Financial Abnormal Data Monitoring and Analysis Algorithm Based on Data Mining and Neural Network

Abstract

The monitoring and analysis algorithm of financial abnormal data based on data mining and neural network is a machine learning algorithm to monitor the abnormal data of financial market. It also uses historical stock price data to analyze the performance of various trading strategies to determine whether any strategy is better or lower than its benchmark. These algorithms are intended to be used as early warning systems for potential security problems in financial markets, such as fraud, insider trading, manipulation or other illegal activities. This technology uses AI to identify unusual trading patterns in the market, so that traders can make better decisions. What are the types of financial abnormal data monitoring and analysis algorithms based on data mining? There are two kinds of financial anomaly data monitoring and analysis algorithms based on Data Mining: time series anomaly detection and event based anomaly detection. Financial anomaly detection (fade) is a term that describes an automated process designed to detect abnormal patterns in financial market activities.