3 research outputs found
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An agent-based system with temporal data mining for monitoring financial stability on insurance markets
We describe an expert system to monitor the stability of insurance markets. It consists of two components: an agent-based simulation component and a temporal data mining component. Like other financial markets, insurance markets experience destabilizing cycles and suffer episodic crises. The expert system assists market regulators by monitoring the financial position of individual insurers and of the overall market, and by forecasting cycles and impending insolvencies. The agent-based simulation component runs a forward simulation allowing for interaction among insurers in a competitive market, and between insurers and customers. The temporal data mining component extracts useful information for market regulators from the simulations. A prototype of the system is applied to the automobile insurance market. We show how the system may be used to forecast cycles, investigate stability, and analyze insurers’ herding behavior on the market. A practical policy conclusion is that regulators should monitor individual insurers’ pricing pattern because aggressive price undercutting creates a “winner’s curse”, with subsequent losses and market instability
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Time Series Data Mining with an Application to the Measurement of Underwriting Cycles
Underwriting cycles are believed to pose a risk management challenge to property casualty insurers. The classical statistical methods that are used to model these cycles and to estimate their length assume linearity and give inconclusive results. Instead, we propose to use novel Time Series Data Mining algorithms to detect and estimate periodicity on U.S. property-casualty insurance markets. These algorithms are in increasing use in Data Science and are applied to Big Data. We describe several such algorithms and focus on two periodicity detection schemes. Estimates of cycle periods on industry-wide loss ratios, for all lines combined and for four specific lines, are provided. One of the methods appears to be robust to trends and to outliers
A predictive method to determine incomplete electronic medical records
© 2018 Association for Computing Machinery. This paper is utilizing predictive models to determine missing electronic medical records (EMR) at general practice offices. Prior research has addressed the missing values problem in the EMRs used for secondary analysis. However, health care providers are overlooking the missing records problem that stores the patients’ medical visits information in EMRs. Our study provides a technique to predict the number of EMR entries for each practice based on their past data records. If the number of EMR entries is less than predicted, it warns the occurrence of missing records with the 95% confidence interval. The study uses seven years of EMRs from 14 general practice offices to train the predictive model. The model predicts EMR data entries and accordingly identified missing EMRs for the following year. We compared the actual visits illustrated by de-identified billing data to the predictive model. The study found auto-correlation method improves the performance of identifying missing records by detecting the period of prediction. In addition, artificial neural networks and support vector machines perform better than other predictive methods depending on whether the analysis aims at detecting missing EMRs or when identifying complete EMRs with no missing records. Results suggest that clinicians and medical professionals should be mindful of the potential missing records of EMRs prior any secondary analysis