scholarly journals Using Benfords Law To Detect Fraud In The Insurance Industry

2011 ◽  
Vol 1 (7) ◽  
Author(s):  
Meredith Maher ◽  
Michael Akers
Keyword(s):  
Health Insurance ◽  
Oil And Gas ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Benford’S Law ◽  
Benford's Law ◽  
Health Insurance Companies ◽  
Logarithmic Frequency ◽  
Health Insurance Industry ◽  
Fraudulent Activity

Benford's Law is the mathematical phenomena that states that the first digits or left most digits in a list of numbers will occur with an expected logarithmic frequency. While this method has been used in industries such as oil and gas and manufacturing to identify fraudulent activity, it has not been applied to the health insurance industry. Since health insurance companies process a large number of claims each year and these claims are susceptible to fraud, the use of this method in this industry is appropriate. This paper examines the application of Benford's Law to four health insurance companies located in the Midwest. For each company, analysis was performed on the first digit distribution, the first two-digit distribution, and providers with high volumes of claims. The results show that the populations are similar to the frequencies predicted by Benfords Law. The findings also suggested possible fraudulent activity by specific providers, however, the com-panies determined that these results occurred due to abnormal billing practices and were not frau-dulent. The insurance companies that participated in this study will continue to use this method to further detect fraudulent claims.

scholarly journals Health Insurance as a Healthcare Financing Mechanism in India: Key Strategic Insights and a Business Model Perspective

2021 ◽  
pp. 025609092110270
Author(s):  
Rohit Kumar ◽  
Aditya Duggirala
Keyword(s):  
Health Insurance ◽  
Business Model ◽  
Critical Success Factors ◽  
Success Factors ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Healthcare Financing ◽  
Strategic Choices ◽  
Critical Success ◽  
Health Insurance Industry

This study provides strategic insights and a business model perspective on health insurance as a vehicle for financing healthcare. It uses both primary (expert interview) and secondary data to investigate the overall disease burden and healthcare industry trends and track healthcare financing through the health insurance mechanism in India. To identify the critical success factors and to gain a business model perspective within the health insurance industry, telephonic and face-to-face interviews were held with 27 experts in the healthcare, insurance, and strategic management field. The study’s findings suggest that the growth of health insurance as a healthcare financing mechanism in India has been challenged continuously and impacted by multiple changes in the health insurance and healthcare industry over the last decade. One of the critical challenges faced by insurance companies is the high incurred claim ratio. We find the Indian health insurance industry to be very competitive and that the focus on critical success factors can help insurance companies gain a competitive advantage. The health insurance business model is unique, with varying configurations, and broadly comprises strategic choices and consequences. In this article, drawing from the strategic management literature on the resource-based view (RBV) and insights gained from the interviews of healthcare and health insurance experts, we highlight the six critical success factors relevant for competing in the health insurance business. We also list five strategic choices that can help health insurance companies improve their profitability and gain a sustained competitive advantage. We recommend that the insurance companies design and develop an innovative business model centred around lowering the claim ratio and simultaneously increasing the customer willingness to pay. To increase the customer willingness to pay and reduce the claim ratio, the insurance companies should focus on the six critical success factors and invest in the five strategic choices.

scholarly journals Comparison of Machine Learning Algorithms for Processing of Health Insurance Claim

2020 ◽  
Vol 5 (2) ◽  
pp. 01-19
Author(s):  
Paridhi Saxena ◽  
◽  
Abhishek Seth ◽  
Gangesh Chawla ◽  
Ranganath Singari
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Consumer Satisfaction ◽  
Insurance Industry ◽  
Machine Learning Algorithms ◽  
Insurance Companies ◽  
Insurance Claim ◽  
Lifestyle Behaviour ◽  
Customer Lifetime ◽  
Health Insurance Industry

The health insurance industry protects against financial losses resulting from various health conditions. Since a long, it has relied on statistics and data to calculate risks and thereby, centre attention more profoundly on a particular target audience for increasing the operational efficiency of the industry. Technologies like Machine Learning and Artificial Intelligence prove to be an efficient tool for enabling insurance companies to predict the Customer Lifetime Value (CLV). This can be done using customer lifestyle behaviour data allowing to assess the customer's potential profitability for insurance companies. This creates a more personalised marketing offer within the audience. The insurance industry and its components constitute a dynamic and competitive sector representing approximately 2.7 percent of the US Gross Domestic Product (GDP). As customers have become progressively scrupulous about narrowing down their specific requirements, insurers and insurance companies are scrutinizing techniques for improving business operations and consumer satisfaction. An attempt in this regard has been made to analyse the “sample insurance claim prediction dataset" using various machine learning models including Decision tree, Random Forest algorithms, Naïve Bayes, K-nearest neighbour algorithm, Supper Vector machines and Neural Networks. A comparative analysis is performed to generate reports.

scholarly journals An Empirical Study on the Equity Performance of China's Health Insurance Companies During the COVID-19 Pandemic—Based on Cases of Dominant Listed Companies

2021 ◽  
Vol 9 ◽  
Author(s):  
Rendao Ye ◽  
Na An ◽  
Yichen Xie ◽  
Kun Luo ◽  
Ya Lin
Keyword(s):  
Neural Network ◽  
Health Insurance ◽  
Stock Price ◽  
Short Term Memory ◽  
Insurance Industry ◽  
Back Propagation ◽  
Listed Companies ◽  
Insurance Companies ◽  
Neural Network Models ◽  
Health Insurance Companies

The health insurance industry in China is undergoing great shocks and profound impacts induced by the worldwide COVID-19 pandemic. Taking for instance the three dominant listed companies, namely, China Life Insurance, Ping An Insurance, and Pacific Insurance, this paper investigates the equity performances of China's health insurance companies during the pandemic. We firstly construct a stock price forecasting methodology using the autoregressive integrated moving average, back propagation neural network, and long short-term memory (LSTM) neural network models. We then empirically study the stock price performances of the three listed companies and find out that the LSTM model does better than the other two based on the criteria of mean absolute error and mean square error. Finally, the above-mentioned models are used to predict the stock price performances of the three companies.

Capitalizing on Illness: The Health Insurance Industry

1974 ◽  
Vol 4 (4) ◽  
pp. 583-598 ◽  
Author(s):  
Thomas Bodenheimer ◽  
Steven Cummings ◽  
Elizabeth Harding
Keyword(s):  
United States ◽  
Health Care ◽  
Health Insurance ◽  
Low Income ◽  
Access To Health Care ◽  
Insurance Industry ◽  
Private Insurance ◽  
The United States ◽  
Insurance Companies ◽  
Health Insurance Industry

The private health insurance industry in the United States began as a money-collection mechanism for hospitals and doctors, and has evolved into an important profit-making sector of the economy. Blue Cross is dominated by hospital representatives and serves to channel money into the nation's hospitals. Physicians control Blue Shield and are its principal beneficiaries. And commercial insurance companies are closely linked to banks and industrial corporations through the country's large financial empires. Some effects of this elite control over the health insurance industry have been inadequate and distorted insurance coverage, discrimination against the elderly, the sick, and the poor, and rapidly rising medical costs. In addition, the control of Medicare and Medicaid by private insurance institutions has contributed to the enormous inflation produced by these programs. Though governments, consumers, and even the insurance industry itself are beginning to apply controls to the unprecedented medical inflation of the late 1960s, these controls tend to limit access to health care, especially for low-income people. Unless insurance companies are barred from the health care field and a public financing mechanism based on progressive taxation is introduced, health care will never be an equal right for everyone in the United States.

scholarly journals Fraud and Abuse in the Saudi Healthcare System: A Triangulation Analysis

2020 ◽  
Vol 57 ◽  
pp. 004695802095462
Author(s):  
Wadi B. Alonazi
Keyword(s):  
Health Insurance ◽  
Healthcare System ◽  
Insurance Industry ◽  
Rejection Rate ◽  
Healthcare Services ◽  
Macro Level ◽  
Insurance Companies ◽  
Structured Interviews ◽  
Health Insurance Companies ◽  
Micro Level

In the insurance industry, the majority of fraud and abuse cases fall into a limited number of patterns, yet false claims normally lead to negative national, local, and organizational effects. Through monitoring the exploitative and abusive behavior commonly found in healthcare services, this paper aims to analyze initiatives implemented by governmental and related healthcare insurance agencies in Saudi Arabia to reduce moral offenses. To accomplish this objective, major governmental health insurance policy documents were analyzed at the macro-level. At the meso-level, semi-structured interviews were conducted with five health insurance professionals on measures undertaken to prevent such incidents. At the micro-level, the critical factors of fraudulent behaviors were analyzed using a retrospective analysis. Data were retrieved from anti-fraud records of ten leading health insurance companies and the focus was mainly on individuals involved in unethical practices between 2014 and 2019. After a full audit was completed, the results concluded that the Saudi healthcare system is composed of twenty-six cooperative health insurance agencies and over 5,202 health services providers. The official documents contain the details of various moral hazard measures. On annual average, more than 196 fraudulent cases were reported with a claim rejection rate of approximately 15%. The majority of fraud cases were reported in dental services with invalid card usage, followed by obstetrics-gynecology services (47 and 113 cases, respectively). Females tended to make up most deceit cases in obstetrics-gynecology with a high level of abuse (95% confidence interval: −83.398 to −24.202; P < .003 and −28 > 638 to −7.362; P < .005, respectively). This study ultimately identifies basic measures employed at the macro-level to reduce moral hazards. However, such measures are not intended to be coherently implemented at the micro-level, especially by health insurance companies and healthcare providers.

scholarly journals The Private Insurance Market: Not Very Big and Not Insuring Much, Either

2018 ◽  
Vol 46 (4) ◽  
pp. 877-882 ◽  
Author(s):  
Jacqueline Fox
Keyword(s):  
Health Insurance ◽  
National Health Insurance ◽  
National Health ◽  
Private Health Insurance ◽  
Insurance Industry ◽  
Private Insurance ◽  
Insurance Market ◽  
Insurance Companies ◽  
Medicare Program ◽  
Health Insurance Industry

Creating a single national health insurance pool is not likely to destabilize the economy by supplanting the private health insurance industry. This industry insures a relatively small percentage of the population and holds very little of the risk such insurance implies. In effect, insurance companies function as middlemen, bundling risk packages to distribute to other, larger companies and so serve a limited purpose. Were insurers to handle claims for a national pool as they do for the Medicare program, any destabilization to the economy more broadly would be further minimized.

scholarly journals Which health-related reasons lead to prehospital emergency care and how does subjective emergency status connect to subsequent care?

2021 ◽  
Author(s):  
Silke Piedmont ◽  
Anna Katharina Reinhold ◽  
Jens-Oliver Bock ◽  
Enno Swart ◽  
Bernt-Peter Robra
Keyword(s):  
Health Insurance ◽  
Emergency Care ◽  
Third Party ◽  
Insurance Companies ◽  
Prehospital Emergency Care ◽  
Health Related ◽  
Life Threatening ◽  
Prehospital Emergency ◽  
Health Insurance Companies ◽  
Reported Data

Abstract Objectives/Background In many countries, the use of emergency medical services (EMS) increases steadily each year. At the same time, the percentage of life-threatening complaints decreases. To redesign the system, an assessment and consideration of the patients’ perspectives is helpful. Methods We conducted a paper-based survey of German EMS patients who had at least one case of prehospital emergency care in 2016. Four health insurance companies sent out the questionnaire to 1312 insured persons. We linked the self-reported data of 254 respondents to corresponding claims data provided by their health insurance companies. The analysis focuses a.) how strongly patients tend to call EMS for themselves and others given different health-related scenarios, b.) self-perceived health complaints in their own index case of prehospital emergency care and c.) subjective emergency status in combination with so-called “objective” characteristics of subsequent EMS and inpatient care. We report principal diagnoses of (1) respondents, (2) 57,240 EMS users who are not part of the survey and (3) all 20,063,689 inpatients in German hospitals. Diagnoses for group 1 and 2 only cover the inpatient stay that started on the day of the last EMS use in 2016. Results According to the survey, the threshold to call an ambulance is lower for someone else than for oneself. In 89% of all cases during their own EMS use, a third party called the ambulance. The most common, self-reported complaints were pain (38%), problems with heart and circulation (32%), and loss of consciousness (17%). The majority of respondents indicated that their EMS use was due to an emergency (89%). We could detect no or only weak associations between patients’ subjective urgency and different items for objective care. Conclusion Dispatchers can possibly optimize or reduce the disposition of EMS staff and vehicles if they spoke directly to the patients more often. Nonetheless, there is need for further research on how strongly the patients’ perceived urgency may affect the disposition, rapidness of the service and transport targets.

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