Automating banking sector monitoring procedures for exceptional situations
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Date
2023
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Abstract
The need to detect anomalous events and react to them immediately in real time is becoming increasingly important in the banking sector. The main objective of this thesis is to propose the development of a real time alert notification system that uses outlier detection algorithms to discover unexpected trends in the key performance indicators of a financial industry. In order to enable real-time monitoring of data streams and notify users of anomalous occurrences as they happen, the system will take advantage of the capabilities of cloud computing and big data technologies. The proposed system will be evaluated against traditional outlier identification techniques. The efficacy of the outlier detection algorithms for the banking dataset is assessed using precision, recall, and Fl score measurements. The approaches of sending alerts are evaluated, with the strengths and weaknesses of each method taken into account. This thorough evaluation approach aims to emphasise the advantages and disadvantages of the suggested system as well as identify potential areas for improvement. The suggested system will allow users to take proactive action to lessen the consequences of abnormal events, reduce the risk of costly downtime and other adverse effects.
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events, banking sector, financial industry, data technologies