Tag: data analytics

In today's data-driven business landscape, leveraging advanced targeting techniques is crucial for effective consumer engagement, particularly in the financial services sector. Prescreen targeting solutions have evolved significantly, offering a competitive edge through more precise and impactful outreach strategies. The power of data analytics and predictive modeling At the heart of modern prescreen targeting solutions lies the integration of extensive data analytics and predictive modeling. These systems combine detailed consumer information, including purchasing behaviors and credit scores, with sophisticated algorithms to identify potential customers most likely to respond positively to specific promotional campaigns. This approach not only streamlines campaign efforts but also enhances the tactical effectiveness of each interaction. Direct mail: a proven channel for financial services In the competitive North American financial services market, direct mail has demonstrated distinct advantages as a targeting channel. Its tangible nature helps cut through digital noise, capturing consumer attention effectively. For credit products, direct mail typically achieves engagement rates of 0.2-2% for prime consumers and 1-3% for near-prime and subprime consumers[1]. Key advantages of prescreen targeting solutions Enhanced response rates Custom response models can significantly boost prospect response rates by targeting a well-defined, high-propensity audience. These models have the potential to improve average response rates of prescreen direct mail campaigns by 10-25%. Risk mitigation By focusing on well-defined, high-propensity audiences, prescreen targeting via direct mail aims to attract the right prospects, minimizing fraud and delinquency risks. This targeted approach can lead to substantial savings on underwriting costs. Improved customer engagement and retention Personalized direct mail strengthens customer relationships by making recipients feel valued, leading to higher engagement and loyalty – crucial factors for long-term business success. Regulatory compliance and security Prescreen solutions come equipped with compliance safeguards, simplifying adherence to industry regulations and consumer privacy standards. This is particularly critical in the highly regulated financial sector. The future of targeting and enhancement As markets continue to evolve, the strategic importance of precise and efficient marketing techniques will only grow. Financial institutions leveraging optimized prescreen targeting and enhancement solutions can gain a significant competitive advantage, achieving higher immediate returns and fostering long-term customer loyalty and brand strength. Future advancements in AI and machine learning are expected to further refine prescreen targeting capabilities, offering even more sophisticated tools for marketers to engage effectively with their target audiences. Ascend Intelligence Services™ Target Ascend Intelligence Services Target is a sophisticated prescreening solution that boosts direct mail response rates. It uses comprehensive trended and alternative data, capturing credit and behavior patterns to iterate through direct mail response models and mathematical optimization. This enhances the target strategy and maximizes campaign response, take-up rates, and ROI within business constraints. Visit our website to learn more [1] Experian Research, Data Science Team, July 2024 

This article was updated on February 23, 2024. First impressions are always important – whether it’s for a job interview, a first date or when pitching a client. The same goes for financial services onboarding as it’s an opportunity for organizations to foster lifetime loyalty with customers. As a result, financial institutions are on the hunt now more than ever for frictionless online identity verification methods to validate genuine customers and maintain positive experiences during the online onboarding process. In a predominantly digital-first world, financial companies are increasingly focused on the customer experience and creating the most seamless online onboarding process. However, according to Experian’s 2023 Identity and Fraud Report, more than half of U.S. consumers considered dropping out during account opening due to friction and a less-than positive experience. And as technology continues to advance, digital financial services onboarding, not surprisingly, increases the demand for fraud protection and authentication methods – namely with digital identity (ID) verification processes. According to Experian’s report, 64% of consumers are very or somewhat concerned with online security, with identity theft being their top concern. So how can financial institutions guarantee a frictionless online onboarding experience while executing proper authentication methods and maintaining security and fraud detection? The answer? While a “frictionless” experience can seem like a bit of a unicorn, there are some ways to get close: Utilizing better data - Digital devices offer an extensive amount of data that’s useful in determining risk. Characteristics that allow the identification of a specific device, the behaviors associated with the device and information about a device’s owner can be captured without adding friction for the user. Analytics – Once the data is collected, advanced analytics uses information based on behavioral data, digital intelligence, phone intelligence and email intelligence to analyze for risk. While there’s friction in the initial ask for the input data, the risk prediction improves with more data. Document verification and biometric identity verification – Real-time document verification used in conjunction with facial biometrics, behavioral biometrics and other physical characteristics allows for rapid onboarding and helps to maintain a low friction customer journey. Financial institutions can utilize document verification to replace manual long-form applications for rapid onboarding and immediately verify new data at the point of entry. Using their mobile phones, consumers can photograph and upload identity documents to pre-fill applications. Document authenticity can be verified in real-time. Biometrics, including facial, behavioral, or other physical characteristics (like fingerprints), are low-touch methods of customer authentication that can be used synchronously with document verification. Optimize your financial services onboarding process Experian understands how critical identity management and fraud protection is when it comes to the online onboarding process and identity verification. That’s why we created layered digital identity verification and risk segmentation solutions to help legitimize your customers with confidence while improving the customer experience. Our identity verification solutions use advanced technology and capabilities to correctly identify and verify real customers while mitigating fraud and maintaining frictionless customer experiences. Learn more

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

This article was updated on January 31, 2024. Debt. For many, it’s a struggle – and a constant one. In fact, total consumer debt balances have increased year-over-year.1 High inflation and fears of a recession aren't letting up either. Successful third-party debt collections can be achieved by investing in the right data and technologies. Overcoming debt collections challenges While third-party debt collectors may take a more specialized approach to collections, they face unique challenges. Debt collectors must find the debtor, get them to respond, collect payment, and stay compliant. With streamlined processes and enhanced strategies, lending institutions and collection agencies can recoup more costs.  Embrace automationAutomation, artificial intelligence, and machine learning are at the forefront of the continued digital transformation within the world of collections. When implemented well, automation can ease pressure on call center agents and improve the customer experience. Automated systems can also help increase recovery rates while minimizing the risk of human error and the corresponding liability. READ: Three Tips for Successful Automated Debt CollectionsMaximize digitalizationIntegrating and expanding digital technologies is mandatory to be successful in the third-party debt collections space. Third-party debt collectors must be at the forefront of adopting digital communication tools (i.e., email, text, chatbots, and banking apps), to connect more easily with debtors and provide a frictionless customer experience. A digital debt recovery solution helps third-party debt collectors streamline processes, maintain debt collection compliance, and maximize collections efforts. READ: The Ultimate Guide to Successful Debt Collection TechniquesLeverage the best data Consumer data is ever-changing, especially during times of economic distress. Capturing accurate consumer information through a combination of data sources — and continually evaluating the data’s validity — is key to reducing risk throughout the consumer life cycle. By gaining a fresher, more complete view of existing and potential customers, third-party debt collectors can better determine an individual’s propensity to pay and enhance their overall decisioning. Keep pace with changing regulations With increasing scrutiny on the financial services industry and ever-evolving consumer protection and privacy regulations, remaining compliant is a top priority for third-party debt collections departments and agencies. The increased focus on regulations and compliance has also brought to the surface the need for teams to include debt collectors with soft skills who can communicate effectively with indebted consumers. With the right processes and third-party debt collections tools, you can better develop a robust compliance management strategy that works to prevent reputational risk and minimize costly violations. Finding the right debt collections partner In today's climate, it's never been more important to build the right third-party debt collections strategies for your business. By creating a more effective, consumer-focused collections process, you can maximize your recovery efforts, make more profitable decisions and focus your resources where they’re needed most. Our third-party debt management solutions empower your organization to see the complete behavioral, demographic, and emerging view of customer portfolios through extensive data assets, debt collection predictive analytics innovative platforms. For more insights to strengthen your debt collection strategy, download our tip sheet. Access tip sheet

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

‘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.

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

The fraud problem is ever-present, with 94% of businesses reporting it as a top priority, and fraudsters constantly finding new targets for theft. Preventing fraud requires a carefully orchestrated strategy that can recognize and treat a variety of types — without adding so much friction that it drives customers away. Experian’s fraud prevention and detection platform, CrossCore®, was recently named an Overall Leader, Product Leader in Fraud Reduction Intelligence Platforms, Innovation Leader and Market Leader in Fraud Reduction by KuppingerCole. CrossCore is an integrated digital identity and fraud risk platform that enables organizations to connect, access, and orchestrate decisions that leverage multiple data sources and services. CrossCore combines risk-based authentication, identity proofing, and fraud detection into a single, state-of-the-art cloud platform. It engages flexible decisioning workflows and advanced analytics to make real-time risk decisions throughout the customer lifecycle. This recognition highlights Experian’s comprehensive approach to combating fraud and validates that CrossCore offers best-in-class capabilities by augmenting Experian’s industry-leading identity and fraud offerings with a highly curated ecosystem of partners which enables further optionality for organizations based on their specific needs. To learn more about how CrossCore can benefit your organization, read the report or visit us. Learn more

The science of turning historical data into actionable insights is far from magic. And while organizations have successfully used predictive analytics for years, we're in the midst of a transformation. New tools, vast amounts of data, enhanced computing power and decreasing implementation costs are making predictive analytics increasingly accessible. And business leaders from varying industries and functions can now use the outcomes to make strategic decisions and manage risk. What is predictive analytics? Predictive analytics is a type of data analytics that uses statistical modeling and machine learning techniques to make predictions based on historical data. Organizations can use predictive analytics to predict risks, needs and outcomes. You might use predictive analytics to make an immediate decision. For example, whether or not to approve a new credit application based on a credit score — the output from a predictive credit risk model. But organizations can also use predictive analytics to make long-term decisions, such as how much inventory to order or staff to hire based on expected demand. How can predictive business analytics help a business succeed? Businesses can use predictive analytics in different parts of their organizations to answer common and critical questions. These include forecasting market trends, inventory and staffing needs, sales and risk. With a wide range of potential applications, it’s no surprise that organizations across industries and functions are using predictive analytics to inform their decisions. Here are a few examples of how predictive analytics can be helpful: Financial services: Financial institutions can use predictive analytics to assess credit risk, detect fraudulent applicants or transactions, cross-sell customers and limit losses during recovery. Healthcare: Using data from health records and medical devices, predictive models can predict patient outcomes or identify patients who need critical care. Manufacturing: An organization can use models to predict when machines need to be turned off or repaired to improve their longevity and avoid accidents. Retail: Brick-and-mortar retailers might use predictive analytics when deciding where to expand, what to cross-sell loyalty program members and how to improve pricing. Hospitality: A large hospitality group might predict future reservations to help determine how much staff they need to hire or schedule. Advanced techniques in predictive modeling for financial services Emerging technologies, particularly AI and machine learning (ML), are revolutionizing predictive modeling in the financial sector by providing more accurate, faster and more nuanced insights. Taking a closer look at financial services, consider how an organization might use predictive credit analytics and credit risk scores across the customer lifecycle. Marketing: Segment consumers to run targeted marketing campaigns and send prescreened credit offers to the people who are most likely to respond. AI models can analyze customer data to offer personalized offers and product recommendations. Underwriting: AI technologies enable real-time data analysis, which is critical for underwriting. The outputs from credit risk models can help you to quickly approve, deny or send applications for manual review. Explainable machine learning models may be able to expand automation and outperform predictive models built with older techniques by 10 to 15 percent.1 Fraud detection models can also raise red flags based on suspicious information or behaviors. Account management: Manage portfolios and improve customer retention, experience and lifetime value. The outputs can help you determine when you should adjust credit lines and interest rates or extend offers to existing customers. AI can automate complex decision-making processes by learning from historical data, reducing the need for human intervention and minimizing human error. Collections: Optimize and automate collections based on models' predictions about consumers' propensity to pay and expected recovery amounts. ML models, which are capable of processing vast amounts of unstructured data, can uncover complex patterns that traditional models might miss. Although some businesses can use unsupervised or “black box" models, regulations may limit how financial institutions can use predictive analytics to make lending decisions. Fortunately, there are ways to use advanced analytics, including AI and ML, to improve performance with fully compliant and explainable credit risk models and scores. WHITE PAPER: Getting AI-driven decisioning right in financial services Developing predictive analytics models Going from historical data to actionable analytics insights can be a long journey. And if you're making major decisions based on a model's predictions, you need to be confident that there aren’t any missteps along the way. Internal and external data scientists can oversee the process of developing, testing and implementing predictive analytics models: Define your goal: Determine the predictions you want to make or problems you want to solve given the constraints you must act within. Collect data: Identify internal and external data sources that house information that could be potentially relevant to your goal. Prepare the data: Clean the data to prepare it for analysis by removing errors or outliers and determining if more data will be helpful. Develop and validate models: Create predictive models based on your data, desired outcomes and regulatory requirements. Deciding which tools and techniques to use during model development is part of the art that goes into the science of predictive analytics. You can then validate models to confirm that they accurately predict outcomes. Deploy the models: Once a model is validated, deploy it into a live environment to start making predictions. Depending on your IT environment, business leaders may be able to easily access the outputs using a dashboard, app or website. Monitor results: Test and monitor the model to ensure it's continually meeting performance expectations. You may need to regularly retrain or redevelop models using training data that better reflects current conditions. Depending on your goals and resources, you may want to start with off-the-shelf predictive models that can offer immediate insights. But if your resources and experience allow, custom models may offer more insights. CASE STUDY: Experian worked with one of the largest retail credit card issuers to develop a custom acquisition model. The client's goal was to quickly replace their outdated custom model while complying with their model governance requirements. By using proprietary attribute sets and a patented advanced model development process, Experian built a model that offered 10 percent performance improvements across segments. Predictive modeling techniques Data scientists can use different modeling techniques when building predictive models, including: Regression analysis: A traditional approach that identifies the most important relationships between two or more variables. Decision trees: Tree-like diagrams  show potential choices and their outcomes. Gradient-boosted trees: Builds on the output from individual decision trees to train more predictive trees by identifying and correcting errors. Random forest: Uses multiple decision trees that are built in parallel on slightly different subsets of the training data. Each tree will give an output, and the forest can analyze all of these outputs to determine the most likely result. Neural networks: Designed to mimic how the brain works to find underlying relationships between data points through repeated tests and pattern recognition. Support vector machines: A type of machine learning algorithm that can classify data into different groups and make predictions based on shared characteristics. Experienced data scientists may know which techniques will work well for specific business needs. However, developing and comparing several models using different techniques can help determine the best fit. Implementation challenges and solutions in predictive analytics Integrating predictive analytics into existing systems presents several challenges that range from technical hurdles to external scrutiny. Here are some common obstacles and practical solutions: Data integration and quality: Existing systems often comprise disparate data sources, including legacy systems that do not easily interact. Extracting high-quality data from these varied sources is a challenge due to inconsistent data formats and quality. Implementing robust data management practices, such as data warehousing and data governance frameworks, ensure data quality and consistency. The use  of APIs can facilitate seamless data integration. Scalability: Predictive business analytics models that perform well in a controlled test environment may not scale effectively across the entire organization. They can suffer from performance issues when deployed on a larger scale due to increased data volumes and transaction rates. Invest in scalable infrastructure, such as cloud-based platforms that can dynamically adjust resources based on demand. Regulatory compliance: Financial institutions are heavily regulated, and any analytics tool must comply with existing laws — such as the Fair Credit Reporting Act in the U.S. — which govern data privacy and model transparency. Including explainable AI capabilities helps to ensure transparency and compliance in your predictive models. Compliance protocols should be regularly reviewed to align with both internal audits and external regulations. Expertise: Predictive analytics requires specialized knowledge in data science, machine learning and analytics. Develop in-house expertise through training and development programs or consider partnerships with analytics firms to bridge the gap. By addressing these challenges with thoughtful strategies, organizations can effectively integrate predictive analytics into their systems to enhance decision-making and gain a competitive advantage. From prediction to prescription While prediction analytics focuses on predicting what may happen, prescription analytics focuses on what you should do next. When combined, you can use the results to optimize decisions throughout your organization. But it all starts with good data and prediction models. Learn more about Experian's predictive modeling solutions. 1Experian (2020). Machine Learning Decisions in Milliseconds *This article includes content created by an AI language model and is intended to provide general information.

 With nearly seven billion credit card and personal loan acquisition mailers sent out last year, consumers are persistently targeted with pre-approved offers, making it critical for credit unions to deliver the right offer to the right person, at the right time. How WSECU is enhancing the lending experience As the second-largest credit union in the state of Washington, Washington State Employees Credit Union (WSECU) wanted to digitalize their credit decisioning and prequalification process through their new online banking platform, while also providing members with their individual, real-time credit score. WSECU implemented an instant credit decisioning solution delivered via Experian’s Decisioning as a ServiceSM environment, an integrated decisioning system that provides clients with access to data, attributes, scores and analytics to improve decisioning across the customer life cycle. Streamlined processes lead to upsurge in revenue growth   Within three months of leveraging Experian’s solution, WSECU saw more members beginning their lending journey through a digital channel than ever before, leading to a 25% increase in loan and credit applications. Additionally, member satisfaction increased with 90% of members finding the simplified process to be more efficient and requiring “low effort.” Read our case study for more insight on using our digital credit solutions to: Prequalify members in real-time at point of contact Match members to the right loan products Increase qualification, approval and take rates Lower operational and manual review costs Read case study

Mortgage lenders are no stranger to income and employment verification. Leveraging a third-party solution provider for automated verifications is a standard practice in mortgage underwriting. Yet many lenders still struggle with time-consuming and complex verification experiences, which can be manual, inefficient and painful for borrowers. Since introducing Experian Verify™ to the market, we’ve had countless conversations with key players in the industry – from the largest banks to small independent mortgage brokers and everything in between. Through these conversations, we’ve learned quite a bit about some of the dos and don’ts when it comes to implementing a successful strategy for income and employment verification for mortgage. Lead with instant verification Digital transformation has forever changed borrower expectations for online experiences. The first key to a successful verification strategy is starting your workflow with an instant verification solution. This allows you to verify information in real time, delivering a completely frictionless experience for you and your borrowers. Consider building a waterfall process For instances when a borrower’s income and employment information is unable to be verified through an instant verification solution, add a consumer-permissioned (bank or payroll) option as a backup. Cascading from one digital solution to the next will ensure you can verify borrower information in seconds or minutes, as opposed to days or weeks. The goal is to prevent as many borrowers as possible from going through a costly manual process. Tap into unique data sources Many verification solutions in market today tap into the same data sources, which can make it difficult to differentiate between solutions and measure additive benefits. When evaluating options, look for verification solutions that leverage unique and exclusive data sources – allowing you to optimize hit rates and maximize value. Avoid a “one-size-fits-all” approach There is no silver bullet. Every market is unique and every lender has different needs. Your verification requirements are likely specific to your business, which means you need to leverage verification solutions that offer flexible options and enable you to build a verification experience that works best for you and your borrowers. Find a solution provider who’s all in It’s important to find a solution provider where income and employment verification isn’t just a “side hustle,” but is core to their business strategy. Find a provider who is fully committed – delivering new innovations, investing in key partnerships, maximizing accessibility through leading LOS / POS technology providers, and offers eligibility for key industry programs, such as Day 1 Certainty® from Fannie Mae. Challenge the status quo Many lenders have an existing relationship with a third-party solution provider. But it’s important not to put all your eggs in one basket. If your existing provider is not meeting all your needs, challenge the status quo. Consider adding a second provider to the top of your waterfall to help contain costs and tap into unique data that is not available from your existing provider. Ready for further insight? Learn more about income and employment verification for mortgage.

With consumers continuing to take a digital-first approach to everything from shopping to dating and investing, fraudsters are finding new and innovative ways to commit fraud. To help businesses anticipate and prepare for the road ahead, we created the 2022 Future of Fraud Forecast. Here are the fraud trends we expect to see over the coming year: Buy Now, Pay Never: Buy now, pay later lenders will see an uptick in identity theft and synthetic identity fraud. Beware of Cryptocurrency Scams: Fraudsters will set up cryptocurrency accounts to extract, store and funnel stolen funds, such as the billions of stimulus dollars swindled by criminals. Double the Trouble for Ransomware Attacks: Fraudsters will not only ask for a hefty ransom to cede control back to the companies they’ve hacked but also steal and leverage data from the hacked company. Love, Actually?: Romance scams will continue to see an uptick, with fraudsters asking victims for money or loans to cover fabricated travel costs, medical expenses and more. Digital Elder Abuse Will Rise: Older consumers and other vulnerable digital newbies will be hit with social engineering and account takeover fraud. “Businesses and consumers need to be aware of the creativity and agility that fraudsters are using today, especially in our digital-first world,” said Kathleen Peters, Chief Innovation Officer at Experian Decision Analytics in North America. “Experian continues to leverage data and advanced analytics to develop innovative solutions to help businesses prevent fraudulent behavior and protect consumers.” To learn more about how to protect your business and customers from rising fraud trends, download the Future of Fraud Forecast and check out Experian’s fraud prevention solutions. Future of Fraud Forecast Read Press Release

Artificial intelligence is here to stay, and businesses who are adopting the newest AI technology are ahead of the game. From targeting the right prospects to designing effective collections efforts, AI-driven strategies across the entire customer lifecycle are no longer a nice to have - they are a must.  Many organizations are late to the game of AI and/or are spending too much time and money designing and redesigning models and deploying them over weeks and months. By the time these models are deployed, markets may have already shifted again, forcing strategy teams to go back to the drawing board. And if these models and strategies are not being continuously monitored, they can become less effective over time and lead to missed opportunities and lost revenue. By implementing artificial intelligence in predictive modeling and strategy optimization, financial institutions and lenders can design and deploy their decisioning strategies faster than ever before and make incremental changes on the fly to adapt to evolving market trends.  While most organizations say they want to incorporate artificial intelligence and machine learning into their business strategy, many do not know where to start. Targeting, portfolio management, and collections are some of the top use cases for AI/ML strategy initiatives.  Targeting  One way businesses are using AI-driven modeling is for targeting the audiences that will most likely meet their credit criteria and respond to their offers. Financial institutions need to have the right data to inform a decisioning strategy that recognizes credit criteria, can respond immediately when prospects meet that criteria and can be adjusted quickly when those factors change. AI-driven response models and optimized decision strategies perform these functions seamlessly, giving businesses the advantage of targeting the right prospects at the right time.  Credit portfolio management  Risk models optimized with artificial intelligence and machine learning, built on comprehensive data sets, are being used by credit lenders to acquire new revenue and set appropriate balance limits. Strategies built around AI-driven risk models enable businesses to send new offers and cross-sell offers to current customers, while appropriately setting initial credit limits and managing limits over time for increased wallet share and reduced risk.   Collections  AI- and ML-driven analytics models are also optimizing collections strategies to improve recovery rates. Employing AI-powered balance and response models, credit lenders can make smarter collections decisions based on the most predictive and accurate information available.   For lending businesses who are already tight on resources, or those whose IT teams cannot meet the demand of quickly adapting to ever-changing market conditions and decisioning criteria, a managed service for AI-powered models and strategy design might be the best option. Managed service teams work closely with businesses to determine specific use cases, develop models to meet those use cases, deploy models quickly, and monitor models to ensure they keep producing and predicting optimally.  Experian offers Ascend Intelligence Services, the only managed service solution to provide data, analytics, strategy and performance monitoring. Experian’s data scientists provide expert guidance as they collaborate with businesses in developing and deploying models and strategies around targeting, acquisitions, limit-setting, and collections. Once those strategies are deployed, Experian continually monitors model health to ensure scores are still predictive and presents challenger models so credit lenders can always have the most accurate decisioning models for their business. Ascend Intelligence Services provides an online dashboard for easy visibility, documentation for regulatory compliance, and cloud capabilities to deliver scores and decisions in real-time.  Experian’s Ascend Intelligence Services makes getting into the AI game easy. Start realizing the power of data and AI-driven analytics models by using our ROI calculator below: initIframe('611ea3adb1ab9f5149cf694e'); For more information about Ascend Intelligence Services, visit our webpage or join our upcoming webinar on October 21, 2021.  Learn more Register for webinar

The tax gap—the difference between what taxpayers should pay and what they actually pay on time—can have a substantial impact on states’ budgets. Tax agencies and other state departments are responsible for helping states manage their budgets by minimizing expected revenue shortfalls. Underreported income is a significant budget complication that continues to frustrate even the most effective tax agencies, until the right tools are brought into play.   The Problem Underreporting is a large, complex issue for agencies. The IRS currently estimates the annual tax gap at $441 billion. There are multiple factors that comprise that total, but the most prevalent is underreporting, which represents 80% of the total tax gap. Of that, 54% is due to underreporting of individual income tax. In addition to being the largest contributor to the tax gap, underreporting is also extremely challenging to identify out of the millions of returns being filed. With 85% of taxes owed correctly reported and paid, finding underreporting can be like trying to locate a needle in the proverbial haystack. Making this even more challenging is the limited resources available for auditing returns, which makes efficiency key. The Solution Data, combined with artificial intelligence (AI) equals efficient detection. The problem with trying to detect which returns are most likely to have underreported income is similar to many other challenges Experian has solved with AI. Partnerships between Experian and state agencies combine what we know about consumers with what their agency knows about their population. We can take the data and use AI to separate the signal from the noise, finding opportunities to recoup lost revenue. Read our case study on how Experian was able to help an agency identify instances of underreporting, detecting an estimated $80 million annual lost revenue from underreported income. Download case study Contact us

This is the fourth in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty, the second with predicting consumer payment behavior, and the third with validating consumer credit scores. This post describes some specific Experian solutions that are especially timely for lenders strategizing their response to the COVID Recession. Will the US economy recover from the pandemic recession?  Certainly yes. When will the economy recover? There is a lot more uncertainty around that question. Many people are encouraged by positive indicators, such as the initial rebound of the stock market, a return of many of the jobs lost at the beginning of the pandemic, and a significant increase in housing starts. August’s retail spending and homebuilder confidence are very encouraging economic indicators. Other experts doubt that the “V-shaped” recovery can survive flare-ups of the virus in various parts of the US and the world, and are calling for a “W-shaped” recovery.  Employment indicators are alarming: many people remain out of work, some job losses are permanent, and there are more initial jobless claims each week now than at the height of the Great Recession. Serious hurdles to economic recovery may remain until a vaccine is widely available: childcare, urban transportation, and global trade, for example. I’m encouraged by the resilience of many of our country’s consumer lenders. They are generally responding well to these challenges. If past recessions are a guide, some lenders will not survive these turbulent times. This time, many lenders—whether or not they have already adopted the CECL accounting standards—have been increasing allowances for their anticipated credit losses. At least one rating agency believes major banks are prepared to absorb those losses from earnings.  The lenders who are most prepared for the eventual recovery will be those that make good decisions during these volatile times and take action to put themselves in the best position in anticipation of the recovery that will certainly follow. The best lenders are making smart investments now to be prepared to capitalize on future opportunities. Experian’s analytics and consulting experts are continuously improving our suite of solutions that help consumer lenders and others assess consumer behavior and respond quickly to the rapidly fluctuating market conditions as well as changing regulations and credit reporting practices. Our newly announced Economic Response and Recovery Suite includes the ABCD’s that lenders need to be resilient and competitive now and to prepare to thrive during the eventual recovery: A – Analytics. As I’ve written about in prior blog posts, data is a prerequisite to making good business decisions, but data alone is not enough. To make wise, insightful decisions, lenders need to use the most appropriate analytical techniques, whether that means more meaningful attributes, more predictive and compliant credit scores, more accurate and defensible loss forecasting solutions, or optimization systems that help develop strategies in a world where budgets, regulations, and other constraints are changing. For example, Experian has released a set of Spotlight 2020 Attributes that help consumer lenders create a positive experience for customers who have received an accommodation during the pandemic. In many cases motivated by the new race to improve customer experience online, and in other cases as a reaction to new and creative fraud schemes, some clients are using this period as an opportunity to explore or deploy ethical and explainable Artificial Intelligence. B – Business Intelligence. Credit bureaus like Experian are uniquely situated to understand the impact of the COVID recession on America’s consumers. With impact reports, dashboards, and custom business intelligence solutions, lenders are working during the recession to gain an even better understanding of their current and prospective customers. We’re helping many of them to proactively help consumers when they need it most. For example, lenders have turned to us to understand their customer’s payment hierarchy—which bills they pay first when times are tough. Our free COVID-19 US Business Risk Index helps make lending options available to the businesses who need them most. And we’ve armed lenders with recommendations for which of our pre-existing attributes and scores are most helpful during trying times. Additional reporting tools such as the Auto Market Tracker, Ascend Market Insights Dashboard, and the weekly economic update video provide businesses with information on new market trends—information that helps them respond during the recession and promises to help them grow during the eventual recovery. C – Consulting. It’s good to turn data into information and information into insight, but how do these lenders incorporate these insights in their business strategies? Lenders and other businesses have been turning to Experian’s analytics and Advisory services consultants to unlock the information hidden in credit and other data sources—finding ways to make their business processes more efficient and more effective while developing quick response plans and more long-term recovery strategies. D – Delivery.  Decision science is the practice of using advanced analytics, artificial intelligence, and other techniques to determine the best decision based on available data and resources. But putting those decisions into action can be a challenge. (Organizations like IBM and Gartner estimate that a great majority of data science projects are never put into production.) Experian technologies—from our analytics platform to our attribute integration and decision management solutions ensure that data-driven decisions can be quickly implemented to make a real difference. Treating each customer optimally has a number of benefits—whether you are trying to responsibly grow your portfolio, reduce credit losses and allowances, control servicing costs, or simply staying in compliance during dynamic times. In the age of COVID, IT departments have placed increased priority on agility, security, customer experience, and cost control, and appreciate cloud-first approach to deploying analytics. It’s too early to know how long this period of extreme uncertainty will last. But one thing is certain: it will come to an end, and the economy will recover someday. I predict that many of the companies that make the best use of data now will be the ones who do the best during the recovery. To hear more ways your organization can navigate this downturn and the recovery to follow, please watch our on-demand webinar and check out our Economic Response and Recovery Suite. Watch the Webinar