Correlating Medi-Claim Service by Deep Learning Neural Networks

Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims

DRIP - Data Rich, Information Poor: A Concise Synopsis of Data Mining

As production of data is exponentially growing with a drastically lower cost, the importance of data mining required to extract and discover valuable information is becoming more paramount. To be functional in any business or industry, data must be capable of supporting sound decision-making and plausible prediction. The purpose of this paper is concisely but broadly to provide a synopsis of the technology and theory of data mining, providing an enhanced comprehension of the methods by which massive data can be transferred into meaningful information

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

Detecting and Combating Fraudulent Health Insurance Claims Using ANN

This work was funded by the National Nature Science Foundation of China (71774069), 2014 “Six Talent Peaks” Project of Jiangsu Province (2014- JY-004) Abstract While governments and private sector stakeholders are taking steps to improve the access and quality of health care service to its citizenry, a lot of resources are lost every year due to fraudulent health insurance claims. The aim of this paper is to explore a more robust and accurate ways of predicting fraudulent health insurance claims by the use of artificial neural network (ANN). Using the fraud diamond theory (FDT)’s fraud elements as fraud indicators, a fraud prediction model was created to determine whether a claim presented by a subscriber (individual) is fraudulent or non-fraudulent by varying severally the number of epoch, hidden layer number and threshold of the artificial neural network on a 14 input data to obtain an optimal parameter for the model.The model was able to predict accurately 98.98% with an MSE of 0.0086, which outperformed other artificial neural network (ANN) methods used to predict fraudulent health care claims. The incorporation of the capacity indicator of the fraud diamond theory (FDT) makes this model a tool not only for prediction but also pre-empting the occurrence of fraud. This study is the first to adopt the fraud diamond theory’s fraud elements as fraud indicators together with artificial neural network (ANN) in predicting fraudulent health insurance claims. Keywords: health insurance claim, ANN, fraud prediction model, fraud diamond theor