Financial early warning system model and data mining application for risk detection

One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an early warning system (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

► We developed a financial early warning system by using data mining.
► SMEs were classified in 31 risk profiles via CHAID.
► 15 risk indicators that affected financial distress were detected.
► We determined 2 financial early warning signs; profit before tax to own funds and return on equity.
► Four road maps were developed for risk prevention and improve financial performance.