Big data has revolutionized the access to and management of credit. The use of big data is significantly related to reduced credit risk as it is more effective a means to assess the creditworthiness of loanees and reduce loan default rates. This study integrates the use of both primary and secondary data collection plus a linear regression analysis and correlation analysis to assess the change in credit risk as a bank adopts the use of big data. Such assessment would also consider other bank and household‐based factors that could affect credit risk such as debt to equity ratio and debt to income ratio respectively.