Learn how big data analytics is used for fraud detection by defining what is fraud detection and analyzing how data science technologies are used to discover fraud. Explore a comparison of the conventional and new big data–driven fraud detection technique
- [Voiceover] Data science marketplace is diverse.…For example, one of its key markets is fraud detection.…As we move toward the digital economy,…criminals and crooks are finding various and ingenious…ways to commit fraud against the banking sector.…The stakes are high.…The loss due to unauthorized credit card transactions alone…is estimated to be billions of dollars each year.…
Therefore, banks are extremely interested…in figuring out what's fraudulent and what's not…as fast as possible or as they occur.…Until very recently, fraud detection…involved significant human intervention.…Suspicious activities would be…flagged for additional scrutiny.…Then a fraud detection specialist…looked into the case more closely.…One of the major challenges in this approach…has been the number of false positives,…that is, there tend to be too many cases…for a human operator to review,…and a significant number of them turn out…to be normal transactions anyway.…
Therefore, improving the accuracy of fraud detection…is a key to success in this case.…
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why data analytics is important
- How data science is used in fraud detection, disease control, network security, and other fields
- Data science skills
- Data science roles
- Data science certifications
- The future of data science
Skill Level Beginner
Insights on Data Science: Lillian Piersonwith Lillian Pierson, P.E.23m 51s Intermediate
Learning Data Science: Understanding the Basicswith Doug Rose1h 16m Appropriate for all
1. Define Data Science
6. Future of Data Science
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