Dynamic Premium Pricing Using Real-Time Geospatial Risk Analysis in P&C Insurance
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I2P104Keywords:
Property and Casualty Insurance, Geospatial Risk Analysis, Dynamic Premium Pricing, Machine Learning, IOT, Predictive Modeling, Climate RiskAbstract
Traditional Property and casualty P&C insurance pricing has involved the utilization of the risky-based pricing which is conducted with the use of historical data, and demographic averages. However, risk in the modern context is dynamic nature and requires a responsive system, real-time, location-aware system being responsive to geospatial risk variables. As this paper will show, this can be achieved by a framework of Dynamic Premium Pricing with Real-Time Geospatial Risk Analysis (DR-GRA) to achieve fairness, profitability and customer-centricity of the insurance pricing. The paper suggests a structure of geospatial integration of data comprising of IoT devices, weather fore-casts, satellite images, and mobility profiles that would alter the premiums in real-time. This strategy utilizes machine learning (ML) models, predictive hazard model, geospatial risk heatmaps and exposure assessment on an individualized basis. The suggested system is compared with traditional actuarial modus operandi using simulated data of floods, wild fires and urban crimes. The results show that DR-GRA is effective to boost the price accuracy rate (32 percent), and enhance reliability of the forecasts of losses and alleviates the adverse selection. The plan has countered the regulatory compliance, privacy and ethical forces and offer insurers a competitive advantage in regards to precision underwriting. The paper concludes by pointing out that geospatial analytics can revolutionize the insurance sector, make the industry resilient and accommodative to changes in climate risks
References
[1] Ohlsson, E., & Johansson, B. (2010). Non-life insurance pricing with generalized linear models (Vol. 174). Berlin: Springer.
[2] Kopalle, P. K., Pauwels, K., Akella, L. Y., & Gangwar, M. (2023). Dynamic pricing: Definition, implications for managers, and future research directions. Journal of Retailing, 99(4), 580-593.
[3] So, B., Boucher, J. P., & Valdez, E. A. (2020). Cost-sensitive multi-class adaboost for understanding driving behavior with telematics. arXiv preprint arXiv:2007.03100.
[4] Pesantez-Narvaez, J., Guillen, M., & Alcañiz, M. (2019). Predicting motor insurance claims using telematics data—XGBoost versus logistic regression. Risks, 7(2), 70.
[5] Palem, G. (2013). Condition-based maintenance using sensor arrays and telematics. arXiv preprint arXiv:1309.1921.
[6] Reddy, A. S. (2024). Beyond the Claims: Emerging AI Models and Predictive Analytics in Property & Casualty Insurance Risk Assessment.
[7] Lin, K. Y. (2006). Dynamic pricing with real-time demand learning. European Journal of Operational Research, 174(1), 522-538.
[8] Faruqui, A. (2012). The ethics of dynamic pricing. In Smart grid (pp. 61-83). Academic Press.
[9] Chen, Q., Jasin, S., & Duenyas, I. (2016). Real-time dynamic pricing with minimal and flexible price adjustment. Management Science, 62(8), 2437-2455.
[10] Balbás, A., Balbás, B., Balbás, R., & Heras, A. (2023). Actuarial pricing with financial methods. Scandinavian Actuarial Journal, 2023(5), 450-476.
[11] Mao, H., Carson, J. M., Ostaszewski, K. M., & Shoucheng, L. (2004). Pricing life insurance: Combining economic, financial, and actuarial approaches. Journal of Insurance Issues, 134-159.
[12] Ballestra, L. V., D'Amato, V., Fersini, P., Forte, S., & Greco, F. (2024). Pricing Cyber Insurance: A Geospatial Statistical Approach. Applied Stochastic Models in Business and Industry, 40(5), 1365-1376.
[13] Kumari, A. (2024, March). Dynamic pricing: Trends, challenges and new frontiers. In 2024 IEEE international conference on contemporary computing and communications (InC4) (Vol. 1, pp. 1-7). IEEE.
[14] Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022). Machine learning-enabled internet of things (IoT): Data, applications, and industry perspective. Electronics, 11(17), 2676.
[15] Bian, J., Al Arafat, A., Xiong, H., Li, J., Li, L., Chen, H., ... & Guo, Z. (2022). Machine learning in real-time Internet of Things (IoT) systems: A survey. IEEE Internet of Things Journal, 9(11), 8364-8386.
[16] Renard, B., Kochanek, K., Lang, M., Garavaglia, F., Paquet, E., Neppel, L., ... & Auffray, A. (2013). Data‐based comparison of frequency analysis methods: A general framework. Water Resources Research, 49(2), 825-843.
[17] Cullen, A. C., & Frey, H. C. (1999). Probabilistic techniques in exposure assessment: a handbook for dealing with variability and uncertainty in models and inputs. Springer Science & Business Media.
[18] Haimes, Y. Y. (2011). Risk modeling, assessment, and management. John Wiley & Sons.
[19] Emms, P. (2007). Dynamic pricing of general insurance in a competitive market. ASTIN Bulletin: The Journal of the IAA, 37(1), 1-34.
[20] Sousa, M. R., Gama, J., & Brandão, E. (2016). A new dynamic modeling framework for credit risk assessment. Expert Systems with Applications, 45, 341-351.
[21] Xin, X., & Huang, F. (2024). Antidiscrimination insurance pricing: Regulations, fairness criteria, and models. North American Actuarial Journal, 28(2), 285-319.
[22] Rusum, G. P., Pappula, K. K., & Anasuri, S. (2020). Constraint Solving at Scale: Optimizing Performance in Complex Parametric Assemblies. International Journal of Emerging Trends in Computer Science and Information Technology, 1(2), 47-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I2P106
[23] Pappula, K. K., & Rusum, G. P. (2020). Custom CAD Plugin Architecture for Enforcing Industry-Specific Design Standards. International Journal of AI, BigData, Computational and Management Studies, 1(4), 19-28. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P103
[24] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[25] Pappula, K. K., & Anasuri, S. (2021). API Composition at Scale: GraphQL Federation vs. REST Aggregation. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 54-64. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P107
[26] Pedda Muntala, P. S. R. (2021). Integrating AI with Oracle Fusion ERP for Autonomous Financial Close. International Journal of AI, BigData, Computational and Management Studies, 2(2), 76-86. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P109
[27] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[28] Karri, N., Pedda Muntala, P. S. R., & Jangam, S. K. (2021). Predictive Performance Tuning. International Journal of Emerging Research in Engineering and Technology, 2(1), 67-76. https://doi.org/10.63282/3050-922X.IJERET-V2I1P108
[29] Rusum, G. P. (2022). Security-as-Code: Embedding Policy-Driven Security in CI/CD Workflows. International Journal of AI, BigData, Computational and Management Studies, 3(2), 81-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P108
[30] Pappula, K. K. (2022). Containerized Zero-Downtime Deployments in Full-Stack Systems. International Journal of AI, BigData, Computational and Management Studies, 3(4), 60-69. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P107
[31] Jangam, S. K. (2022). Role of AI and ML in Enhancing Self-Healing Capabilities, Including Predictive Analysis and Automated Recovery. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 47-56. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P106
[32] Anasuri, S., Rusum, G. P., & Pappula, kiran K. (2022). Blockchain-Based Identity Management in Decentralized Applications. International Journal of AI, BigData, Computational and Management Studies, 3(3), 70-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I3P109
[33] Pedda Muntala, P. S. R. (2022). Natural Language Querying in Oracle Fusion Analytics: A Step toward Conversational BI. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 81-89. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I3P109
[34] Rahul, N. (2022). Optimizing Rating Engines through AI and Machine Learning: Revolutionizing Pricing Precision. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 93-101. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P110
[35] Karri, N. (2022). Predictive Maintenance for Database Systems. International Journal of Emerging Research in Engineering and Technology, 3(1), 105-115. https://doi.org/10.63282/3050-922X.IJERET-V3I1P111
[36] Tekale, K. M. T., & Enjam, G. reddy . (2022). The Evolving Landscape of Cyber Risk Coverage in P&C Policies. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 117-126. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P113
[37] Rusum, G. P. (2023). Large Language Models in IDEs: Context-Aware Coding, Refactoring, and Documentation. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 101-110. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P110
[38] Pappula, K. K. (2023). Edge-Deployed Computer Vision for Real-Time Defect Detection. International Journal of AI, BigData, Computational and Management Studies, 4(3), 72-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P108
[39] Jangam, S. K. (2023). Importance of Encrypting Data in Transit and at Rest Using TLS and Other Security Protocols and API Security Best Practices. International Journal of AI, BigData, Computational and Management Studies, 4(3), 82-91. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P109
[40] Anasuri, S., & Pappula, K. K. (2023). Green HPC: Carbon-Aware Scheduling in Cloud Data Centers. International Journal of Emerging Research in Engineering and Technology, 4(2), 106-114. https://doi.org/10.63282/3050-922X.IJERET-V4I2P111
[41] Pedda Muntala, P. S. R. (2023). AI-Powered Chatbots and Digital Assistants in Oracle Fusion Applications. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 101-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P111
[42] Rahul, N. (2023). Personalizing Policies with AI: Improving Customer Experience and Risk Assessment. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 85-94. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P110
[43] Tekale, K. M., & Rahul, N. (2023). Blockchain and Smart Contracts in Claims Settlement. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 121-130. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P112
[44] Karri, N. (2023). Intelligent Indexing Based on Usage Patterns and Query Frequency. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 131-138. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P113
[45] Rusum, G. P., & Anasuri, S. (2024). AI-Augmented Cloud Cost Optimization: Automating FinOps with Predictive Intelligence. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 82-94. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P110
[46] Pappula, K. K., & Rusum, G. P. (2024). AI-Assisted Address Validation Using Hybrid Rule-Based and ML Models. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 91-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P110
[47] Rahul, N. (2024). Revolutionizing Medical Bill Reviews with AI: Enhancing Claims Processing Accuracy and Efficiency. International Journal of AI, BigData, Computational and Management Studies, 5(2), 128-140. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P113
[48] Partha Sarathi Reddy Pedda Muntala, "AI-Powered Expense and Procurement Automation in Oracle Fusion Cloud" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 3, pp. 62-75, 2024.
[49] Jangam, S. K. (2024). Research on Firewalls, Intrusion Detection Systems, and Monitoring Solutions Compatible with QUIC’s Encryption and Evolving Protocol Features . International Journal of AI, BigData, Computational and Management Studies, 5(2), 90-101. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P110
[50] Anasuri, S., Pappula, K. K., & Rusum, G. P. (2024). Sustainable Inventory Management Algorithms in SAP ERP Systems. International Journal of AI, BigData, Computational and Management Studies, 5(2), 117-127. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P112
[51] Karri, N. (2024). ML Algorithms that Dynamically Allocate CPU, Memory, and I/O Resources. International Journal of AI, BigData, Computational and Management Studies, 5(1), 145-158. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P115
[52] Tekale, K. M., & Enjam, G. R. (2024). AI Liability Insurance: Covering Algorithmic Decision-Making Risks. International Journal of AI, BigData, Computational and Management Studies, 5(4), 151-159. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P116
[53] Pappula, K. K. (2020). Browser-Based Parametric Modeling: Bridging Web Technologies with CAD Kernels. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 56-67. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P107
[54] Rahul, N. (2020). Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 46-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106
[55] Pappula, K. K., & Rusum, G. P. (2021). Designing Developer-Centric Internal APIs for Rapid Full-Stack Development. International Journal of AI, BigData, Computational and Management Studies, 2(4), 80-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P108
[56] Pedda Muntala, P. S. R., & Jangam, S. K. (2021). End-to-End Hyperautomation with Oracle ERP and Oracle Integration Cloud. International Journal of Emerging Research in Engineering and Technology, 2(4), 59-67. https://doi.org/10.63282/3050-922X.IJERET-V2I4P107
[57] Rahul, N. (2021). Strengthening Fraud Prevention with AI in P&C Insurance: Enhancing Cyber Resilience. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 43-53. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P106
[58] Karri, N. (2021). AI-Powered Query Optimization. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 63-71. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P108
[59] Rusum, G. P., & Pappula, kiran K. . (2022). Event-Driven Architecture Patterns for Real-Time, Reactive Systems. International Journal of Emerging Research in Engineering and Technology, 3(3), 108-116. https://doi.org/10.63282/3050-922X.IJERET-V3I3P111
[60] Pappula, K. K. (2022). Modular Monoliths in Practice: A Middle Ground for Growing Product Teams. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 53-63. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P106
[61] Jangam, S. K., & Karri, N. (2022). Potential of AI and ML to Enhance Error Detection, Prediction, and Automated Remediation in Batch Processing. International Journal of AI, BigData, Computational and Management Studies, 3(4), 70-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P108
[62] Anasuri, S. (2022). Formal Verification of Autonomous System Software. International Journal of Emerging Research in Engineering and Technology, 3(1), 95-104. https://doi.org/10.63282/3050-922X.IJERET-V3I1P110
[63] Pedda Muntala, P. S. R., & Jangam, S. K. (2022). Predictive Analytics in Oracle Fusion Cloud ERP: Leveraging Historical Data for Business Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 86-95. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P110
[64] Rahul, N. (2022). Enhancing Claims Processing with AI: Boosting Operational Efficiency in P&C Insurance. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 77-86. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P108
[65] Karri, N., Pedda Muntala, P. S. R., & Jangam, S. K. (2022). Forecasting Hardware Failures or Resource Bottlenecks Before They Occur. International Journal of Emerging Research in Engineering and Technology, 3(2), 99-109. https://doi.org/10.63282/3050-922X.IJERET-V3I2P111
[66] Tekale, K. M. (2022). Claims Optimization in a High-Inflation Environment Provide Frameworks for Leveraging Automation and Predictive Analytics to Reduce Claims Leakage and Accelerate Settlements. International Journal of Emerging Research in Engineering and Technology, 3(2), 110-122. https://doi.org/10.63282/3050-922X.IJERET-V3I2P112
[67] Rusum, G. P., & Anasuri, S. (2023). Synthetic Test Data Generation Using Generative Models. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 96-108. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P111
[68] Pappula, K. K., & Rusum, G. P. (2023). Multi-Modal AI for Structured Data Extraction from Documents. International Journal of Emerging Research in Engineering and Technology, 4(3), 75-86. https://doi.org/10.63282/3050-922X.IJERET-V4I3P109
[69] Jangam, S. K. (2023). Data Architecture Models for Enterprise Applications and Their Implications for Data Integration and Analytics. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 91-100. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P110
[70] Anasuri, S., Rusum, G. P., & Pappula, K. K. (2023). AI-Driven Software Design Patterns: Automation in System Architecture. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 78-88. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P109
[71] Pedda Muntala, P. S. R., & Karri, N. (2023). Managing Machine Learning Lifecycle in Oracle Cloud Infrastructure for ERP-Related Use Cases. International Journal of Emerging Research in Engineering and Technology, 4(3), 87-97. https://doi.org/10.63282/3050-922X.IJERET-V4I3P110
[72] Rahul, N. (2023). Transforming Underwriting with AI: Evolving Risk Assessment and Policy Pricing in P&C Insurance. International Journal of AI, BigData, Computational and Management Studies, 4(3), 92-101. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P110
[73] Tekale , K. M. (2023). AI-Powered Claims Processing: Reducing Cycle Times and Improving Accuracy. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 113-123. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P113
[74] Karri, N., & Pedda Muntala, P. S. R. (2023). Query Optimization Using Machine Learning. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 109-117. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P112
[75] Rusum, G. P., & Anasuri, S. (2024). Vector Databases in Modern Applications: Real-Time Search, Recommendations, and Retrieval-Augmented Generation (RAG). International Journal of AI, BigData, Computational and Management Studies, 5(4), 124-136. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P113
[76] Kiran Kumar Pappula, "Transformer-Based Classification of Financial Documents in Hybrid Workflows" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 3, pp. 48-61, 2024.
[77] Rahul, N. (2024). Improving Policy Integrity with AI: Detecting Fraud in Policy Issuance and Claims. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 117-129. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P111
[78] Reddy Pedda Muntala, P. S., & Jangam, S. K. (2024). Automated Risk Scoring in Oracle Fusion ERP Using Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 105-116. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P111
[79] Jangam, S. K. (2024). Scalability and Performance Limitations of Low-Code and No-Code Platforms for Large-Scale Enterprise Applications and Solutions. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 68-78. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P107
[80] Anasuri, S., & Rusum, G. P. (2024). Software Supply Chain Security: Policy, Tooling, and Real-World Incidents. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 79-89. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P108
[81] Karri, N., & Pedda Muntala, P. S. R. (2024). Using Oracle’s AI Vector Search to Enable Concept-Based Querying across Structured and Unstructured Data. International Journal of AI, BigData, Computational and Management Studies, 5(3), 145-154. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P115
[82] Tekale, K. M. (2024). Generative AI in P&C: Transforming Claims and Customer Service. International Journal of Emerging Trends in Computer Science and Information Technology, 5(2), 122-131. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I2P113.
