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Companies depend on quality information to make decisions that move their business objectives forward while minimizing risk exposure. And in today’s modern, tech-driven, innovation-led world, there’s more  information available than ever before. Expansive datasets from sources, both internal and external, allow decision-makers to leverage a wide range of intelligence to fuel how they plan, forecast and set priorities. But how can business leaders be sure that their data is as robust, up-to-date and thorough as they need — and, most importantly, that they’re able to use it to its fullest potential? That’s where the power of advanced analytics comes in. By making use of cutting-edge datasets and analytics insights, businesses can stay on the vanguard of business intelligence and ahead of their competitors. What is advanced analytics? Advanced analytics is a form of business intelligence that takes full advantage of the most modern data sources and analytics tools to create forward-thinking analysis that can help businesses make well-informed, data-driven decisions that are tailored to their needs. Simply put, advanced analytics is an essential component of any proactive business strategy that aims to maximize the future potential of both customers and campaigns. These advanced business intelligence and analytics solutions  help leaders make profitable decisions no matter the state of the current economic climate. They use both traditional and non-traditional data sources to provide businesses with actionable insights in the formats best suited to their needs and goals. One key aspect of advanced analytics is the use of AI analytics solutions. These efficient and effective tools help businesses save time and money by harnessing the power of cutting-edge technologies and deploying them in optimal use-case scenarios. These AI and machine-learning solutions use a wide range of tools, such as neural network methodologies, to help organizations optimize their allocation of resources, expediting and automating some processes while creating valuable insights to help human decision-makers navigate others. Benefits of advanced analytics Traditional business intelligence tends to be limited by the scope and quality of available data and ability of analysts to make use of it in an effective, comprehensive way. Modern business intelligence analytics, on the other hand, integrates machine learning and analytics to maximize the potential of data sets that, in today's technology-driven world, are often overwhelmingly large and complex: think not just databases of customer decisions and actions but behavioral data points tied to online and offline activity and the internet of things. What's more, advanced analytics does this in a way that's accessible to an entire organization — not just those who know their way around data, like IT departments and trained analysts. With the right advanced analytics solution, decision-makers can access convenient cloud-based dashboards designed to give them the information they want and need — with no clutter, noise or confusing terminology. Another key advantage of advanced analytics solutions is that they don't just analyze data — they optimize it, too. Advanced analytics offers the ability to clean up and integrate multiple data sets to remove duplicates, correct errors and inaccuracies and standardize formats, leading to high-quality data that creates clarity, not confusion. The result? By analyzing and identifying relationships across data, businesses can uncover hidden insights and issues. Advanced analytics also automate some aspects of the decision-making process to make workflows quicker and nimbler. For example, a business might choose to automate credit scoring, product recommendations for existing customers or the identification of potential fraud. Reducing manual interventions translates to increased agility and operational efficiency and, ultimately, a better competitive advantage. Use cases in the financial services industry Advanced analytics gives businesses in the financial world the power to go deeper into their data — and to integrate alternative data sources as well. With predictive analytics models, this data can be transformed into highly usable, next-level insights that help decision-makers optimize their business strategies. Credit risk, for instance, is a major concern for financial organizations that want to offer customers the best possible options while ensuring their credit products remain profitable. By utilizing advanced analytics solutions combined with a broad range of datasets, lenders can create highly accurate credit risk scores that forecast future customer behavior and identify and mitigate risk, leading to better lending decisions across the credit lifecycle. Advanced analytics solutions can also help businesses problem-solve. Let's say, for instance, that uptake of a new loan product has been slower than desired. By using business intelligence analytics, companies can determine what factors might be causing the issue and predict the tweaks and changes they can make to improve results. Advanced analytics means better, more detailed segmentation, which allows for more predictive insights. Businesses taking advantage of advanced analytics services are simply better informed: not only do they have access to more and better data, but they're able to convert it into actionable insights that help them lower risk, better predict outcomes, and boost the performance of their business. How we can help Experian offers a wide range of advanced analytics tools aimed at helping businesses in all kinds of industries succeed through better use of data. From custom machine learning models that help financial institutions assess risk more accurately to self-service dashboards designed to facilitate more agile responses to changes in the market, we have a solution that's right for every business. Plus, our advanced analytics offerings include a vast data repository with insights on 245 million credit-active individuals and 25 million businesses, as well as the industry's largest alternative data set from non-traditional lenders. Ready to explore? Click below to learn about our advanced analytics solutions. Learn more

Are you ready to talk football? And read some Taylor Swift puns? And hear about a big automotive ad measurement success story? 'Don’t Blame Me’, I warned you…. As we are all aware, Travis Kelce will be playing for the Chiefs on Superbowl Sunday. Whether you are cheering for the Chiefs, the 49er’s, or you’re just there to watch the commercials and half-time show, ‘You Need to Calm Down’ and just expect to hear a couple references during the game about Taylor and Travis’ ‘Love Story’. Ad Measurement at the Top of Its Game Before the final match-up of this season though, let’s go ‘Back to December’ and talk about a “Red” hot advertising success story from Thursday Night Football. An automotive advertiser wanted to better understand the impact of advertising frequency on vehicle purchase activity. To do this, the advertiser initiated an Amazon Prime streaming advertising campaign to reach Thursday Night Football (TNF) audience viewers. They reached these viewers by leveraging in-game media to raise awareness with new and TNF-engaged audience viewers. In addition, the advertiser used remarketing techniques and other Amazon ads to re-engage the previously exposed TNF viewers. Campaign Results—More Than Just Karma! After the campaign was over, the automotive advertiser wanted to learn whether the results were more than just ‘Karma’. They used Experian’s Vehicle Purchase Insights data within Amazon Marketing Cloud (AMC) to fill in the ‘Blank Space’ and attribute vehicle sales to the exposed TNF audience. Customers exposed 2-3 times to their ads were 1.3X more likely to purchase a vehicle than viewers who were exposed to the advertising only once Customers exposed to their ads 4 times were 1.6X more likely to purchase a vehicle than viewers who were exposed to the advertising only once The advertiser understood ‘All Too Well’ the results, the campaign was a success! Even if you’re over all the pop culture references and the Swift romance, ‘Shake it Off’, there’s no reason for any ‘Bad Blood.” The ‘Fearless’ leader of the scoreboard will have their ‘Wildest Dreams’ come true and be named the Super Bowl LVIII champion. To read more about this case study, (without the Taylor Swift song title puns), click here.  

This article was updated on February 6, 2024. Lenders looking to gain a competitive edge need to improve their credit underwriting process in the coming years. The most obvious developments are the advances in artificial intelligence (AI) — machine learning in particular — the increased available computing capacity, and access to vast amounts of data. But when it comes to credit underwriting models, those are tools you can use to reach your goals, not a strategy for success. The evolution of credit underwriting Credit underwriters have had the same goal for millennia — assess the creditworthiness of a borrower to determine whether to offer them a loan. But the process has changed immensely, and the pace of change has recently increased. Fewer than 50 years ago, an underwriter might consider an applicant's income, occupation, marital status, and sex to make a decision. The Equal Credit Opportunity Act didn't pass until 1974. And it wasn't expanded to prohibit lending discrimination based on other factors, such as color, age, and national origin, until two years later. Regulatory changes can have an immediate and immense impact on credit underwriting, but there were also slower changes developing. As credit bureaus centralized and computers became more readily available, credit decisioning systems offered new insights. The systems could segment groups and help lenders make more complex and profitable decisions at scale, such as setting risk-appropriate credit limits and terms. INFOGRAPHIC: Data-driven decisioning journey map With access to more data and computing power, lenders get a more complete picture of applicants and their current customers. Technological advances also lead to automated decisions, which can improve lenders' workflows and customer satisfaction. In the late 2000s, fintech lenders entered the scene and disrupted the ecosystem with a completely online underwriting and funding process. More recently, AI and machine learning started as buzzwords, but quickly became business necessities. In fact, 66% of businesses believe advanced analytics, including machine learning and artificial intelligence, are going to rapidly change the way they do business.1 The latest explainable machine learning models can increase automation and efficiency while outperforming traditional modeling approaches. Access to increased computing power is, once again, helping power this shift.2 But it's also only possible because of the lenders access to alternative credit data.* WATCH: Why Advanced Analytics is Now Available for All Future-proofing your credit underwriting strategy Today's leading lenders use innovative technology and comprehensive data to improve their credit decisioning — including fraud detection, underwriting, account management, and collections. To avoid getting left behind, you need to consider how you can incorporate new tools and processes into your strategy. Get comfortable with machine learning models Although machine learning models have repeatedly shown they can offer performance improvements, lenders may hesitate to adopt them if they can't explain how the models work. It's smart to be cautious as so-called “black box" models generally don't pass regulatory muster — even if they can offer a greater lift. But there is a middle ground, and credit modelers use machine learning techniques to develop more effective models that are fully explainable. READ MORE: Explainability: ML and AI in credit decisioning Explore new data sources Machine learning models are great at recognizing patterns, but you need to train them on large data sets if you want to unlock their full potential. Lenders' internal data can be important, especially if they're developing custom models. But lenders should also try leveraging various types of alternative credit data to train models and more accurately assess an applicant's creditworthiness. This can include data from public records, rental payments, alternative financial services, and consumer-permissioned data. READ MORE: 2023 State of Alternative Credit Data Report Focus on financial inclusion Using new data sources can also help you more accurately understand the risk of an applicant who isn't scorable with traditional models. For example, Lift Premium™ uses machine learning and a combination of traditional consumer bureau credit data and alternative credit data to score 96 percent of U.S. consumers — 15 percent more than conventional scores.3 As a result, lenders can expand their lending universe and offer right-sized terms to people and groups who might otherwise be overlooked. Use AI to fuel automation Artificial intelligence can accelerate automation throughout the credit life cycle. Machine learning models do this within underwriting by more precisely estimating the creditworthiness of applicants. The more accurate a model is, the better it will be at identifying applicants who lenders want to approve or deny. Consider your decisioning strategy Although a machine learning model might offer more precise insight, lenders still need to set their decisioning strategy and business rules, including the cutoff points. Credit decisioning software can help lenders implement these decisions with speed, accuracy, and scalability. CASE STUDY: Experian partnered with OneAZ Credit Union to upgrade to an advanced credit decisioning platform and automate its underwriting strategy. The credit union increased load funding rates by 26 percent within one month and reduced manual reviews by 25 percent. Use underwriting as a component of strategic optimization Advanced analytics allow companies to move away from simpler rule-based decisions and toward strategies that take the business's overall goals into account. For example, lenders may be able to optimize decisions that involve competing goals — such as targets for volume and bad debt — to help the business reach its goals. Test and benchmark Underwriting is an iterative process. Lenders can use machine learning techniques to build and test challenger models and see how well they perform. You can also compare the results to industry benchmarks to see if there's likely room for more improvement. Why lenders choose Experian Lenders have used Experian's consumer and business credit data to underwrite loans for decades, but Experian is also a leader in advanced analytics. As lenders try to figure out how they'll approach underwriting in the coming years, they can partner with Experian's data scientists, who understand how to develop and deploy the latest types of compliant and explainable credit underwriting models. Experian also offers credit underwriting software and cloud-based and integrated decisioning platforms, along with modular solutions, such as access to alternative credit data, predictive attributes and scores. And lenders can explore collaborative approaches to developing ML-aided models that incorporate internal and third-party data. If you're not sure where to start, a business review can help you identify a few quick wins and create a road map for future improvements. Explore our credit decisioning solutions. * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably. 1Experian (2022). Explainability: ML and AI in credit decisioning2Experian (2022). Webinar: Driving Growth During Economic Uncertainty with AI/ML Strategies 3Experian (2022). Lift Premium