Vol. 2 No. 2 (2022): African Journal of Artificial Intelligence and Sustainable Development
Articles

Navigating the Rising Tide: The Impact of Inflation on Property & Casualty Insurance and Strategies for Resilience

Ravi Teja Madhala
Senior Software Developer Analyst at Mercury Insurance Services, LLC, USA
Nivedita Rahul
Business Architecture Manager at Accenture, USA
Cover

Published 29-07-2022

Keywords

  • Inflation,
  • Property & Casualty Insurance

How to Cite

[1]
Ravi Teja Madhala and Nivedita Rahul, “Navigating the Rising Tide: The Impact of Inflation on Property & Casualty Insurance and Strategies for Resilience”, African J. of Artificial Int. and Sust. Dev., vol. 2, no. 2, pp. 467–492, Jul. 2022, Accessed: Dec. 29, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/238

Abstract

Inflation significantly impacts the property and casualty (P&C) insurance industry, influencing everything from premium rates to claims processing, underwriting, and broader risk management strategies. Rising inflationary pressures have brought challenges and opportunities for insurers, requiring them to adapt to shifting economic conditions. The surge in costs for materials, labour, and services, often linked to inflation, directly affects the cost of repairs, replacements, and overall claims expenses. As a result, insurers face rising claim payouts, which can pressure profitability if premiums do not align with the increased cost of claims. Inflation can complicate underwriting processes, as insurers must carefully assess the actual value of properties and assets to avoid under or over-insuring them. Insurance companies are adjusting pricing models to counteract the negative impacts, often through more frequent rate adjustments & targeted pricing strategies. These adaptations aim to keep pace with inflation but require insurers to balance competitiveness with profitability. Another crucial aspect is an investment strategy, as inflation affects the returns on fixed-income investments, traditionally a cornerstone of P&C insurers’ portfolios. Inflation can erode the value of bonds and other conservative investments, prompting insurers to diversify their investment portfolios into higher-yielding, albeit riskier, assets. Insurers also explore operational efficiencies and innovative technologies to reduce costs and improve customer service. These efforts help mitigate the pressure of rising expenses, allowing insurers to maintain profitability & retain a competitive edge. Risk diversification becomes essential as insurers look beyond traditional insurance products and explore new avenues for growth and resilience. To build long-term stability, the key lies in adaptability, with insurers being agile enough to revise their strategies in real time while ensuring they can still provide reliable coverage and services to their customers. Adaptive pricing, operational improvements, and diversified investments are crucial for navigating inflation’s challenges and positioning insurers for success in an unpredictable economic environment.

Downloads

Download data is not yet available.

References

  1. Meyer, R., & Kunreuther, H. (2017). The ostrich paradox: Why we underprepare for disasters. University of Pennsylvania Press.
  2. Smolka, A. (2006). Natural disasters and the challenge of extreme events: risk management from an insurance perspective. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2147-2165.
  3. Kunreuther, H., & Useem, M. (2018). Mastering catastrophic risk: How companies are coping with disruption. Oxford University Press.
  4. Mills, E. (2009). A global review of insurance industry responses to climate change. The Geneva Papers on Risk and Insurance-Issues and Practice, 34, 323-359.
  5. Thierer, A. (2013). Technopanics, threat inflation, and the danger of an information technology precautionary principle. Minn. JL Sci. & Tech., 14, 309.
  6. Béland, D., Howlett, M., Rocco, P., & Waddan, A. (2020). Designing policy resilience: lessons from the Affordable Care Act. Policy Sciences, 53, 269-289.
  7. Schoemaker, P. (2012). Profiting from uncertainty: Strategies for succeeding no matter what the future brings. Simon and Schuster.
  8. Ragland, K. (2017). " Hoi Toide" Sustaining Adaptations and Mandating Action in Historic Flood-Prone Communities.
  9. Chang, S. E., Stone, J., Demes, K., & Piscitelli, M. (2014). Consequences of oil spills: a review and framework for informing planning. Ecology and Society, 19(2).
  10. Schwartz, J. (2021). Work disrupted: Opportunity, resilience, and growth in the accelerated future of work. John Wiley & Sons.
  11. Mitchell, T., Mechler, R., & Peters, K. (2014). Disaster risk management and adaptation to extreme events: Placing disaster risk management at the heart of national economic and fiscal policy. In Routledge handbook of the economics of climate change adaptation (pp. 417-436). Routledge.
  12. Mills, E., & Lecomte, E. (2006). How Insurers Can Proactively and Profitably Manage Climate Change.
  13. Chamberlin, S. (2009). The Transition Timeline for a local, resilient future. Chelsea Green Publishing.
  14. Maynard, T., & Ranger, N. (2012). What role for “Long-term Insurance” in adaptation? An analysis of the prospects for and pricing of multi-year insurance contracts. The Geneva Papers on Risk and Insurance-Issues and Practice, 37, 318-339.
  15. Hay, A. H. (2016). After the Flood: Exploring operational resilience. FriesenPress.
  16. Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
  17. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
  18. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
  19. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
  20. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
  21. Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-10
  22. Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
  23. Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77
  24. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
  25. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
  26. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
  27. , G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
  28. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
  29. Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
  30. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
  31. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
  32. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  33. Gade, K. R. (2021). Cost Optimization Strategies for Cloud Migrations. MZ Computing Journal, 2(2).
  34. Gade, K. R. (2021). Cloud Migration: Challenges and Best Practices for Migrating Legacy Systems to the Cloud. Innovative Engineering Sciences Journal, 1(1).
  35. Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
  36. Gade, K. R. (2021). Data-Driven Decision Making in a Complex World. Journal of Computational Innovation, 1(1).
  37. Gade, K. R. (2021). Migrations: Cloud Migration Strategies, Data Migration Challenges, and Legacy System Modernization. Journal of Computing and Information Technology, 1(1).
  38. Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
  39. Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
  40. Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
  41. Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
  42. Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
  43. Naresh Dulam, et al. “The AI Cloud Race: How AWS, Google, and Azure Are Competing for AI Dominance ”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Dec. 2021, pp. 304-28
  44. Naresh Dulam, et al. “Kubernetes Operators for AI ML: Simplifying Machine Learning Workflows”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, June 2021, pp. 265-8
  45. Naresh Dulam, et al. “Data Mesh in Action: Case Studies from Leading Enterprises”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Dec. 2021, pp. 488-09
  46. Naresh Dulam, et al. “Real-Time Analytics on Snowflake: Unleashing the Power of Data Streams”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 91-114
  47. Naresh Dulam, et al. “Serverless AI: Building Scalable AI Applications Without Infrastructure Overhead ”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, May 2021, pp. 519-42
  48. Thumburu, S. K. R. (2021). The Future of EDI Standards in an API-Driven World. MZ Computing Journal, 2(2).
  49. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
  50. Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
  51. Thumburu, S. K. R. (2021). Transitioning to Cloud-Based EDI: A Migration Framework, Journal of Innovative Technologies, 4(1).
  52. Thumburu, S. K. R. (2021). Integrating Blockchain Technology into EDI for Enhanced Data Security and Transparency. MZ Computing Journal, 2(1).
  53. Sarbaree Mishra. “The Age of Explainable AI: Improving Trust and Transparency in AI Models”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 212-35
  54. Sarbaree Mishra, et al. “A New Pattern for Managing Massive Datasets in the Enterprise through Data Fabric and Data Mesh”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Dec. 2021, pp. 236-59
  55. Sarbaree Mishra. “Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, Jan. 2021, pp. 286-0
  56. Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
  57. Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-37
  58. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
  59. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  60. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
  61. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
  62. Komandla, Vineela. "Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction." Available at SSRN 4983012 (2018).