MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud

320 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “With regards to external process sensing, BDACs can contribute toward identifying bottlenecks or other potential hazards in supply chains [87], detect market disturbances and monitor the financial environment [88,89], and help predict prices and availability of key resources for production [90,91].”
Metalearning: a survey of trends and technologies (2013, background): “(2013) for the problem of electricity load forecasting, Abbasi et al. (2012) for financial fraud detection or Tsai and Hsu (2013) for bankruptcy prediction.”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, unknown): “In contrast, Abbasi, Albrecht, Vance, and Hansen (2012) model financial fraud using meta-leaning, which is a specialized form of machine learning that combines the outputs of multiple machine learning techniques in a self-adaptive way to improve accuracy.”