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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.

We already know over 286 million cars and light-duty trucks are in operation in the United States and that 4 out of 10 of those vehicles have been in an accident. That’s over 114 million vehicles on the road that have been in an accident. However, have you ever wondered what happens to a vehicle after it’s been involved in an accident? Does the owner keep the vehicle or get rid of it? If they decide to purchase a different vehicle, how soon after the reported accident do they purchase it? Does the frequency and timing in which an owner household purchases a different vehicle vary based on the accident's severity? Does this vary by other factors, such as vehicle type? If so, what vehicles are owners most likely, and least likely, to dispose of after a reported accident? To answer these questions, we completed an internal 3-year analysis of over 15 million unique VINs from all 50 states, Washington D.C., and Puerto Rico. We created a new detailed report with all the data insights! Check out our complimentary Vehicle Accident & Damage Insights report today!


