This article was updated on March 6, 2024.
Advances in analytics and modeling are making credit risk decisioning more efficient and precise. And while businesses may face challenges in developing and deploying new credit risk models, machine learning (ML) — a type of artificial intelligence (AI) — is paving the way for shorter design cycles and greater performance lifts.
LEARN MORE: Get personalized recommendations on optimizing your decisioning strategy
Limitations of traditional lending models
Traditional lending models have worked well for years, and many financial institutions continue to rely on legacy models and develop new challenger models the old-fashioned way. This approach has benefits, including the ability to rely on existing internal expertise and the explainability of the models. However, there are limitations as well.
- Slow reaction times: Building and deploying a traditional credit risk model can take many months. That might be okay during relatively stable economic conditions, but these models may start to underperform if there’s a sudden shift in consumer behavior or a world event that impacts people’s finances.
- Fewer data sources: Traditional scoring models may be able to analyze some types of FCRA-regulated data (also called alternative credit data*), such as utility or rent payments, that appear in credit reports. Custom credit risk scores and models could go a step further by incorporating data from additional sources, such as internal data, even if they’re designed in a traditional way. But AI-driven models can analyze vast amounts of information and uncover data points that are more highly predictive of risk.
- Less effective performance: Experian has found that applying machine learning models can increase accuracy and effectiveness, allowing lenders to make better decisions. When applied to credit decisioning, lenders see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.1
Leveraging machine learning-driven models to segment your universe
From initial segmentation to sending right-sized offers, detecting fraud and managing collection efforts, organizations are already using machine learning throughout the customer life cycle. In fact, 79% are prioritizing the adoption of advanced analytics with AI and ML capabilities, while 65% believe that AI and ML provide their organization with a competitive advantage.2
While machine learning approaches to modeling aren’t new, advances in computer science and computing power are unlocking new possibilities.3 Machine learning models can now quickly incorporate your internal data, alternative data, credit bureau data, credit attributes and other scores to give you a more accurate view of a consumer’s creditworthiness.
By more precisely scoring applicants, you can shrink the population in the middle of your score range, the segment of medium-risk applicants that are difficult to evaluate. You can then lower your high-end cutoff and raise your low-end cutoff, which may allow you to more confidently swap in good accounts (the applicants you turned down with other models that would have been good) and swap out bad accounts (those you would have approved who turned bad).
Machine learning models may also be able to use additional types of data to score applicants who don’t qualify for a score from traditional models. These applicants aren’t necessarily riskier — there simply hasn’t been a good way to understand the risk they present.
Once you can make an accurate assessment, you can increase your lending universe by including this segment of previously “unscorable” consumers, which can drive revenue growth without additional risk. At the same time, you’re helping expand financial inclusion to segments of the population that may otherwise struggle to access credit.
READ MORE: Is Financial Inclusion Fueling Business Growth for Lenders?
Connecting the model to a decision
Even a machine learning model doesn’t make decisions.4 The model estimates the creditworthiness of an applicant so lenders can make better-informed decisions. AI-driven credit decisioning software can take your parameters (such cutoff points) and the model’s outputs to automatically approve or deny more applicants.
Models that can more accurately segment and score populations will result in fewer applications going to manual review, which can save you money and improve your customers’ experiences.
CASE STUDY: Atlas Credit, a small-dollar lender, nearly doubled its loan approval rates while decreasing risk losses by up to 20 percent using a machine learning-powered model and increased automation.
Concerns around explainability
One of the primary concerns lenders have about machine learning models come from so-called “black box” models.5 Although these models may offer large lifts, you can’t verify how they work internally. As a result, lenders can’t explain why decisions are made to regulators or consumers — effectively making them unusable.
While it’s a valid concern, there are machine learning models that don’t use a black box approach. The machine learning model doesn’t build itself and it’s not really “learning” on its own — that’s where the black box would come in. Instead, developers can use machine learning techniques to create more efficient models that are explainable, don’t have a disparate impact on protected classes and can generate reason codes that help consumers understand the outcomes.
LEARN MORE: Explainability: Machine learning and artificial intelligence in credit decisioning
Building and using machine learning models
Organizations may lack the expertise and IT infrastructure required to develop or deploy machine learning models. But similar to how digital transformations in other parts of the business are leading companies to use outside cloud-based solutions, there are options that don’t require in-house data scientists and developers.
Experian’s expert-guided options can help you create, test and use machine learning models and AI-driven automated decisioning;
- Ascend Intelligence Services™ Acquire: Our model development service allows you to prebuild and test the performance of a new model before Experian data scientists complete the model. It’s collaborative, and you can upload internal data through the web portal and make comments or suggestions. The service periodically retrains your model to increase its effectiveness.
- Ascend Intelligence Services™ Pulse: Monitor, validate and challenge your existing models to ensure you’re not missing out on potential improvements. The service includes a model health index and alerts, performance summary, automatic validations and stress-testing results. It can also automatically build challenger models and share the estimated lift and financial benefit of deployment.
- PowerCurve® Originations Essentials: Cloud-based decision engine software that you can use to make automated decisions that are tailored to your goals and needs.
A machine learning approach to credit risk and AI-driven decisioning can help improve outcomes for borrowers and increase financial inclusion while reducing your overall costs. With a trusted and experienced partner, you’ll also be able to back up your decisions with customizable and regulatorily-compliant reports.
Learn more about our credit decisioning 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.
1Experian (2024). Improving Your Credit Risk Machine Learning Model Deployment
2Experian and Forrester Research (2023). Raising the AI Bar
3Experian (2022). Driving Growth During Economic Uncertainty with AI/ML Strategies
4Ibid
5Experian (2020). Explainability ML and AI in Credit Decisioning