A near miss is a negative and anomalous event that causes an accident without damage to people, corporates or environmental assets due to fortunate and/or random circumstances. A series of these events can be the precursor to a harmful incident with serious consequences because of inadequacies in internal processes. The management of a company should mitigate such inadequacies promptly to avoid future disasters. Near miss analysis is a milestone of the operational risk management framework in financial institutions. Therefore, near misses represent a primary information source to analyze the operational risk exposure of the company, since they can reveal gaps in the control environment. The model proposed in this paper aims at identifying the most dangerous events that could happen in a financial institution using near miss data collection. The output of the model is a near miss ranking, in decreasing order of significance in terms of possible damage, which supports the management in prioritizing mitigation actions, in line with the principles of parsimony and efficiency.