All posts by Julie Lee

The state of digital banking is a story of fragmentation and technology that's often outdated or poorly integrated. Customer journeys are often suboptimal, and multiple layers of technological solutions often translate to problems like poor data hygiene, lack of regulatory compliance and missed opportunities. In addition, the use of legacy software can make it challenging to integrate up-to-date methods such as AI analytics solutions. However, demand on both the front and back ends for better digital services and more-efficient processes is driving banks to take on digital transformations that will help them stay competitive in an evolving technological landscape. Customers expect a frictionless, personalized and highly functional digital experience. To match strength with digital-native competitors, banks and lenders must transform how their organizations do business. What is digital transformation, and what does it mean for banks and lenders? A comprehensive digital transformation strategy is more than just investing in new digital tools. It's about rebuilding the structure and infrastructure of your business so that online and digital services and processes form the core of your competencies and offerings. Digital transformation is an ongoing journey rather than an end goal. It's a continuous process that iterates as you steadily improve and streamline operations and integrate new and improved technologies. One of the key aspects of digital transformation in banking is better gathering and leveraging of data. Banks, especially larger ones with a longer business history, possess large quantities of data that may be siloed or poorly utilized. By improving how they collect, analyze and make use of data, banks and financial institutions can enhance their decision-making abilities and engage with consumers in a more authentic, personalized way. Perhaps most important, digital transformation is customer-centric. While upgrading, merging and integrating back-end technologies and data solutions is a key component of the process, it's all done with the customer experience top of mind. Centralizing, streamlining and modernizing digital operations help to create a seamless, secure and highly targeted customer journey. The core pillars of digital transformation Multiple core pillars are involved in undergoing a successful digital transformation. Each of these should be integrated into a comprehensive strategy that considers the transformation as an integrated process, rather than a series of individual projects. In fact, one common error banks make when upgrading their digital infrastructure and offerings is failing to coordinate digital initiatives. A true digital transformation is holistic, resulting in apps, infrastructure, digital systems and customer experience platforms that are all part of one coherent, consistent approach. Data: Data is at the heart of digital transformation. It's through maximizing and optimizing usable data that financial institutions can truly make an impact on their ability to reach and connect with target consumers. Using data the right way means prioritizing security and privacy while taking advantage of opportunities to improve consumer targeting and engagement and personalization of offers. Analytics: Data can't do its job if it's not interpreted in a way that makes sense for your business. Quality analytics software and comprehensive analysis are what turn a set of disparate data points into usable information that informs smart decision-making and improves KPIs. Automation: Machine learning is improving by leaps and bounds, and it's only going to get more useful for businesses looking to increase the efficiency of their sales, marketing and engagement efforts. AI solutions are no longer a fringe tool but are quickly becoming part of the mainstream and a key component of digital strategies. Customers: With the array of digital tools available today, it's easy to lose sight of the main purpose of your business — connecting with people. Customers today expect digital engagement experiences that feel personalized and real, which is why a consistent, appealing digital customer journey should be top of mind in any digital transformation strategy. How can banks benefit? New, digitally native fintech solutions abound in the contemporary landscape. Overall, they tend to be highly competent when it comes to making the most of state-of-the-art tools like artificial intelligence, mobile apps and blockchain. By combining their brand longevity with a well-executed digital transformation, traditional banks can capitalize on their established reputations by reaching consumers with compelling offerings that utilize and are based on best-in-class digital tools and data analysis. Digital transformation in banking can have numerous benefits. For one, operations will be more streamlined. For another, enhanced security will make customers feel more secure while minimizing losses from fraud. In addition, integrating top-of-the-line data and analysis will result in better overall decision-making. The ultimate goal? Boosting lead generation and conversion rates and improving customer onboarding while reducing churn, thereby maximizing the efficiency of budget spend across multiple departments, from marketing to customer service. Get started with Experian Implementing a digital transformation that truly improves your business can be a daunting task, but it's achievable with the right partner. Experian's connectable and configurable solutions and technology can help drive your digital transformation. With offerings like our cloud platform solutions, you'll be well-positioned to move forward and take advantage of up-to-date technologies to serve your customers better. Learn more about how you can benefit from the digital transformation in banking. 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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

‘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.

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.

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.

From chatbots to image generators, artificial intelligence (AI) has captured consumers' attention and spurred joy — and sometimes a little fear. It's not too different in the business world. There are amazing opportunities and lenders are increasingly turning to AI-driven lending decision engines and processes. But there are also open questions about how AI can work within existing regulatory requirements, how new regulations will impact its use and how to implement advanced analytics in a way that increases equitable inclusion rather than further embedding disparities. How are lenders using AI today? Many financial institutions have embraced — or at least tested — AI within several parts of their organization. The most advanced use of machine learning (ML) models tends to occur with credit card and unsecured personal loan underwriting.1 However, by late 2021, nearly three-quarters of businesses had used AI and machine learning, and 81 percent felt confident in using advanced analytics and AI in credit risk decisioning.2 READ MORE: AI and Machine Learning for Financial Institutions Today, lenders are implementing AI-driven tools throughout the customer lifecycle to: Target the right consumers: Lenders can sift through vast amounts of data to find consumers who match their credit criteria and send right-sized offers, which enables them to maximize their acceptance rates.Detect and prevent fraud:  Fraud detection tools have used AI and machine learning techniques to detect and prevent fraud for years. These systems may be even more important as fraudsters invest in technology and conduct increasingly sophisticated attacks.Assess creditworthiness: Machine learning-based models can incorporate a range of internal and external data points to more precisely evaluate creditworthiness and create a 10 to 15 percent performance lift compared with traditional linear and logistic regression models.3Automate decisions:  More precise evaluations can increase how many applications flow into your automated approval and denial process rather than requiring a manual review.Manage portfolios: Lenders can also use a more complete picture of their current customers to make better decisions. For example, AI-driven models can help lenders set initial credit limits and suggest when a change could help them increase wallet share or reduce risk. Lenders can also use AI to help determine which up- and cross-selling offers to present and when (and how) to reach out.Improve collections: Models can be built to ease debt collection processes, such as choosing where to assign accounts, which accounts to prioritize and how to contact the consumer. Additionally, businesses around the world have recognized improving customer acquisition and digital engagement as top priorities. In a recent Experian survey, companies ranked investing in AI second, behind investing in decisioning software, as the best way to improve their digital experiences.2 The benefits of AI in lending Although lenders can use machine learning models in many ways, the primary drivers for adoption in underwriting are:1 Improving credit risk assessmentFaster development and deployment cycles for new or recalibrated modelsUnlocking the possibilities within large datasetsKeeping up with competing lenders Some of the use cases for machine learning solutions have a direct impact on the bottom line — improving credit risk assessment can decrease charge-offs. Others are less direct but still meaningful. For instance, machine learning models might increase efficiency and allow further automation. This takes the pressure off your underwriting team, even when application volume is extremely high, and results in faster decisions for applicants, which can improve your customer experience. CASE STUDY:  Atlas Credit, a small-dollar lender, used a machine learning-powered model and automation to nearly double its loan approval rates and decrease credit losses by up to 20 percent. Incorporating large data sets into their decisions also allows lenders to expand their lending universe without taking on additional risk. For example, they may now be able to offer risk-appropriate credit lines to consumers that traditional scoring models can't score. And machine learning solutions can increasecustomer lifetime value when they're incorporated throughout the customer lifecycle by stopping fraud, improving retention, increasing up- or cross-selling and streamlining collections. Hurdles to adoption of machine learning in lending There are clear benefits and interest in machine learning and analytics, but adoption can be difficult, especially within credit underwriting. In August 2021, Forrester Consulting conducted a study commissioned by Experian and found the main barriers to adopting machine learning were:4 Explainability of machine learning models (35 percent)Model deployment into decisioning strategy management systems (34 percent)Model deployment into live operational runtime environment (31 percent)Lack of access to in-house transactional data (30 percent)Lack of access to a wide range of traditional and non-traditional data assets (30 percent) Explainability comes down to transparency and trust. Financial institutions have to trust that machine learning models will continue to outperform traditional models to make them a worthwhile investment. The models also have to be transparent and explainable for financial institutions to meet regulatory fair lending requirements.1 WATCH:  Explainable Artificial Intelligence: The Case of Fair Lending A lack of resources and expertise could hinder model development and deployment. It can take around nine months to build and deploy a custom model, and there's a lot of overhead to cover during the process.5 Large lenders might have in-house credit modeling teams that can take on the workload, but they also face barriers when integrating new models into legacy systems. Small- and mid-sized institutions may be more nimble, but they rarely have the in-house expertise to build or deploy models on their own. The models also have to be trained on appropriate data sets. Similar to model building and deployment, organizations might not have the human or financial resources to clean and organize internal data. And although vendors offer access to a lot of external data, sometimes sorting through and using the data requires a large commitment. How Experian is shaping the future of AI in lending Lenders are finding new ways to use AI throughout the customer lifecycle and with varying types of financial products. However, while the cost to create custom machine learning models is dropping, the complexities and unknowns are still too great for some lenders to manage. But that's changing.5 Experian built the Ascend Intelligence Services™ to help smaller and mid-market lenders access the most advanced analytics tools. The managed service platform won a Fintech Breakthrough Award in 2021, and it can significantly reduce the cost and deployment time for lenders who want to incorporate AI-driven strategies and machine learning models into their lending process. The end-to-end managed analytics service gives lenders access to Experian's vast data sets and can incorporate internal data to build and seamlessly deploy custom machine learning models. The platform can also continually monitor and retrain models to increase lift, and there's no “black box" to obscure how the model works. Everything is fully explainable, and the platform bakes regulatory constraints into the data curation and model development to ensure lenders stay compliant.5 Learn more about our machine learning 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. 1FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context2Experian (2021). Global Insights Report September/October 2021 3Experian (2020). Machine Learning Decisions in Milliseconds 4Experian (2022). Explainability: ML and AI in credit decisioning 5Experian (2021). Podcast: Advanced Analytics, Artificial Intelligence and Machine Learning in Lending