Data & Analytics

Changes in your portfolio are a constant. To accelerate growth while proactively identifying risk, you’ll need a well-informed portfolio risk management strategy. What is portfolio risk management? Portfolio risk management is the process of identifying, assessing, and mitigating risks within a portfolio. It involves implementing strategies that allow lenders to make more informed decisions, such as whether to offer additional credit products to customers or identify credit problems before they impact their bottom line. Leveraging the right portfolio risk management solution Traditional approaches to portfolio risk management may lack a comprehensive view of customers. To effectively mitigate risk and maximize revenue within your portfolio, you’ll need a portfolio risk management tool that uses expanded customer data, advanced analytics, and modeling. Expanded data. Differentiated data sources include marketing data, traditional credit and trended data, alternative financial services data, and more. With robust consumer data fueling your portfolio risk management solution, you can gain valuable insights into your customers and make smarter decisions. Advanced analytics. Advanced analytics can analyze large volumes of data to unlock greater insights, resulting in increased predictiveness and operational efficiency. Model development. Portfolio risk modeling methodologies forecast future customer behavior, enabling you to better predict risk and gain greater precision in your decisions. Benefits of portfolio risk management Managing portfolio risk is crucial for any organization. With an advanced portfolio risk management solution, you can: Minimize losses. By monitoring accounts for negative performance, you can identify risks before they occur, resulting in minimized losses. Identify growth opportunities. With comprehensive consumer data, you can connect with customers who have untapped potential to drive cross-sell and upsell opportunities. Enhance collection efforts. For debt portfolios, having the right portfolio risk management tool can help you quickly and accurately evaluate collections recovery. Maximize your portfolio potential Experian offers portfolio risk analytics and portfolio risk management tools that can help you mitigate risk and maximize revenue with your portfolio. Get started today. Learn more

From science fiction-worthy image generators to automated underwriting, artificial intelligence (AI), big data sets and advances in computing power are transforming how we play and work. While the focus in the lending space has often been on improving the AI models that analyze data, the data that feeds into the models is just as important. Enter: data-centric AI. What is a data-centric AI? Dr. Andrew Ng, a leader in the AI field, advocates for data-centric AI and is often credited with coining the term. According to Dr. Ng, data-centric AI is, ‘the discipline of systematically engineering the data used to build an AI system.’1 To break down the definition, think of AI systems as a combination of code and data. The code is the model or algorithm that analyzes data to produce a result. The data is the information you use to train the model or later feed into the model to request a result. Traditional approaches to AI focus on the code — the models. Multiple organizations download and use the same data sets to create and improve models. But today, continued focus on model development may offer a limited return in certain industries and use cases. A data-centric AI approach focuses on developing tools and practices that improve the data. You may still need to pay attention to model development but no longer treat the data as constant. Instead, you try to improve a model's performance by increasing data quality. This can be achieved in different ways, such as using more consistent labeling, removing noisy data and collecting additional data.2 Data-centric AI isn't just about improving data quality when you build a model — it's also part of the ongoing iterative process. The data-focused approach should continue during post-deployment model monitoring and maintenance. Data-centric AI in lending Organizations in multiple industries are exploring how a data-centric approach can help them improve model performance, fairness and business outcomes. For example, lenders that take a data-centric approach to underwriting may be able to expand their lending universe, drive growth and fulfill financial inclusion goals without taking on additional risk. Conventional credit scoring models have been trained on consumer credit bureau data for decades. New versions of these models might offer increased performance because they incorporate changes in the economic landscape, consumer behavior and advances in analytics. And some new models are built with a more data-centric approach that considers additional data points from the existing data sets — such as trended data — to score consumers more accurately. However, they still solely rely on credit bureau data. Explainability and transparency are essential components of responsible AI and machine learning (a type of AI) in underwriting. Organizations need to be able to explain how their models come to decisions and ensure they are behaving as expected. Model developers and lenders that use AI to build credit risk models can incorporate new high-quality data to supplement existing data sets. Alternative credit data can include information from alternative financial services, public records, consumer-permissioned data, and buy now, pay later (BNPL) data that lenders can use in compliance with the Fair Credit Reporting Act (FCRA).* The resulting AI-driven models may more accurately predict credit risk — decreasing lenders' losses. The models can also use alternative credit data to score consumers that conventional models can't score. Infographic: From initial strategy to results — with stops at verification, decisioning and approval — see how customers travel across an Automated Loan Underwriting Journey. Business benefit of using data-centric AI models Financial services organizations can benefit from using a data-centric AI approach to create models across the customer lifecycle. That may be why about 70 percent of businesses frequently discuss using advanced analytics and AI within underwriting and collections.3 Many have gone a step further and implemented AI. Underwriting is one of the main applications for machine learning models today, and lenders are using machine learning to:4 More accurately assess credit risk models. Decrease model development, deployment and recalibration timelines. Incorporate more alternative credit data into credit decisioning. AI analytics solutions may also increase customer lifetime value by helping lenders manage credit lines, increase retention, cross-sell products and improve collection efforts. Additionally, data-centric AI can assist with fraud detection and prevention. Case study: Learn how Atlas Credit, a small-dollar lender, used a machine learning model and loan automation to nearly doubled its loan approval rates while decreasing its credit risk losses. How Experian helps clients leverage data-centric AI for better business outcomes During a presentation in 2021, Dr. Ng used the 80-20 rule and cooking as an analogy to explain why the shift to data-centric AI makes sense.5 You might be able to make an okay meal with old or low-quality ingredients. However, if you source and prepare high-quality ingredients, you're already 80% of the way toward making a great meal. Your data is the primary ingredient for your model — do you want to use old and low-quality data? Experian has provided organizations with high-quality consumer and business credit solutions for decades, and our industry-leading data sources, models and analytics allow you to build models and make confident decisions. If you need a sous-chef, Experian offers services and has data professionals who can help you create AI-powered predictive analytics models using bureau data, alternative data and your in-house data. Learn more about our AI analytics solutions and how you can get started today. 1DataCentricAI. (2023). Data-Centric AI.2Exchange.scale (2021). The Data-Centric AI Approach With Andrew Ng.3Experian (2021). Global Insights Report September/October 2021.4FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context. 5YouTube (2021). A Chat with Andrew on MLOps: From Model-Centric to Data-Centric AI *Disclaimer: When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

This article was originally published on multifamilyinsiders.com One of the challenges currently facing the rental housing industry is the amount of lease application fraud. An Entrata study found a 111% increase in lease application fraud between 2019 and 2020. In the same study, 55% of surveyed apartment managers and rental operators said their properties experience fraudulent lease application attempts every few months, and 15% said their communities were subjected to multiple attempts each month. One-third of respondents described themselves as "very concerned" about application fraud. Just as alarming as the rise in attempts is the apparent likelihood of success. In the study, 65% of apartment managers said they are not confident in their current fraud prevention efforts. Some applicants can use a range of tools to commit fraud such as fake pay stubs, bank statements, employment records, and other falsified documents. Unfortunately, readily available computer technology makes it all too easy for applicants to produce these falsified documents. Tools to fight against fraud Apartment communities that rely on an overly manual screening process may find themselves at a disadvantage in the current landscape. Relying on associates to manually verify things like income and employment history can increase the risk of a deceitful applicant being successful. In addition, these processes can be extraordinarily time-consuming, which means leasing associates have less bandwidth for their many other important duties and responsibilities. Not to mention, the units stay unoccupied while these time-consuming verifications are being done manually. Among the general screening technologies that operators should consider: Automated verification of income, assets and employment — These solutions eliminate the need for operators to collect this kind of documentation from applicants. Furthermore, it eliminates the opportunity for applicants to supply falsified supporting documentation. Frictionless authentication — A multi-layered identity verification process for those applying for rental housing, frictionless authentication detects the subtle and not-so-subtle signs that an applicant is, to one degree or another, using a false identity. By highlighting discrepancies, the process assigns a “score” to quantify the likelihood that misrepresentation is taking place. Additional confirmation of the applicant’s identity can be completed using a one-time passcode (OTP) or knowledge-based authentication (KBA). This technology also uses device intelligence to recognize the risks associated with the physical devices (such as computers, tablets, and smartphones) that consumers use for online applications to identify potential imposters. In today's landscape, apartment owners and operators need to make sure they're protecting themselves against fraudulent applicants, who may not fulfill their financial obligations as outlined in their leases. By embracing the ever-growing array of advanced screening tools and technologies, owners and operators can achieve that protection and reduce their risk significantly — and save their associates time and energy.

Investing in a strong customer acquisition strategy is critical to attracting leads and converting them into high-value customers. In this blog post, we’ll be focusing on one of the first stages of the customer acquisition process: the application stage. Challenges with online customer application processes When it comes to the customer application stage, speed, ease, and convenience are no longer nice-to-haves — they are musts. But various challenges exist for lenders and consumers in terms of online credit or account application processes, including: Limited digital capabilities. Consumers have grown more reliant on digital channels, with 52% preferring to use digital banking options over banking at branches. That said, financial institutions should prioritize the digital customer experience or risk falling behind the competition. The length of applications. Whether it’s a physical or digital application, requiring consumers to provide a substantial amount of information about themselves and their past can be frustrating. In fact, 67% of consumers will abandon an application if they experience complications. Potential human error. Because longer, drawn-out applications require various steps and data inputs, consumers may leave fields blank or make errors along the way. This can create more friction and delays as consumers may potentially be driven offline and into branches to get their applications sorted out. Improve the speed and accuracy of online credit applications Given that consumers are more likely to abandon their applications if their experience is friction-filled, financial institutions will need an automated, data-driven solution to simplify and streamline the online form completion process. Some of the benefits of leveraging an automated solution include: Improved customer experiences. Shortening time-to-value starts with faster decisioning. By using accurate consumer data and automation to prefill parts of the online credit application, you can reduce the amount of information applicants are required to enter, leading to lower abandonment rates, less potential for manual error, and enhanced user experiences. Fraud prevention. Safeguarding consumer information throughout the credit application process is crucial. By leveraging intelligent identity verification solutions, you can securely and compliantly identify consumer identities while ensuring data isn’t released in risky situations. Then by using identity management solutions, you can gain a connected, validated customer view, resulting in minimized end-user friction. Faster approvals. With automated data prefill and identity verification, you can process applications more efficiently, leading to faster approvals and increased conversions. Choosing the right partner Experian can help optimize your customer application process, making it faster, more efficient, and less error prone. This way, you can win more customers and improve digital experiences. Learn more about Experian’s customer acquisition solutions.

Using data to understand risk and make lending decisions has long been a forte of leading financial institutions. Now, with artificial intelligence (AI) taking the world by storm, lenders are finding innovative ways to improve their analytical capabilities. How AI analytics differs from traditional analytics Data analytics is analyzing data to find patterns, relationships and other insights. There are four main types of data analytics: descriptive, diagnostic, predictive and prescriptive. In short, understanding the past and why something happened, predicting future outcomes and offering suggestions based on likely outcomes. Traditionally, data analysts and scientists build models and help create decisioning strategies to align with business needs. They may form a hypothesis, find and organize relevant data and then run analytics models to test their hypothesis. However, time and resource constraints can limit the traditional analytics approach. As a result, there might be a focus on answering a few specific questions: Will this customer pay their bills on time? How did [X] perform last quarter? What are the chances of [Y] happening next year? AI analytics isn't completely different — think of it as a complementary improvement rather than a replacement. It relies on advances in computing power, analytics techniques and different types of training to create models more efficient than traditional analytics. By leveraging AI, companies can automate much of the data gathering, cleaning and analysis, saving them time and money. The AI models can also answer more complex questions and work at a scale that traditional analytics can't keep up with. Advances in AI are additionally offering new ways to use and interact with data. Organizations are already experimenting with using natural language processing and generative AI models. These can help even the most non-technical employees and customers to interact with vast amounts of data using intuitive and conversational interfaces. Benefits of AI analytics The primary benefits of AI-driven analytics solutions are speed, scale and the ability to identify more complex relationships in data. Speed: Where traditional analytics might involve downloading and analyzing spreadsheets to answer a single question, AI analytics automates these processes – and many others.Scale: AI analytics can ingest large amounts of data from multiple data sources to find analytical insights that traditional approaches may miss. When combined with automation and faster processing times, organizations can scale AI analytics more efficiently than traditional analytics.Complexity: AI analytics can answer ambiguous questions. For example, a marketing team may use traditional analytics to segment customers by known characteristics, such as age or location. But they can use AI analytics to find segments based on undefined shared traits or interests, and the results could include segments that they wouldn't have thought to create on their own. The insights from data analytics might be incorporated into a business intelligence platform. Traditionally, data analysts would upload reports or update a dashboard that business leaders could use to see the results and make educated decisions. Modern business intelligence and analytics solutions allow non-technical business leaders to analyze data on their own. With AI analytics running in the background, business leaders can quickly and easily create their own reports and test hypotheses. The AI-powered tools may even be able to learn from users' interactions to make the results more relevant and helpful over time. WATCH: See how organizations are using business intelligence to unlock better lending decisions with expert insights and a live demo. Using AI analytics to improve underwriting From global retailers managing supply chains to doctors making life-changing diagnoses, many industries are turning to AI analytics to make better data-driven decisions. Within financial services, there are significant opportunities throughout customer lifecycles. For example, some lenders use machine learning (ML), a subset of AI, to help create credit risk models that estimate the likelihood that a borrower will miss a payment in the future. Credit risk models aren't new — lenders have used models and credit scores for decades. However, ML-driven models have been able to outperform traditional credit risk models by up to 15 percent.1 In part, this is because the machine learning models might use traditional credit data and alternative credit data* (or expanded FCRA-regulated data), including information from alternative financial services and buy now pay later loans. They can also analyze the vast amounts of data to uncover predictive attributes that logistic regression (a more traditional approach) models might miss. The resulting ML models can score more consumers than traditional models and do so more accurately. Lenders that use these AI-driven models may be able to expand their lending universe and increase automation in their underwriting process without taking on additional risk. However, lenders may need to use a supervised learning approach to create explainable models for credit underwriting to comply with regulations and ensure fair lending practices. Read: The Explainability: ML and AI in credit decisioning report explores why ML models will become the norm, why explainability is important and how to use machine learning. Experian helps clients use AI analytics Although AI analytics can lead to more productive and efficient analytics operations over time, the required upfront cost or expertise may be prohibitive for some organizations. But there are simple solutions. Built with advanced analytics, our Lift Premium™ scoring model uses traditional and alternative credit data to score more consumers than conventional scoring models. It can help organizations increase approvals among thin-file and credit-invisible consumers, and more accurately score thick-file consumers.2 Experian can also help you create, test, deploy and monitor AI models and decisioning strategies in a collaborative environment. The models can be trained on Experian's vast data sources and your internal data to create a custom solution that improves your underwriting accuracy and capabilities. Learn more about machine learning and AI analytics. * 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. 1. Experian (2020). Machine Learning Decisions in Milliseconds 2. Experian (2022). Lift PremiumTM product sheet

As 2023 unfolds, rental housing owners and operators find themselves faced with a slightly different market than in the recent past. While rents are still high, rent growth has slowed somewhat, and the prospect of a cooler U.S. economy means more renters could be facing economic hardships in the months ahead. So, who is today's renter? In The State of the U.S. Rental Housing Market, a new report from Experian, we uncover that today’s renters are typically younger. According to our data derived from Experian RentBureau® and our analysis, 68.8% of today’s renters are either millennials (41.8%) or Gen Z (27%). Meanwhile, 17.3% are Gen X, 11.9% are baby boomers and only 2.2% are from the Silent Generation. Similarly, when you look at the renters who have a higher propensity to move — and thus need a new apartment or home to rent — they tend to skew younger. Our analysis shows that, of the renters who made two or more moves during the last two years, 43.2% were Gen Y (millennials). The younger Gen Y segment accounts for 25.2% of the frequent movers. As the population of renters has increased over the past decade, the concentration of growth appears to be among households earning $75,000 or more in annual income. About 7.6 million of these households were renters in 2009; by 10 years later, that figure had increased to 11.2 million. What is their financial status? Also, by some measurements, U.S. consumers — and, by extension, renters — improved their financial standing during the pandemic era. Credit scores rose as consumers used stimulus payments to pay down debt and save, but this trend is starting to normalize. The median conventional credit score rose above 700 in 2022, up from just above 680 in 2019. Still, according to Experian RentBureau, 63% of all renter households are low- to moderate-income earners, meaning they make less than 80% of the area median income. Furthermore, the average renter spends 38.6% of their income on rent. Households that spend more than 30% of their income on housing costs — including rent or mortgage payments, utilities and other fees — are considered “housing cost burdened” by the U.S. Department of Housing and Urban Development. For more insight and analysis of today’s rental-housing market, click here to download your free copy of The State of the U.S. Rental Housing Market report.

After a period of historic, double-digit rent growth and razor-thin vacancy rates, the rental housing market has shown some signs of softening in recent months. And economic uncertainty still looms. The potential of a downturn this year and the existing economic strains faced by large swaths of renters may impact many rental-housing owners and managers nervous about their ability to find renters who can fulfill their lease terms. In The State of the U.S. Rental Housing Market, a new report from Experian, our data scientists and analysts offer key insights into the U.S. housing market and its impact on renters. The analysis in this report is derived from synthesizing various data samples and sources, including Experian credit attributes and models as well as data from the U.S. Census Bureau and Experian RentBureau®. Experian RentBureau is the largest rental payment database and contains over 4.4 million transactions and more than 25 million renter profiles. This report yields three major takeaways: Soaring interest rates and a slowing mortgage sector over the last year have taken heat out of the homebuying market, leading to more renters remaining in the renter pool. Inflation and other economic strains continue to squeeze renters’ finances. As rent prices increase and negative payment activity becomes more frequent, rental-housing owners and operators are striving to grow without expanding default risk and need to find renters with the best chances of fulfilling the terms of their leases. Among the report’s other notable findings: The average renter spends 38.6% of their income on rent. Households that spend more than 30% of their income on housing costs — including rent or mortgage payments, utilities and other fees — are considered “housing cost burdened” by the U.S. Department of Housing and Urban Development. Experian data shows 28% of renters with negative payment activity in 2022 (negative payment activity is defined as having late charges, insufficient funds, write-offs or outstanding balances). The figure represented an increase of 5.7 percentage points from 2021 and 3.8 percentage points from 2020. Also of note, low-to-moderate income renters are twice as likely to have a negative payment activity compared to other renters. Rent-to-income ratios are highest in the West and the Northeast. Among all 50 states, the leaders are Washington D.C. (40.9%), California (39.7%), Washington state (35.6%), Utah (35.6%) and New York (35.3%). Keep pace with trends in future blog posts that will dive deeper into the current conditions affecting the rental housing market and renters. In the meantime, click here to download your free copy of The State of the U.S. Rental Housing Market Report in full.

More than half of U.S. businesses say they discuss fraud management often, making fraud detection in banking top-of-mind. Banking fraud prevention can seem daunting, but with the proper tools, banks, credit unions, fintechs, and other financial institutions can frustrate and root out fraudsters while maintaining a positive experience for good customers. What is banking fraud? Banking fraud is a type of financial crime that uses illegal means to obtain money, assets, or other property owned or held by a bank, other financial institution, or customers of the bank. This type of fraud can be difficult to detect when misclassified as credit risk or written off as a loss rather than investigated and prevented in the future. Fraud that impacts financial institutions consists of small-scale one-off events or larger efforts perpetrated by fraud rings. Not long ago, many of the techniques utilized by fraudsters required in-person or phone-based activities. Now, many of these activities are online, making it easier for fraudsters to disguise their intent and perpetrate multiple attacks at once or in sequence. Banking fraud can include: Identity theft: When a bad actor steals a consumer’s personal information and uses it to take money, open credit accounts, make purchases, and more. Check fraud: This type of fraud occurs when a fraudster writes a bad check, forges information, or steals and alters someone else’s check. Credit card fraud: A form of identity theft where a bad actor makes purchases or gets a cash advance in the name of an unsuspecting consumer. The fraudster may takeover an existing account by gaining access to account numbers online, steal a physical card, or open a new account in someone else’s name.  Phishing: These malicious efforts allow scammers to steal personal and account information through use of email, or in the case of smishing, through text messages. The fraudster often sends a link to the consumer that looks legitimate but is designed to steal login information, personally identifiable information, and more. Direct deposit account fraud: Also known as DDA fraud, criminals monetize stolen information to open new accounts and divert funds from payroll, assistance programs, and more. Unfortunately, this type of fraud doesn’t just lead to lost funds – it also exposes consumer data, impacts banks’ reputations, and has larger implications for the financial system. Today, top concerns for banks include generative AI (GenAI) fraud, peer-to-peer (P2P) payment scams, identity theft and transaction fraud. Without the proper detection and prevention techniques, it’s difficult for banks to keep fraudsters perpetrating these schemes out. What is banking fraud prevention? Detecting and preventing banking fraud consists of a set of techniques and tasks that help protect customers, assets and systems from those with malicious intent. Risk management solutions for banks identify fraudulent access attempts, suspicious transfer requests, signs of false identities, and more. The financial industry is constantly evolving, and so are fraudsters. As a result, it’s important for organizations to stay ahead of the curve by investing in new fraud prevention technologies. Depending on the size and sophistication of your institution, the tools and techniques that comprise your banking fraud prevention solutions may look different. However, every strategy should include multiple layers of friction designed to trip up fraudsters enough to abandon their efforts, and include flags for suspicious activity and other indicators that a user or transaction requires further scrutiny.   Some of the emerging trends in banking fraud prevention include: Use of artificial intelligence (AI) and machine learning (ML). While these technologies aren’t new, they are finding footing across industries as they can be used to identify patterns consistent with fraudulent activity – some of which are difficult or time-consuming to detect with traditional methods. Behavioral analytics and biometrics. By noting standard customer behaviors — e.g., which devices they use and when — and how they use those devices — looking for markers of human behavior vs. bot or fraud ring activity — organizations can flag riskier users for additional authentication and verification. Leveraging additional data sources. By looking beyond standard credit reports when opening credit accounts, organizations can better detect signs of identity theft, synthetic identities, and even potential first-party fraud.     With real-time fraud detection tools in place, financial institutions can more easily identify good consumers and allow them to complete their requests while applying the right amount and type of friction to detect and prevent fraud.   How to prevent and detect banking fraud In order to be successful in the fight against fraud and keep yourself and your customers safe, financial institutions of all sizes and types must: Balance risk mitigation with the customer experience Ensure seamless interactions across platforms for known consumers who present little to no risk Leverage proper identity resolution and verification tools Recognize good consumers and apply the proper fraud mitigation techniques to riskier scenarios With Experian’s interconnected approach to fraud detection in banking, incorporating data, analytics, fraud risk scores, device intelligence, and more, you can track and assess various activities and determine where additional authentication, friction, or human intervention is required. 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‘Big data’ might not be the buzzword du jour, but it's here to stay. Whether trying to improve your customer experience, portfolio performance, automation, or new AI capabilities, access to quality data from varying data sources can create growth opportunities. 85 percent of organizations believe that poor-quality customer contact data negatively affects their operations and efficiencies, which leads to wasted resources and damages their brand. And 77 percent said that inaccurate data hurt their response to market changes during the pandemic.1 If you want to use data to drive your business forward, consider where the data comes from and how you can glean useful insights. What is a data source? A data source is a location where you can access information. It's a broad description because data sources can come in different formats — the definition depends on how the data is being used rather than a specific storage type. For example, you can get data from a spreadsheet, sensors on an internet of things device or scrape it from websites. You might store the data you gather using different types of databases. And in turn, those databases can be data sources for other programs or organizations. Types of data sources Many organizations have chief data officers, along with data engineers, scientists and analysts who gather, clean, organize and manage data. This important work relies on understanding the technical aspects of varying data sources and connections. And it can turn a disorganized pool of data into structured databases that business leaders can easily access and analyze. From a non-technical point of view, it’s important to consider where the data comes from and the pros and cons of these data sources. For instance, marketers might define data sources as: First-party data: The data collected about customers and prospects, such as account details, transaction history and interactions with your website or app. The data can be especially valuable and insightful when you can connect the dots between previously siloed data sources within your organization.Zero-party data: Some organizations have a separate classification for information that customers voluntarily share, such as their communication preferences and survey results. It can be helpful to view this data separately because it reflects customers' desires and interests, which can be used to further customize your messaging and recommendations.Second-party data: Another organization's first-party data can be your second-party data if you purchase it or have a partnership that involves data sharing or data collaboration. Second-party data can be helpful because you know exactly where the information comes from and it can complement information you already have about customers or prospects.Third-party data: Third-party data comes from aggregators that collect and organize information from multiple sources. It can further enrich your customer view to improve marketing, underwriting, customer service and collection efforts. READ: The Realizing a Single Customer View white paper explores how organizations can use high-quality data to better understand their customers. How can a data-driven approach benefit your business? Organizations use data science to make sense of the increasingly large flow of information from varying data sources. A clear view can be important for driving growth and responding to changing consumer preferences and economic uncertainty. A 2022 survey of U.S. organizations found high-quality data can help:2 Grow your business: 91 percent said investing in data quality helped business growth.Improve customer experience: 90 percent said better data quality led to better customer experiences.Increase agility: 89 percent said best practices for data quality improved business agility. You can see these benefits play out in different areas. For example, you can more precisely segment customers based on reliable geographic, demographic, behavioral and psychographic data. Or combine data sources to get a more accurate view of consumer risk and increase your AI-powered credit risk decisioning capabilities. But building and scaling data systems while maintaining good quality isn't easy. Many organizations have to manage multiple internal and external data sources, and these can feed into databases that don't always communicate with one another. Most organizations (85 percent) are looking toward automation to improve efficiency and make up for skill shortages. Most are also investing in technology to help them monitor, report and visualize data — making it easier to understand and use.3 WATCH: See how you can go from data to information to insight and foresight in the Using Business Intelligence to Unlock Better Lending Decisions webinar. Access high-quality data from Experian Digital acceleration has made accessing quality data more important than ever. This includes learning how to collect and manage your zero- and first-party data. Experian's data quality management solutions can help you aggregate, cleanse and monitor your data. And the business intelligence tools and platform democratize access, allowing non-technical business leaders to find meaningful insights. You can also enhance your data sets with second- and third-party data. Our industry-leading data sources have information on over 245 million consumers and 32 million businesses, including proprietary data assets. These include traditional credit bureau data, alternative credit data, automotive data, commercial credit data, buy now pay later data, fraud data and residential property data. And you can use our API developer portal to access additional third-party data sources within the same interface. Learn more about Experian's data sources. 1. Experian (2022). 2022 Global Data Management Research Report2. Experian (2022). The Data Quality Imperative3. Ibid.

After being in place for more than three years, the student loan payment pause is scheduled to end 60 days after June 30, with payments resuming soon after. As borrowers brace for this return, there are many things that loan servicers and lenders should take note of, including: Potential risk factors demonstrated by borrowers. About one in five student loan borrowers show risk factors that suggest they could struggle when scheduled payments resume.1 These include pre-pandemic delinquencies on student loans and new non-medical collections during the pandemic. The impact of pre-pandemic delinquencies. A delinquent status dating prior to the pandemic is a statistically significant indicator of subsequent risk. An increase in non-student loan delinquencies. As of March 2023, around 2.5 million student loan borrowers had a delinquency on a non-student loan, an increase of approximately 200,000 borrowers since September 2022.2 Transfers to new servicers. More than four in ten borrowers will return to repayment with a new student loan servicer.3 Feelings of anxiety for younger borrowers. Roughly 70% of Gen Z and millennials believe the current economic environment is hurting their ability to be financially independent adults. However, 77% are striving to be more financially literate.4 How loan servicers and lenders can prepare and navigate Considering these factors, lenders and servicers know that borrowers may face new challenges and fears once student loan payments resume. Here are a few implications and what servicers and lenders can do in response: Non-student loan delinquencies can potentially soar further. Increased delinquencies on non-student loans and larger monthly payments on all credit products can make the transition to repayment extremely challenging for borrowers. Combined with high balances and interest rates, this can lead to a sharp increase in delinquencies and heightened probability of default. By leveraging alternative data and attributes, you can gain deeper insights into your customers' financial behaviors before and during the payment holidays. This way, you can mitigate risk and improve your lending and servicing decisions. Note: While many student loan borrowers have halted their payments during forbearance, some have continued to pay anyway, demonstrating strong financial ability and willingness to pay in the future. Trended data and advanced modeling provide a clearer, up-to-date view of these payment behaviors, enabling you to identify low-risk, high-value customers. Streamlining your processes can benefit you and your customers. With some student loan borrowers switching to different servicers, creating new accounts, enrolling in autopay, and confirming payment information can be a huge hassle. For servicers that will have new loans transferred to them, the number of queries and requests from borrowers can be overwhelming, especially if resources are limited. To make transitions as smooth as possible, consider streamlining your administrative tasks and processes with automation. This way, you can provide fast and frictionless service for borrowers while focusing more of your resources on those who need one-on-one assistance. Providing credit education can help borrowers take control of their financial lives. Already troubled by higher costs and monthly payments on other credit products, student loan payments are yet another financial obligation for borrowers to worry about. Some borrowers have even stated that student loan debt has delayed or prevented them from achieving major life milestones, such as getting married, buying a home, or having children.5 By arming borrowers with credit education, tools, and resources, they can better navigate the return of student loan payments, make more informed financial decisions, and potentially turn into lifelong customers. For more information on effective portfolio management, click here. 1Consumer Financial Protection Bureau. (June 2023). Office of Research blog: Update on student loan borrowers as payment suspension set to expire. 2Ibid. 3Ibid. 4Experian. (May 2023). Take a Look: Millennial and Gen Z Personal Finance Trends 5AP News. (June 2023). The pause on student loan payment is ending. Can borrowers find room in their budgets?

Credit portfolio management has often involved navigating uncertainty, but some periods are more extreme than others. With the right data and analytics you can gain deeper insight into financial behaviors and risk to make better decisions and drive profitable growth. Along with access to an increasing amount of data, advanced analytics can help lenders more accurately: Forecast losses under different economic scenarios to estimate liquidity requirements. Identify fraud by detecting behaviors that could indicate identity theft, account takeover fraud, first-party or synthetic identity fraud. Incorporate real-time and alternative data,1 such as cash flow transaction data and specialty bureau data, in decisioning and scoring to accurately assess creditworthiness and expand your lending pool without taking on undue risk. Precisely segment consumers using internal and external data to increase automation during underwriting and identify cross-sell opportunities. Improve collections using AI-driven strategies and automated debt collection software to enhance operations and increase recovery rates. It’s imperative to take a proactive approach to portfolio monitoring. Monthly portfolio reviews with bureau scores, credit attributes and specialized scores — and using the results to manage credit lines and loan terms — are critical during volatile times. View our interactive e-book for the latest economic and consumer trends and learn how to set your portfolio up to succeed in any economic cycle. Download e-book 1"Alternative credit data" refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “expanded FCRA data" may also apply in this instance, and both can be used interchangeably.

Banking uncertainty creates opportunity for fraud The recent regional bank collapses left anxious consumers scrambling to withdraw their funds or open new accounts at other institutions. Unfortunately, this situation has also created an opportunity for fraudsters to take advantage of the chaos. Criminals are exploiting the situation and posing as legitimate customers looking to flee their current bank to open new accounts elsewhere. Financial institutions looking to bring on these consumers as new clients must remain vigilant against fraudulent activity. Fraudsters also prey on vulnerable individuals who may be financially stressed and uncertain about the future. This creates a breeding ground for scams as fear and uncertainty cloud judgment and make people more susceptible to manipulation. Beware of fraudulent tactics Now, it is more important than ever for financial institutions to be vigilant in their due diligence processes. As they navigate this period of financial turbulence, they must take extra precautions to ensure that new customers are who they say they are by verifying customer identities, conducting thorough background checks where necessary, and monitoring transactions for any signs of suspicious activity. Consumers should also maintain vigilance — fraudulent schemes come in many forms, from phishing scams to fake investment opportunities promising unrealistic returns. To protect yourself against these risks, it is important to remain vigilant and take precautions such as verifying the legitimacy of any offers or investments before investing, monitoring your bank and credit card statements regularly for suspicious activity, and being skeptical of unsolicited phone calls, emails, or text messages. Security researcher Johannes Ulrich reported that threat actors are jumping at the opportunity, registering suspicious domains related to Silicon Valley Bank (SVB) that are likely to be used in attacks. Ulrich warned that the scammers might try to contact former clients of SVB to offer them a support package, legal services, loans, or other fake services relating to the bank's collapse. Meanwhile, on the day of the SVB closure, synthetic identity fraud began to climb from an attack rate of .57 to a first peak of 1.24% on the Sunday following the closure, or an increase of 80%. After the first spike reduced on March 14, we only saw a return of an even higher spike on March 21 to 1.35%, with bumps continuing since then. As the economy slows and fraud rises, don’t let your guard down The recent surge in third-party attack rates on small business and investment platforms is a cause for concern. There was a staggering nearly 500% increase in these attacks between March 7th and 11th, which coincided with the release of negative news about SVB. Bad actors had evidently been preparing for this moment and were quick to exploit vulnerabilities they had identified across our financial system. They used sophisticated bots to create multiple accounts within minutes of the news dropping and stole identities to perpetrate fraudulent activities. This underscores the need for increased vigilance and proactive measures to protect against cyber threats impacting financial institutions. Adopting stronger security measures like multi-factor authentication, real-time monitoring, and collaboration with law enforcement agencies for timely identification of attackers is of paramount importance to prevent similar fraud events in the future. From frictionless to friction-right As businesses seek to stabilize their operations in the face of market turbulence, they must also remain vigilant against the threat of fraud. Illicit activities can permeate a company's ecosystem and disrupt its operations, potentially leading to financial losses and reputational damage. Safeguarding against fraud is not a simple task. Striking a balance between ensuring a smooth customer experience and implementing effective fraud prevention measures can be a challenging endeavor. For financial institutions in particular, being too stringent in fraud prevention efforts may drive customers away, while being too lenient can expose them to additional fraud risks. This is where a waterfall approach, such as that offered by Experian CrossCore®, can prove invaluable. By leveraging an array of fraud detection tools and technologies, businesses can tailor their fraud prevention strategies to suit the specific needs and journeys of different customer segments. This layered, customized approach can help protect businesses from fraud while ensuring a seamless customer experience. Learn more

With an ever-present need for efficiency, security, and seamless citizen services, many agencies are looking at the benefits of a data-driven government. Last year, the federal government kicked off a unified effort to enable data-driven decision making. The goal at that level – and across all agencies – is to serve citizens more efficiently and effectively. By embracing the power of data and analytics, agencies of all sizes can set themselves up to better serve their citizens. What is a data-driven government? Agencies collect citizen data from a variety of service-based sources, including the Postal Service, Census Bureau, social welfare departments, and agencies that issue government IDs. When properly leveraged, this data holds many possibilities. However, many agencies face challenges when it comes to efficient collection, sharing, usage, integrity, and accessibility. Due to the amount of data collected and the potential lack of consistency in the collection and storage techniques, the data may not be usable. Without proper management and analysis, there’s little government agencies can do with their data to improve their processes. A data-driven government has well-managed data and uses that data to drive their decisions as they relate to citizen requests for benefits, tax collection, elections, and more. What are the benefits of data-driven decision making? Data management and government data analytics enable agencies to react quickly to citizen demands and concerns and proactively anticipate an issue before it becomes a crisis. With the right tools, agencies can gain a holistic view of their citizens, communicate effectively internally, provide digitally-driven services and improve overall efficiency through government-wide data integration and management. These changes have a wide range of benefits, including reduction of cost, fraud, waste and abuse, the automation of manual processes, and better service delivery. Why is a data-driven strategy required? In addition to the benefits listed above, a data-driven strategy also helps agencies align with published NIST guidelines and the need to monitor, evaluate, and maintain digital identity systems. Proper use of data-driven digital identity strategies will enhance equity and the usability of the solutions agencies provide to their citizens. Building an effective data-driven strategy The right strategy starts with ensuring that all departments about the need for proper data management and analytics and the guidelines that will govern it, such as maintaining up-to-date data, removing silos, and leveraging the right tools. The next step is finding the right partner. An effective partner can help agencies develop and maintain data management systems and implement the right tools and analytics – things like machine learning in government – to help each agency function efficiently and safeguard the data of its citizens. To learn how Experian can help your agency improve its use of data, visit us or request a call. Visit us

Every data-driven organization needs to turn raw data into insights and, potentially, foresight. There was a time when lack of data was a hindrance, but that's often no longer the case. Many organizations are overwhelmed with too much data and lack clarity on how to best organize or use it. Modern business intelligence platforms can help. And financial institutions can use business intelligence analytics to optimize their decisioning and uncover safe growth opportunities. What is business intelligence? Business intelligence is an overarching term for the platforms and processes that organizations use to collect, store, analyze and display data and information. The ability to go from raw data to useful insights and foresight presents organizations with a powerful advantage, and can help them greatly improve their operations and efficiencies. Let’s pause and break down the below terms before expanding on business intelligence. Data: The raw information, such as customers' credit scores. Many organizations collect so much data that keeping it all can be an expensive challenge. Access to new types of data, such as alternative credit data, can assist with decisioning — but additional data points are only helpful if you have the resources or expertise to process and analyze them.Information: Once you process and organize data points, you can display the resulting information in reports, dashboards, and other visualizations that are easier to understand. Therefore, turning raw data into information. For example, the information you acquire might dictate that customers with credit scores over 720 prefer one of your products twice as much as your other products.Insight: The information tells you what happened, but you must analyze it to find useful and actionable insights. There could be several reasons customers within a specific score band prefer one product over another, and insights offer context and help you decide what to do next. In addition, you could also see what happened to the customers who were not approved.Foresight: You can also use information and insights to make predictions about what can happen or how to act in the future given different scenarios. For example, how your customers' preferences will likely change if you offer new terms, introduce a new product or there's a large economic shift. Business intelligence isn't new — but it is changing. Traditionally, business intelligence heavily relied on IT teams to sift through the data and generate reports, dashboards and other visualizations. Business leaders could ask questions and wait for the IT team to answer the queries and present the results. Modern business intelligence platforms make that process much easier and offer analytical insights. Now even non-technical business leaders can quickly answer questions with cloud-based and self-service tools. Business intelligence vs. business intelligence analytics Business intelligence can refer to the overall systems in place that collect, store, organize and visualize your data. Business intelligence tends to focus on turning data into presentable information and descriptive analytics — telling you what happened and how it happened. Business intelligence analytics is a subset of business intelligence that focuses on diagnostics, predictive and prescriptive analytics. In other words, why something happened, what could happen in the future, and what you should do. Essentially, the insights and foresight that are listed above. How can modern business intelligence benefit lenders? A business intelligence strategy and advanced analytics and modeling can help lenders precisely target customers, improve product offerings, streamline originations, manage portfolios and increase recovery rates. More specifically, business intelligence can help lenders uncover various trends and insights, such as: Changes in consumers' financial health and credit behavior.How customers' credit scores migrate over time.The risk performance of various portfolios.How product offerings and terms compare to competitors.Which loans are they losing to peers?Which credit attributes are most predictive for their target market? Understanding what's working well today is imperative. But your competitors aren't standing still. You also need to monitor trends and forecast the impact — good or bad — of various changes. WATCH: Webinar: Using Business Intelligence to Unlock Better Lending Decisions Using business intelligence to safely grow your portfolio Let's take a deeper dive into how business intelligence could help you grow your portfolio without taking on additional risk. It's an appealing goal that could be addressed in different ways depending on the underlying issue and business objective. For example, you might be losing loans to peers because of an acquisition strategy that's resulting in declining good customers. Or, perhaps your competitors' products are more appealing to your target customers. Business intelligence can show you how many applications you received, approved, and booked — and how many approved or declined applicants accepted a competitor's offer. You can segment and analyze the results based on the applicant’s credit scores, income, debt-to-income, loan amounts, loan terms, loan performance and other metrics. An in-depth analysis might highlight meaningful insights. For example, you might find that you disproportionately lost longer-term loans to competitors. Perhaps matching your competitors' long-term loan offerings could help you book more loans. READ: White paper: Getting AI-driven decisioning right in financial services Experian's business intelligence analytics solutions Lenders can use modern business intelligence platforms to better understand their customers, products, competitors, trends, and the potential impact of shifting economic circumstances or consumer behavior. Experian's Ascend Intelligence Services™ suite of solutions can help you turn data points into actionable insights. Ascend Intelligence Services™ Acquire Model: Create custom machine learning models that can incorporate internal, bureau and alternative credit data to more accurately assess risk and increase your lending universe.Ascend Intelligence Services™ Acquire Strategy: Get a more granular view of applicants that can help you improve segmentation and increase automation.Ascend Intelligence Services™ Pulse: A model and strategy health dashboard that can help you proactively identify and remediate issues and nimbly react to market changes.Ascend Intelligence Services™ Limit: Set and manage credit limits during account opening and when managing accounts to increase revenue and mitigate risk.Ascend Intelligence Services™ Foresight: A modern business intelligence platform that offers easy-to-use tools that help business leaders make better-informed decisions. Businesses can also leverage Experian's industry-leading data assets and expertise with various types of project-based and ongoing engagements. Learn more about how you can implement or benefit from business intelligence analytics.

On average, the typical global consumer owns three or more connected devices.1 80% of consumers bounce between devices, while 31% who turned to digital channels for their last purchase used multiple devices along the way.2 Considering these trends, many lenders are leveraging multiple channels in addition to direct mail, including email and mobile applications, to maximize their credit marketing efforts. The challenge, however, is effectively engaging consumers without becoming overbearing or inconsistent. In this article, we explore what identity resolution for credit marketing is and how the right identity tools can enable financial institutions to create more cohesive and personalized customer interactions. What is identity resolution? Identity resolution connects unique identifiers across touchpoints to build a unified identity for an individual, household, or business. This requires an identity graph, a proprietary database that collects, stitches, and stores identifiers from digital and offline sources. As a result, organizations can create a persistent, high-definition customer view, allowing for more consistent and meaningful brand experiences. What are the types of identity resolution? There are two common approaches to identity resolution: probabilistic ID matching and deterministic ID matching. Probabilistic ID matching uses multiple algorithms and data sets to match identity profiles that are most likely the same customer. Data points used in probabilistic models include IP addresses and device types. Deterministic ID matching uses first-party data that customers have produced, enabling you to merge new data with customer records and identify matches among existing identifiers. Examples of this type of data include phone numbers and email addresses. What role does identity resolution play in credit marketing? Maintaining a comprehensive customer view is crucial to credit marketing — the insights gained allow lenders to determine who they should engage and the type of offer or messaging that would resonate most. But there are many factors that can prevent financial institutions from doing this effectively: poor data quality, consumers bouncing between multiple devices, and so on. Seven out of 10 consumers find it important that companies they interact with online identify them across visits. Identity resolution for credit marketing solves these issues by matching and linking customer data from disparate sources back to a single profile. This enables lenders to: Create highly targeted campaigns. If your data is incomplete or inaccurate, you may waste your marketing spend by engaging the wrong audience or sending out irrelevant credit offers. An identity resolution solution that leverages expansive, regularly updated data gives you access to high-definition views of individuals, resulting in more personalization and greater campaign engagement. Deliver seamless, omnichannel experiences. To further improve your credit marketing efforts, you’ll need to keep up with consumers not only as their needs or preferences change, but also as they move across channels and devices. Instead of creating multiple identity profiles for the same person, identity resolution can recognize an individual across touchpoints, allowing you to create consistent offers and cohesive experiences. Picking the right marketing identity resolution solution While the type of identity resolution for marketing solution can vary depending on your business’s goals and challenges, Experian can help you get started. To learn more, visit us today. 1 Global number of devices and connections per capita 2018-2023, Statista. 2 Cross Device Marketing - Statistics and Trends, Go-Globe.