![]() These predictive improvements in turn can translate into substantial portfolio profit gains for a much more precisely targeted retention strategy. In addition, another 15% performance improvement was found by applying machine learning with a much larger set of features relating to event-specific recencies and frequencies. The option to include this interaction as a nonlinear input feature in an interpretable fashion into a scorecard led to a substantial improvement (~10%) of the lift measure, used to characterize the performance of attrition models. When developing a credit card churn model, FICO data scientists used machine learning to discover a powerful interaction between recency and frequency of card usage. The two examples below illustrate how you can achieve better performance and explainability by combining machine learning and scorecard approaches. Improving Results with AI and Machine Learning You can then drive these new inputs into a traditional scorecard model to ensure explainability. For example, utilization is always an important feature in a credit model, as is delinquency, but a nonlinear combination of these can produce more optimal results in a machine learning model. Our approach builds on mature, time-tested analytic models and scorecards, enhancing them with advanced AI technology to drive better segments and feature creation in models.Īnother approach is to use AI and machine learning to “train” models to discover maximum predictive power, and find new relationships amongst input features that could produce a stronger model. The way that we can capture these subtle changes in behavior, and can incorporate them into the credit risk model, presents a distinct advantage for FICO customers. those who have higher risk and covering misuse of credit elsewhere in their history. For example, collaborative profiles derive behavioral archetype distributions - these could include archetypes that point to credit seekers building credit histories vs. We can then group customers into micro-segments based on that similarity, instead of typical segmentation approaches that rely on hard business attributes. Our data science teams are now using techniques such as collaborative profiles to reveal entity segmentation based on customer behaviors. This allows us to apply AI to improve risk prediction without creating “black box” models that don’t give risk managers, customers and regulators the required insights into why individuals score the way they do. To build the models in FICO Origination Solution, our data scientists used AI and machine learning algorithms to discover a better way to segment the scorecards. ![]() This doesn’t capture the behavior of certain individual entities or more optimal ways to segment scoring models. In traditional risk modeling, customer segmentation is based on “hard” lines and broad categories, such as new customer vs. How FICO Uses AI to Build Better Credit Risk ModelsĪs with our other origination products, FICO Origination Solution includes credit risk models, and these models are segmented - different types of small business customers and different credit products require different models to assess their credit risk. This new FICO product combines our well-established scorecard technology with AI and ML to build better credit risk models, algorithms that better predict the probability that customers will pay on time. It’s designed to help lenders make faster origination decisions without increasing risk. Take, for example, our new credit decisioning solution, FICO Origination Solution, Powered by FICO Platform. While some new market entrants may have a vested interest in pushing AI solutions, the fact is that traditional scorecard methods and AI bring different advantages to credit risk modeling - if you know how to use them together. Given the excitement around AI today, this question is inevitable. Which works better for modeling credit risk: traditional scorecards or artificial intelligence and machine learning? Transportation Dealer Groups Original Equipment Manufacturers Parts Manufacturers Vehicle Finance.Telecommunications, Media and Entertainment.Banking Credit Card Deposits Merchant Services Mortgage Lending Personal Lending Vehicle Finance.Partner with the FICO® Scoring solutions team and leverage our scoring and analytic expertise to help industries reduce risk, improve experiences, and support growth. Learn how to gain better industry risk insights using data-driven analytic solutions with FICO® Scoring Solutions for Industry Risk. ![]() FICO delivers a range of products and services globally that empower the development of enhanced credit risk strategies.
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