Over the last decade, bank industry has made a significant investment on mobile banking (MB) as an innovative tool with an expectation that MB services increase customer satisfaction. While the focus has been increasingly on MB adoption, banking research shows more value is generated with frequent and continued usage of MB services, an area that has been given little attention. This study integrates privacy and personalization into TAM theoretical model to address this gap. SEM analysis of a sample of 486 MB customers from a US local bank reveals that perceived usefulness and perceived ease of use are significant predictors of satisfaction, while satisfaction can determine continued usage intention of MB. However, the interaction effect shows statistical significance for privacy, but not for personalization. Limitations and implications for academia and industry are discussed.

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Objective: Financial fraud has been a big concern for many organizations across industries; billions of dollars are lost yearly because of this fraud. So businesses employ data mining techniques to address this continued and growing problem. This paper aims to review research studies conducted to detect financial fraud using data mining tools within one decade and communicate the current trends to academic scholars and industry practitioners. Method: Various combinations of keywords were used to identify the pertinent articles. The majority of the articles retrieved from Science Direct but the search spanned other online databases (e.g., Emerald, Elsevier, World Scientific, IEEE, and Routledge-Taylor and Francis Group). Our search yielded a sample of 65 relevant articles (58 peer-reviewed journal articles with 7 conference papers). Onefifth of the articles was found in Expert Systems with Applications (ESA) while about one-tenth found in Decision Support Systems (DSS). Results: 41 data mining techniques were used to detect fraud across different financial applications such as health insurance and credit card. Logistic regression model appeared to be the leading data mining tool in detecting financial fraud with a 13% of usage.In general, supervised learning tool have been used more frequently than the unsupervised ones. Financial statement fraud and bank fraud are the two largest financial applications being investigated in this areaabout 63%, which corresponds to 41 articles out of the 65 reviewed articles. Also, the two primary journal outlets for this topic are ESA and DSS. Conclusion: This review provides a fast and easy-to-use source for both researchers and professionals, classifies financial fraud applications into a highlevel and detailed-level framework, shows the most significant data mining techniques in this domain, and reveals the most countries exposed to financial fraud.