Risk Assessment Models for Protecting Automated Accounting Systems from Cyber Threats

With rising financial processes that are digitized and robotized, the accounting systems are operating in a constantly changing landscape of cyber threats that threaten data integrity, financial validity, and organizational reputation. This study focuses on the elaboration of risk assessment models that use artificial intelligence to guard the automated accounting systems against cyber vulnerability and safeguard them against fraud, data theft, unauthorized accessibility, and misstatement of financial statements. This study investigates how to detect and predict risk behaviors in accounting in record-keeping processes using the Financial Transaction and Risk Management Dataset, a fully labelled multilingual risk incident Dataset that contains detailed transactional data and system metadata so far. The steps of data preprocessing and feature engineering take place in Python and Excel, thus facilitating the conversion of raw data into analyzable predictors, such as abnormal patterns within transactions, login peculiarities, and risk scores in particular categories. Unsupervised anomaly search with the help of Isolation Forest is also carried out to improve the detection of new threats. Furthermore, Tableau dashboard is also used to show vital trends in charts, such as risk heat map, trend lines, and category-wise distribution of the frauds. The findings show that ensemble models can be better than baseline classifiers and have a high accuracy rate in high-risk transaction detection, which could yield fruitful results and enlighten the real-time financial security monitoring. Visual analytics also help in decision-making since complex outputs on models are readable and understandable by finance people and those verifying the accounts. This study advances the accounting cyber security field of study by providing a flexible and understandable model of cyber risk to be addressed in the automated financial systems. The study also emphasizes the need to integrate machine learning, data visualization, and domain knowledge as a combined effort to protect computerized accounting infrastructure against relatively advanced cyber hacking.