The Financial Risk Analysis And Forewarning Research Based On Data Mining Technology

With the rapid development of Information Technology and significantimprovement of management theory research, Information Technology hasbeen paid more and more attention in the area of enterprise managementdecision. Confronting the fierce competition in the market, the enterprisemakes increasing requirements for risk management, therefore how toevaluate the existing financial risks objectively and timely forewarning in theenterprise management process becomes the goal the enterprise is alwaysseeking for. The traditional methods for financial risk analysis andforewarning include statistical analysis method and neural network model,however with the enlargement of the enterprise in scale and more frequentinformation disclosure, the traditional statistical analysis methods have beenunable to meet the requirement of massive data analysis; also the neuralnetwork model doesn't consider the time continuity of financial data. Inaddition, the enterprise financial risk analysis and forewarning research isinfluenced by a variety of internal and external factors with high uncertainty,however, the excellent performance of data mining technology in uncertaintytheoretical study makes them closely linked. In order to solve the above problems existed in the traditional methods, after further research on theassociation rule mining methods, we present3improved algorithms based onnew association rules, which improved the mining efficiency greatly. In themeanwhile, we applied them to the enterprise financial risk analysis and crisisforewarning research and proposed both conception hierarchical tree modelfor enterprise financial risks and financial crisis forecasting model based ontime series. The paper is organized as follows.1. Hash based association rule interactive mining algorithm (HIUA)The present association rules mining algorithms are mainly based on thesupport and confidence framework. For the same database, different supportand confidence threshold will generate different frequent item sets anddifferent numbers of association rule with the same algorithm. Since the userscannot in advance know which support-confidence threshold is appropriate,they need to constantly test different thresholds to get the ideal frequent itemsets and association rule. The new algorithm is aimed to improve theassociation rule maintenance issues when the support threshold is changed. Inother words, the previous algorithm involves scanning the database multipletimes and repeated calculation issues while the users adjusted the threshold, soHash based association rule interactive mining algorithm HIUA is proposed,which improves the pruning process of the original IUA algorithm and useHash structure to quickly access to the support counting during the execution.By this way, the algorithm efficiency is improved. 2. Association rule incremental updating algorithm based on PS_Tree(IUPS_Miner)In general, association rule mining algorithm assumes that the database isstatic, in the condition of specifying fixed threshold; it needs re-scan thedatabase to compute new association rules once the database has been updated.Towards the above-mentioned association rule maintenance issues, we presentan efficient association rule incremental updating algorithm based on PS_Tree(IUPS_Miner) that only needs mining the new database. By merging theretained PS_Tree with the new PS_Tree to reduce the cost of scanning theoriginal database and repeated calculation of associations, it efficientlymaintained the previously discovered association rules and improved thealgorithm efficiency.3. Association rule dynamic maintenance algorithm (ARDM)The dynamic maintenance of association rules refers to the maintenanceand update issues of association rules when both the database and supportthreshold are changed at the same time. The present mining methods usuallyhave the problems that involve multiple scanning of database or repeatedlytraversing the complex structure. In this paper, for the situations the databaseand support threshold are changed simultaneously, we present an associationrule dynamic maintenance algorithm based on interactive mining andincremental mining, which uses the already generated associations forincremental mining and interactive mining. Basically the algorithm applies interactive mining to the original databases and then applies incrementalmining to the new added databases using the new support threshold;furthermore, the efficiency is optimized and improved with Hash structure andpattern growth methods.4The application of association rule interactive mining algorithms inenterprise financial risk analysisThe objective of the enterprise financial risk research is to constructingfinancial risk index system, and then determines the high support patterns inthe index system to help for the enterprise management decision. Thetraditional method for enterprise financial risk is usually based on statisticalanalysis model, which drawback is many assumptions so that it cannot processmass data. Aim at the above-mentioned problems, association rule interactivemining is proposed in this paper, which choose multiple wider ranges offinancial indexes first, and then ultimately determine the most representativefinancial risk indicator by mining the rules between all financial indexes. Thedetailed steps can refer to the following: firstly establish financial risk indexsystem in which the selection of financial index is based on variablecorrelation analysis; then build a risk conception hierarchical tree to find therules between the finance risk indicators with interactive mining strategies ofdecreasing support threshold; finally, select the ST companies in the domesticlisted companies for the empirical research of enterprise financial risk analysis,and propose10key indicators that influence the enterprise financial risk and suggestions for avoiding financial risks.5. The Application of association rule dynamic maintenance mining algorithmin financial crisis forecasting.The research of financial crisis forecasting mainly focuses on tracking thefinancial index fluctuations and trends, and the system is supposed to providewarning alert once the financial index fluctuates beyond a certain range. Theexisting methods for financial crisis forecasting are mainly based on artificialintelligence data mining models, which own the drawback that doesn't takethe time continuity of the financial index data into account. In this paper,considering the time series characteristic for the financial index, we present adynamic-maintained enterprise financial crisis forecasting model based ontime series. The concrete steps refer to the following: firstly construct thefinancial data mining model based on time series; then based on time seriesincremental mining and interactive mining mechanism, find the rules betweenthe financial index and predict the development trend for the crisis enterprisewith association rule dynamic maintenance mining; finally, we select the STcompanies in the domestic listed companies for the empirical research ofenterprise financial crisis forecasting, and determine the key indicator todefine different phase of crisis enterprise.