Data mining has an important role in the sector of financial organizations as it helps to change the data (raw) into extremely valuable information. In the present research, the study highlights the application of data mining technique in the detection of financial fraud and the current research will aim to critically analyse the implication of data mining techniques. There are some techniques which are mentioned and elaborated in this study which are regression, linear, SPSS analysis along with peer group analysis and breakpoint analysis. According to the articles, it has been found that such kind of techniques positively are used to prevent the financial fraud in organisational function. In another article the author has explained the implementation process of the AI and other expert system in decision making process and in creating a neural network. This research proposal consists the overall analysis of the of the financial fraud detection with the help of Big data and other machine learning techniques. The visuals and representations of the data in the simple forms of graphs and charts which helped in analysing processes is also elaborated in this research paper.
Data mining has a pivotal role in the financial sector of organizations as it helps in turning data (raw) into highly useful information. In the present research, the study emphasizes the application of data mining technique in the detection of financial fraud. The focus is made on the background of the research, as well as its significance for future researchers. Different techniques and tools are used for eradicating fraud from the financial department of organizations.
Data mining techniques are important for discovering patterns as well as relationships in data for making better decisions. Based on the viewpoint of Herawati (2015), techniques of data mining are beneficial for anticipating and quickly detecting frauds, besides taking immediate actions for minimising costs. In the modern era of globalization, financial frauds are increasing for meeting earnings expectation. In addition to this, it is also noted that financial frauds generally occur in the financial sector of organisations for increasing management compensation. For example, Kmart's accounting scandal of 2002 is widely common as the company has overstated 10% of its profit for fascinating a enormous investors (Villanueva, 2015). It is noted that before the accounting scandal, Kmart had more investment and higher stocks.
The above graph shows that after the financial fraud in 2002, the stocks and sales of Kmart dropped, thereby leading the company to bankruptcy. Moreover, the debt of the company increases, besides losing 57,000 employees (Villanueva, 2015). Other financial frauds that are widely noticed among today's organisations include merging of long-term and short-term debt into a single account for improving the perceived organizational liquidity. Over-recording revenue sales, hidden liabilities, unsuitable disclosure and under-recording expenses are different financial frauds that are widely practiced in organisations.
The impact of financial fraud results in misappropriation of the assets by employees. Moreover, fraudulent report of finance is disseminated to stakeholders, public and investors. Therefore, in order to overcome the negative impact of financial fraud, organisations need to detect it within time by using techniques. The sophisticated techniques of data mining are useful for organisations to search millions of transactions for spotting patterns as well as detecting fraudulent transactions (Hassani et al. 2016). Different data mining techniques, such as Random Forests, boosting trees and Classification trees, need to be used by organisations for detecting any fraudulent behaviour in organisational financial department. Hence, the present topic of research will be interesting and highly important for future researchers as it can help them in carrying forward the study and bringing a satisfactory end to it.
The contemporary research will aim to critically analyse the implication of data mining techniques in financial fraud detection.
Q.1. What is the concept of data mining and its technique?
Q.2. What is the conception related to detection of financial fraud?
Q.3. What are the challenges that are faced using data mining techniques?
Q.4. What is the core influencing factors of financial fraud?
Q.5. What is data mining impact’s techniques over financial detection of fraud?
• To analyse the concept of data mining and its technique.
• To determine the concept of financial fraud detection.
• To evaluate the challenges within the data mining technique.
• To analyse the core influencing factor of financial fraud.
• To determine the impact of data mining technique over financial fraud detection.
Article 1: Data mining applications in accounting: A review of the literature and organizing framework.
This article mostly discovers the application of data mining technique within the accounting as well as it tends to propose an organisation framework within these applications. Moreover, the article highlights that data mining has a particular algorithm which is used to extract a pattern from data. In addition to this, it shows that data mining key focus is mainly leveraging the data assets for deriving the non-financial or financial benefits. In the technologically advanced market, it is very difficult to detect or prevent fraud in organisational activity. Therefore, most of the organisations emphasised on the selection and application of proper techniques to detect the fraud activities within an organisation which impacts negatively on the organisational profit. However, it has been found that there is various kind of fraud activities are existing which directly on the organisational monetary fund. The fraud type mainly includes credit card frauds, medical insurance fraud, and so on. Most of the fraud activities are associated with the credit cards, including a duplicate of signatures, wrong information of address and identity along with skimming (Amani and Fadlalla, 2017). On the other hand, fraud medical claims also become a significant issue for life insurance organisations.
Therefore, the organisations started to adopt various kinds of data mining techniques like regression, linear, SPSS analysis along with peer group analysis and breakpoint analysis. It has been found that such kind of techniques positively are used to prevent the financial fraud in organisational function. In the study, the main data sources were the leading accounting journals and in here there are a total of 209 papers satisfied in terms of inclusion criteria. Moreover, they also used the method of article filtering to make sure all the article is connected to topic of mining application in accounting department. Moreover, main debate in here is showcase of data mining application within the accounting sector.
From the article the results mainly denote that between the year 1989 to 2014 there were a total of 209 applications from their 23 which was in the papers of proceeding conference and in the journal articles 186 were described. It was also found that there was a leap within the modelling sophistically which is following a major worldwide financial crisis of the year 2008. It is also found that financial accounting mainly tends towards examining financial performance as well as analysis. Moreover, it was found that the earliest data mining application was built in term of neutral networks model for forecasting the quarterly accounting earnings.
Article 2: Introduction to data mining
Data mining generally stands for a process for discovering actionable data from the large sets of information. The data mining mostly utilises the mathematical or technical technique for deriving designs and styles which exist in data (Tan, 2018). On the other hand, the data mining process or technique generally associated with the business process of an organisation in the context of mining their financial data. However, data mining of an organisation can be done by following some significant technique for extracting potential data from the bunch of organisational data. The data mining technique mainly includes tracking patterns, clustering, prediction and regression (Lausch et al. 2015). In respect of the research topic, the data mining techniques are mainly liable for the collection of transactional details and financial data of the Australian organisations.
Hence, in this article the main methodology used in terms of data sets which were available within the UCI Machine Learning Repository and it is applied in different visualising technique. Hence, this analysis shows that it is relatively highly developed and it is accurate if it is widely available. Moreover, in here the analysis is done in respect of basic concepts and algorithms, advanced concepts as well as basic concepts. Apart from this, the algorithms and additional issue analysis is also made for finding the results. Apart from this, there can be showcase of the grounded theory for system methodology associated with analysis a data collection. The grounded theory also helps to analyse the hypothesis by analysing the collected data through interview or survey from the market (Zamawe, 2015). Moreover, this theory helps to analyse data by following four different methods such as
● Data coding
● Code system customizing
● Creative coding and building of category
● Construction theory making T
Based on the above-mentioned steps, it can be analysed that by analysing the theory, the research will can analyse the collected data in future from the respondent regarding data mining and financial fraud. Moreover, it also helps in analysing the hypothesis based on influence of data mining for detecting financial fraud within organisations.
Article 3: Financial fraud detection model: Based on random forest
Business's accelerated globalisation has simply tended towards weakened regulatory capacity of law towards the paid attention of fraud detection within the recent years. It has been found that the peer group analysis helps an organisation to monitor and understand customer behaviour with respect to a certain time. The organisation can create peer group of account for every credit cardholder which helps in measurement of same behaviour exhibition. Moreover, it helps in all the customers by dividing their peer groups with respect to similar behaviours (Van Vlasselaer et al. 2015). In this technique, if any of the account from peer groups performs subsequent behaviour that strongly deviates can be considered as the fraudulent. Therefore, it can be analysed that this data mining technique impacts positively on the detection of fraudulent.On the contrary, if the peer group account is hacked by an individual, it will be impossible to detect the anonymous behaviour of any individual which may fail to detect the fraudulent.
Another technique is called "Breakpoint analysis" which helps in distinguishing the spending activities which is supported by the transaction information within a single account. It impacts positively on the matching of new transactions with the prior spending activities for sporting features. Therefore, it can be analysed that this data mining technique positively on detecting financial fraud detection. On the contrary, if an individual put any wrong inputs regarding financial activities can impact negatively on the identification of financial faults due to the improper balancing of the activities (Liu et al. 2015). Moreover, it also has been found that most of the significant data mining models help organisations to find out the cost frauds at the time of saving adjusters time. Based on some techniques, it can be analysed that it provides a different kind of benefits to the organisation. However, it has been found that the banks and financial institutions are profited by data mining in the context of identifying probable defaulters. It helps the organisations to issue credit cards for the customers who are based on past transaction records and user behaviour.
The latent class model generally associated with the multivariate variables for setting latent variables; besides, it also can be recognised as the latent variable models. As the giving variable is discrete is able to characterise the design of restrictive prospects, which indicate chance which the certain values are taken by variables (Bakk and Vermunt, 2016). Moreover, the model also can be recognised as structural equation modelling, which helps to find groups within the multivariate categorical data. Besides the latent class model also helps in identifying dependent and independent variable both. Therefore, it can understand that by utilising this model, the researcher can find out the independent and dependent variable both. It helps the researchers to understand what kind of factors can influence financial fraud detection.
In this article it can be seen there is the use of Ballpark Matrix of Data along with the measurement method estimation. Apart from this method there was also used the CSMAR Data which includes the financial frauds aspects. The analysis highlights the introduction of Random Forest model for financial fraud in terms of data mining. Hence, to detect the financial it is found that the use of Random forest method has the highest accuracy regarding detecting frauds. The main debate focused within this article is where Random forest method provided the correct fraud detection or not (Liu et al. 2015).
Article 4: Fraud triangle theory and fraud diamond theory. Understanding the convergent and divergent for future research.
Financial fraud can be recognised as the international deceptive act associated with the financial transaction along with personal profit-making. As per the business context, financial fraud mainly stands for the loss of monetary fund of an organisation due to some illegal activity of the employees and customers. It has been found if an organisation did not track the financial record of each and every transaction related to the business can face the financial challenges or difficulties (Mansor, 2015). It can impact negatively on the market position of any particular organisation or organisational profit. Besides, mostly that organisation who deals with their customer through online transactions suffers more than traditional organisations (Isukul and Chizea, 2015).
Moreover, the frauding of financial data can be successful if the data mining technique of an organisation was not strong. Therefore, the financial report of the particular organisation would be affected due to loss of financial records. Besides, the poor data mining technique was unable to detect the small transaction data from a bulk number of organisational data (Baxter, 2016). In the research study, most of the Australian organisations were facing financial fraud cases because of having improper data mining technique. Therefore, it aims to analyse the impact of these techniques over financial fraud detection.
This theory mainly based on the core concept of three factors which are directly associated with the high risk, However, this theory helps to understand the significant factors which can lead an organisation to unethical behaviour and fraud activities (Ruankaew, 2016). Based on the above-mentioned picture it can be seen that the theory emphasis on the three significant factors such as
The main methodology used in here is the secondary sources by suing the journals for getting the information's regarding fraud. In addition to this, the discussion also shows the usage of two theories which contributes for understanding fraud especially through forensic accountants and fraud examiners. The main highlighted debate is between the similarities between FDT and FTT (Mansor, 2015).
Article 5: Beyond the fraud diamond
Fraud is mainly debilitating factor which affects the growth of business and continues towards a major problem for organisation. In addition to this, the article showcases that fraud schemes are seems to be highly sophisticated as well complex with the implementation of technological advancement. Moreover, the financial concept can be analysed with the three statement model concept which analyse the financial factor. Moreover, it also has been found that most of the gaming industries of the various countries including Australia faced lots of financial frauding due to having poor standardized online identity verification and authentication tool (Mangala and Kumari, 2015).
Based on the above-mentioned picture, it has understood that the three-statement model helps to link among three factors of financial accounting. The model mainly emphasised on the linking among income statement, cash flow and balance sheet one connection (Guay et al. 2016). Therefore, it can be understood that by implementing this model, an organisation can analyse their financial data by seeing the information of three accounting statement together. It impacts positively on customer data tracking and financial report making. The main methodology used within this article is the use of different theories to showcase the fraud within the financial sector. The main debate in here highlights the combat fraud which tends to make the business understand regarding the elements and risk factors.
Article 6: Big data: Dimensions, evolution, impacts, and challenges
The article mainly highlights the big data where new technology paradigm regarding the data which are generated at a high-level volume and velocity. The article integrated towards the view of data mining regarding within the past 20 years. However, in the context of mining data, some criteria must be matched with the data mining techniques; otherwise, it may create challenges to mine data such as
It has been found that more collected data were incomplete and noisy to understand. Therefore, the dependability and rationality of the particular data were not potential for mining. In contrast, the spelling mistakes at the time of putting data impacts negatively on the finding of real mail IDs of the customers (Jin et al. 2015). Therefore, it can be understood that it can create challenges for mining previous customer data records.
The real-world data was usually stored on various platforms in the context of distributed computing environments. Therefore, it will be very difficult to find or bring entire data to the centralised data respiratory for an organisational and technological reason. As an example, all the different regional organisations utilise or their local server to access customer data (Van Der Aalst, 2016). Therefore, it will be very difficult for an organisation to centralise all customer data into one server.
It has been found that every country has different activities or rules related to the data protection act of consumers. Moreover, the different country holds a different policy for utilising customer data (Bello-Orgaz et al. 2016). Therefore, it will create a great problem for a globalised organisation to utilise the data of the customer transaction of their different branch located in a different country (refer to appendix 1).
The main methodology used in here is the lexical based method along with the machine learning method for the sentiment analysis. These were used in terms of showcasing the data mining techniques. The main debate in this article highlights the impacts and challenges which are faced by the big data in terms of data mining.
Article 7:- The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature.
This article consists the detailed analysis and review about the implementation of the data mining and big data in financial fraud detection. In this article the learner is going to the in-depth analysis of different case studies and methodologies related to the detection process and the data mining process. Frauds and other theft related to the data and other things is getting very common. With every coming day users are getting different types of threats related to their data and information. Above all these they are also getting threats related to financial thefts and thugs. In the recent days there are a lot of case registered which directly relates to the financial fraud. In the year 2014 -15 more than 400 financial frauds case has been registered in a local police station of the Queensland, Australia. Most of the cases were related to the online theft and frauds. In most of the cases which are detected by the, local police they have found that these frauds were related to the frauds and other theft from the context of the data mining (Okeke,2018).
In the process of fraud detector with the data mining a number of key words and other they are used in this process. They related and attached these keywords with the available data bases to compare the existing records of the victims and the criminals. IN this process lot of new outcomes are generated which helped the organization in getting new results and other outcomes. From the processes of data mining they detected or overviewed the outcome of the whole database.
Article 8:- Data mining techniques in fraud detection
This article contains the detailed analysis of the fraud detection with the help of data mining. There they have explained the number of mining algorithm which are used in the detecting process and in data mining or data warehousing process. The author of the article has explained in detail about the implementation of different types of algorithm which will help them in analysing and mining the data in proper manner. There are different algorithms like Bayesian algorithms which allows the data process in creating the risk network for this process.
Highlighting more on the concept of the Bayesian algorithm which the author has discussed in this article, they are using this for generating the new risk table from the clustered data. They are processing these clustered data as a trainee data and creating new risk table and using as test data. With all these test data and the trainee data users can get the outcomes and profound related to the big data analysis (Roiger,2018).
In the application of data mining in fraud detection there are mainly two types of data which can be found, on these supervised data which has the direct relation with the lists and data of the frauds cases and other one is unsupervised data where the users have to process these data to their desired results and outcomes related to the fraud detection. In this article they have found that both datatypes are helping them in different ways .In the case of the unsupervised data they are able to create a new matrix with the same data for the purpose of creating different matrices and tables (Abdallah,2016).
Discussing about the existing fraud detection system, according to Williams et. al.(1997) with using one of the most used algorithms in the process of the data analysis and other technologies algorithm “KNN algorithm”. From the KNN (K nearest algorithm) they can implement the process of “hot spots methodology”. In this methodology users finds the nearest and the most suitable data from the test data set. In this article the author has explained this thing with an example. In this example the author has explained a case study of the bank fraud which took place in the year 2004. In this case a cyber-criminal withdrawn the amount of $ 2million form the different accounts of the same bank. And was using these amounts in the transaction process for and was transferring process. The police and the local cybercrime experts collected the data of the transaction location and matched withal the available data set. It provided them the accessibility to get the set of IP address which the attacker was using to perform the transaction. With the help of these IP they traced all of them one by one and finally they were able to catch that person. After some time, the bank faced the same issue of fraud and this time they matched the data with the.
With all these explained case studies and implementation it can be concluded that the process of data mining with applying the different types of algorithms and paradigms can helping many efficient ways in solving the financial cases which the organizations and individuals are facing. Here the author has also explained about the future scope of these data mining processes which will provide them the easiness to solve the cases in future. In the last section the author has explained about some recommendation which they have to follow. They can keep the record of the access and the transaction process and are advised to match with the available data set. This step will help the banks in verifying their customers and account holders also in the case of the transaction process they need to implement the same system, they can check the frauds details and the location to verify whether the customers who is accessing this account is either getting matched with any of the risk table which the banks have or not. If they are getting a match, then the process will be automatically aborted unless it will proceed as a normal process.
In the end of this article the author has concluded with the simple statement of Bayesian belief network, which says that in the process of data mining and data ware housing there is nothing like the perfect outcome. With the available clustered data base system, one must analyse and must create a network for this system so that they can get the nearest and the most suitable data set which will help them in getting the nearest and suitable solution.
In interruption recognition terms, abuse location is for coordinating known assaults; and irregularity location is for finding obscure assaults. The flow explore in both interruption recognition and spam discovery are on abnormality recognition (semi-regulated) and unaided methodologies. In interruption recognition examine, the utilization of grouping to decrease information and Gee for inconsistency identification had been famous for detail that k-intends to pack information and report that well performed somewhat better than occurrence-based learning (IBL) for semi-genuine client level information. Thus use SOM to diminish information for well displaying. The creator demonstrate that various Well models with fluffy rationale can be utilized to lessen false positive rates. Additionally, advocate Scanty Markov Transducers. Be that as it may, reason that straightforward static approaches, for example, event recurrence conveyances and cross entropy between appropriations, outflank Well. Other irregularity discovery thinks about what's more, most investigations finish up that inconsistency recognition doesn't execute just as abuse identification. Solo methodologies incorporate what’s more, which supporter replicator neural systems to recognize exceptions. To recognize spam from the email server, encodes email as hash-based content, and stays away from marks using report space thickness to discover enormous volumes of comparative messages. On a little example of marked messages, the creator’s remark that SVM is the best regulated calculation in any case, the location time is unreasonably long for an occasion driven framework. The utilization of game hypothesis is to display the key cooperation between the framework and enemy has been as of late brought into interruption and spam identification inquire about. apply hypothetical game hypothesis to represent the collaboration between one assailant and one hub to distinguish interruptions from versatile impromptu systems. In spam discovery, adjust game hypothesis to consequently re-get familiar with a cost-touchy regulated calculation given the cost-touchy enemy's ideal technique. It characterizes the enemy and classifier ideal technique by making some legitimate suppositions. Tried under various false positives costs, the game-theoretic guileless Bayes classifier beats the traditional classifier by proficiently predicting its positives and negatives.
Article 9: - Decision Tree in data mining and fraud detection
In the process of decisions making and other methodologies one can implement the concept of the data mining and other processes. In this article the author has discussed the implantation process of decision tree and other concepts in data mining process which will be further implemented for the use of fraud detection and other activities related to the detection of crimes and thefts.
Decision trees are machine learning concepts which are implemented as a key attribute in creating the decisions and other concepts.in this article the author has highlighted the concept of the decision-making process with the help of the big data and machine learning concepts.
Initiating from the normal programming concept of IF THEN and ELSE which is widely used in most of the available programming languages. The author has highlighted the implementation of this concept in the big data process and machine learning for creating the suitable decision with the most appropriate solutions. Pointing out the process of decision making the author has discussed about the process of decision-making step by step. In his context the author has explained that from the available clustered data they can create some simple sets of data and then can create the decision from all these sets first and then will analyse all the generated sets with this process. For the implementation in the fraud detection system for the financial cases the author has also explained one of the case studies where they sued this decision-making process in solving the cases related to the financial fraud. In this case study the author has discussed the case of United Bank fraud case where their own employees have created a trouble for their company. In this process they have used the decision making, text mining process to solve the situation or the case.
It can be concluded from the above discussion that financial fraud detection is the major topic of concern in today's era of globalization. The increasing number of Australian financial risks is degrading economy of business by reducing the number of sales and brand image in market. Currently, it is important to reduce the incidents of financial fraud by using data mining technique as it can, therefore, help in improving organisational growth and productivity. In the present study, main data will be used for making the study highly valid and reliable. Furthermore, both Qualitative, Quantitative data analysis techniques will be selected to gain deep vision into the topic of research.
Abdallah, A., Maarof, M.A. and Zainal, A., 2016. Fraud detection system: A survey. Journal of Network and Computer Applications, 68, pp.90-113.
Abe, N., Abrams, C.E., Apte, C.V., Bhattacharjee, B., Goldman, K.A., Gruetzner, M., Hilbert, M.A., Langford, J., Padmanabhan, S.K., Tresser, C.P. and Troidle, K.M., 2015. Confidential fraud detection system and method. U.S. Patent 9,064,364.
Abe, N., Abrams, C.E., Apte, C.V., Bhattacharjee, B., Goldman, K.A., Gruetzner, M., Hilbert, M.A., Langford, J., Padmanabhan, S.K., Tresser, C.P. and Troidle, K.M., 2015. Confidential fraud detection system and method. U.S. Patent Application 14/711,183.
Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, pp.278-288.shttps://www.researchgate.net/profile/Mohiuddin_Ahmed2/publication/270968895
Albashrawi, M. and Lowell, M., 2016. Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015. Journal of Data Science, 14(3), pp.553-569.https://www.researchgate.net/profile/Mousa_Albashrawi/publication/311557850
Amani, F.A. and Fadlalla, A.M., 2017. Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, pp.32-58https://fardapaper.ir/mohavaha/uploads/2017/12/Fardapaper-Data-mining-applications-in-accounting-A-review-of-the-literature-and-organizing-framework.pdf
Bakk, Z. and Vermunt, J.K., 2016. Robustness of stepwise latent class modelling with continuous distal outcomes. Structural Equation Modelling: A Multidisciplinary Journal, 23(1), pp.20-31. https://openaccess.leidenuniv.nl/bitstream/handle/1887/44101/zs3.pdf?sequence=1
Baxter, L.G., 2016. Adaptive financial regulation and regtech: a concept article on realistic protection for victims of bank failures. Duke LJ, 66, p.567.https://scholarship.law.duke.edu/cgi/viewcontent.cgi?referer=https://scholar.google.co.in/&httpsredir=1&article=3873&context=dlj
Bello-Orgaz, G., Jung, J.J. and Camacho, D., 2016. Social big data: Recent achievements and new challenges. Information Fusion, 28, pp.45-59. http://www.ttcenter.ir/ArticleFiles/ENARTICLE/3404.pdf
Dua, S. and Du, X., 2016. Data mining and machine learning in cybersecurity. Auerbach Publications.
García, S., Luengo, J. and Herrera, F., 2015. Data preprocessing in data mining (pp. 59-139). New York: Springer.
Fabijan, A., Olsson, H.H. and Bosch, J., 2015, June. Customer feedback and data collection techniques in software R&D: a literature review. In International Conference of Software Business (pp. 139-153). Springer, Cham.https://muep.mau.se/bitstream/handle/2043/19593/ICSOB2015_34_CR.pdf?sequence=2&isAllowed=n
Graue, C., 2015. Qualitative data analysis. International Journal of Sales, Retailin &Marketing, 4(9)pp.5-14. http://www.ijsrm.com/ijsrm/Current_&_Past_Issues_files/IJSRM4-9.pdf#page=9
Guay, W., Samuels, D. and Taylor, D., 2016. Guiding through the fog: Financial statement complexity and voluntary disclosure. Journal of Accounting and Economics, 62(2-3),pp.234-269. https://daneshyari.com/article/preview/5086575.pdf
Gupta, M. and George, J.F., 2016. Toward the development of a big data analytics capability. Information & Management, 53(8), pp.1049-1064.http://iranarze.ir/wp-content/uploads/2017/01/E3296.pdf
Hassani, H., Huang, X., Silva, E.S. and Ghodsi, M., 2016. A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(3),pp.139-154.https://www.researchgate.net/profile/Xu_Huang7/publication/301579904_
Herawati, N., 2015. Application of Beneish M-Score models and data mining to detect financial fraud. Procedia-Social and Behavioral Sciences, 211, pp.924-930.https://www.sciencedirect.com/science/article/pii/S1877042815054622/pdf?md5=601c5e1f00b351a9860ed63b979e7848&pid=1-s2.0-S1877042815054622-main.pdf&_valck=1
Isukul, A.C. and Chizea, J.J., 2015. Environmental Factors Influencing Corporate Governance: The Nigerian Reality. Sage Open, 5(2), p.2158244015581380.https://journals.sagepub.com/doi/pdf/10.1177/2158244015581380
Jin, X., Wah, B.W., Cheng, X. and Wang, Y., 2015. Significance and challenges of big data research. Big Data Research, 2(2), pp.59-64. https://www.researchgate.net/profile/Benjamin_Wah/publication/272946686
Johnston, M.P., 2017. Secondary data analysis: A method of which the time has come. Qualitative and quantitative methods in libraries, 3(3), pp.619-626. Available at: http://www.qqml-journal.net/index.php/qqml/article/download/169/170 [Accessed on 08 August 2019]
Jolliffe, I.T. and Cadima, J., 2016. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), p.20150202.https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2015.0202
Lausch, A., Schmidt, A. and Tischendorf, L., 2015. Data mining and linked open data–New perspectives for data analysis in environmental research. Ecological Modelling, 295, pp.5-17.https://www.researchgate.net/profile/Angela_Lausch/publication/266260663_Data_mining_and_linked_open_data_-_New_perspectives_for_data_analysis_in_environmental_research/links/5bf2b196a6fdcc3a8de14100/Data-mining-and-linked-open-data-New-perspectives-for-data-analysis-in-environmental-research.pdf
Lee, I., 2017. Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), pp.293-303. https://e-tarjome.com/storage/panel/fileuploads/2019-02-27/1551256718_E10700-e-tarjome.pdfLiu, C., Chan, Y., Alam Kazmi, S.H. and Fu, H., 2015. Financial fraud detection model: Based on random forest. International journal of economics and finance, 7(7).https://escholarship.org/content/qt3tz2s53v/qt3tz2s53v.pdf
Mangala, D. and Kumari, P., 2015. Corporate fraud prevention and detection: Revisiting the literature. Journal of Commerce & Accounting Research, 4(1), pp.35-45. https://s3.amazonaws.com/academia.edu.documents/52739389/06.pdf?response-content-disposition=inline%3B%20filename%3DCorporate_Fraud_Prevention_and_Detection.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20190809%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20190809T060559Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=742b15a584bffac171a9085c4cf9f75b05ec25a36bf89ff51f14216e6ec2e18c
Mansor, N., 2015. Concomitant debacle of fraud incidences in the Nigeria public sector: Understanding the power of fraud triangle theory. International journal of academic research in business and social sciences, 5(9), pp.2222-6990.
Mansor, N., 2015. Fraud triangle theory and fraud diamond theory. Understanding the convergent and divergent for future research. International Journal of Academic Research in Accounting, Finance and Management Science, 1, pp.38-45.http://hrmars.com/hrmars_papers/Article_05_Fraud_Triangle_Theory_and_Fraud_Diamond_Theory.pdf
McCusker, K. and Gunaydin, S., 2015. Research using qualitative, quantitative or mixed methods and choice based on the research. Perfusion, 30(7), pp.537-542.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.968.2338&rep=rep1&type=pdf
Muda, I., Dharsuky, A., Siregar, H.S. and Sadalia, I., 2017, March. Combined Loadings and Cross-Dimensional Loadings Timeliness of Presentation of Financial Statements of Local Government. In IOP Conference Series: Materials Science and Engineering (Vol. 180, No. 1, p. 012099). IOP Publishing.https://iopscience.iop.org/article/10.1088/1757-899X/180/1/012099/pdf
Oberski, D., 2016. Mixture models: Latent profile and latent class analysis. In Modern statistical methods for HCI (pp. 275-287). Springer, Cham. https://www.semanticscholar.org/paper/Mixture-Models%3A-Latent-Profile-and-Latent-Class-Oberski/d474fcaae5ea4b32fa4596086e5d805406ebf38d Okeke, M.S.O., 2018. ROLE OF FRAUD MANAGEMENT IN THE PROFITABILITY OF NIGERIAN BANKS (Doctoral dissertation, GODFREY OKOYE UNIVERSITY, ENUGU).
Pierson, G. and DeHaan, J., iovationInc, 2015. Network security and fraud detection system and method. U.S. Patent 9,203,837.
Roiger, R.J., 2017. Data mining: a tutorial-based primer. Chapman and Hall/CRC.
Tan, P.N., 2018. Introduction to data mining. Pearson Education India.
Ruankaew, T., 2016. Beyond the fraud diamond. International Journal of Business Management and Economic Research (IJBMER), 7(1), pp.474-476.http://www.ijbmer.com/docs/volumes/vol7issue1/ijbmer2016070102.pdf
Scutt, D. (2016) 1.6 million Australians were victims of personal fraud last year https://www.businessinsider.com.au/1-6-million-australians-were-the-victim-of-personal-fraud-last-year-2016-4
Tan, P.N., 2018. Introduction to data mining. Pearson Education India.https://www-users.cs.umn.edu/~kumar001/dmbook/sol.pdf
Van Der Aalst, W., 2016. Data science in action. In Process Mining (pp. 3-23). Springer, Berlin, Heidelberg.https://nnov.hse.ru/data/2017/03/15/1112283148/program-1732155265-xsPmoe7O2q.pdf
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M. and Baesens, B., 2015. APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, pp.38-48. https://eprints.soton.ac.uk/376989/1/VanVlasselaer_APATE.pdf
Villanueva, A. (2015) Kmart Accounting Scandal (2002) https://prezi.com/elfzj78s-_8g/kmart-accounting-scandal-2002/
Zamawe, F.C., 2015. The implication of using NVivo software in qualitative data analysis: Evidence-based reflections. Malawi Medical Journal, 27(1), pp.13-15. https://www.ajol.info/index.php/mmj/article/download/116229/105755
Zareapoor, M. and Shamsolmoali, P., 2015. Application of credit card fraud detection: Based on bagging ensemble classifier. Procedia computer science, 48(2015), pp.679-685. https://cyberleninka.org/article/n/324468.pdf
Computer network security assignment help, Computer network assignment solution help, Computer science assignment help, Computer science homework help, Computer application online help, Computer technology assignment help, Computer hardware and software assignment help, Computer security software assignment help, Computer hardware assignment homework help