Acuon Savings Bank Successfully Develops Alternative Credit Scoring Model

Providing Customized Financial Services Such as Interest Rates and Loan Conditions"

Accuon Savings Bank announced on the 23rd that it has successfully developed and advanced an alternative credit evaluation model for individuals and sole proprietors to expand financial accessibility and strengthen risk management capabilities.

With this alternative credit evaluation model, Accuon Savings Bank has improved the limitations of the existing credit evaluation methods and established a more rational evaluation system. The model enables more sophisticated credit assessments than the previous system, allowing for customized financial services such as interest rates and loan conditions.

Accuon Savings Bank applied non-financial alternative data, machine learning algorithms, and time series (spatial) clustering analysis techniques to the alternative credit evaluation model. To build a rich alternative data system, it incorporated information provided by domestic credit rating agencies Korea Credit Bureau (KCB) and NICE Information Service, as well as additional alternative data such as automatic transfer information from the Korea Financial Telecommunications & Clearings Institute and CreditTree from Korea Evaluation Data. The machine learning algorithm applied was ‘ExML (Explainable Machine Learning),’ and time series clustering was utilized to closely analyze the financial transaction characteristics of new subscribers.

Since the appointment of CEO Kim Jeong-su last year, Accuon Savings Bank has been planning and developing alternative data centered on the Credit Communication Division and the Data Science Team to strengthen credit evaluation capabilities.

Kim Jeong-su, CEO of Accuon Savings Bank, stated, “Based on differentiated information technology (IT) competitiveness within the savings bank sector, we have independently developed an alternative credit evaluation model. With this newly introduced model, we will expand personal credit loans and business loans, and continue to research new technologies to develop various alternative credit evaluation models, while also striving to strengthen support for low-income financial services.”