Big data and machine learning-based decision support system to reshape the vaticination of insurance claims

被引:3
作者
Jaiswal, Rachana [1 ]
Gupta, Shashank [2 ]
Tiwari, Aviral Kumar [3 ]
机构
[1] HNB Garhwal A Cent Univ, Dept Business Management, Srinagar, Uttarakhand, India
[2] Morgan Stanley Advantage India Pvt Ltd, Nirlon Knowledge Pk,Goregaon E, Mumbai, Maharashtra, India
[3] Indian Inst Management Bodh Gaya IIMBG, Dept Econ, Bodh Gaya, Bihar, India
关键词
Claim frequency; Risk management; Predictive insurance analytics; Sustainable development goals; Machine learning; Big data; LightGBM; PROPERTY-LIABILITY INSURANCE; ECONOMIC-GROWTH; PREDICTION; INSOLVENCY; SELECTION;
D O I
10.1016/j.techfore.2024.123829
中图分类号
F [经济];
学科分类号
02 ;
摘要
Based on actuarial science theory, decision-making theory, and anonymous big data, this study employs machine learning to advance insurance claim forecasting, aiming to enhance pricing accuracy, mitigate adverse selection risks, and optimize operational efficiency for improved customer satisfaction and global competitiveness. The study utilized the Boruta algorithm with LightGBM for feature selection, analyzing a 57-dimensional dataset and identifying an optimal subset of 24 features. The improved LightGBM model achieved superior results (AUC similar to 0.9272 and accuracy similar to 92.94 %), surpassing other models evaluated. Beyond operational improvements, the proposed model holds the potential to contribute to various United Nations SDGs, such as promoting financial inclusion (SDG 1; SDG 10), reducing fraud, improving public safety (SDG 3; SDG 11; SDG 13), and encouraging sustainable practices (SDG 9; SDG 11). By utilizing data-driven insights to make more informed and accurate decisions, insurance companies can provide better services to their policyholders and contribute to a more equitable and sustainable society.
引用
收藏
页数:13
相关论文
共 115 条
  • [1] Abdelhadi S., 2020, J. Theor. Appl. Inf. Technol., V98
  • [2] How can organizations leverage big data to innovate their business models? A systematic literature review
    Acciarini, Chiara
    Cappa, Francesco
    Boccardelli, Paolo
    Oriani, Raffaele
    [J]. TECHNOVATION, 2023, 123
  • [3] Ahmed K, 2021, Int. J. Law Manag. Human., V4, P4194
  • [4] Ai Cheo Yeo, 2001, International Journal of Intelligent Systems in Accounting, Finance and Management, V10, P39, DOI 10.1002/isaf.196
  • [5] Optuna: A Next-generation Hyperparameter Optimization Framework
    Akiba, Takuya
    Sano, Shotaro
    Yanase, Toshihiko
    Ohta, Takeru
    Koyama, Masanori
    [J]. KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 2623 - 2631
  • [6] Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia
    AL-Musaylh, Mohanad S.
    Deo, Ravinesh C.
    Adamowski, Jan F.
    Li, Yan
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2019, 113
  • [7] Alamir E, 2021, INT J ADV COMPUT SC, V12, P457
  • [8] Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector
    Alfiero, Simona
    Battisti, Enrico
    Hadjielias, Elias
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2022, 183
  • [9] Antonio K., 2017, SEM H WATT U
  • [10] Arden F., 2022, P 2022 6 INT C INF T, P183, DOI [10.1109/ICITISEE57756.2022.10057630, DOI 10.1109/ICITISEE57756.2022.10057630]