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IRJET-Financial Distress Prediction of a Company using Data Mining
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 265 Financial Distress Prediction of a Company using Data Mining Lakshmi S1, Mahalakshmi A2, Pavithra R3, R.S.Rajkumar4 1,2,3Third year B.E CSE, Sri Ramakrishna Engineering College 4Assistant Professor, Dept. of CSE, Sri Ramakrishna Engineering College ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Data mining tools become important in finance and accounting. This paper analyses the classification and prediction is used for bankruptcy prediction, financialdistress prediction, management fraud detection, credit risk estimation, and corporate performance prediction. The existing financial analysis model does not address the issue of Bankruptcy. Thus, an efficient financial analysis model should be generated to know about the financial distress of a company. Key Words: Financial data, Bankruptcy, Data Mining, Z- score ,F-score etc. 1. INTRODUCTION Data mining tools become important in finance and accounting. The Classification and prediction is used for bankruptcy prediction, financial distress prediction, management fraud detection, credit risk estimation, and corporate performance prediction. Data mining is the practice of automatically searching large stores of data to discover patterns and trends that gobeyondsimpleanalysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD). The key properties of data mining are: Automatic discovery of patterns Prediction of likely outcomes Creation of actionable information Focus on large data sets and data bases 1.1 The Data Mining Process The process flow shows that a data mining project does not stop when a particularsolutionisdeployed.Theresultsof data mining trigger new business questions, which in turn can be used to develop more focused models. 1.2 Data mining applications for Finance A huge amount of data is generated in onlinetransactions, so the ability to identify the right information at the right time can mean the difference between gaining or losing millions of dollars: Increase customer loyalty by collecting and analysing customer behaviour data. Help banks predict customer behaviour and launch relevant services and products Discover hidden correlations between various financial indicators to detect suspicious activities with a high potential risk. Improve due diligence to speed alerts and support real-time decision-making Identify fraudulent or non-fraudulent actions by collecting historical data and turning it into valid and useful information. 2. Bankruptcy Prediction Predicting bankruptcy is of great benefit to those who have some relations to a firm concerned, for bankruptcy is a final state of corporate failure. In the 21st Century, corporate bankruptcy in the world has reachedanunprecedentedlevel. It resultsinhugeeconomiclossestocompanies,stockholders, employees, and customers, together with tremendous social and economic cost to the nation. Therefore, accurate prediction of bankruptcy has become an important issue in finance. Companiesare strongly demanding explanationsfor the logic of prediction. Fig -1: KDD process The breakthroughbankruptcypredictionmodelwastheZ - score model developed by Altman. The five - variable Z - score model using multiple discriminant analysis showed very strong predictive power. Since then, the discriminant analysis has been approved to be the most widely accepted and successful method in bankruptcy prediction literature. In addition, numerous studies have tried to develop different bankruptcy prediction models by applying other data mining techniquesincludinglogisticregressionanalysis, genetic algorithms, decision trees, classification and regression trees (CART),andotherstatisticalmethods.Those
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 266 techniques can generally provide good interpretabilityofthe prediction models. In the past two decades, a number of studies have also applied neural network approach to bankruptcy prediction, most centring on the comparison of predictive performance of neural networks and other methodologies such as discriminant analysis and logic analysis. Some havereported that the performance of neural networks is slightly better than that of other techniques,butresultsarecontradictoryor inconclusive. The existing financial analysismodeldoesnotaddressthe issue of Bankruptcy. Thus, an efficient financial analysis model should be generated to know about the financial distress of a company. The dramatic growth of the information available online and stored in enterprise databases has made data mining a critical task for enhancing knowledge management and, generally, for gaining insight to drive decision making. A significant source of thisinsightderivesfromthecapabilityto identify hidden patterns and relationships in data. Data mining drills the staticdatadeeperandexaminesthe historic business activities. Ad hoc reporting spotlights analysis of both. Thereby, the pattern and trendsaretracked. Mining software spotlights the algorithms thereafter. This way, unknown businessstrategiesareidentified.The deduction of current liabilities from current assets gives out working capital. Capital fuels a business to run. Extract the idea of how working capital is being utilized. If the current liabilities exceed current assets, the business can encounter bankruptcy. Therefore, this working capital gives ideas of how the company’s efficaciesareperformingandhowmuchitgainsin short interval.Operationalefficiencycanbeachievedwiththe forecast that mined data provides.Thewayofachievinggoals can be identified this way. The proposed system forpredictionoffinancialdistressis F-Score system and if f score > -0.05 the company is considered to be healthy case and if not f score<=-0.05 Distressed. The methodology’s assumption isthewillingnesstomake the process of data mining reliable and usable bypeoplewith few skills in the field but with a high degree of knowledge of the business. Fig -2: Methodology The methodology providesa framework that includes six stages, which can be repeated as in a loop with the aim to review and refine the forecasting model: Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment 3. CONCLUSIONS In this study we use financial data as inputs to the failure corporate prediction model using structured and heterogeneous information grouped by the company’s financial statuses. Thus, using F – Score in Data mining financial distress of the companies is displayed. REFERENCES [1] F.Y. Lin and S. McClean: “A Data Mining Approach to the Prediction of Corporate Failure”, Knowledge-Based Systems, Volume 14, Issues 3-4,June,2001,pp.189-195. [2] T. McKee: “Rough Sets BankruptcyPredictionModelsvs. Auditor SignallingRates”, Journal ofForecasting,Volume 22. Issue 8, December, 2003, pp.569-586.
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