مدلی برای تشخیص ادعاهای غیرعادی خسارت در بیمه کشاورزی با استفاده از یادگیری عمیق

موارد کلاهبرداری در سال‌های اخیر به ویژه در زمینه‌های مهم و حساس مالی و بیمه‌ای افزایش یافته است. از این رو، برای مقابله با این‌گونه کلاهبرداری‌ها نیاز به اقدامات متفاوتی نسبت به روش‌های بازرسی سنتی وجود دارد. بیمه کشاورزی نیز با توجه به ماهیت و گستردگی وسیع آن از این تهدید مستثنا نبوده و سالانه هزینه‌‌های زیادی صرف پرداخت به خسارت‌‌های ساختگی می‌شود. این پژوهش با هدف ارائه مدلی برای کشف ادعاهای خسارت غیرواقعی در بیمه کشاورزی با بکارگیری تکنیک‌های داده کاوی و یادگیری ماشین ارائه شد. برای ساخت مدل یادگیری عمیق مورد استفاده قرار گرفت. داده‌‌های مورد استفاده از صندوق بیمه کشاورزی اخذ شد و مربوط به بیمه‌‌نامه‌‌های گندم آبی و دیم استان خوزستان بود که در سال زراعی 1399 1398 برای آنها غرامت پرداخت شده بود. بعد از آماده‌‌سازی و پیش‌‌پردازش داده‌ها، با استفاده از یادگیری عمیق نسبت به کشف موارد غیرعادی اقدام و نتایج توسط کارشناسان صندوق بیمه کشاورزی مورد ارزیابی قرار گرفت. بعد از تحلیل نتایج مشخص شد یک درصد از خسارت‌‌های پرداختی مربوط به درخواست‌های غیرواقعی بوده و در پرداخت خسارت بایستی دقت و بررسی بیشتری انجام شود. دقت مدل در تشخیص موارد غیرعادی برای گندم آبی و دیم به ترتیب برابر با 53/53 و 63/37 درصد بدست آمد. در بررسی نتایج مشخص شد 5 دسته رفتار غیرعادی منجر به پرداخت خسارت غیرواقعی شده‌اند که رفتار عدم ارائه مستندات خسارت فراوانی بیشتری نسبت به بقیه داشت.

a model for detecting abnormal claims in crop insurance using deep learning

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