Artificial Intelligence and Machine Learning Are Transforming Credit Scoring in the US Financial Sector

Traditional credit scoring methods long used in the US financial industry are undergoing a quiet transformation with the integration of artificial intelligence and machine learning. While the three-digit numbers seen by consumers remain the same, institutions are incorporating transaction data and new analytical tools into their systems.

Traditional Credit Scoring and Scorecards

Traditional credit scoring relies on scorecards that allow for human oversight and assign weighted values to inputs. While payment history carries the largest weight, factors such as debt amounts and the length of credit history also shape the process.

Challenges and Speed Brought by Machine Learning

Machine learning models can process much more data compared to traditional methods, but their explainability is low. The obligation to provide consumers with clear reasons in the event of a credit denial constitutes one of the biggest obstacles to using these complex models.

New Data Sources and Open Banking

In addition to traditional bureau data, alternative data such as rent, utility payments, and bank account transaction history have started to be used in credit modeling. Open banking allows lenders to access real-time financial behaviors.

Tools Developed for Explainability

Game theory-based tools such as SHAP and LIME are being developed for complex models in financial services to meet auditing requirements. These tools help determine the contribution of each input variable to individual predictions.

The Future of the Sector and the Transition Process

While advanced tree-based models are becoming widespread in financial institutions, studies on the integration of large language models and foundational systems into credit risk processes continue. While traditional scores persist, the underlying mechanisms are evolving.