<![CDATA[The 2008 global financial crisis affected the pace of the economy by reducing the level of public trust in banks. Seeing these conditions, Bank XYZ needs to know and analyze the factors that influence bank lending. Debtor data processing at Bank XYZ is well integrated, but in fact, Bank XYZ still needs to pay attention to the principle of prudence in making decisions to provide credit facilities to debtors with minimal risk so that credit can be extended consistently and based on sound credit principles. In monitoring and analyzing the credit system at Bank XYZ, there is one factor that serves as a reference for measuring a bank's ability to bear the risk of credit failure by debtors through NPLs (non-performing loans). In addition to NPLs, the growth in the number of debtors, the amount of credit disbursement, the amount of outstanding credit provided in various sectors, and the amount of collectibility of debtors also play an important role in decision-making by management; therefore, the author proposes a business intelligence model to analyze credit data at XYZ Bank in the form of data visualization in dashboard form. The dashboard was built using the Tablue for Students software. The results of this study are a business intelligence model in the form of a dashboard application to be able to monitor debtor data growth, analyze loans extended in various economic sectors, and analyze outstanding loans using the NPL value as a reference based on credit quality as a decision support tool.]]>
