P&C's Small & Medium Enterprise (SMEs) Cyber Protection
Keywords:
Cyber insurance, SMEs, P&C insurance, data breaches, cyber risk exposureAbstract
Cyber insurance has grown crucial as SMEs adopt digital technology & confront increased cyber dangers such as ransomware, data breaches, & business disruptions. SMEs frequently lack the means to protect themselves from these threats, which makes them appealing targets for cybercriminals. This is in contrast to major organizations that have specialized cybersecurity teams. By giving access to professional assistance, particularly risk evaluations & incident response, & by covering monetary losses, legal responsibilities, & recovery expenses, cyber protection helps to safeguard SMEs. By incorporating cyber insurance into previous P&C protection, SMEs can handle several risks under one roof. However, adoption is still tricky because of complicated regulatory terminology, low awareness, & financial concerns. To become effective, insurers must create modified reasonably priced coverage, use data analytics to improve the underwriting, & evaluate risk. Building confidence & acceptance among SMEs also requires educating customers about cyber threats & preventive security measures. Insurers must adapt their results to help the businesses remain resilient as cyber risks change. Besides offering financial securities, cyber insurances build partnership between insurers & SMEs by providing them with resources & assistance they need to succeed in the business landscape that grows more digital & linked by the day.
References
1. Granato, A., & Polacek, A. (2019). The growth and challenges of cyber insurance. Chicago Fed Letter, 426, 1-6.
2. Chester, A., Ebert, S., Kauderer, S., & McNeill, C. (2019). From art to science: The future of underwriting in commercial P&C insurance.
3. Chester, A., Johansson, S., Kauderer, S., Michel-Kerjan, E., & Pinkes, A. (2020, April). Coronavirus response: Short-and long-term actions for P&C insurers.
4. Kanavas, A. (2023). Cyberinsurance as a risk management tool (Master's thesis, Πανεπιστήμιο Πειραιώς).
5. Camillo, M. (2017). Cyber risk and the changing role of insurance. Journal of Cyber Policy, 2(1), 53-63.
6. Cremer, F., Sheehan, B., Fortmann, M., Mullins, M., & Murphy, F. (2022, April). Cyber exclusions: An investigation into the cyber insurance coverage gap. In 2022 Cyber Research Conference-Ireland (Cyber-RCI) (pp. 1-10). IEEE.
7. Sayre, M. (2023). Impossible Math: The Need for Government-Backed Cyber Insurance. Tort Trial & Insurance Practice Law Journal, Fall.
8. Romanosky, S., Ablon, L., Kuehn, A., & Jones, T. (2019). Content analysis of cyber insurance policies: how do carriers price cyber risk?. Journal of Cybersecurity, 5(1), tyz002.
9. Park, H. J. Incentivizing Cybersecurity Through Cyber Insurance: Benefits and Pitfalls of Mandating Cyber Insurance. Available at SSRN 4065565.
10. Itty, M. S. (2023). Cyber Insurance in the US Market: Assessing Cyber Risks and Reducing Risks for Insurers (Master's thesis, Utica University).
11. Matejka, V., Soto, J., & Franco, M. (2021). A framework for the definition and analysis of cyber insurance requirements. Master Project, University of Zurich, Communication Systems Group, Department of Informatics, Zurich, Switzerland.
12. Inpoint, A. (2017, June). Global cyber market overview.
13. Yu, Q. (2022). Exploration of Combination of Cyber Insurance and Commercial Property Insurance. Temple University.
14. Taplin, R. (2020). Cyber risk, intellectual property theft and cyberwarfare: Asia, Europe and the USA. Routledge.
15. Pitcock, R. W. (2015). Evaluating the cyber security capabilities of senior managers employed by companies located in the united states (Doctoral dissertation, Jones International University).
16. Katari, A., & Rodwal, A. NEXT-GENERATION ETL IN FINTECH: LEVERAGING AI AND ML FOR INTELLIGENT DATA TRANSFORMATION.
17. Katari, A. Case Studies of Data Mesh Adoption in Fintech: Lessons Learned-Present Case Studies of Financial Institutions.
18. Katari, A. (2023). Security and Governance in Financial Data Lakes: Challenges and Solutions. Journal of Computational Innovation, 3(1).
19. Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
20. Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
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. (2023). Zero-Trust Security Frameworks: The Role of Data Encryption in Cloud Infrastructure. MZ Computing Journal, 4(1).
25. Nookala, G. (2023). Real-Time Data Integration in Traditional Data Warehouses: A Comparative Analysis. Journal of Computational Innovation, 3(1).
26. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
27. Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
28. 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).
29. Boda, V. V. R., & Immaneni, J. (2023). Automating Security in Healthcare: What Every IT Team Needs to Know. Innovative Computer Sciences Journal, 9(1).
30. Immaneni, J. (2023). Best Practices for Merging DevOps and MLOps in Fintech. MZ Computing Journal, 4(2).
31. Immaneni, J. (2023). Scalable, Secure Cloud Migration with Kubernetes for Financial Applications. MZ Computing Journal, 4(1).
32. Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
33. Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
34. Gade, K. R. (2023). Data Lineage: Tracing Data's Journey from Source to Insight. MZ Computing Journal, 4(2).
35. Gade, K. R. (2023). Security First, Speed Second: Mitigating Risks in Data Cloud Migration Projects. Innovative Engineering Sciences Journal, 3(1).
36. Gade, K. R. (2023). Data Governance in the Cloud: Challenges and Opportunities. MZ Computing Journal, 4(1).
37. Gade, K. R. (2023). The Role of Data Modeling in Enhancing Data Quality and Security in Fintech Companies. Journal of Computing and Information Technology, 3(1).
38. Gade, K. R. (2023). Event-Driven Data Modeling in Fintech: A Real-Time Approach. Journal of Computational Innovation, 3(1).
39. Muneer Ahmed Salamkar. Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, Mar. 2023, pp. 668-94
40. Muneer Ahmed Salamkar, et al. Data Transformation and Enrichment: Utilizing ML to Automatically Transform and Enrich Data for Better Analytics. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, July 2023, pp. 613-38
41. Muneer Ahmed Salamkar. Real-Time Analytics: Implementing ML Algorithms to Analyze Data Streams in Real-Time. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, Sept. 2023, pp. 587-12
42. Muneer Ahmed Salamkar. Feature Engineering: Using AI Techniques for Automated Feature Extraction and Selection in Large Datasets. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, Dec. 2023, pp. 1130-48
43. Naresh Dulam. Snowflake: A New Era of Cloud Data Warehousing. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Apr. 2015, pp. 49-72
44. Naresh Dulam. The Shift to Cloud-Native Data Analytics: AWS, Azure, and Google Cloud Discussing the Growing Trend of Cloud-Native Big Data Processing Solutions. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Feb. 2015, pp. 28-48
45. Naresh Dulam. DataOps: Streamlining Data Management for Big Data and Analytics . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Oct. 2016, pp. 28-50
46. Naresh Dulam. Machine Learning on Kubernetes: Scaling AI Workloads . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Sept. 2016, pp. 50-70
47. Naresh Dulam. Data Lakes Vs Data Warehouses: What’s Right for Your Business?. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Nov. 2016, pp. 71-94
48. Thumburu, S. K. R. (2023). Leveraging AI for Predictive Maintenance in EDI Networks: A Case Study. Innovative Engineering Sciences Journal, 3(1).
49. Thumburu, S. K. R. (2023). AI-Driven EDI Mapping: A Proof of Concept. Innovative Engineering Sciences Journal, 3(1).
50. Thumburu, S. K. R. (2023). EDI and API Integration: A Case Study in Healthcare, Retail, and Automotive. Innovative Engineering Sciences Journal, 3(1).
51. Thumburu, S. K. R. (2023). Quality Assurance Methodologies in EDI Systems Development. Innovative Computer Sciences Journal, 9(1).
52. Thumburu, S. K. R. (2023). Data Quality Challenges and Solutions in EDI Migrations. Journal of Innovative Technologies, 6(1).
53. Sarbaree Mishra. “Incorporating Automated Machine Learning and Neural Architecture Searches to Build a Better Enterprise Search Engine”. African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 2, Dec. 2023, pp. 507-2
54. Sarbaree Mishra, et al. “Hyperfocused Customer Insights Based On Graph Analytics And Knowledge Graphs”. Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, Oct. 2023, pp. 1172-93
55. Sarbaree Mishra, and Jeevan Manda. “Building a Scalable Enterprise Scale Data Mesh With Apache Snowflake and Iceberg”. Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, June 2023, pp. 695-16
56. Sarbaree Mishra. “Scaling Rule Based Anomaly and Fraud Detection and Business Process Monitoring through Apache Flink”. Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, Mar. 2023, pp. 677-98
57. Komandla, V. Crafting a Clear Path: Utilizing Tools and Software for Effective Roadmap Visualization.
58. Komandla, V. (2023). Safeguarding Digital Finance: Advanced Cybersecurity Strategies for Protecting Customer Data in Fintech.
59. Komandla, Vineela. "Crafting a Vision-Driven Product Roadmap: Defining Goals and Objectives for Strategic Success." Available at SSRN 4983184 (2023).
60. Komandla, Vineela. "Critical Features and Functionalities of Secure Password Vaults for Fintech: An In-Depth Analysis of Encryption Standards, Access Controls, and Integration Capabilities." Access Controls, and Integration Capabilities (January 01, 2023) (2023).
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
