Financial Accounting Detection Techniques †Myassignmenthelp.Com

Question: Discuss About The Financial Accounting Detection Techniques? Answer: Introduction Business intelligence deals with different technologies, applications and practices for the integration and collection of business information. Business Intelligence helps in better decision making (Chen, Chiang Storey, 2012). It further helps the company in collecting data from internal systems and external resources, encompassing a variety of tools and applications. The fraud detection techniques in business Intelligence, different security issues associated with business intelligence, data security and different methods and strategies for mitigating the security concerns associated with Business Intelligence are elaborated in the following paragraphs (Anandarajan, Anandarajan Srinivasan, 2012). Using business Intelligence for fraud detection Fraud is a term to define activities of a person or an organization that have been intentionally done for gaining personal benefits. There are various methods of conducting a fraud and therefore, there must be a process of fraud detection as well. Business intelligence can be effectively used to identify the different fraudulent transactions. BI can help in unveiling the fraudulent transactions persisting in an organization by tracking the trends of the frauds with the help of advanced analytics (Copeland et al., 2012). Furthermore, it helps in detection of manuals and report that does not abide by the pre set standards. Therefore, the usage of Business Intelligence tools helps in pointing at the accuracy of the analyzed data, which helps in identifying the fraud and help in determining the level of fraud conducted (Sharma Panigrahi, 2013). Data Security in BI Business Intelligence is a catchphrase for next generation data warehousing. While data warehousing focuses on data integration, business Intelligence is concerned with the governance of data. Therefore security in BI is utmost essential. In order to achieve data security in BI, timely information gathering of vulnerabilities, accessing of threats and responding to an attack with appropriate measures are necessary. Risk prioritizing is another essential step for nursing data security in BI. Risk prioritization is a process of rating the risks according to the severity and vulnerability of the threats according to the applicable standard and regulations (Minelli, Chambers Dhiraj, 2012). Security issues associated with Business Intelligence The security issues associated with Business Intelligence are listed below (Chen, Chiang Storey, 2012)- 1) Business Intelligence can trigger targeted cyber attacks as different company analyses raw data for effective decision making, which in increases the number of security risks. 2) Breach of data is most common in the Business Intelligence as the different data are mined by the organization for analyzing the trends and frauds. 3) Social networks and Business Intelligence go hand in hand, as different data from these networks are collected and analyzed for business purposes. 4) Business Intelligence is largely dependent of cloud computing as the big data stores in cloud is mined for analyzing. This involves different security risks and issues, which includes data loss, modification of data and so on. 5) Business Intelligence may not be used by certain organizations appropriately, which may give rise to different security risks. User adoption is poor which results in certain risks. 6) Certain BI tools do not live up to the hype resulting in fetching incorrect data. Security Methods and Strategies The methods and strategies for ensuring data security in Business Intelligence are listed below (Kimball et al., 2015) 1) Encryption: Encryption is a process of converting information into an unreadable form of information in order to prevent unauthorized access. 2) Authentication: Authentication is a process of proving or showing something as true and genuine. Multiphase authentication is a process of securing a data by different techniques such as fingerprint authentication, security questions and security code of 4 to 6 digits. This helps in limiting the access of the data only to authorized persons (Boyd Mathuria, 2013). 3) Access control or knowing the person who is accessing the data is another important methodology for mitigation of risk in BI. 4) Furthermore, the use of both logical and physical security helps in protecting the confidentiality of the data. Physical security includes installation of CCTV and maintaining manual records. Logical security deals with safeguarding the documents with the help of user identification, passwords and authentication. 5) Tiered data protection and enabling multilayered security is another effective means of data security in BI. Conclusion Therefore, from the above discussion it can be concluded that the process of BI deals with the integration of business processes and operations, which further helps in effective decision-making. The report discusses the different security risks and concerns associated with BI and the process of mitigation of the risk. Different BI tools are effectively used for detection of fraudulent transaction and further help in effective decision-making. References Anandarajan, M., Anandarajan, A., Srinivasan, C. A. (Eds.). (2012).Business intelligence techniques: a perspective from accounting and finance. Springer Science Business Media. Boyd, C., Mathuria, A. (2013).Protocols for authentication and key establishment. Springer Science Business Media. Chen, H., Chiang, R. H., Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact.MIS quarterly,36(4). Copeland, L., Edberg, D., Panorska, A. K., Wendel, J. (2012). Applying business intelligence concepts to medicaid claim fraud detection.Journal of Information Systems Applied Research,5(1), 51. Kimball, R., Ross, M., Mundy, J., Thornthwaite, W. (2015).The Kimball Group Reader: Relentlessly psychology Tools for Data Warehousing and Business Intelligence Remastered Collection. John Wiley Sons. Minelli, M., Chambers, M., Dhiraj, A. (2012).Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley Sons. Sharma, A., Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques.arXiv preprint arXiv:1309.3944.