Segmentation of life insurance customers based on their profile using fuzzy clustering

In the current competitive environment, companies will be able to adjust business strategies, they use market segmentation based on practical ways rather than using traditional approaches or incomplete and impractical mass marketing. In recent years, mining has gained attention and popularity in the business world. The goal of data mining projects is to convert the raw data into useful information. Clustering can also be used to explore differences in attitudes and intentions of the clients. In this study, we used fuzzy clustering on 1071 life insurance customers during March to October 2014. Results show that the optimal number of clusters was 2 which were named as "investment" and "life safety". Some suggestions are presented to improve the performance of the insurance company

DETERMINAN KECURANGAN LAPORAN KEUANGAN (PERSPEKTIF FRAUD HEXAGON THEORY)

The purpose of this study is to determine the effect of the fraud hexagon theory on financial statement fraud. The variables used are external pressure, financial target, capability, collusion, nature of industry, rationalization, and arrogance. The population is healthcare sector companies listed on the IDX from 2016-2019. The sampling technique used purposive sampling and obtained 13 companies with 52 analysis units. The data analysis technique uses multiple linear regression. The results of this study show that variables of external pressure, financial target, capability, collusion, rationalization, and arrogance are not able to affect fraudulent financial statements. In contrast, the nature of industry variables has a negative effect on financial statement fraud. The nature of the industry, which is reflected by the ratio of changes in receivables, gives the result that the increase in receivables that occurs as a result of the increase in sales does not make the company commit fraudulent financial statements. Otherwise, the increase in receivables can be used as an opportunity to attract investors and compete with competitors. The increase in receivables does not cause the amount of cash to be reduced and is still considered sufficient for the company's operations, so the risk of fraud is lower.Penelitian ini memiliki tujuan untuk mengetahui pengaruh fraud hexagon theory terhadap kecurangan laporan keuangan. Variabel yang digunakan meliputi tekanan eksternal, target keuangan, kemampuan, kolusi, nature of industry, rasionalisasi, dan arogansi. Populasi penelitian ini adalah perusahaan sektor kesehatan di BEI selama tahun 2016–2019. Teknik pengambilan sampel menggunakan purposive sampling dan diperoleh 13 perusahaan dengan 52 unit analisis. Pengujian data dilakukan menggunakan metode regresi linier berganda. Hasil penelitian menunjukkan bahwa variabel tekanan eksternal, target keuangan, kemampuan, kolusi, rasionalisasi, dan arogansi tidak mampu memengaruhi kecurangan laporan keuangan. Namun, variabel nature of industry berpengaruh negatif terhadap kecurangan laporan keuangan. Nature of industry yang direfleksikan dengan rasio perubahan piutang memberikan hasil bahwa kenaikan piutang yang terjadi sebagai akibat dari kenaikan penjualan tidak membuat perusahaan untuk melakukan kecurangan laporan keuangan. Sebaliknya, kenaikan piutang tersebut dapat dijadikan peluang untuk menarik investor dan bersaing dengan kompetitor. Kenaikan piutang tidak menyebabkan jumlah kas menjadi berkurang dan masih dianggap cukup untuk operasional perusahaan, sehingga risiko kecurangan semakin rendah

FLAME: trusted fire brigade service and insurance claim system using blockchain for enterprises

Smart fire detection and insurance systems have gained considerable attention from researchers and industries. At the same time, automatic requests for fire brigade services to cure fire and instant claims settlement to defend insurance fraud are lacking in literatures. We propose a trusted fire brigade service and insurance claim (FLAME) framework using blockchain for enterprises to provide immediate fire brigade services and prevent insurance frauds. A system model is presented to explain architecture, and overall functionality of FLAME using blockchain. Further, a sensing network and connectivity model is proposed to detect true fire and send an emergency service request to monitoring station. Smart contracts are designed to automate fire brigade service and insurance claim processes. A prototype of the FLAME is implemented on Hyperledger Besu blockchain using Istanbul Byzantine Fault Tolerance 2.0 consensus protocol. Simulation results show that latency and throughput of the FLAME are better compared to state-of-the-art models

DETERMINAN KECURANGAN LAPORAN KEUANGAN (PERSPEKTIF FRAUD HEXAGON THEORY)

Penelitian ini memiliki tujuan untuk mengetahui pengaruh fraud hexagon theory terhadap kecurangan laporan keuangan. Variabel yang digunakan meliputi tekanan eksternal, target keuangan, kemampuan, kolusi, nature of industry, rasionalisasi, dan arogansi. Populasi penelitian ini adalah perusahaan sektor kesehatan di BEI selama tahun 2016–2019. Teknik pengambilan sampel menggunakan purposive sampling dan diperoleh 13 perusahaan dengan 52 unit analisis. Pengujian data dilakukan menggunakan metode regresi linier berganda. Hasil penelitian menunjukkan bahwa variabel tekanan eksternal, target keuangan, kemampuan, kolusi, rasionalisasi, dan arogansi tidak mampu memengaruhi kecurangan laporan keuangan. Namun, variabel nature of industry berpengaruh negatif terhadap kecurangan laporan keuangan. Nature of industry yang direfleksikan dengan rasio perubahan piutang memberikan hasil bahwa kenaikan piutang yang terjadi sebagai akibat dari kenaikan penjualan tidak membuat perusahaan untuk melakukan kecurangan laporan keuangan. Sebaliknya, kenaikan piutang tersebut dapat dijadikan peluang untuk menarik investor dan bersaing dengan kompetitor. Kenaikan piutang tidak menyebabkan jumlah kas menjadi berkurang dan masih dianggap cukup untuk operasional perusahaan, sehingga risiko kecurangan semakin rendah

Analysing of temporal and spatial data in life insurance

In this master’s thesis we present the development of the methodology for the analysis of insurance data. Due to the fact that insurance data are presented temporally and spatially, a special approach is necessary for the development of the methodology. For this purpose we use approaches of spatio-temporal data mining, which enable us the appropriate treatment of temporal and spatial attributes. When analysing data we limit ourselves on the data of personal insurance. Into the treatment we capture data on reports of the loss events in the field of accidents and diseases. Insurance data are linked to the data on weather conditions on the day of the report of the claim of compensation. By this linkage we wish to use the thesis on the influence of the weather on the accidents. We hope that in this way we shall predict the number of the claims of compensation and the average amount of the disbursed claims of compensation more easily. Firstly, we deal with the solving of the classification problem. We use some of the basic classification algorithms, but the level of successfulness of predicting in case of all algorithms proves to be extremely low. Due to the fact that the problems are by nature regression, we try to solve the regression problem too. Even regression algorithms do not offer much better results. We check the adequacy of training set time window. We get the confirmation that the time windows is selected appropriately with respect to the data. Furthermore, we check if the instances deal with the appropriate time scale. We come to the conclusion that the time scale is selected appropriately. We try to localize the problem: we divide the data. In different cases of the cut of Slovenia we come to the ascertainment that the estimate of the prediction for each of the local areas is worse than for the entire Slovenia. The set methodology proves to be partially useful. It can be used for predicting the number of accidents. By means of diagnostics we receive the confirmation that the failure is due to the smallness of the multitude of treated events. We give proposals regarding the possibility of improvements. We hope that the usefulness of the set methodology will become evident in the future. For the needs of the master’s thesis we also prepare the maps for following the accidents and diseases in Slovenia through time. On the basis of the maps we ascertain the trends for the future. We identify the most important factors, responsible for the emergence of the accidents. At the end we perform the analysis of the influence of the altitude for the emergence of the accidents

Analysing of temporal and spatial data in life insurance

In this master’s thesis we present the development of the methodology for the analysis of insurance data. Due to the fact that insurance data are presented temporally and spatially, a special approach is necessary for the development of the methodology. For this purpose we use approaches of spatio-temporal data mining, which enable us the appropriate treatment of temporal and spatial attributes. When analysing data we limit ourselves on the data of personal insurance. Into the treatment we capture data on reports of the loss events in the field of accidents and diseases. Insurance data are linked to the data on weather conditions on the day of the report of the claim of compensation. By this linkage we wish to use the thesis on the influence of the weather on the accidents. We hope that in this way we shall predict the number of the claims of compensation and the average amount of the disbursed claims of compensation more easily. Firstly, we deal with the solving of the classification problem. We use some of the basic classification algorithms, but the level of successfulness of predicting in case of all algorithms proves to be extremely low. Due to the fact that the problems are by nature regression, we try to solve the regression problem too. Even regression algorithms do not offer much better results. We check the adequacy of training set time window. We get the confirmation that the time windows is selected appropriately with respect to the data. Furthermore, we check if the instances deal with the appropriate time scale. We come to the conclusion that the time scale is selected appropriately. We try to localize the problem: we divide the data. In different cases of the cut of Slovenia we come to the ascertainment that the estimate of the prediction for each of the local areas is worse than for the entire Slovenia. The set methodology proves to be partially useful. It can be used for predicting the number of accidents. By means of diagnostics we receive the confirmation that the failure is due to the smallness of the multitude of treated events. We give proposals regarding the possibility of improvements. We hope that the usefulness of the set methodology will become evident in the future. For the needs of the master’s thesis we also prepare the maps for following the accidents and diseases in Slovenia through time. On the basis of the maps we ascertain the trends for the future. We identify the most important factors, responsible for the emergence of the accidents. At the end we perform the analysis of the influence of the altitude for the emergence of the accidents