Tag: custom model

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

Believe it or not, 2023 is underway, and the new year could prove to be a challenging one for apartment operators in certain ways. In 2021 and into the beginning of 2022, demand for apartment rentals approached record levels, which shrunk vacancy rates and increased monthly rents. The rest of the year remained stagnant while other regions saw some decline, but inflation and other economic factors have many apartment communities confronted with labor shortages, and other challenges which can certainly make leasing and operating properties difficult. Against that backdrop, here are some of the technologies and solutions operators should consider for optimizing their success and efficiencies in 2023 and beyond. Tools that allow prospective residents to have a fully digital and contactless leasing experience — During the pandemic, many operators rushed to implement virtual tours, onsite self-guided tours and other solutions that allowed prospects to apply for and finalize their leases remotely. Prospective renters have undoubtedly grown fond of navigating the leasing process from their homes and taking self-guided tours when onsite, and the demand for digital solutions will surely continue even after COVID distancing is no longer a factor. Therefore, apartment owners and operators should think of these capabilities as long-term investments and always seek ways to optimize the digital leasing experience they provide. Along those lines, forward-thinking operators are employing solutions that allow them to embed credit functionality into their websites and mobile apps using modern, RESTful APIs like the Experian ConnectSM API. Not only does it enhance the information included in a lease application with credit report data, but it also allows prospective renters to easily apply for more than one property at once, enhancing their experience at the same time. Automated lease application form fill — By using information entered by a lease applicant (such as first name, last name, postal code and the last four digits of a Social Security number), this technology uses information from credit files to automatically fill other data fields in a lease application. This tool reduces the effort required by prospective renters to complete the application process, resulting in a better user experience, faster completions, greater accuracy and reduced application abandonment. Automated verification of income, assets, and employment — These solutions eliminate the need for associates to manually verify these components of a lease application. Manual verification is both time-consuming and prone to human error. In addition, automated tools eliminate the opportunity for applicants to supply falsified supporting documentation. The best part about verification is the variety of options available; leasing managers can pick and choose verification options that meet their needs. Renter Risk Score™ and custom-built scores and models applying RentBureau data — These options offer a score designed expressly to predict the likelihood that an applicant will pay rent. Renter risk score can be purchased with preset score logic, or for high-volume decisions, a model can be built calibrated for your specific leasing decisioning needs. A rental payment history report — The RentBureau Consumer Profile tool can provide detailed insight into a lease applicant's history of meeting their lease obligations, which is invaluable information during the lease application process. Having a tool to report rental payment histories to credit bureaus can be a powerful financial amenity. By reporting these payments, operators can help residents build credit histories and improve financial well-being. Such an amenity can attract and retain residents and provide them with a powerful incentive to pay rent on time and in full. In the end, tools that seek to manage risk and create improved experiences for prospective renters have a multitude of benefits. They create meaningful efficiencies for onsite staff by greatly reducing the time, resources and paperwork required to process applications and verify applicant information. This gives overextended associates more time to handle their many other responsibilities. Beyond just efficiency savings, these technologies and solutions also can help operators avoid the complications and loss of income that result from evictions. In fact, the National Association of Realtors estimates that average eviction costs $7,685. Managing risk and providing the best possible customer experience should always be top of mind for rental housing operators. And with the solutions outlined above, they can effectively accomplish those goals in 2023 and beyond.

It is no news that businesses are increasing their focus on advanced analytics and models. Whether looking to increase resources or focus on artificial intelligence (AI) and machine learning (ML), growth is the name of the game. But how do you maximize impact while minimizing risk? And how can you secure expertise and ROI when budgets are strapped?  Does your organization have the knowledge and talent in-house to remain competitive? No matter where you are on the analytics maturity curve, (outlined in detail below), your organization can benefit from making sure your machine learning models solution consists of: Regulatory documentation: Documentation for model and strategy governance is critical, especially as there is more conversation surrounding fair lending and how it relates to machine learning models. How does your organization ensure your models are explainable, well documented and making fair decisions? These are all questions you must be asking of your partners and solutions. Integrated services: For some service providers, “integrated,” is merely a marketing ploy, but it is essential that your solution truly integrates attributes, scores, models and decisions into one another. Not only does this serve as a “checks and balances” system of sorts, but it also is a primary driver for the speed of decisioning, which is crucial in today’s digital-first world. Deep expertise: Models are a major component for your decisioning, but ensuring those models are built and backed by experts is the one-two punch your strategies depend on. Make sure your services are managed by data scientists with extensive experience to take the best approach to solving your business problems. Usability: Does your solution close the loop? To future proof your processes, your solution must analyze the performance of attributes, scores and strategies. On top of that, your solution should make sure the items being built are useable and can be modified when needed. A one-and-done model does not suit the unique needs of your organization, so ensure your solution provides actionable analysis for continual refinement. Does your machine learning model solution check these boxes? Do you want to transform your existing system into a state-of-the-art AI platform? Learn more about how you can take your business challenges head-on by rapidly developing, deploying and monitoring sophisticated models and strategies to more accurately predict risk and achieve better outcomes. Learn more Access infographic   More information: What’s the analytics maturity curve? “Analytics” is the discovery, interpretation and communication of meaningful patterns in data; the connective tissue between data and effective decision-making within an organization. You can be along this journey for different decision points you’re making or product types, said Mark Soffietti, Director of Analytics Consulting at Experian, at our recent AI-driven analytics and strategy optimization webinar. Where you are on this curve often depends on your organization’s use of generic versus custom scores, the systems currently engaged to make those decisions and the sophistication of an organization’s models and/or strategies. Here’s a breakdown of each of the four stages: Descriptive Analytics – Descriptive analytics is the first step of the analytics maturity curve. These analytics answer the question “What is happening?” and typically revolve around some form of reporting. An example would be the information that your organization received 100 applications. Diagnostic Analytics – These analytics move from what happened to, “Why did it happen?” By digging into the 100 applications received, diagnostic analytics answer questions like “Who were we targeting?” and “How did those people come into our online portal/branch?” This information helps organizations be more strategic in their practices. Predictive Analytics – Models come into play at this stage as organizations try to predict what will happen. Based on the data set and an understanding of what the organization is doing, effort is put towards automating information to better solve business problems. Prescriptive Analytics – Optimization is key for prescriptive analytics. At this point in the maturity curve, there are multiple models and/or information that may be competing against one another. Prescriptive analytics will attempt to prescribe what an organization is doing and how it can drive more desired behaviors. For more information and to get personalized recommendations throughout your analytics journey, visit our website.

Consumer behavior is constantly evolving — from the channels they prefer to the economic trends spurring varying interest and activity. It’s no surprise that businesses find it challenging to know what their customers want today or tomorrow. But knowing and understanding this information is essential to growing your bottom line. Through years of working with businesses across every vertical, we’ve found that a solid approach to growing your business revolves around your customers. The better you know your customers, the better you can achieve your goals. Seeing the future. How well can you identify and rank your current customer population? Are you leveraging that insight to acquire new customers, manage current customers and prioritize collections efforts? If so, you’re probably using custom models in your business strategy. But if your organization is like many businesses, you may use a more traditional approach. In our highly competitive market, strategy and decisions must be based on the right data and insights. No excuses. The data is there, and we can help you turn it into actionable insights. Implementing a custom model can maximize your return on investment and help you make more profitable business decisions — now and in the future. No palm reading required. Without visiting your local fortuneteller, you still can predict the future. You need a model, but not the “runway” type. What constitutes a highly predictive and effective model? Many factors. A highly predictive custom model should incorporate robust data, advanced modeling methodologies, analytical expertise and attributes. Having these foundational components is essential to knowing your customers and making confident decisions. Models aren’t one-size-fits-all. When you take an innovative approach to model development, the model is targeted to support your specific business goals while providing the documentation required for regulatory reviews. Consider these items as you develop your custom model: Data — It all starts with the right data. Combining multiple data assets — your master-file data, our credit data and any additional data sources — is key to developing a robust model development sample. In other words, a model development sample should represent your future through-the-door population. Model design — To ensure the custom model is designed to help you achieve your specific goals, you’ll want to incorporate the latest analytics and modeling methodologies. An experienced analytics team will be essential here. Segmentation — With the right model development and segmentation strategies, you can identify optimal segments that will result in a more predictive custom model. This way, each consumer is scored on a scorecard developed using a credit profile similar to theirs. Validation — To ensure the model’s predictive ability and longevity, validate each custom model on a holdout sample and compare it with other scores to ensure it accounts for the current and future (through-the-door) consumer populations, as well as policy rule and behavioral changes. Regulatory review — Don’t forget about the documentation needed for compliance. While audits are unpleasant , fines and extensive scrutiny can significantly impact your business. Take your fortunetelling to the next level. Machine learning is all the rage. This cutting-edge technology can be embedded in your predictive models to help uncover patterns in data that may not be apparent otherwise. This can be done by comparing the performance of the machine learning model with your existing models. Once you know that machine learning can add the lift you’re looking for, you can apply that methodology to develop a custom model focused on stability, cost-efficiency, transparency and predictive performance. Predicting behavior across the Customer Life Cycle. How can a custom model benefit you? From improving baseline performance and increasing profitability by approving more good accounts to uncovering opportunities within your target market, custom models can provide the confidence needed to grow your business. Which one of these models can help you achieve your business goals? When it comes to accurately predicting customer behavior, you don’t need a crystal ball. You need a well-built, highly predictive custom model. Use the data that’s available to gain insight into your customers and grow your bottom line. If you need help, we’re here. We have the data, analytics and expertise to help you get started.