A Study on Financial Frauds and Anti Money Laundering

INTRODUCTION

Data Mining plays a very important role in prevention of financial statement fraud because the aim of data mining is to find out potential knowledge, unknown patterns and unsuspected relationship from a large set of data. This capability of data mining has been utilized in this research for preventing fraudulent financial reporting. Data mining tasks can be divided in two subgroups: predictive tasks and descriptive tasks. With predictive tasks, the objective is to predict the value of one attribute, based on the values of other attributes. Due to this nature, predictive data mining along with machine learning is best suited for fraud detection. Predictive tasks make a prediction for every observation. Descriptive tasks however, describe the data set as a whole. It aims to describe the underlying relationships in the data set. This fact accounts for the use of descriptive data mining instead of predictive data mining for fraud prevention. An advantage of the use of descriptive data mining techniques is that it is easier to apply it on unsupervised data. Therefore, this research recommends the use of descriptive data mining techniques for prevention of financial statement fraud. Descriptive data mining techniques namely association rules, clustering and anomaly detection are suggested as appropriate candidates for prevention of financial statement fraud in this research. Financial statement fraud is a deliberate misstatement of material facts by the management in the books of accounts of a company with the aim of deceiving investors and creditors. This illegitimate task performed by management has a severe impact on the economy throughout the world because it significantly dampens the confidence of investors. Despite the presence of strong internal control and various internal as well as external audit committees, detecting fraudulent financial reporting fraud is a difficult task when using normal audit procedures due to the following reasons. First, there is a shortage of knowledge concerning the characteristics of financial statement fraud. Secondly, given its infrequency, most auditors lack the experience necessary to detect it. Finally, managers deliberately try to deceive auditors. For such managers, who comprehend the limitations of any audit, standard auditing procedures may prove insufficient. It has also been noted that the increased emphasis on system assessment is at odds with the profession‗s prevalent and effective. These limitations suggest that there is a need for additional analytical procedures for the effective detection of financial statement fraud. Cost of financial statement fraud is very high both in terms of finance as well as the goodwill of the organisation and related country. Therefore, this research work discusses various causes and consequences of financial statement fraud. Detection of financial statement fraud comes in to play only if the prevention mechanism has failed to stop the management in perpetrating the fraud. Therefore, this research work proposes a data mining framework for prevention of financial statement fraud at the first place and detecting it in case of failure of prevention methods. Data mining methods could possibly assist auditors in detection of fraud, because data mining can use previous instances of fraud to build models in order to identify and detect the risk of fraud. Detection of financial statement fraud is an instance of classification and decision problem and the efficacy of the detection depends on the classification algorithms and the fraud predictors used and how they are combined. Therefore, this research work identifies various financial ratios / variables as input vector to the classification algorithms and further implements three classification algorithms i.e. Decision Tree (CART), Naïve Bayesian Classifier and Genetic Programming for identification and detection of financial statement fraud. Fraud detection is one of the most important applications of Data Mining. It is widely accepted by both researchers‗ community and practitioners that there is a requirement of analytical procedures and data mining techniques along with traditional auditing procedures for prevention and detection of financial statement fraud. Auditing firms and procedures are not capable enough to prevent and detect financial statement fraud, since detection of fraud is not their primary objective and auditors have a very little knowledge about the management of the organization. Moreover, standard auditing procedures may prove insufficient because auditors use sampling technique and do not examine each and every transaction. Researchers are working hard for designing and implementing new data mining methodologies for prevention and detection of fraudulent financial reporting. Enormous research work has been already done by many researchers in this area. However, due to upgrading and changing technology and new methods used by management in perpetrating financial statement fraud, there is always a necessity of more research work in this area. This motivated us to work in this area. should complement and supplement each other. However, Bologna states that prevention should take precedence over detection. Auditing procedures are not skilled for prevention and detection of financial statement fraud, because it is not their key objective. Ethically, management of an organisation is supposed to be accountable for prevention and detection of financial statement fraud but it is almost always accomplished with the consent or knowledge of management. Failure to detect or prevent financial statement fraud can damage the reputation and the credibility of the audit profession . In order to help auditors, analytical techniques of data mining can estimate the risk of fraud and can help them in understanding the reasons behind the fraudulent financial reporting. Hence, various techniques of data mining are being used to ease out this extra pressure of prevention and detection of financial statement fraud, from the mind of the auditors. Data mining is a confluence of the various disciplines such as statistics, artificial intelligence, and pattern recognition. With the coming of data mining as a new field of data analysis, data analyzing techniques can be divided into two groups: reporting techniques and data mining techniques. Reporting techniques refers to the techniques used before, where quantitative and statistical data characteristics are extracted from data and human analysts turn this information into knowledge. These are the techniques currently used in internal control settings. Data mining techniques emphasizes on the semi- automatic process to discover meaningful patterns in large data sets. Especially the data mining characteristic of revealing latent knowledge is very typical and valuable. This characteristic comes forward in the fact that no hypotheses are needed to mine the data, as opposed to pure statistics or data reporting. This is the main reason why these techniques are selected for preventing and detecting financial statement fraud in this research. Data mining techniques can be used for assisting auditors in prevention and detection of financial statement fraud because these methods are capable of constructing self-learned models from historical cases of fraud, which identify and detect the risk of fraud. Data Mining is an iterative process within which progress is defined by discovery of knowledge, which helps in finding the reasons behind financial statement fraud. Data Mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an interesting outcome.

enforcement agencies and special investigative units have used data mining techniques successfully for detection of financial frauds. Traditional methods of auditing and internal control are not capable enough for prevention and detection of financial statement fraud, because it is a type of management fraud and management is adaptive and usually find easy ways to circumvent the auditing measures. Therefore, several data mining techniques have been implemented by number of researchers for preventing and detecting fraudulent financial reporting. In this research work extensive literature survey is carried out in the area of applicability of data mining methods for prevention and detection of financial statement fraud by focusing on nature of data mining techniques and data specifications.

After the review of literature, three major areas in usage of data mining methods for prevention and detection of fraud were targeted for future research. These targeted areas include, comparative analysis of four commonly used data mining techniques, identification of financial variables, and designing and implementation of a data mining framework for prevention and detection of fraudulent financial reporting. Seven specific objectives were achieved as a part of this research work. First objective was achieved by performing thorough study of existing data mining techniques by focusing on purpose, nature of data mining techniques used, data sample specifications and empirical results. Second objective was realised by elaborating the concept of financial statement fraud along with various factors responsible for financial statement fraud. Third objective was accomplished by comparative analysis of four commonly used data mining techniques namely Neural Networks, Decision Tree, Genetic Programming and Bayesian Belief Network on the basis of eight performance criteria. Fourth objective was fulfilled by identifying financial variables from publically available financial statements. These variables were further used as input vector to the proposed framework for realising the primary objective of prevention and detection of financial statement fraud. Fraud industry incurs a substantial loss to the global economy. Measures to stop fraud from occurring in the first place is termed as fraud prevention whereas identifying fraud as quickly as possible once it has been perpetrated is known as fraud detection. Fraud detection comes into picture once fraud prevention has failed. In practice, of course fraud detection must Considering the influence of the loss incurred due to fraud, effective measures and methods should be employed for prevention and detection of financial statement fraud. Data mining methods could possibly assist auditors in reducing the risk of fraud, because data mining techniques are capable of building self-learning models by using previous instances of fraud for classifying organisations into fraud or non – fraud. Moreover, data mining methods can design new techniques for preventing and detecting fraudulent financial reporting. The presence of unexpected deviations or outliers in the financial statement fraud is most likely the indicators of errors, but may also indicate unethical, illegal act of fraud. The perpetrators of financial statement fraud show signs of behavioural characteristics such as living beyond one‗s means, high personnel turnover. Traditional auditing procedures will not be able to identify these factors and hence unable to prevent financial statement fraud at the first place. In view of the fact that victim organisations are unable to recover their losses, cost effective measures to prevent fraud are critical. The substantial cost of financial statement fraud and considerable threat to the economy of a country inspires us to deduce a data mining framework for prevention and detection of financial statement fraud. This research work conducts study and survey of existing use of data mining techniques for prevention and detection of financial statement fraud, which provides better understanding of existing use of data mining technique and their applicability. It helps in setting objectives for conducting the research work. Further whole research work is divided into three parts –comparative analysis of four commonly used data mining techniques, identification of financial variables, and designing and implementation of a data mining framework for prevention and detection of fraudulent financial reporting. Specific findings and significance of each part are given as follows: First part analyses the data mining techniques commonly used for detection of financial statement fraud. In order to understand the seriousness and cost of financial statement fraud. This part present a brief overview of financial statement fraud, its causes, consequences and various methods of perpetrating it. This part is useful in understanding the concept of fraud especially financial statement fraud along with its causes and consequences. In order to have better understanding the process of fraudulent financial reporting, fraud triangle and a flow chart for financial statement production is also presented in this part. Four data mining techniques are identified as a result of extensive literature survey. Data mining techniques namely Neural performance criteria includes: classification accuracy, ease of problem encoding, flexibility, computation complexity, interpretability, optimization capability, scalability and accessibility. Neural Network outperforms the other data mining techniques. This part is useful in selecting a data mining technique for identifying organisations into fraud or non – fraud organisation. Financial variables for prevention and detection of financial statement fraud have been identified. Sixty two financial ratios and variables were identified on the basis of behavioural characteristics and profitability, liquidity, safety and efficiency of the organisation concerned. These financial variables and ratios are used as input vector to the proposed data mining framework. This study suggests the use of descriptive data mining techniques for prevention of financial statement fraud. Three techniques namely Association Rule, Cluster Analysis and Anomaly Detection were proposed for preventing fraudulent financial reporting. This study is useful in identifying financial variables and selecting a data mining technique for prevention of financial statement fraud. Third part proposes a data mining framework for prevention and detection of financial statement fraud. This framework suggests the use of descriptive data mining techniques for prevention and predictive data mining for detection of fraudulent financial statements. This framework was implemented by using Association Rule as descriptive data mining method for prevention and three predictive methods namely Decision Trees, Naïve Bayesian Classifier and Genetic Programming for detection of financial statement fraud. This implementation results in seven Association Rules and five Decision Rules for prevention and detection respectively. This part also finds the applicability of the framework by evaluating the performance of the data mining techniques. The performance of classification techniques used in this research has been evaluated by using sensitivity, specificity of the method. This part is useful to both researchers and practitioners in preventing fraudulent financial reporting at the first place and detecting it in case of failure of prevention mechanism.

This research work focuses on the design and applicability of data mining framework for prevention and detection of financial statement fraud. Data used for implementation of the data mining framework are extracted from publically available financial statements namely balance sheet, income statement and cash flow statements of 114 organizations. Financial variables used in this research are confined to financial ratios available from financial statements; research proposes the use of descriptive data mining techniques as opposed to the use of predictive data mining techniques for prevention of financial statement fraud. Text mining algorithms for sentiment analysis of the textual description of financial statements are not in the scope of this research work. The proposed framework considers the use of individual data mining techniques only. Hybrid systems which integrate two or more data mining techniques are not taken in the scope of this research work.

Management should continually evaluate its anti – fraud programs and indicators of fraud for successful fraud prevention. Indicators of fraud are the clues that may warrant further review of a specific area or activity. Some Indicators of fraud are mentioned below : 1. No separation of duties 2. Lack of internal controls 3. High turnover of personnel 4. Unexplained entries or altered records 5. Unusually large amount of payment for cash 6. Inadequate or missing documentation 7. Non – serial number transactions 8. Unauthorized transactions The above stated occurrences are indicators of fraud which can often lead to opportunities to commit fraud. The incident of fraudulent financial reporting is possible by an employee / manager who have the adequate opportunity and sufficient motive to misstate the numbers in the books of accounts. In order to prevent financial statement fraud, management of an organisation should assess the above mentioned risk factors continuously. Every organisation should implement anti - fraud controls for creating a work environment that values honesty and empower the fraud prevention program. Anti - fraud environment will help the organisations in deterring and preventing fraudulent financial reporting. Anti - fraud control includes the following : 1. Establish an appropriate tone at the top. 2. Maintain an adequate and effective system of checks and balances

of directors. 5. Hire an objective and independent auditing firm. 6. Establish an independent whistle – blower system. 7. Maintain an independent and effective internal audit.

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Research Scholar, Department of Commerce, Maharaj Vinayak Global University, Jaipur