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.…
Author
Jungwoo Ryoo
Released
1/26/2018Jungwoo 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
Duration
Views
-
Introduction
-
Welcome1m 9s
-
-
1. Define Data Science
-
Introduction1m 24s
-
A brief history2m 37s
-
Fundamentals3m 15s
-
Big data analytics1m 44s
-
Enabling technologies2m 51s
-
-
2. Marketplace
-
Introduction to marketplace1m 26s
-
Fraud detection2m 5s
-
Social media analytics2m 9s
-
Disease control1m 24s
-
Dating services1m 50s
-
Simulations1m 28s
-
Climate research1m 24s
-
Network security1m 16s
-
-
3. Skills
-
Required skills2m 42s
-
Data mining and analytics1m 49s
-
Machine learning1m 33s
-
Statistics1m 10s
-
Visualization1m 35s
-
-
4. Roles
-
Introduction to roles1m 49s
-
Data scientist or engineer1m 48s
-
Data visualization developer2m 26s
-
Salaries1m 32s
-
-
5. Certifications
-
6. Future of Data Science
-
Emerging technologies1m 44s
-
Emerging careers1m 34s
-
Ethics1m 51s
-
Professional development1m 45s
-
Conclusion
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: Fraud detection