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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.

Despite economic uncertainty, new-customer acquisition remains a high priority in the banking industry, especially with increasing competition from fintech and big tech companies. For traditional banks, standing out in this saturated market doesn’t just involve enhancing their processes — it requires investing in the future of their business: Generation Z. Explore what Gen Z wants from financial technology and how to win them over in 2023 and beyond: Accelerate your digital transformation As digital natives, many Gen Zers prefer interacting with their peers and businesses online. In fact, more than 70% of Gen Zers would consider switching to a financial services provider with better digital offerings and capabilities.1 With a credit prescreen solution that harnesses the power of digital engagement, you can extend and represent firm credit offers through your online and mobile banking platforms, allowing for greater campaign reach and more personalized digital interactions. READ: Case study: Drive loan growth with digital prescreen Streamline your customer onboarding process With 70% of Gen Z and millennials having already opened an account online, it’s imperative that financial institutions offer a digital onboarding experience that’s quick, intuitive, and seamless. However, 44% of Gen Z and millennials state that their digital customer experience has been merely average, noting that the biggest gaps exist in onboarding and account opening.2 To improve the onboarding process, consider leveraging a flexible decisioning platform that accepts applications from multiple channels and automates data collection and identity verification. This way, you can reduce manual activity, drive faster decisions, and provide a frictionless digital customer experience. WATCH: OneAZ Credit Union saw a 25% decrease in manual reviews after implementing an integrated decisioning system Provide educational tools and resources Many Gen Zers feel uncertain and anxious about their financial futures, with their top concern being the cost of living. One way to empower this cohort is by offering credit education tools like step-by-step guides, score simulators, and credit alerts. These resources enable Gen Z to better understand their credit and how certain choices can impact their score. As a result, they can establish healthy financial habits, monitor their progress, and gain more control of their financial lives. By helping Gen Z achieve financial wellness, you can establish trust and long-lasting relationships, ultimately leading to higher customer retention and increased revenue for your business. To learn how Experian can help you engage the next generation of consumers, check out our credit marketing solutions. Learn more 1Addressing banking’s key business challenges in 2023.

The rise of the digital channel lead to a rise in new types of fraud – like cryptocurrency and buy now, pay later scams.  While the scams themselves are new, they’re based on tried-and-true schemes like account takeover and synthetic identity fraud that organizations have been working to thwart for years, once again driving home the need for a robust fraud solution.   While the digital channel is extremely attractive to many consumers due to convenience, it represents a balancing act for organizations – especially those with outdated fraud programs who are at increased risk for fraud. As organizations look for ways to keep themselves and the consumers they serve safe, many turn to fraud risk mitigation. What are fraud risk management strategies? Fraud risk management is the process of identifying, understanding, and responding to fraud risks. Proper fraud risk management strategies involve creating a program that detects and prevents fraudulent activity and reduces the risks associated with fraud. Many fraud risk management strategies are built on five principles: Fraud Risk AssessmentFraud Risk GovernanceFraud PreventionFraud DetectionMonitoring and Reporting By understanding these principles, you can build an effective strategy that meets consumer expectations and protects your business. Fraud risk assessment Fraud protection begins with an understanding of your organization’s vulnerabilities. Review your top risk areas and consider the potential losses you could face. Then look at what controls you currently have in place and how you can dial those up or down to impact both risk and customer experience. Fraud risk governance Fraud risk governance generally takes the form of a program encompassing the structure of rules, practices, and processes that surround fraud risk management. This program should include the fraud risk assessment, the roles and responsibilities of various departments, procedures for fraud events, and the plan for on-going monitoring. Fraud prevention “An ounce of prevention is worth a pound of cure.” This adage certainly rings true when it comes to fraud risk management. Having the right controls and procedures in place can help organizations stop a multitude of fraud types before they even get a foot in the door. Account takeover fraud prevention is an ideal example of how organizations can keep themselves and consumers safe. Fraud detection The only way to stop 100% of fraud is to stop 100% of interactions. Since that’s not a sustainable way to run a business, it’s important to have tools in place to detect fraud that’s already entered your ecosystem so you can stop it before damage occurs. These tools should monitor your systems to look for anomalies and risky behaviors and have a way to flag and report suspicious activity. Monitoring and reporting Once your fraud detection system is in place, you need active monitoring and reporting set up. Some fraud detection tools may include automatic next steps for suspicious activity such as step-up authentication or another risk mitigation technique. In other cases, you’ll need to get a person involved. In these cases it’s critical to have documented procedure and routing in place to ensure that potential fraud is assessed and addressed in a timely fashion. How to implement fraud risk management By adhering to the principles above, you can gain a holistic view of your current risk level, determine where you want your risk level to be, and what changes you’ll need to make to get there. While you might already have some of the necessary tools in place, the right next step is usually finding a trusted partner who can help you review your current state and help you use the right fraud prevention services that fit your risk tolerance and customer experience goals. To learn more about how Experian can help you leverage fraud prevention solutions, visit us or request a call. Learn more