Anomaly Detection of Financial Data using Machine Learning

Anomaly detection is critical in the financial sector, especially as financial environments evolve with increasing digitization, posing challenges for real -time anomaly detection. Recently, deep learning (DL) algorithms have emerged as promising solutions for this problem. This study presents a DL -based anomaly detection model utilizing various algorithms, including LSTM, GRU, and 1dCNN, applied to Tesla's stock market and Ethereum cryptocurrency data sets. Hyperparameter optimization is performed using grid search. Results show that the GRU algorithm achieves the highest prediction score in both datasets, while the 1dCNN algorithm performs the lowest. Additionally, anomaly values are graphically demonstrated using GRU for both datasets. Accurate bookkeeping is essential for legitimate business operations, yet the complexity of financial auditing requires new solutions. Supervised and unsupervised machine learning techniques are increasingly applied to detect fraud and anomalies in accounting data. This paper addresses the challenge of detecting financial misstatements in general ledger (GL) data, proposing seven supervised ML techniques, including deep learning, and two unsupervised ML techniques. Models are trained and evaluated on real -life GL datasets, demonstrating high potential in detecting predefined anomaly types and efficiently sampling data. Practical implications of these solutions in accounting and auditing contexts are discussed. The rapid development of computer networks brings both convenience and security challenges due to various abnormal flows. Traditional detection systems, like intrusion detection systems (IDS), have limitations, necessitating real -time updates to function effectively. With the advent of machine learning and data mining, new methods for abnormal network flow detection have emerged. This paper introduces the random forest algorithm for detecting abnormal samples, proposing the concept of an abnormal point scale to measure sample abnormality based on similarity. Simulation experiments demonstrate the superiority of random forest -based detection in terms of model accuracy and computing efficiency compared to other methods.