Using Self-Organizing Maps to Identify Operational Risk

Sammanfattning: In recent years, the awareness and concern for operational risk in financial institutions have increased, and several disastrous events in the last two decades been caused by human error. With this, the regulatory demands have increased on the financial institutions to control operational risk. One operational risk that Svenska Handelsbanken AB (SHB) has detected are the audit changes of trades which occur when a trade need some form of altering from its original state, which can lead to losses for the bank. The bank has looked into identifying and forecasting these losses with the use of a neural network clustering method called Self-Organizing Map. This thesis expands on a previous project initialized by SHB on the po- tential of using this method to identify operational risk, and research the robustness and effectiveness of the Self-Organizing Map and trying to ob- tain an optimal solution by using quantifiable measurements like Matthew’s Correlation Coefficient. By evaluating the algorithm through visualizations of the generated maps and evaluating its prediction ability through Cross-Validation, the results obtained from this thesis indicate that the Self-Organizing Map has great potential in this area and is able to identify these risks with a relatively high accuracy.