Tag: custom models

Getting customers to respond to your credit offers can be difficult. With the advent of artificial intelligence (AI) and machine learning (ML), optimizing credit prescreen campaigns has never been easier or more efficient. In this post, we'll explore the basics of prescreen and how AI and ML can enhance your strategy.  What is prescreen?  Prescreen involves evaluating potential customers to determine their eligibility for credit offers. This process takes place without the consumer’s knowledge and without any negative impact on their credit score.  Why optimize your prescreen strategy?  In today's financial landscape, having an optimized prescreen strategy is crucial. Some reasons include:  Increased competition: Financial institutions face stiff competition in acquiring new customers. An optimized prescreen strategy helps you stand out by targeting the right individuals with tailored offers, increasing the chances of conversion.  Customer expectations: Modern customers expect personalized and relevant offers. An effective prescreen strategy ensures that your offers resonate with the specific needs and preferences of potential customers.  Strict budgets: Organizations today are faced with a limited marketing budget. By determining the right consumers for your offers, you can minimize prescreen costs and maximize the ROI of your campaigns.  Regulatory compliance: Compliance with regulations such as the Fair Credit Reporting Act (FCRA) is essential. An optimized prescreen strategy helps you stay compliant by ensuring that only eligible individuals are targeted for credit offers.  Financial inclusion: 49 million American adults don’t have conventional credit scores. An optimized prescreen strategy allows you to send offers to creditworthy consumers who you may have missed due to a lack of traditional credit history.  How AI and ML can enhance your strategy  AI and ML can revolutionize your prescreen strategy by offering advanced analytics and custom response modeling capabilities.  AI-driven data analytics  AI analytics allow financial institutions to analyze vast amounts of data quickly and accurately. This enables you to identify patterns and trends that may not be apparent through traditional analysis. By leveraging data-centric AI, you can gain deeper insights into customer behavior and preferences, allowing for more precise targeting and increased response rates.  LEARN MORE: Explore the benefits of AI for credit unions.  Custom response modeling  Custom response models enable you to better identify individuals who fall within your credit criteria and are more likely to respond to your credit offers. These models consider various factors such as credit history, spending habits, and demographic information to predict future behavior. By incorporating custom response models into your prescreen strategy, you can select the best consumers to engage, including those you may have previously overlooked.  LEARN MORE: AI can be leveraged for numerous business needs. Learn about generative AI fraud detection.   Get started today  Incorporating AI and ML into your prescreen campaigns can significantly enhance their effectiveness and efficiency. By leveraging Experian's Ascend Intelligence Services™ Target, you can better target potential customers and maximize your marketing spend.   Our optimized prescreen solution leverages:  Full-file credit bureau data on over 245 million consumers and over 2,100 industry-leading credit attributes.  Exclusive access to the industry's largest alternative datasets from nontraditional lenders, rental data inputs, full-file public records, and more.  24 months of trended data showing payment patterns over time and over 2,000 attributes that help determine your next best action.  When it comes to compliance, Experian leverages decades of regulatory experience to provide the documentation needed to explain lending practices to regulators. We use patent-pending ML explainability to understand what contributed most to a decision and generate adverse action codes directly from the model.  For more insights into Ascend Intelligence Services Target, view our infographic or contact us at 855 339 3990. View infographic This article includes content created by an AI language model and is intended to provide general information. 

Machine learning (ML) is a powerful tool that can consume vast amounts of data to uncover patterns, learn from past behaviors, and predict future outcomes. By leveraging ML-powered credit risk models, lenders can better determine the likelihood that a consumer will default on a loan or credit obligation, allowing them to score applicants more accurately. When applied to credit decisioning, lenders can achieve a 25 percent reduction in exposure to risky customers and a 35 percent decrease in non-performing loans.1 While ML-driven models enable lenders to target the right audience and control credit losses, many organizations face challenges in developing and deploying these models. Some still rely on traditional lending models with limitations preventing them from making fast and accurate decisions, including slow reaction times, fewer data sources, and less predictive performance. With a trusted and experienced partner, financial institutions can create and deploy highly predictive ML models that optimize their credit decisioning. Case study: Increase customer acquisition with improved predictive performance Looking to meet growth goals without increasing risk, a consumer goods retailer sought out a modern and flexible solution that could help expand its finance product options. This meant replacing existing ML models with a custom model that offers greater transparency and predictive power. The retailer partnered with Experian to develop a transparent and explainable ML model. Based on the model’s improved predictive performance, transparency, and ability to derive adverse action reasons for declines, the retailer increased sales and application approval rates while reducing credit risk. Read the case study Learn about our custom modeling capabilities 1 Experian (2020). The Art of Decisioning in Uncertain Times

Credit reports and conventional credit scores give lenders a strong starting point for evaluating applicants and managing risk. But today's competitive environment often requires deeper insights, such as credit attributes. Experian develops industry-leading credit attributes and models using traditional methods, as well as the latest techniques in machine learning, advanced analytics and alternative credit data — or expanded Fair Credit Reporting Act (FCRA)-regulated data)1 to unlock valuable consumer spending and payment information so businesses can drive better outcomes, optimize risk management and better serve consumers READ MORE: Using Alternative Credit Data for Credit Underwriting Turning credit data into digestible credit attributes Lenders rely on credit attributes — specific characteristics or variables based on the underlying data — to better understand the potentially overwhelming flow of data from traditional and non-traditional sources. However, choosing, testing, monitoring, maintaining and updating attributes can be a time- and resource-intensive process. Experian has over 45 years of experience with data analytics, modeling and helping clients develop and manage credit attributes and risk management. Currently, we offer over 4,500 attributes to lenders, including core attributes and subsets for specific industries. These are continually monitored, and new attributes are released based on consumer trends and regulatory requirements. Lenders can use these credit attributes to develop precise and explainable scoring models and strategies. As a result, they can more consistently identify qualified prospects that might otherwise be missed, set initial limits, manage credit lines, improve loyalty by applying appropriate treatments and limit credit losses. Using expanded credit data effectively Leveraging credit attributes is critical for portfolio growth, and businesses can use their expanding access to credit data and insights to improve their credit decisioning. A few examples: Spot trends in consumer behavior: Going beyond a snapshot of a credit report, Trended 3DTM attributes reveal and make it easier to understand customers' behavioral patterns. Use these insights to determine when a customer will likely revolve, transact, transfer a balance or fall into distress. Dig deeper into credit data: Making sense of vast amounts of credit report data can be difficult, but Premier AttributesSM  aggregates and summarizes findings. Lenders use the 2,100-plus attributes to segment populations and define policy rules. From prospecting to collections, businesses can save time and make more informed decisions across the customer lifecycle. Get a clear and complete picture: Businesses may be able to more accurately assess and approve applicants, simply by incorporating attributes overlooked by traditional credit bureau reports into their decisioning process. Clear View AttributesTM uses data from the largest alternative financial services specialty bureau, Clarity Services, to show how customers have used non-traditional lenders, including auto title lenders, rent-to-own and small-dollar credit lenders. The additional credit attributes and analysis help lenders make more strategic approval and credit limit decisions, leading to increased customer loyalty, reduced risk and business growth. Additionally, many organizations find that using credit attributes and customized strategies can be important for measuring and reaching financial inclusion goals. Many consumers have a thin credit file (fewer than five credit accounts), don’t have a credit file or don’t have information for conventional scoring models to score them. Expanded credit data and attributes can help lenders accurately evaluate many of these consumers and remove barriers that keep them from accessing mainstream financial services. There's no time to wait Businesses can expand their customer base while reducing risk by looking beyond traditional credit bureau data and scores. Download our latest e-book on credit attributes to learn more about what Experian offers and how we can help you stay ahead of the competition.  Download e-book  Learn more 1When 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. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

The fact that the last recession started right as smartphones were introduced to the world gives some perspective into how technology has changed over the past decade. Organizations need to leverage the same technological advancements, such as artificial intelligence and machine learning, to improve their collections strategies. These advanced analytics platforms and technologies can be used to gauge customer preferences, as well as automate the collections process. When faced with higher volumes of delinquent loans, some organizations rapidly hire inexperienced staff. With new analytical advancements, organizations can reduce overhead and maintain compliance through the collections process. Additionally, advanced analytics and technology can help manage customers throughout the customer life cycle. Let’s explore further: Why use advanced analytics in collections? Collections strategies demand diverse approaches, which is where analytics-based strategies and collections models come into play. As each customer and situation differs, machine learning techniques and constraint-based optimization can open doors for your organization. By rethinking collections outreach beyond static classifications (such as the stage of account delinquency) and instead prioritizing accounts most likely to respond to each collections treatment, you can create an improved collections experience. How does collections analytics empower your customers? Customer engagement, carefully considered, perhaps comprises the most critical aspect of a collections program—especially given historical perceptions of the collections process. Experian recently analyzed the impact of traditional collections methods and found that three percent of card portfolios closed their accounts after paying their balances in full. And 75 percent of those closures occurred shortly after the account became current. Under traditional methods, a bank may collect outstanding debt but will probably miss out on long-term customer loyalty and future revenue opportunities. Only effective technology, modeling and analytics can move us from a linear collections approach towards a more customer-focused treatment while controlling costs and meeting other business objectives. Advanced analytics and machine learning represent the most important advances in collections. Furthermore, powerful digital innovations such as better criteria for customer segmentation and more effective contact strategies can transform collections operations, while improving performance and raising customer service standards at a lower cost. Empowering consumers in a digital, safe and consumer-centric environment affects the complete collections agenda—beginning with prevention and management of bad debt and extending through internal and external account resolution. When should I get started? It’s never too early to assess and modernize technology within collections—as well as customer engagement strategies—to produce an efficient, innovative game plan. Smarter decisions lead to higher recovery rates, automation and self-service tools reduce costs and a more comprehensive customer view enhances relationships. An investment today can minimize the negative impacts of the delinquency challenges posed by a potential recession. Collections transformation has already begun, with organizations assembling data and developing algorithms to improve their existing collections processes. In advance of the next recession, two options present themselves: to scramble in a reactive manner or approach collections proactively. Which do you choose? Get started

I believe it was George Bernard Shaw that once said something along the lines of, “If economists were laid end-to-end, they’d never come to a conclusion, at least not the same conclusion.” It often feels the same way when it comes to big data analytics around customer behavior. As you look at new tools to put your customer insights to work for your enterprise, you likely have questions coming from across your organization. Models always seem to take forever to develop, how sure are we that the results are still accurate? What data did we use in this analysis; do we need to worry about compliance or security? To answer these questions and in an effort to best utilize customer data, the most forward-thinking financial institutions are turning to analytical environments, or sandboxes, to solve their big data problems. But what functionality is right for your financial institution? In your search for a sandbox solution to solve the business problem of big data, make sure you keep these top four features in mind. Efficiency: Building an internal data archive with effective business intelligence tools is expensive, time-consuming and resource-intensive. That’s why investing in a sandbox makes the most sense when it comes to drawing the value out of your customer data.By providing immediate access to the data environment at all times, the best systems can reduce the time from data input to decision by at least 30%. Another way the right sandbox can help you achieve operational efficiencies is by direct integration with your production environment. Pretty charts and graphs are great and can be very insightful, but the best sandbox goes beyond just business intelligence and should allow you to immediately put models into action. Scalability and Flexibility: In implementing any new software system, scalability and flexibility are key when it comes to integration into your native systems and the system’s capabilities. This is even more imperative when implementing an enterprise-wide tool like an analytical sandbox. Look for systems that offer a hosted, cloud-based environment, like Amazon Web Services, that ensures operational redundancy, as well as browser-based access and system availability.The right sandbox will leverage a scalable software framework for efficient processing. It should also be programming language agnostic, allowing for use of all industry-standard programming languages and analytics tools like SAS, R Studio, H2O, Python, Hue and Tableau. Moreover, you shouldn’t have to pay for software suites that your analytics teams aren’t going to use. Support: Whether you have an entire analytics department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best sandbox solution for your company will have a robust support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. Look for solutions and data partners that also offer the consultative help of industry experts when your company needs it. Data, Data and More Data: Any analytical environment is only as good as the data you put into it. It should, of course, include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a sandbox solution, pick a system that will include the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data.The optimum solutions will have years of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a big data partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%. Solving the business problem around your big data can be a daunting task. However, investing in analytical environments or sandboxes can offer a solution. Finding the right solution and data partner are critical to your success. As you begin your search for the best sandbox for you, be sure to look for solutions that are the right combination of operational efficiency, flexibility and support all combined with the most robust national data, along with your own customer data. Are you interested in learning how companies are using sandboxes to make it easier, faster and more cost-effective to drive actionable insights from their data? Join us for this upcoming webinar. Register for the Webinar

In my first blog post on the topic of customer segmentation, I shared with readers that segmentation is the process of dividing customers or prospects into groupings based on similar behaviors. The more similar or homogeneous the customer grouping, the less variation across the customer segments are included in each segment’s custom model development. A thoughtful segmentation analysis contains two phases: generation of potential segments, and the evaluation of those segments. Although several potential segments may be identified, not all segments will necessarily require a separate scorecard. Separate scorecards should be built only if there is real benefit to be gained through the use of multiple scorecards applied to partitioned portions of the population. The meaningful evaluation of the potential segments is therefore an essential step. There are many ways to evaluate the performance of a multiple-scorecard scheme compared with a single-scorecard scheme. Regardless of the method used, separate scorecards are only justified if a segment-based scorecard significantly outperforms a scorecard based on a broader population. To do this, Experian® builds a scorecard for each potential segment and evaluates the performance improvement compared with the broader population scorecard. This step is then repeated for each potential segmentation scheme. Once potential customer segments have been evaluated and the segmentation scheme finalized, the next step is to begin the model development. Learn more about how Experian Decision Analytics can help you with your segmentation or custom model development needs.