Microsoft Uses Machine Learning and Optimization to Reduce E-Commerce Fraud

Published Online:https://doi.org/10.1287/inte.2019.1017

References

  • Bertsekas DP (2005) Dynamic Programming and Optimal Control, 3rd ed., vol. 1 (Athena Scientific, Belmont, MA).Google Scholar
  • Bonet B (1998) Solving large POMDPs using real time dynamic programming. Proc. AAAI Fall Sympos. POMDPs (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 61–68.Google Scholar
  • Cowan R, Nicolas J (2004) Network structure and the diffusion of knowledge. J. Econom. Dynam. Control 28(8):1557–1575.Google Scholar
  • eMarketer (2018) Worldwide retail and ecommerce sales: eMarketer’s forecast and new ecommerce estimates for 2016–2021. Report, eMarketer, New York.Google Scholar
  • Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Ann. Statist. 29(5):1189–1232.Google Scholar
  • Jia Y, Wang SJ, Marcjan C, Nanduri J (2019b) Long-term short-term cascade modeling for fraud detection. U.S. Patent US20190066109, filed August 22, 2017, issued February 28, 2019.Google Scholar
  • Jia Y, Mao H, Wang SJ, Marcjan C, Nanduri J (2019a) Hierarchical profiling inputs and self-adaptive fraud detection system. U.S. Patent US20190087821, filed September 21, 2017, and issued March 21, 2019.Google Scholar
  • LexisNexis Risk Solutions (2018) 2018 true cost of fraudSM study. Accessed August 29, 2019, https://risk.lexisnexis.com/-/media/files/financial%20services/research/2018-true-cost-of-fraud-overall-rep%20pdf.pdf?la=en-us.Google Scholar
  • Li J, Liu YW, Jia Y, Nanduri J (2018) Discriminative data-driven self-adaptive fraud control decision system with incomplete information. Microsoft J. Appl. Res. 10(November):14–27.Google Scholar
  • Lin Z-Z, Bean J, White CC (1998) A hybrid genetic/optimization algorithm for finite horizon partially observed Markov decision processes. Technical Report 98-25, University of Michigan, Ann Arbor.Google Scholar
  • Nanduri J, Mao H, Liu YW, Yang K, Jia Y (2020) Adaptive fraud detection system using dynamic risk features. J. Systems Sci. Inform. Forthcoming.Google Scholar
  • Puterman ML (2014) Markov Decision Processes: Discrete Stochastic Dynamic Programming (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Smallwood RD, Edward JS (1973) The optimal control of partially observable Markov processes over a finite horizon. Oper. Res. 21(5):1071–1088.LinkGoogle Scholar
  • Venkatesan R, Er MJ (2016) A novel progressive learning technique for multi-class classification. Neurocomputing 207(May):310–321.Google Scholar
  • West DB (2001) Introduction to Graph Theory, vol. 2 (Prentice Hall, Upper Saddle River, NJ).Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.