Detection of Financial Reporting Fraud Based on Clustering Algorithm of Automatic Gained Parameter K Value

Listed companies' financial reporting fraud has been a major problem in the research history of accounting. It produces an extremely bad and wide range of influence on the development of securities market. With the continuous development and progress of the stock market, the requirements for strictly controlling and preventing financial reporting fraud are also increasingly high. There have been a lot of studies of financial fraud at home and abroad. They are usually about motivations, means, identification and controlling of financial fraud. Financial fraud recognition is usually divided into signal judgment and model identification. However, the existing recognition models’ accuracy is generally not high. There is a large room for improvement and the models are not applicable enough. In addition, in the era of knowledge economy, with the continuous development of information networks, computer network technology is more and more generally applied in the field of finance. Especially the use of computers in financial reporting fraud investigation can greatly reduce the manpower and resources as well as improve the efficiency of identification. But it is not known that what kind of method combined with computer technology can better identify financial reporting fraud. In this case, the paper aims at establishing an accurate financial reporting fraud recognition model based clustering method.