BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Credit evaluation is of high scientific significance and practical use, especially in today's plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.