Thursday 23 January 2025
A team of researchers has made a significant breakthrough in developing an algorithm that can accurately predict credit scores, even when faced with imbalanced data. Imbalance refers to the situation where there are significantly more instances of one class than another, making it challenging for machine learning models to learn from the data.
The new algorithm, called ASIG (Adaptive Sampling-based Imbalanced Gradient), uses a novel approach to tackle this issue by adaptively sampling the minority class and adjusting the loss function. This allows the model to focus on the most informative samples, leading to improved performance.
In traditional credit scoring models, imbalanced data can lead to poor predictions, with high-risk individuals being incorrectly classified as low-risk or vice versa. This can have serious consequences for financial institutions, consumers, and the overall economy.
The ASIG algorithm was tested on two real-world datasets: the CHN Banks dataset, which contains information about borrowers in China, and the Credit Card dataset, which includes data from a popular credit card company. The results showed that ASIG outperformed other state-of-the-art models in terms of accuracy, particularly in scenarios with high imbalance ratios.
For instance, in the CHN Banks dataset, ASIG achieved an area under the receiver operating characteristic curve (AUC) of 0.827, while the next best-performing model had an AUC of 0.813. Similarly, in the Credit Card dataset, ASIG had an AUC of 0.965, compared to the next best-performing model’s AUC of 0.954.
The researchers believe that their algorithm has significant potential for real-world applications, particularly in industries where data is often imbalanced, such as finance and healthcare. By improving credit scoring models, financial institutions can make more informed lending decisions, reducing defaults and increasing overall profitability.
Moreover, the ASIG algorithm can be used to address other imbalance-related challenges in machine learning, such as class imbalance in text classification or object detection tasks.
In summary, the ASIG algorithm is a powerful tool for addressing imbalanced data in credit scoring models, with significant potential for real-world applications. Its adaptability and ability to focus on informative samples make it an attractive solution for financial institutions seeking to improve their lending decisions.
Cite this article: “Adaptive Algorithm Improves Credit Score Predictions in Imbalanced Data”, The Science Archive, 2025.
Algorithm, Credit Scoring, Imbalanced Data, Sampling, Gradient Descent, Machine Learning, Financial Institutions, Credit Risk Assessment, Classification, Text Classification
