consumers. EngagementEconomic headwinds and fierce competition in the banking industry have acquisition costs soaring, making it more important than ever to be critical of your organization’s advertising spend. Meanwhile, consumers are trending back toward the convenience of banking bundles, and they expect their financial institutions to help them improve their financial health. These conditions create a unique opportunity to extract value from digital experiences. CollectionsData, advanced analytics and machine learning are transforming all aspects of collections during a time when consumer debt pressure is building. Organizations can harness the power of data-driven insights, predictive modeling, enhanced segmentation and optimized decisions to ensure they have the strongest contact data and best collections strategy to reduce delinquencies and boost recovery rates. See you next year for Vision 2025!

This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies. After again announcing no change in the target fed funds rate during their May meeting, the Federal Reserve continues to face the decision of when to begin cutting rates. The economic data released this month only complicated this decision, as growth came in well-below expectations and the labor market seemed to ease on several fronts. However, there was only minimal downward progress in inflation, especially considering the high prices seen over the past few months. In this month’s report, we dive into the data developments that comprise this economic story. Data highlights from this month’s report include: Economic growth in Q1 came in at 1.6%, under economists’ expectations. Underlying components of consumer spending and business investment remained solid. Inflation cooled in April, with annual headline inflation down from 3.5% to 3.4% and annual core inflation down from 3.8% to 3.6%. Consumer sentiment fell 13% in May, due to stubborn inflation, low growth, and easing in the labor market. Check out our report for a deep dive into the rest of this month’s data, including the latest trends in job creation, spending, and the fed funds rate. Download May's State of the Economy report  To have a holistic view of our current environment, it’s important to view the economy from different angles and through different lenses. Watch our team of experts discuss the latest economic and credit trends in the recording of our latest macroeconomic forecasting webinar, download our latest forecast scenario report, or listen to our latest Econ to Action podcast for views on the economic environment in different market segments. For more economic trends and market insights, visit Experian Edge.

Experian’s award-winning platform now brings together market-leading data, generative AI and cutting-edge machine learning solutions for analytics, credit decisioning and fraud into a single interface — simplifying the deployment of analytical models and enabling businesses to optimize their practices. The platform updates represent a notable milestone, fueled by Experian’s significant investments in innovation over the last eight years as part of its modern cloud transformation.  “The evolution of our platform reaffirms our commitment to drive innovation and empower businesses to thrive. Its capabilities are unmatched and represent a significant leap forward in lending technology, democratizing access to data in compliant ways while enabling lenders of all sizes to seamlessly validate their customers’ identities with confidence, help expand fair access to credit and offer awesome user and customer experiences,” said Alex Lintner CEO Experian Software Solutions. The enhanced Experian Ascend Platform dramatically reduces time to install and offers streamlined access to many of Experian's award-winning integrated solutions and tools through a single sign-on and a user-friendly dashboard. Leveraging generative AI, the platform makes it easy for organizations of varying sizes and experience levels to pivot between applications, automate processes, modernize operations and drive efficiency. In addition, existing clients can easily add new capabilities through the platform to enhance business outcomes. Read Press Release Learn More Check out Experian Ascend Platform in the media: Transforming Software for Credit, Fraud and Analytics with Experian Ascend Platform™ (Episode 160) Reshaping the Future of Financial Services with Experian Ascend Platform Introducing Experian’s Cloud-based Ascend Technology Platform with GenAI Integration 7 enhancements of Experian Ascend Platform

“Learn how to learn.” One of Zack Kass’, AI futurist and one of the keynote speakers at Vision 2024, takeaways readily embodies a sentiment most of us share — particularly here at Vision. Jennifer Schulz, CEO of Experian, North America, talked about AI and transformative technologies of past and present as she kicked off Vision 2024, the 40th Vision. Keynote speaker: Dr. Mohamed El-Erian Dr. Mohamed El-Erian, President of Queens’ College, Cambridge and Chief Economic Advisor at Allianz, returned to the Vision stage to discuss the labor market, “sticky” inflation and the health of consumers. He emphasized the need to embrace and learn how to talk to AI engines and that AI can facilitate content, creation, collaboration and community Keynote speaker: Zack Kass Zack Kass, AI futurist and former Head of Go-To-Market at OpenAI, spoke about the future of work and life and artificial general intelligence. He said AI is aiding in our entering of a superlinear trajectory and compared the thresholds of technology versus those of society. Sessions – Day 1 highlights The conference hall was buzzing with conversations, discussions and thought leadership. Some themes definitely rose to the top — the increasing proliferation of fraud and how to combat it without diminishing the customer experience, leveraging AI and transformative technology in decisioning and how Experian is pioneering the GenAI era in finance and technology. Transformative technologiesAI and emerging technologies are reshaping the finance sector and it's the responsibility of today's industry leaders to equip themselves with cutting-edge strategies and a comprehensive understanding to master the rapidly evolving landscape. That said, transformation is a journey and aligning with a partner that's agile and innovative is critical. Holistic fraud decisioningGenerative AI, a resurgence of bank branch transactions, synthetic identity and pig butchering are all fraud trends that today's organizations must be acutely aware of and armed to protect their businesses and customers against. Leveraging a holistic fraud decisioning strategy is important in finding the balance between customer experience and mitigating fraud. Unlocking cashflow to grow, protect and reduce riskCash flow data can be used not only across the lending lifecycle, but also as part of assessing existing portfolio opportunities. Incorporating consumer-permissioned data into models and processes powers predicatbility and can further assess risk and help score more consumers. Navigating the economyAmid a slowing economy, consumers and businesses continue to struggle with higher interest rates, tighter credit conditions and rising delinquencies, creating a challenging environment for lenders. Experian's experts outlined their latest economic forecasts and provided actionable insights into key consumer and commercial credit trends. More insights from Vision to come. Follow @ExperianVision and @ExperianInsights to see more of the action.

With e-commerce booming and more transactions occurring online, the threat of chargeback fraud has never been more significant. In this article, we'll explore chargeback fraud, why it's a growing problem, and, most importantly, how to prevent it. Whether you're a small or large business, understanding and implementing robust chargeback fraud prevention measures is critical to protecting your organization. Understanding chargeback fraud Before we can prevent chargeback fraud, we need to know what we're dealing with. A chargeback happens when a cardholder disputes a transaction, or files a chargeback request, leading to the reversal of the payment to the merchant. Chargebacks can occur for various reasons including: Fraudulent transactions: If a card is stolen or its information is used without authorization, like in the case of account takeover fraud or card not present fraud, the cardholder can dispute the charges. Unauthorized transactions: Even if the cardholder didn't lose their card, they might notice charges they didn't make. Quality issues: If the product or service doesn't meet the cardholder's expectations or has a defect, they might dispute the charge. Billing errors: Sometimes, billing mistakes happen, such as being charged multiple times for the same transaction. Subscription cancellations: When a cardholder cancels a subscription but continues to be billed they can dispute the charges. While there are legitimate reasons for chargebacks, chargeback fraud, also known as friendly fraud, occurs when a customer makes a legitimate purchase with their credit card and then disputes the charge by filing a chargeback request. Unlike third-party fraud, where the cardholder's information is stolen or used without permission, in chargeback fraud, the cardholder initiates the dispute to avoid paying for goods or services they legitimately received. Chargeback fraud can take various forms: False claims of non-receipt: The cardholder claims they never received the purchased item, even though they did. Unauthorized transaction claims: The cardholder denies making the purchase, even though they did so legitimately. Product/service dissatisfaction: The cardholder claims dissatisfaction with the product or service as a reason for disputing the charge, even if the product or service was as described. Subscription services: The cardholder signs up for a subscription service and then disputes the recurring charges as unauthorized or unwanted. Why chargeback fraud is on the rise Chargeback fraud is becoming more pervasive for a couple of reasons. First, as e-commerce grows, so does the opportunity for fraud. Without face-to-face interactions, fraudsters can pull off their schemes more easily. Second, the process of issuing chargebacks has become consumer-friendly, with banks often siding with the cardholder without deep scrutiny of the claim. Finally, with the rise of subscription-based services and digital goods, the incidence of "friendly fraud" is increasing. The impact and repercussions of chargeback fraud The impact of chargeback fraud can be felt across several areas within a business. Financially, it's a clear and direct loss. There are also significant operational costs associated with managing chargebacks, including potential product loss, bank and related fees, and administrative work. However, the less tangible, more insidious repercussions involve damage to the business's reputation. A high chargeback rate can lead to a merchant account being suspended or terminated, causing a loss of the ability to process credit card payments. A tarnished reputation can further lead to losing consumer trust, which can be hard to regain. How to prevent chargeback fraud Preventing and managing chargeback fraud often involves implementing fraud prevention solutions, providing clear communication and customer support, and disputing illegitimate chargebacks with evidence when possible. Here are key actions you can take to protect your business against chargeback fraud: Educate and communicate with customers: Ensure your customers are fully aware of your return and refund policies. Be clear and transparent in your communications about what happens in the event of a disputed transaction. This can significantly reduce misunderstandings that often lead to legitimate chargebacks. Implement stringent transaction verification processes: Utilize Address Verification Services (AVS) and Card Verification Value (CVV2) verification for online and over-the-phone transactions. These credit card authentication services add an extra layer of security and can establish the validity of a purchase. Keep meticulous records: Document all transactions, including emails, phone calls, and any other purchase-related correspondence. In the event of a dispute, these records can serve as compelling evidence to defend the transaction. Immediate shipment and tracking: Ship products as quickly as possible after purchase and provide tracking information to customers. This delights customers and provides tangible proof of delivery should a chargeback be disputed. Utilize advanced fraud detection tools: Many fraud detection services are available that can instantly flag potentially fraudulent transactions, from monitoring for suspicious spending patterns to IP tracking for online orders. Examples include: Tokenization: Tokenization replaces sensitive card data with a "token," a random string of characters that is useless to fraudsters. This token can be stored or transmitted easily, with the actual payment information securely kept off-site. Machine learning and AI: Machine learning and AI fraud detection solutions can analyze vast amounts of transaction data to detect patterns and anomalies, thus flagging potentially fraudulent activity in real-time. The role of customer support in chargeback prevention While the above tools can help your organization prevent fraudulent charge backs, you likely already have a key tool in your company that can help mitigate chargebacks altogether. Your customer support team is your front line in chargeback prevention. Train them to handle customer inquiries effectively and resolve issues before they escalate. Offer multiple contact channels: Give customers several ways to reach your support team, such as email, phone, and live chat. The more easily they can contact you, the less likely they are to resort to a chargeback. Ensure prompt and courteous service: A positive and responsive customer service experience can turn a potential chargeback into a loyalty-building opportunity. Make refunds and returns as easy as possible for your customers. Additionally, clear and generous policies will reduce dissatisfaction and the likelihood of chargebacks. How Experian can help with chargeback fraud prevention Chargeback fraud can be a daunting prospect for any business, but with the right strategies in place, you can protect your business, your customers and your bottom line. Experian’s fraud management solutions provide robust verification options and layered risk management to help reduce the risk of chargeback fraud. Our advanced fraud detection solutions leverage machine learning algorithms and behavioral analytics to confirm the identity of customers during transactions, identify suspicious patterns and activities, and offer deeper insights that enhance fraud prevention strategies. These solutions can help detect potential instances of chargeback fraud in real-time or during post-transaction analysis. Learn More *This article includes content created by an AI language model and is intended to provide general information.