Insurance claims

Healthcare fraud threatens the interests of both healthcare facilities and the patients that they serve. In turn, such actions usually cause enormous financial damage and compromise the trustworthiness of healthcare systems. The proposed study intends, by adding a machine learning framework, to counter the issue of healthcare fraud. The systematic analysis of healthcare data shows patterns and anomalies which can be used to identify fraudulent behavior with more precision and speed. Based on the XG boost algorithm, this work describes a machine learning approach to irregularity detection and health insurance premium estimation. XG Boost algorithm was evaluated using the standardized performance measures such as R², MAE, RMSE and MAPE. Model's 89. 47% R² coupled with MAE of 1. XGBoost showed more predictive performance than Random Forest and Genetic Support Vector Machines (GSVMs) when compared. Support for generalization of the model was also offered when learning curves and prediction error plots were considered. From these results it is evident that XGBoost is a reliable approach for detecting insurance fraud and pricing within structured healthcare settings.

Healthcare fraud inflicts substantial financial losses and undermines the
integrity of healthcare systems, making its effective detection a critical
challenge. This is particularly true for identifying complex, coordinated
schemes that exploit patient-provider networks. We propose a Bipartite
Graph Autoencoder (BGAE) that models patient-provider interactions as
heterogeneous graphs to detect relational fraud patterns missed by traditional
analyses. The framework uses attention mechanisms to learn representations
of patients, providers, and claims through structural and attribute recon-
struction. We evaluated our framework against feature-based, structure-
based, and homogeneous GNN baselines using synthetically injected anomaly
patterns. Experimental results demonstrated that our heterogeneous, graph-
based BGAE approach offers notable advantages. The method showed
improved performance in detecting anomalous providers, yielding a∼20%
improvement in mean Average Precision (AP) over the strongest performing
baseline for this task (0.257 ± 0.023 vs. 0.214 ± 0.024). The model performed
effectively with combined structural and attribute anomalies and uniquely
provides anomaly scores for individual claims, which may assist investigators.
While also leading in AP for highly imbalanced member anomalies (0.398 ±
0.021), its advantage over well-augmented feature-based baselines was less
pronounced, and overall model efficacy varied for certain subtle fraud
patterns. The findings suggest that explicitly incorporating structured,
heterogeneous network information significantly enhances capabilities for
detecting complex provider-centric fraud and offers finer-grained claim-level
insights for healthcare insurers. While absolute performance metrics indicate
room for improvement and highlight the task’s inherent difficulty, this work
contributes to graph-based anomaly detection research by demonstrating a
tailored approach for bipartite healthcare networks. Future work should focus
on enhancing model stability for nuanced fraud patterns and further improving
detection in extremely imbalanced scenarios. This research may directly
inform the development of more robust fraud mitigation strategies in health
insurance.

Insurance fraud is a major issue facing today's global insurance industry, not only destabilizing its finances but also eroding the trust which forms the foundation for any insurance policyholder-institution relationship. This paper, therefore, investigates how insurance fraud relates to two areas it highly affects: consumer trust erosion and premium rate inflation. Fraudulent activities may range from inflated claims to totally fictitious events. They account for costs very much injected into claims processing. Insurers seek

The implementation of AI-driven LLMs in the healthcare industry has had a profound effect and will continue to shape the healthcare and AI analytics sector. According to Straits research the healthcare analytics field is "valued at USD 17.61 billion in 2024 and is projected to reach USD 21.78 billion in 2025". The rapidly shaping industry is just starting to grow. These implementations help increase cost effectiveness, implement fraud preventive measures, and risk reduction. The AI-driven implementation process of predictive analytics and finding patterns helps not only the healthcare industry but the beneficiaries indirectly. This study analyzes the use of supervised machine learning algorithms to detect fraudulent claims. This paper explains AI powered fraud Medicaid claims detection framework using machine learning algorithms applying to Medicare synthetic claims data set. Through Supervised Learning, focusing on random classification, along with explainable AI methods, this paper highlights how Medicare fraudulent claims are effectively found with high precision. In addition, this paper demonstrates important prerequisites such as Data preparation, model training, and evaluating performance. Our approach and results highlight the efficiency of AI in automating claims fraud detection, reducing manual laborers' work and improving overall claims authentication processes. In addition, the paper also highlights statistical analysis and graphical representations that evaluate the efficacy of the generated model, contributing to real time issues with Medicare claims fraud.

Despite increasing instances of fraudulent activities within the Nepalese insurance sector being periodically revealed by government bodies, regulatory authorities, and investigative journalists, a systematic academic inquiry into this issue has remained notably absent. To address this gap, an exploratory cross-sectional quantitative investigation was conducted to examine stakeholder perceptions regarding the effectiveness of fraud control mechanisms and the primary repercussions of insurance fraud on insurers in Nepal. Data were collected through a structured questionnaire administered to 200 respondents including insurance employees, policyholders, agents, insurance technicians, surveyors, and domain experts within the Pokhara Valley, selected via convenience sampling. Analytical procedures included descriptive statistics, Mann-Whitney U tests, and Kruskal-Wallis H tests. It was identified that robust legal enforcement, particularly the enactment and strict implementation of antifraud legislation, was perceived as the most effective control strategy. Institutional reforms, such as the establishment of a dedicated Fraud Investigation Bureau and a centralized Insurance Information Centre, were also emphasized as critical to improving the detection and monitoring of fraudulent activities. Although technologyenabled solutions, including AI-driven digital claim management and anomaly detection systems, were acknowledged for their importance, they were ranked marginally lower in perceived efficacy compared to legal and institutional measures. Fraud was reported to exert significant detrimental effects on insurers, most prominently through the erosion of public trust and social credibility. Additional impacts included claim settlement delays, reduced profitability, destabilization of share prices, and increased insurance premiums, collectively threatening both the short-term financial performance and long-term sustainability of the sector. To safeguard stakeholder interests and ensure sectoral stability, a multi-pronged anti-fraud framework has been recommended. Main recommendations include strengthening the legal framework with stringent penalties, developing a centralised fraud registry for inter-insurer information sharing, enhancing underwriting and claims verification procedures, and investing in intelligent fraud detection technologies. These findings offer empirical insights that can guide policy reform and institutional development in emerging insurance markets.

The impact of life insurance claim settlement on the insurance penetration rate in Nigeria was investigated in this study. The study examined the relationship between the insurance penetration rate and the trend of life insurance claims in Nigeria from 2000 to 2023, as well as the impact of paid life insurance claims on the insurance penetration rate in Nigeria over the same period. The study's data, which was gathered between 2000 and 2023, was examined using charts and the Autoregressive Distributed Lag (ARDL) model co-integration technique. The findings showed that, although the insurance penetration rate fluctuated, both the amount of life insurance claims paid and the insurance penetration rate in Nigeria showed an upward trend from 2000 to 2023. Additionally, the study confirmed that life insurance claims significantly influenced Nigeria's insurance penetration rate, though negatively over the long term. Hence, this study conclude that life insurance claim settlement plays significant role on insurance penetration in Nigeria. By identifying the factors that influence public trust in life insurance and highlighting the link between claim efficiency and policy uptake, this study makes unique contributions to the body of research on insurance penetration. These findings are particularly valuable for policymakers, as they illustrate the importance of balancing consumer needs with the operational sustainability of life insurance firms. Therefore, this study recommended among others that financial authorities should design a framework of life insurance claim that put into consideration the interest of the people and life insurance business in terms of time and amount, in ways that promote penetration rate in the insurance companies.

This study assessed the influence of life insurance premium on insurance penetration in Nigeria. Specifically, this study examined the trend of life insurance premium and insurance penetration in Nigeria, using data of 1981 to 2023. These data were extracted from secondary sources for insurance penetration, life insurance penetration and control variables (inflation rate, exchange rate, interest rate, gross domestic product growth rate and population growth rate). Data were analysed with charts and ARDL co-integration methods based on the objectives formulated. Accordingy, result demonstrated upward trend in life insurance premium as well as downward trend in insurance penetration. On the other hand, the result revealed that life insurance premium has significant positive effect on insurance penetration on the short run, but insignificant negative effect on the long run. Nevertheless, this study concluded that life insurance premium has influence on insurance penetration, although significance is limited to the short run. Hence, this study recommended that life insurance businesses in Nigeria should ensure to put in more effort into creating awareness, advertisement and promotions to enhance life insurance demand as well as facilitate penetration rate

Businesses in the insurance sector use DLT technology to open transaction flows and make job efficiency better. Researchers verified that blockchain technology for reinsurance solves fraud and claim processing issues to produce efficient insurance services. We examine how blockchain technology optimizes reinsurance contracts by combining insurance facts and blockchain pilot results using statistical procedures. Companies can complete insurance claims more rapidly at a 30% to 40% lower expense level while spotting 85% of possible fraud. Using this service reduces business expenses and processes run faster since it manages settlements directly plus records all system activities. Significant insurance companies like B3i, IBM, Swiss Re and Etherisc test blockchain-based reinsurance systems in their projects. Bitcoin offers benefits to the industry but users encounter three main difficulties due to unclear rules regarding transactions, several control limitations, and problems with integrating legacy systems. Through scientific data we learn that Distributed Ledger Technology provides dependable partnership programs to reinsurance through a trusted central platform. With fraudresistant technology from blockchain systems the reinsurance and retrocession industry will develop better protection at lower prices and improved trustworthiness.

Epidemiological aspects of Parkinson's disease (PD), co-occurring diseases and medical healthcare utilization of PD patients are still largely elusive. Based on claims data of 3.7 million statutory insurance members in Germany in 2015 the prevalence and incidence of PD was determined. PD cases had at least one main hospital discharge diagnosis of PD, or one physician diagnosis confirmed by a subsequent or independent diagnosis or by PD medication in 2015. Prevalence of (co-)occurring diseases, mortality, and healthcare measures in PD cases and matched controls were compared. In 2015, 21,714 prevalent PD cases (standardized prevalence: 511.4/100,000 persons) and 3,541 incident PD cases (standardized incidence: 84.1/100,000 persons) were identified. Prevalence of several (co-)occurring diseases/complications, e.g., dementia (PD/controls: 39/13%), depression (45/22%), bladder dysfunction (46/22%), and diabetes (35/31%), as well as mortality (10.7/5.8%) differed between PD cases and controls. The annual healthcare utilization was increased in PD cases compared to controls, e.g., regarding mean ± SD physician contacts (15.2 ± 7.6/12.2 ± 7.3), hospitalizations (1.3 ± 1.8/0.7 ± 1.4), drug prescriptions (overall: 37.7 ± 24.2/21.7 ± 19.6; anti-PD medication: 7.4 ± 7.4/0.1 ± 0.7), assistive/therapeutic devices (47/30%), and therapeutic remedies (57/16%). The standardized prevalence and incidence of PD in Germany as well as mortality in PD may be substantially higher than reported previously. While frequently diagnosed with co-occurring diseases/complications, such as dementia, depression, bladder dysfunction and diabetes, the degree of healthcare utilization shows Parkinson's Disease Epidemiology and Healthcare in Germany large variability between PD patients. These findings encourage a rethinking of the epidemiology and healthcare utilization in PD, at least in Germany. Longitudinal studies of insurance claims data should further investigate the individual and epidemiological progression and healthcare demands in PD.

Epidemiological aspects of Parkinson's disease (PD), co-occurring diseases and medical healthcare utilization of PD patients are still largely elusive. Based on claims data of 3.7 million statutory insurance members in Germany in 2015 the prevalence and incidence of PD was determined. PD cases had at least one main hospital discharge diagnosis of PD, or one physician diagnosis confirmed by a subsequent or independent diagnosis or by PD medication in 2015. Prevalence of (co-)occurring diseases, mortality, and healthcare measures in PD cases and matched controls were compared. In 2015, 21,714 prevalent PD cases (standardized prevalence: 511.4/100,000 persons) and 3,541 incident PD cases (standardized incidence: 84.1/100,000 persons) were identified. Prevalence of several (co-)occurring diseases/complications, e.g., dementia (PD/controls: 39/13%), depression (45/22%), bladder dysfunction (46/22%), and diabetes (35/31%), as well as mortality (10.7/5.8%) differed between PD cases and controls. The annual healthcare utilization was increased in PD cases compared to controls, e.g., regarding mean ± SD physician contacts (15.2 ± 7.6/12.2 ± 7.3), hospitalizations (1.3 ± 1.8/0.7 ± 1.4), drug prescriptions (overall: 37.7 ± 24.2/21.7 ± 19.6; anti-PD medication: 7.4 ± 7.4/0.1 ± 0.7), assistive/therapeutic devices (47/30%), and therapeutic remedies (57/16%). The standardized prevalence and incidence of PD in Germany as well as mortality in PD may be substantially higher than reported previously. While frequently diagnosed with co-occurring diseases/complications, such as dementia, depression, bladder dysfunction and diabetes, the degree of healthcare utilization shows Parkinson's Disease Epidemiology and Healthcare in Germany large variability between PD patients. These findings encourage a rethinking of the epidemiology and healthcare utilization in PD, at least in Germany. Longitudinal studies of insurance claims data should further investigate the individual and epidemiological progression and healthcare demands in PD.

Epidemiological aspects of Parkinson's disease (PD), co-occurring diseases and medical healthcare utilization of PD patients are still largely elusive. Based on claims data of 3.7 million statutory insurance members in Germany in 2015 the prevalence and incidence of PD was determined. PD cases had at least one main hospital discharge diagnosis of PD, or one physician diagnosis confirmed by a subsequent or independent diagnosis or by PD medication in 2015. Prevalence of (co-)occurring diseases, mortality, and healthcare measures in PD cases and matched controls were compared. In 2015, 21,714 prevalent PD cases (standardized prevalence: 511.4/100,000 persons) and 3,541 incident PD cases (standardized incidence: 84.1/100,000 persons) were identified. Prevalence of several (co-)occurring diseases/complications, e.g., dementia (PD/controls: 39/13%), depression (45/22%), bladder dysfunction (46/22%), and diabetes (35/31%), as well as mortality (10.7/5.8%) differed between PD cases and controls. The annual healthcare utilization was increased in PD cases compared to controls, e.g., regarding mean ± SD physician contacts (15.2 ± 7.6/12.2 ± 7.3), hospitalizations (1.3 ± 1.8/0.7 ± 1.4), drug prescriptions (overall: 37.7 ± 24.2/21.7 ± 19.6; anti-PD medication: 7.4 ± 7.4/0.1 ± 0.7), assistive/therapeutic devices (47/30%), and therapeutic remedies (57/16%). The standardized prevalence and incidence of PD in Germany as well as mortality in PD may be substantially higher than reported previously. While frequently diagnosed with co-occurring diseases/complications, such as dementia, depression, bladder dysfunction and diabetes, the degree of healthcare utilization shows Parkinson's Disease Epidemiology and Healthcare in Germany large variability between PD patients. These findings encourage a rethinking of the epidemiology and healthcare utilization in PD, at least in Germany. Longitudinal studies of insurance claims data should further investigate the individual and epidemiological progression and healthcare demands in PD.

The new stage of the scientific and technological revolution has led to the development of all industries that provide and serve any sphere of human life. At the same time, manufacturing enterprises are the biggest polluters of the environment. Therefore, finding ways to improve resource efficiency is an important part of improving all economic systems, especially industrial cities. The industrial development of the world economy, which accelerated during the twentieth and early twenty-first century, has largely identified modern global contradictions, when the rapid development of one area has led to problems in another-environmentalone. So there was a need to addressthe effect of the STP on restoration and preservation of the environment, which created the basis for the formation of the seventeen goals of sustainable development by The United Nations for all countries in the coming decades. Ensuring environmental responsibility is associated with defining the goals of sustainable environmental development and a set of measures to achieve them.The Sustainable Development Goals are a global benchmark that every country must achieve by the governments, citizens, entrepreneurship and business. The main institutions involved in the process of forming the strategy of energy supply of cities on the basis of sustainable development are the state, local governments, municipal enterprises. Their interaction, despite the differences in the interests of different groups of stakeholders, is possible if the needs and interests of consumers are met and a regular dissemination of reliable information and active interaction between public authorities and local governments as well as municipal enterprises and civil society institutions is present [1]. The sustainable development goals for energy supply in industrial cities should help reduce the negative impact of pollutants, including the urban environment; reducing the volume of waste generation and increasing the volume of their processing and reuse; ensuring sustainable use of chemicals through the use of innovative technologies in enterprises [2].The dynamics of the number of cities in which the average daily maximum permissible concentrations are exceeded. Furthermore, the dynamics of the emissions indicate a rather low level of innovation and technology in industrial enterprises to protect and restore the country's environment.The number of these cities is growing.For example, in 2020 in 25 cities of Ukraine exceedances of nitrogen dioxide, formaldehyde, ammonia, nitric oxide, carbon monoxide and other substances were recorded. Achieving this goal is possible through the use of new means and technologies of production, training of personnel in the mining and processing industries; a decrease in resource intensity indicates a decrease in resource costs and increase production efficiency.In addition, ensuring the preservation of cultural and natural heritage with the involvement of the private sector is also an important aspect in achieving sustainable development. Thus, the incorporation of the principles of sustainable development for environmental protection in the activities of domestic enterprises, which forms the overall level of pollution, being the most noticeable for the ecology of industrial cities, should occur at all levels of organization with a clear division of roles and responsibilities.In addition, thereisa need to invest in new technologies with less useof energy or better cleaning.

This edition of our insurance law series discusses the analysis of insurance contracts. Insurance is a special kind of contract. The document which embodies the insurance contract is known as the policy. This chapter reviews the component parts of a typical insurance policy.

This study assessed the contribution of fire insurance to the Nigerian economy from 1990 to 2020. It was aimed at examining the effect of fire insurance premium income and claim expenditure on the Nigerian gross domestic product. The study was motivated by the incessant fires in Nigeria which has resulted in an increase in fire insurance premium income in recent times. The research design for the study was ex post facto, while the study population was made up of all insurance companies providing fire insurance cover as of 2020. Data were obtained from Nigeria Insurance Digest, Central Bank of Nigeria statistical bulletin and the National Bureau of Statistics for the study period. Analysis of data was carried out using cointegration and ordinary least square regression. A long-run relationship between fire insurance premium income, claim expenditure and GDP was established while a positive significant relationship was observed between fire insurance premiums and GDP. Claim expenditure had an insignificant relationship with GDP. This study implies that fire insurance serves as a risk diversification tool that has had a positive impact on the Nigerian economy. It is concluded that fire insurance is of importance and its pull of premiums and financial intermediation role boosts economic activities in the country. It is recommended that fire insurance should be encouraged among individuals and businesses in Nigeria.

This study examined the socioeconomic determinants of life assurance purchase in Nigeria. It explored the effect of education, occupation, level of income, and price of life insurance policies on purchasing life insurance products. The study employed a survey research design. The study population was the civil servants in the Enugu State Government ministry, while a convenient sampling technique was used to select 240 civil servants who participated in the study. A suitable sampling technique was used to collect data from the selected respondents (civil servants) from the Enugu State Government Ministries (ESGMs). Linear regression analysis was employed to validate the research hypothesis at a 0.05 significant level. The statistical result showed that education and occupation have 39% and 40.5% respective influence on the purchase of life assurance products in Nigeria. The analysis also revealed that income level and premium (price) have 39.9% and 55.6% influence on the purchase of life assurance products respectively. The most important finding of this study was that socioeconomic variables such as education, occupation, income level and the prices (premium) of life assurance policies are significant determinants of life assurance products purchase in Nigeria. Therefore, it was recommended that the regulatory bodies and stakeholders should consider introducing insurance education in schools, radio and televisions stations where the significance and mechanisms of life assurance can be discussed. That life assurance sellers should focus on creating special products for people whose jobs present greater than average risks. It was equally recommended that occupation, income and prices of life assurance products should be factors in product design and sales since they are all significant determinants of life assurance purchase. The study contributes to knowledge by establishing a significant relationship between education, occupation, income and price (premium) with the purchase of life assurance policies.

Background: The performance of a sample of manufacturing enterprises was measured in terms of return on assets, and this study looked at the impact of exposure to credit risk, liquidity risk, and interest rate exposure. Objective: The aim of this study is to examine how risk exposure affects the productivity of manufacturing companies in Nigeria. The specific objectives of this study are to examine the effect of credit risk exposure on return on asset of manufacturing firms, investigate the effect of liquidity risk exposure on return on asset of manufacturing firms and assess the effect of interest risk exposure on return on asset of manufacturing firms in Nigeria Method: This study employs inferential and descriptive analysis, using metrics like mean, minimum and maximum value, kurtosis, skewness, and Jacque bera for descriptive analysis. Inferential analytic approaches include correlation analysis and panel regression estimation. Panel regression estimation was chosen to determine if heterogeneity affects the influence of risk exposure on performance, a feature not found in previous studies. Results: Results showed that exposure to credit risk and liquidity risk positively affected return on assets, while interest rate risk exposure had a negative effect. However, credit risk exposure was the only factor significantly affecting the return on assets of the chosen enterprises. The study concluded that manufacturing firms' exposure to risk, particularly credit risk exposure, significantly affects performance. Conclusion: To improve performance, the study suggested that manufacturing enterprises should be exposed to a certain level of risk related to debtors and creditors.

As datasets increase radically in size, highly scalable algorithms leveraging modern distributed infrastructures need to be developed for detecting outliers in massive datasets. In this work, we present the first distributed distance-based outlier detection approach using the MapReduce-based infrastructure, called DOD. DOD features a single-pass execution framework that minimizes communication overhead. Furthermore, DOD overturns two fundamental assumptions widely adopted in the distributed analytics literature, namely cardinality-based load balancing and one algorithm for all data. The multi-tactic strategy of DOD achieves a truly balanced workload by taking into account the data characteristics in data partitioning and assigns most appropriate algorithm for each partition based on our theoretical cost models established for distinct classes of detection algorithms. Thus, DOD effectively minimizes the end-to-end execution time. Our experimental study confirms the efficiency of DOD and its scalability to terabytes of data, beating the baseline solutions by a factor of 20x.

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. However, most of the existing studies have a shortage of real data for analysis and focus on a very limited version of the problem by covering only a specific actor, healthcare service, or disease. The purpose of this study is to implement and evaluate a novel framework to detect fraudulent and abusive cases independently from the actors and commodities involved in the claims and an extensible structure to introduce new fraud and abuse types. Interactive machine learning that allows incorporating expert knowledge in an unsupervised setting is utilized to detect fraud and abusive cases in healthcare. In order to increase the accuracy of the framework, several well-known methods are utilized, such as the pairwise comparison method of analytic hierarchical processing (AHP) for weighting the actors and attributes, expectation maximization (EM) for clustering similar actors, two-stage data warehousing for proactive risk calculations, visualization tools for effective analyzing, and z-score and standardization in order to calculate the risks. The experts are involved in all phases of the study and produce six different abnormal behavior types using storyboards. The proposed framework is evaluated with real-life data for six different abnormal behavior types for prescriptions by covering all relevant actors and commodities. The Area Under the Curve (AUC) values are presented for each experiment. Moreover, a cost-saving model is also presented. The developed framework, i.e., the eFAD suite, is actor-and commodity-independent, configurable (i.e., easily adaptable in the dynamic environment of fraud and abusive behaviors), and effectively handles the fragmented nature of abnormal behaviors. The proposed framework combines both proactive and retrospective analysis with an enhanced visualization tool that significantly reduces the time requirements for the fact-finding process after the eFAD detects risky claims. This system is utilized by a company to produce monthly reports that include abnormal behaviors to be evaluated by the insurance company.

Insurance plays an essential role in stimulating economic growth. Insurance is an intangible product, and prompt claim settlement proves that insurers fulfill their promises to the insureds. This paper analyses the impacts of insurance claims settlement on economic growth. It examines the effect of insurance claims' settlements on a nation's economic growth, using Nigeria as a case study. The research utilised an ex-post facto design, using 28-year time series data (1992 – 2019). Gross Domestic Product (GDP) and Nigeria's insurance companies' claims settlement are the dependent and independent variables used for the study, respectively. The Long-run co-integration result revealed that INCLM (Insurance claims) has an insignificant negative effect on GDP. The coefficient shows that a percentage increment in INCLM (Insurance claims) would result in a 1.22 decrease in GDP. The results indicate that insurance claims settlement has an insignificant negative effect on econo...

The statement above suggests that in Nigeria, Insurers are using claims payment as a competitive tool, particularly when premium rates are comparable. This implies that Insurers may seek to differentiate themselves by emphasizing efficient and reliable claims processing to attract and retain customers. It underscores the significance of a prompt and fair claims settlement process as  key factor influencing consumer choice in a competitive insurance market.

Resilience to withstand the destructive impact of the external environment and modern challenges contributes to ensure the economic security of the company. The consequences of the pandemic, military actions in Ukraine, rising inflation, and the transformation of geopolitical processes in the European Union have negatively affected the economic security of insurance companies in Slovakia. The purpose 10 of the study is to assess the economic security of Slovak insurance companies. The study is based on the analysis of profitability, assets and liabilities, premiums, claims and expenses of insurance companies in Slovakia. The forecasting methodology is applied and various scenarios of further functioning of insurance companies in Slovakia, proposed by the National Bank of Slovakia, are analyzed. Insurance companies in Slovakia have sufficient capital, losses in the forecast period can be covered by current profits, and the growth of insurance claims can be transferred to reinsurers. The study found that insurance companies in Slovakia to ensure stability and economic security, and have prospects for further operation and development.

Purpose: With this study, the authors aim to design a measure to determine the effectiveness of having Forensic Auditors in the Insurance Business. Moreover, in doing this to determine whether this measure is affected by different demographic variables. Design/Methodology: To prove the effectiveness of forensic auditors in the insurance industry, the authors designed a questionnaire using criteria of effectiveness established by Bond et al. (2011) which was completed by 402 valid participants. The criteria revolved around having procedural, substantive, transactive and normative effectiveness. Findings: Results show that engaging forensic auditors in the insurance industry are effective. Forensic auditors are needed in the claims process of insurance firms, and possibly, they should further be engaged in the risk management team of an insurance company. However, with regards to determining the compensation amount, respondents showed a lack of trust, and prefer if forensic auditors w...

Govoreći o uredskom poslovanju i njegovu razvoju na području Hrvatske od 1945. do 1958. godine treba odmah napomenuti da sagledavanje toga razvoja nije moguće izvan konteksta FNRJ, jer su u vrijeme centralizirane uprave i unitarnoga sagledavanja društvenih pitanja srodna obilježja i podjednaka razvojna dinamika uredskoga poslovanja prisutni na čitavom jugoslavenskom prostoru. S druge strane upravo zbog manjka objavljenih tekstova koji bi se odnosili isključivo na Hrvatsku primjeri iz drugih republika mogu poslužiti kao dobra ilustracija stanja i razvoja te djelatnosti i na našem području, jer korespondiraju s našim neposrednim iskustvima nastalima u obradi i tijekom uporabe gradiva toga doba. brought to you by CORE View metadata, citation and similar papers at core.ac.uk 2 Postupci su: primanje i pregled pošte, signiranje i uvođenje spisa u evidencije, dostava u rad, tehničkoadministrativna obrada, otpremanje pošte, razvođenje spisa, njihova pohrana u pismohrani (tzv. arhivu), a alati koji se pritom rabe jesu: popisi radnih jedinica i njihovih oznaka, oznake pismohrane, tj. registraturne oznake (registraturni nacrti) ili pak klasifikacijski sustavi.

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical toolssuch as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet 'count' 'count if' and 'filtering' functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

INTRODUZIONE: gli infortuni lavorativi in Italia sono censiti
principalmente da Inail; le statistiche riportano il numero di
eventi suddiviso per tipo di esito (temporaneo, permanente,
mortale). I dati pre-2001 includono i casi mortali solo se indennizzati
a superstiti (tipicamente coniuge, figli).
OBIETTIVI: stimare il numero di infortuni mortali senza superstiti
occorsi a lavoratori, assenti nelle statistiche Inail
1951-2000; valutare gli infortuni lavorativi italiani del periodo
1951-2017, con ogni tipo di esito, nei termini degli indicatori
dello studio Global Burden of Disease (GBD): years
lived with disability (YLD) years of life lost (YLL), disabilityadjusted
life years (DALY), per consentire l’aggregazione e il
paragone di eventi con esiti diversi e degli infortuni con altre
patologie.
DISEGNO: valutazione della stabilità del rapporto tra infortuni
mortali con e senza superstiti del periodo osservabile
(2001-2017); applicazione retrospettiva del rapporto ai dati
1951-2000. Riconduzione dei dati Inail individuali alle categorie
di lesione GBD; calcolo dei corrispondenti YLL, YLD,
DALY; determinazione dei parametri medi per tipo di evento;
applicazione ai dati italiani 1951-2017.
SETTING E PARTECIPANTI: infortuni lavorativi riconosciuti
da Inail in Italia nel periodo 1951-2017.
PRINCIPALI MISURE DI OUTCOME: numero di infortuni,
YLD, YLL, DALY.
RISULTATI: gli infortuni mortali di lavoratori senza superstiti,
assenti nelle statistiche nel periodo 1951-2000, sono stimati
in 26.218 casi, corrispondenti a circa 1,4 milioni di YLL.
Le conseguenze totali degli infortuni (temporanei, permanenti,
mortali con e senza superstiti) di tale periodo assommano
a 11,5 milioni di DALY, di cui la componente mortale
è 5,1 milioni di YLL.
CONCLUSIONI: il numero totale stimato di infortuni lavorativi
mortali del cinquantennio 1951-2000 corrisponde a una
rivalutazione del 28% circa rispetto ai dati censuari. In termini
di YLL, la rivalutazione raggiunge il 37%, per la maggiore
aspettativa di vita dei lavoratori senza superstiti, tipicamente
più giovani. L’aumento riferito ai DALY complessivi è 14%.
Lungo l’intero periodo 1951-2017, si osserva una riduzione
del danno e un suo cambiamento qualitativo, con contrazione
dal 55% al 30% circa della componente dovuta alla mortalità
(YLL/DALY).
Parole chiave: infortuni sul lavoro, carico di infortuni, DALY, Inail

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical toolssuch as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet 'count' 'count if' and 'filtering' functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

Insurance plays an essential role in stimulating economic growth. Insurance is an intangible product, and prompt claim settlement proves that insurers fulfill their promises to the insureds. This paper analyses the impacts of insurance claims settlement on economic growth. It examines the effect of insurance claims' settlements on a nation's economic growth, using Nigeria as a case study. The research utilised an ex-post facto design, using 28-year time series data (1992-2019). Gross Domestic Product (GDP) and Nigeria's insurance companies' claims settlement are the dependent and independent variables used for the study, respectively. The Long-run co-integration result revealed that INCLM (Insurance claims) has an insignificant negative effect on GDP. The coefficient shows that a percentage increment in INCLM (Insurance claims) would result in a 1.22 decrease in GDP. The results indicate that insurance claims settlement has an insignificant negative effect on economic growth. This implies a negative relationship between insurance claims settled by insurance companies and economic growth in Nigeria. The finding is surprising as one expects that settlement of claims by insurers should positively impact economic growth. The implication is that the relationship between insurance claims settlement and economic growth varies depending on several factors, including country-specific factors and the performance of the country's insurance industry.

Extreme value theory is applied to model extreme occurrences, and it is applied in business and finance to measure tail risk. Marine and aviation class of insurance business is an important component of non-life business because it insures all risks relating to aviation and marine vessels which play a significant role in the economic development of Nigeria. Recent claims experience has necessitated the investigation of tail risks with small probability of occurrence but with high potential impact on company's survival. The study employs the Extreme Value Theory (EVT) to estimate the minimum expected claims for the marine and aviation insurance business using historical claims data. Diagnostics plot like the mean excess plot suggest the threshold to chosen, to fit Generalized Pareto model based on EVT and the excess distributions were obtained over a chosen threshold. Linear Q-Q plots and tail plots reveal that parametric model fits the data well. VaR estimate was finally obtained using the extreme value method at 5% confidence interval and the empirical results show that Extreme VaR is most suitable to calculate VaR as against the Historical and Gaussian methods. This will guide proper underwriting process and loss reserving in this class of business.

Over the last 11 years, the Law Commission and the Scottish Law Commission have worked on a joint project to modernise the law of insurance contracts. Due to the size of the project, the Law Commissions proceeded in phases and separated out specific issues for legislative reform. Their proposals have already resulted in the Consumer Insurance (Disclosure and Representations) Act 2012 and the Insurance Act 2015 which brought about significant changes for consumer and non-consumer insureds and insurers alike. This paper examines two further areas of reform: the introduction of an implied term about payment of insurance claims by insurers within a reasonable time and a statutory restatement of the doctrine of insurable interest. It considers the old and new substantive law and provides an insight into the reform process.

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the Network Explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand. The system has been deployed on multiple sites and datasets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month.

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In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical toolssuch as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet 'count' 'count if' and 'filtering' functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

A cross-sectional exploratory survey on occurrence of triple-whammy prescription pattern in Japan ABSTRACT Background: The concurrent use of nonsteroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system blockers, and diuretics, known as a "triple-whammy," is related to the occurrence of acute kidney injury. However, there are few reports regarding the prescription pattern of the triple-whammy. Objective: To elucidate the patterns of the triple-whammy prescription in Japan. Methods: A cross-sectional study was performed using a health-insurance-claims database that included Japanese people under 75 years of age, and enrolled outpatients that were prescribed any nonsteroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system blockers, and diuretics between April 2017 and June 2017. As an outcome, the proportion of triple-whammy prescriptions was evaluated. Among the patients who received triple-whammy prescriptions, we evaluated the prevalence of chronic kidney disease and the proportion of prescriptions provided for these three drugs from different clinical departments and institutions. Results: Overall, 730 of 246,721 (0.3%) patients received triple-whammy prescriptions. Among these patients, 13.3% had underlying chronic kidney disease. The proportions of any of the three drug types prescribed by different clinical departments and institutions was 48.2% and 61.8%, respectively. Conclusions: We examined the patterns of triple-whammy prescriptions and concluded that pharmacists need to pay attention to triple-whammy prescriptions if the prescriptions are provided by multiple clinical departments or institutions.

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical toolssuch as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet 'count' 'count if' and 'filtering' functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient's challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent claims in the health insurance databases. The experimental results have proven that the MR-ISVM classifier outperforms better in classification and detection than other support vector machine (SVM) kernel classifiers. From the results, a positive impact seen in declining the computational time on processing the healthcare insurance claims without compromising the classification accuracy is achieved. The proposed MR-ISVM classifier achieves 87.73% accuracy than the linear (75.3%) and radial basis function (79.98%).

Purpose: With this study, the authors aim to design a measure to determine the effectiveness of having Forensic Auditors in the Insurance Business. Moreover, in doing this to determine whether this measure is affected by different demographic variables. Design/Methodology: To prove the effectiveness of forensic auditors in the insurance industry, the authors designed a questionnaire using criteria of effectiveness established by Bond et al. (2011) which was completed by 402 valid participants. The criteria revolved around having procedural, substantive, transactive and normative effectiveness. Findings: Results show that engaging forensic auditors in the insurance industry are effective. Forensic auditors are needed in the claims process of insurance firms, and possibly, they should further be engaged in the risk management team of an insurance company. However, with regards to determining the compensation amount, respondents showed a lack of trust, and prefer if forensic auditors w...

Purpose: With this study, the authors aim to design a measure to determine the effectiveness of having Forensic Auditors in the Insurance Business. Moreover, in doing this to determine whether this measure is affected by different demographic variables. Design/Methodology: To prove the effectiveness of forensic auditors in the insurance industry, the authors designed a questionnaire using criteria of effectiveness established by Bond et al. (2011) which was completed by 402 valid participants. The criteria revolved around having procedural, substantive, transactive and normative effectiveness. Findings: Results show that engaging forensic auditors in the insurance industry are effective. Forensic auditors are needed in the claims process of insurance firms, and possibly, they should further be engaged in the risk management team of an insurance company. However, with regards to determining the compensation amount, respondents showed a lack of trust, and prefer if forensic auditors w...

Extreme value theory is applied to model extreme occurrences, and it is applied in business and finance to measure tail risk. Marine and aviation class of insurance business is an important component of non-life business because it insures all risks relating to aviation and marine vessels which play a significant role in the economic development of Nigeria. Recent claims experience has necessitated the investigation of tail risks with small probability of occurrence but with high potential impact on company's survival. The study employs the Extreme Value Theory (EVT) to estimate the minimum expected claims for the marine and aviation insurance business using historical claims data. Diagnostics plot like the mean excess plot suggest the threshold to chosen, to fit Generalized Pareto model based on EVT and the excess distributions were obtained over a chosen threshold. Linear Q-Q plots and tail plots reveal that parametric model fits the data well. VaR estimate was finally obtained using the extreme value method at 5% confidence interval and the empirical results show that Extreme VaR is most suitable to calculate VaR as against the Historical and Gaussian methods. This will guide proper underwriting process and loss reserving in this class of business.

Extreme value theory is applied to model extreme occurrences, and it is applied in business and finance to measure tail risk. Marine and aviation class of insurance business is an important component of non-life business because it insures all risks relating to aviation and marine vessels which play a significant role in the economic development of Nigeria. Recent claims experience has necessitated the investigation of tail risks with small probability of occurrence but with high potential impact on company's survival. The study employs the Extreme Value Theory (EVT) to estimate the minimum expected claims for the marine and aviation insurance business using historical claims data. Diagnostics plot like the mean excess plot suggest the threshold to chosen, to fit Generalized Pareto model based on EVT and the excess distributions were obtained over a chosen threshold. Linear Q-Q plots and tail plots reveal that parametric model fits the data well. VaR estimate was finally obtained using the extreme value method at 5% confidence interval and the empirical results show that Extreme VaR is most suitable to calculate VaR as against the Historical and Gaussian methods. This will guide proper underwriting process and loss reserving in this class of business.

The ability to mine relational data has become important in several domains (e.g., counter-terrorism), and a graph-based representation of this data has proven useful in detecting various relational, structural patterns [1]. Here, we analyze the use of graph properties as a method for uncovering anomalies in data represented as a graph.