Data science, intelligence and future analysis
Yaqub Ahmadlou; Alireza pourebrahimi; jafar tanha; Ali Rajabzadeh Ghatari
Abstract
Fraud cases have increased in recent years, especially in important and sensitive financial and insurance fields. Therefore, to deal with such frauds, there is a need for different measures than traditional inspection methods. Agricultural insurance is also not exempted from this threat due to its nature ...
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Fraud cases have increased in recent years, especially in important and sensitive financial and insurance fields. Therefore, to deal with such frauds, there is a need for different measures than traditional inspection methods. Agricultural insurance is also not exempted from this threat due to its nature and wide extent and every year a lot of money is spent on paying fake damages. This research was presented with the aim of providing a model to discover unrealistic damage claims in agricultural insurance by using data mining and machine learning techniques. It was used to build a deep learning model. The data used was obtained from the Agricultural Insurance Fund and related to wet and rainfed wheat insurance policies of Khuzestan province, for which compensation was paid in the 2018-2019 crop year. After preparing and preprocessing the data, using deep learning to discover unusual cases, the action and results were evaluated by the experts of the Agricultural Insurance Fund. After analyzing the results, it was found that 1% of the damages paid were related to unrealistic requests and more care should be taken in paying the damages. The accuracy of the model in detecting unusual cases for wet and dry wheat was 53.53 and 63.37 percent, respectively. In the review of the results, it was found that 5 categories of unusual behavior have led to the payment of unrealistic damages, and the behavior of not providing damage documentation was more frequent than the others.IntroductionInsurance fraud refers to the immoral act of committing a crime with the intention of abusing an insurance policy to obtain illegal profit from an insurance company; In general, insurance is made to protect the assets and business of individuals or organizations against financial loss and may occur at any stage of the insurance process by anyone such as customers or fraudulent agents (Al -Hashedi & Magalingam, 2021). Insurance fraud not only reduces the profit of the insurance company and leads to major losses, but also affects the pricing strategy of the insurance company and its socio-economic benefits in the long term (Yaram, 2016). Every year, significant sums of money are defrauded from the insurance industry, but not all of them are discovered. According to the statistics published by the Insurance Anti-Fraud Coalition, an amount of about eighty billion dollars is added to customers' expenses in the United States through fraud, and they must compensate for the amount of fraud by paying higher insurance premiums in the following year (Fraud statistics, 2020). In Iran, there is no accurate estimate of the amount of compensations paid to unreal damage claims or any other fraud, and one of the goals of this research is to estimate the amount of fraud in wheat crop insurance using deep learning. Research Question(s)This research seeks to find answers to these questions: In rainfed and irrigated wheat crop insurance, what percentage of the paid compensations are related to unrealistic and fictitious damage claims, and what is the accuracy of deep learning detection for this purpose?Literature ReviewGhahari et al. (2019) in their study investigated the use of deep learning in predicting agricultural performance in time and space with unstable weather conditions. They compared the performance of machine learning next to weather stations with conventional methods. Their findings showed that deep learning provides the highest prediction accuracy compared to other approaches. It can also be inferred from this result that the use of deep learning can play a role in reducing agricultural insurance costs by knowing the exact measures of crop yield (Newlands et al., 2019). Gomez et al. (2021) presented a new deep learning method to gain pragmatic insight into the behavior of an insured individual using the unsupervised effective variable. Their proposed method can be used in the fields of pension insurance, investment and other broader areas of the insurance industry. Their proposed method enables auto encoder and variable auto encoder to be used in semi-supervised/unsupervised effective variable analysis to identify cheating agents (Gomes et al., 2021). Xia et al. (2022) in their study proposed a deep learning model to detect car insurance fraud by combining convolutional neural network, long-term and short-term memory, and deep neural network. In their proposed method, more abstract features were extracted and helped the experts in the complex process of feature extraction which is very critical in traditional machine learning algorithms. The results of the experiments showed that their method can effectively improve the accuracy of car insurance fraud detection.MethodologyThe current research method is practical from the point of view of the objective and is data-oriented from the point of view of its nature. For machine learning modeling, the standard CRISP process has been used, which includes the stages of data collection, data preparation and preprocessing, modeling and model evaluation, and obtaining results. Figure 1 shows the general process of anomaly detection and analysis.Figure 1. Anomaly detection process framework In this research, the data related to one agricultural year of wet and dry wheat crop were obtained from the Agricultural Insurance Fund. The national code of the insurers has been removed from the data set to maintain confidentiality. The extracted data is related to the crop insurance policies of wet and rainfed wheat for the crop year 2018-2019 of Khuzestan province. In this crop year, compensation has been paid for these insurance policies according to the claim of the damage they had, in other words, the data set includes those insurance policies of wet and dry wheat whose product is damage Seen and compensated for them. The data were obtained from the comprehensive system of the insurance fund in the form of a csv report. The obtained data set had 23 features.ConclusionThe results of the research show that in wheat insurance, about 1% of the compensations paid are allocated to unrealistic claims, so they need to be further investigated by experts before payment. This amount of compensations paid to unrealistic claims was close to the prediction of insurance fund inspection experts who stated that about 1.5% of claims are unrealistic. Also, according to the results, 5 categories of behavior or methods were identified in the beneficiaries to receive compensation for unrealistic claims, which are mentioned below:Lack of sufficient documentation to prove the damage: This means that the necessary documents that should be uploaded in the system according to the implementation methods are not available or some of them have not been uploaded. Payment of compensation without the existence of documents indicating the occurrence of damage can be caused by the negligence or collusion of the appraiser or broker with the insured.The documents are not in accordance with the declared damage: the documents uploaded in the system according to the relevant instructions do not show the occurrence of the type of registered damage. For example, the speed of storm damage is mentioned as 50 km/h, but in meteorological documents it is 15 km/h.The damage documentation is not true: for example, in some documents, the risk factor is mentioned in the expert form of drought, but the picture sent shows flood damage. In this case, it is probably due to negligence. In another possibility, it is also possible to send the image of damaged agricultural land instead of healthy agricultural land. Non-observance of the damage notification period: According to the executive instructions of the insurance fund, the time limit for the declaration of damage until the time of payment of compensation is one month. Outside of that, it is against the instructions. Sometimes it was observed that the damage had been declared before the accident. The date of damage does not match with the time of its announcement: according to the executive instructions of the insurance fund, in the case of damage to agriculture, the visit must be done one week after the occurrence of the damage; before removing the damage, the type and amount of the damage should be carefully checked. In some cases, it was observed that the announcement date was recorded one month after the damage occurred. It is clear that after removing the effects of damage, the payment of compensation can seem suspicious because there may not have been any damage in the past.Keywords: Anomaly Detection, Crop Insurance, Deep Learning, Auto Encoder.
Maryam Sadat Mazaheri; Changiz Valmohammadi; Alireza Pourebrahimi; Mahnaz Rabeei
Abstract
IntroductionNowadays, cloud computing has attracted the attention of many organizations. So many of them tend to make their business more agile by using flexible cloud services. Currently, the number of cloud service providers is increasing. In this regard, choosing the most suitable cloud service provider ...
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IntroductionNowadays, cloud computing has attracted the attention of many organizations. So many of them tend to make their business more agile by using flexible cloud services. Currently, the number of cloud service providers is increasing. In this regard, choosing the most suitable cloud service provider based on the criteria according to the conditions of the service consumer will be considered one of the most important challenges. Relying on previous studies and using a meta-synthesis approach, this research comprehensively searches past researches and provides a comprehensive framework of factors affecting the choice of cloud service providers including 4 main categories and 10 sub-areas. Then, using the opinions of experts who were selected purposefully and using the snowball method, and using the Lawshe validation method, the framework is finalized.Research Question(s)This research aims to complete the results of previous studies and answer the following questions with a systematic review of the subject literature:-What are the components of the comprehensive framework for choosing cloud service providers?-What are the effective criteria to choose a cloud service provider?-What is the selected framework of effective factors? Literature ReviewMany researchers have looked at the problem of choosing the best CSP from different aspects and have tried to provide a solution in this field. In this regard, we can refer to "Tang and Liu" (2015) who proposed a model called "FAGI" which defines the choice of a trusted CSP through four dimensions: security functions, auditability, management capability, and Interactivity helps. "Kong et al." (2013) presented an optimization algorithm based on graph theory to facilitate CSP selection. Some researchers have also provided a framework for CSP selection, such as "Gash" (2015) who provides a framework called "SelCSP" with the combination of trustworthiness and competence to estimate the risk of interaction. "Brendvall and Vidyarthi" (2014) suggest that in order to choose the best cloud service provider, a customer must first identify the indicators related to the level of service quality related to him and then evaluate different providers. Some researchers have focused on using different techniques for selection. For example: "Supraya et al." (2016) use the MCDM method to rank based on infrastructure parameters (agility, financial, efficiency, security, and ease of use). They investigate the mechanisms of cloud service recommender systems and divide them into four main categories and their techniques in four features of scalability, accessibility, accuracy, and trustIn this research, it has been tried to use the models and variables of the subject literature in developing a comprehensive framework. The codes, concepts, and categories related to the choice of cloud service providers are extracted from previous studies, and a comprehensive framework of the factors influencing the choice of cloud service providers is presented using the meta-composite method. MethodologyIn this research, based on the "Sandusky and Barroso" meta-composite qualitative research method, which is more general, a systematic review of the research literature was conducted, and the codes in the research literature were extracted. Then the codes, categories, and finally the proposed model are formed. The seven-step method of "Sandusky and Barroso" consists of: formulation of the research question, systematic review of the subject literature, search and selection of suitable articles, extraction of article information, analysis and synthesis of qualitative findings, quality control, and presentation of findings. Lawshe validation method has been used to validate the research findings. ResultsIn the meta-synthesis method, all the factors extracted from previous studies are considered as codes and concepts are obtained from the collection of these codes. Using the opinion of experts and considering the concept of each of these codes, codes with similar concepts were placed next to each other and new concepts were formed. This procedure was repeated in converting the concepts into categories and the proposed framework was identified. This framework consists of 27 codes, 10 concepts, and 4 categories (Table 1).Table 1: Codes, concepts, and categories extracted from the sourcescategoryConceptCodeNo.TrustSecurityHardware Security1Network Security2Software Security3Confidentiality4Control5Guarantee and AssuranceAccessibility6Stability7Facing ThreatsTechnical Risk8Center for Security Measures9TechnologyEfficiencyService Delivery Efficiency10Interactivity11Hardware and Network InfrastructureConfiguration and Change12Capacity (Memory, CPU, Disk)13Functionality Flexibility14Usability15Accuracy16Service Response Time17Ease of use18ManagerialMaintenanceEducation and Awareness19Customer Communication Channels20StrategicLegal Issues21Data Analysis22Service Level Agreement23CommercialCustomer SatisfactionResponsiveness24Customer Feedback25CostSubscription Fee26Implementation Cost27The lack of a common framework for evaluating cloud service providers is compounded by the fact that no two providers are the same, so that this issue complicates the process of choosing the right provider for each organization. Figure 1 shows the proposed comprehensive framework including 4 categories and 10 concepts covering the issue of choosing cloud service providers. These factors are useful in determining the provider that best matches the personal and organizational needs of the service recipient. The main categories are: trust building, technology, management, and business, which will be explained in the following.Figure 1: Cloud service provider selection framework 5- ConclusionBy comprehensively examining the factors affecting the choice, this research introduces specific areas such as trust building, technology, management, and business as the main areas of cloud service provider selection and add to the previous areas. The category of building trust between the customer, and the cloud service provider is of particular importance. In this research, the concepts related to trust building are: security (including hardware security, network security, software security, confidentiality and control), (availability, stability and stability), and facing threats (technical risk). In 36% of the articles, the concept of trust is mentioned, but in each study, only a limited number of factors affecting this category are discussed. This research takes a comprehensive look at the category of technology, the concepts of productivity (including service delivery efficiency, interactivity), hardware and network infrastructure (including configuration and repair, capacity (memory, processor, disk)), and performance (including flexibility, usability, accuracy of operation, service response time, ease of use). Considering the variety of services on different cloud platforms, service recipients must ensure that the provision of services is managed easily and in the shortest possible time by the cloud provider. The commercial aspect of service delivery deals with the two concepts of customer satisfaction (including responsiveness, customer feedback) and service rates (including: subscription cost and implementation cost), which are of interest to many businesses. The results of this research will help the decision makers of using the cloud space (both organizational managers and cloud customers) in choosing the best cloud service provider to have a comprehensive view of the effective factors before choosing and plan according to their needs.
