Job Detail

Fraud Examination Conduct and Procedures Week 6 Disc Topic 1 Review Leodan 1 page

Date Posted: October 09th, 2019
Deadline: October 10th, 2019 at at 01:01:AM
Job Budget: 2

Job Description

 The volume of data collected today from all sectors, also known as big data, is immense, involving the process of data collection, data analysis, and data implementation. Data analytics dynamics are evolving over the years, from an individual departments approach to business-driven software strategy, adopting agile technology (Some 2019), and relying more on advanced analytics. To stay ahead in the marketplace, companies need to adopt the right data-driven big data analytics trends. Big data were historically mainly applied by large companies that could afford the technologies and platforms used to capture and analyze the information. Today the scope of big data has expanded leading to large and small companies relying on smart business insights on big data. This has culminated in the evolution of big data at an incredibly fast pace (Some 2019).

     All this is of great help to solve cases of marriage fraud since, social networks can become a key factor in any investigation, currently most people publish their entire lives on these sites and then forget that this information is available and can be use in any investigation, one of the common cases is the child support or ally money, many times the person involved presume to have a higher standard of living in social networks than he or her really has, and the partner can use this information to claim a fraud of your income in front of a judge, just to mention an example (Walden 2014).

References

ACFE (2019) Detecting Fraud with Data Analytics Workshop. Retrieved from: https://www.acfe.com/uploadedFiles/ACFE_Website/Content/review/daw/01-Introduction-to-Data-Analytics-and-the-Data-Analysis-Process.pdf

Some Kamalika (2019) Top 7 Big Data Analytics Trends For 2019. Retrieved from: https://www.analyticsinsight.net/top-7-big-data-analytics-trends-for-2019/

Walden Vincent M. (2014) Big data. Big Risks Require Big Data Thinking. Retrieved from: https://www.acfe.com/article.aspx?id=4294983057

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