Bank failure prediction is important for the regulators (such as the central banks and the finance ministries) of the banking industries. The collapse and failure of a bank could have devasta ting consequences to the entire banking fect on other banks and financial institutions. Some of the negative impacts are the massive bail out cost for a failing bank and the negative sentiments and loss of confidence developed by investors and depositors. Very often, ba nk failures do not occur over night and are usually due to a prolonged period of financial distress. Hence, it is desirable banks through financial distress. Various traditional statistical models have been employed to study bank failures. However, these models have not identified the symptoms of financial distress leading to eventual bank failure. This paper attempts to identify the financial distress (the symptoms) that leads to a bank failure using financial covariates derived from publicly available financial statements using a novel neural fuzzy system named the Generic Self-organising Fuzzy Neural Network (GenSoFNN). Subsequently, the performance of the Cox proportional hazards model is benchmarked against that of the GenSoFNN in predicting ba nk failures based on a population of In addition, it is believed that the event of financial distress does not develop out of the blue. The deterioration of the financial conditions of di stressed banks can be observed over time. Thus, the performance of a bank can be tracked and studied from its annual financial statements over a period of time, which essentially is time-series modeling. However, it may not be possible to obtain all the financial statements or there may be missing information in the observed period of a bank. Hence, as part of the study, the Pseudo-Outer-Product Fuzzy Neural Network (POPFNN) is used to reconstruct missing financia l data that tracks the solvency (financial health) of banking institutions. Th e performances of both the GenSoFNN as construct missing financial data are encouraging.