<![CDATA[Companies worldwide have a significant issue in the form of financial fraud. The repercussions are not limited to financial losses; they also encompass a decline in customer trust and harm to the company's standing. Manual fraud detection has become inefficient due to the intricate nature and high volume of transactions. Artificial intelligence (AI) is already a very efficient technology for detecting and preventing financial crime. This study assesses the efficacy of the Random Forest technique in identifying instances of financial fraud by analysing a dataset of 150 bank transactions. The necessary variation was created by simulating data collected from a hypothetical company's information system. The Random Forest model was trained using optimised parameters and evaluated on the test set. The model's performance was assessed using metrics such as accuracy, precision, and recall. The results demonstrate that AI, particularly the Random Forest algorithm, is highly efficient in accurately identifying fraudulent transactions. Additionally, the use of graph visualisation aids in highlighting patterns associated with fraud detection. The utilisation of line chart visualisation facilitates the comprehension of trends and patterns within the data, hence enabling the early detection and prevention of fraudulent activities.]]>
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