Document Type : Original Research Paper
Authors
1 PhD Student, Department of Computer Engineering, Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 Instructor, Department of Computer Engineering, Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 Assistant Professor, Department of Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
Abstract
BACKGROUND AND OBJECTIVES: The role of the insurance industry is changing nowadays. The reason is that companies are using new analytical methods to predict losses and risks, and these methods help them assess potential risks. In this era, traditional business models and old methods have always been under threat from technology. New insurance companies are using the power of innovative technologies to eliminate the traditional leaders of the insurance market. The protection provided to the insured against risks and the proposed solutions provided to deal with risks are obtained through services designed to identify potential risks, and these services can be used to warn of danger (in high-risk cases). As a result, these services will be the most important distinction of these companies and the key to their success in the future. Powerful artificial intelligence and analysis of large volumes of big data give insurers the power to move towards predicting losses and incidents. The more information insurance companies have about their policyholders, the better they can use these valuable data to predict policyholders’ behavior and create a historical profile for each individual, thereby reducing the volume of claims and associated risks. Insurance companies enjoying leverage innovative technologies have a significant opportunity for growth. However, those that continue to rely on basic questions such as age, gender, and occupation to determine premiums are unlikely to survive in the digital era and amidst the rise of insurtech. Insurers that fail to adopt predictive analytics and continue to use outdated traditional systems may experience longer delays in processing and paying claims compared to innovative companies. This gap will allow tech-driven insurers to attract more customers and cover a wider range of policyholders in the long term. Insurance data often contains nonlinear and complex relationships that simple models—such as linear regression or decision trees—cannot fully capture or model effectively. These companies are also faced with vast volumes of data. Traditional methods such as general linear models often fail to identify complex patterns in insurance data. Therefore, we seek to improve existing methods by applying modern techniques such as deep learning, since deep neural networks can more accurately identify complex patterns in insurance data, process large datasets efficiently, and uncover hidden insights. Deep learning, with the ability to identify nonlinear relationships and complex patterns, can overcome these limitations. In this paper, a method to improve the performance of deep learning using sequential deep regression techniques is presented. The proposed approach is a combination of deep learning and sequential models. Long Short-Term Memory (LSTM) neural networks are used to model time series data.
METHODS: In this study, data spanning the past seven years from Alborz Insurance Company—specifically related to the issuance and loss records of fire insurance policies—has been systematically utilized to analyze and forecast potential losses in this domain. The methodology places a strong emphasis on comprehensive data pre-processing, including cleaning, normalization, and transformation to ensure the reliability and quality of the input data. In the feature engineering stage, various techniques were applied to extract the most informative and relevant attributes from the raw dataset. Out of a total of 40 initially selected features, the top 20 features were identified through statistical analysis and machine learning-based selection methods. These refined features were then used to train the deep learning models. The proposed method is a hybrid approach that combines deep learning with sequential modeling techniques. Specifically, Long Short-Term Memory (LSTM) neural networks were employed due to their ability to capture time-dependent patterns in sequential data, making them particularly suitable for modeling the temporal dynamics inherent in insurance data over multiple years.
FINDINGS :The study involved the evaluation and comparison of multiple machine learning algorithms, including traditional models and advanced deep learning techniques. The results demonstrated that the proposed sequential deep regression approach significantly outperforms conventional models such as general linear models and decision trees. Notably, the LSTM-based model provided higher prediction accuracy and demonstrated superior performance in identifying complex, nonlinear patterns within the data. Key findings highlight the critical role of temporal features in enhancing prediction reliability and show that incorporating time series analysis is essential for improving the accuracy of damage occurrence forecasts in fire insurance.
CONCLUSION: The results of this research underscore the effectiveness of combining deep learning techniques with sequential models for predicting fire insurance losses. Using the confidential and comprehensive issuance and claims dataset from Alborz Insurance Company over seven years, the proposed hybrid model was capable of delivering better performance in comparison to previous methods. The approach not only improved the precision of predictions but also offered a more robust and scalable solution for risk assessment. Overall, the use of LSTM-based deep learning models represents a significant advancement in the insurance industry’s ability to make data-driven decisions regarding premium setting, policy issuance, and risk mitigation strategies.
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