Question

How might using big data improve fraud detection? Are there any challenges with this approach? Would this type of fraud detection have prevented the corporate fraud cases discussed in this week’s readings? Why or why not?

Answer 1

Big data is playing very important significant role to improve fraud detection by doing data mining and analysis of big data. Many dimensions of big data helps to understanding information about unstuctured data for fraud detection. Statistical technique s such as regression analysis, time series, clustering technique and classification can be used in fraud detection. By developing predictive model based on both historical and real time data, we can identify suspected fraudlent. For example if any fake claim is occured then, big data analysis quickly look for pattern of historical claims and identify similarities or bring up question in new claims before the process gets too far along and it dected fraud. (*) there are some challenges with this approach of fraud detection: (1) changing fraud pattern over time : fraudsters are find new ideas to get around system to commit act. This leads to changes pattern of fraud due to this our statistical model performance and efficiency decreases. (2) class Imbalance: there is imbalance in classification of fraud detection models because only small percentage of customer have fraudulent intensions. Due to imbalance ratio it is harder to build perfect statistical model. (3) model lnterpretation: our statistical model of fraud detection only shows number of frud occure it does not explaining why fraud occurs. (4) Time consuming process: it required kong period of time to generate perfect statistical model to detect fraud. (*) this type of fraud detection have prevented the corporate fraud cases discussed in this weaks reading. Because big data analysis techniques can help to find out froud and so by finding solution on fruad companies can prevent fraud cases.