Auto Insurance Fraud Detection with Multimodal Learning

ABSTRACTIn recent years, feature engineering-based machine learning models have made significant progress in auto insurance fraud detection. However, most models or systems focused only on structural data and did not utilize multi-modal data to improve fraud detection efficiency. To solve this problem, we adapt both natural language processing and computer vision techniques to our knowledge-based algorithm and construct an Auto Insurance Multi-modal Learning (AIML) framework. We then apply AIML to detect fraud behavior in auto insurance cases with data from real scenarios and conduct experiments to examine the improvement in model performance with multi-modal data compared to baseline model with structural data only. A self-designed Semi-Auto Feature Engineer (SAFE) algorithm to process auto insurance data and a visual data processing framework are embedded within AIML. Results show that AIML substantially improves the model performance in detecting fraud behavior compared to models that only use structural data

Perceptions of Nurse\u27s Personal Smartphone Use at Work

Distracted nurses who use their personal smartphone at work has resulted in the diversion of attention from patient care. The specific problem is the personal smartphone use by nurses in the hospital settings has resulted in distracted patient care, leading to wrongful release of patient’s information, medical errors, injury or preventable patient death. The purpose of this qualitative study was to describe the perceptions of nurses regarding distracted patient care in their clinical workplace due to personal smartphone use by nurses. The study was grounded in the distraction-conflict theory conceptual framework. The key research question examined the perceptions of nurses regarding distracted patient care in their clinical workplace due to personal smartphone use by nurses. A single case study with embedded units was conducted and involved a total of 54 participants. The trustworthiness of the study’s data was supported by employing methodological triangulation from the study’s three data sources: semi-structured interviews, a focus group, and an open-ended questionnaire. Four themes and 9 subthemes were revealed after thematic analysis. The findings clearly demonstrated that nurses perceive their smartphones as an integral tool to assist in patient care and, if misused, a distraction that may create a negative impact on patient care. This study is likely to promote positive social change by providing guidance for nursing management on defining policies and practices based on nurses’ smartphone use that incorporate the perceptions and insight of the nurses to provide professional application that heighten the awareness of distracted health care