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Financial fraud is a serious challenge in a rapidly evolving digital economy that places increasing demands on detection systems. However, traditional methods are often limited by the dimensional information of the corporations themselves and are insufficient to deal with the complexity and dynamics of modern financial fraud. This study introduces a novel intelligent financial fraud detection support system, leveraging a three-level relationship penetration (3-LRP) method to decode complex fraudulent networks and enhance prediction accuracy, by integrating the fuzzy rough density-based feature selection (FRDFS) methodology, which optimizes feature screening in noisy financial environments, together with the fuzzy deterministic soft voting (FDSV) method that combines transformer-based deep tabular networks with conventional machine learning classifiers. The integration of FRDFS optimizes feature selection, significantly improving the system's reliability and performance. An empirical analysis, using a real financial dataset from Chinese small and medium-sized enterprises (SMEs), demonstrates the effectiveness of our proposed method. This research enriches the financial fraud detection literature and provides practical insights for risk management professionals, introducing a comprehensive framework for early warning and proactive risk management in digital finance. [ABSTRACT FROM AUTHOR]

When applying artificial intelligence technology to quantitative trading, high noise and unpredictability of market environment are the first practical problems to be considered. Therefore, how to select the learning features of the market based on rapidly changing financial data is particularly important. In this paper, the real time financial data are first processed by K-line theory, which uses candlesticks as a generalization of price movements over a period of time, so this process can play the role of de-noising. Then, the candlesticks are decomposed into different subparts by mean of a specified spatio-temporal relationship, based on which cluster analysis of the subparts to get the learning features. Further, the learning features that are clustered by the above K-lines are put into the model, and the online adaptive control of the parameters in the unknown environment is realized by the deep reinforcement learning method, so as to realize the high frequency transaction strategy. In order to verify the performance of the model, the data on different financial derivatives transactions such as stocks, financial futures and commodity futures are used. The proposal approach is compared with other methods which are based on price, fuzzified price and K-lines for features learning. In order to verify the accuracy of the proposal approach, prediction-based methods such as recurrent neural network and fuzzy neural network are used for comparison. Experimental results show that the proposed method has higher robustness and prediction accuracy.