Hybrid Approach to Document Anomaly Detection:An Application to Facilitate RPA in Title Insurance

• 作者: Abhijit Guha Debabrata Samanta
• 来源期刊: 国际自动化与计算杂志（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: Research Articles
• 页码范围: 55-72
• 页数: 18页
• 语种: 英文
• DOI: 10.1007/s11633-020-1247-y

摘要

Anomaly detection(AD)is an important aspect of various domains and title insurance(TI)is no exception.Robotic pro-cess automation(RPA)is taking over manual tasks in TI business processes,but it has its limitations without the support of artificial in-telligence(AI)and machine learning(ML).With increasing data dimensionality and in composite population scenarios,the complexity of detecting anomalies increases and AD in automated document management systems(ADMS)is the least explored domain.Deep learning,being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI.We present a hybrid model for AD,using autoencoders(AE)and a one-class support vector machine(OSVM).In the present study,OSVM receives input features representing real-time documents from the TI business,orchestrated and with dimensions reduced by AE.The results obtained from multiple experiments are comparable with traditional methods and within a business accept-able range,regarding accuracy and performance.