Leveraging AI for Predictive Analytics in Insurance
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Keywords

Leveraging AI
Predictive Analytics in Insurance

How to Cite

[1]
D. S. Mukhopadhyay, “Leveraging AI for Predictive Analytics in Insurance”, Journal of Bioinformatics and Artificial Intelligence, vol. 3, no. 2, pp. 245–265, Dec. 2023, Accessed: Nov. 13, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/116

Abstract

Predictive analytics makes predictions about unknown future events by exploiting patterns within big data. Predictive models examine patterns found in historical and transactional data to identify hazards and opportunities. Models aim to determine the likelihood of certain results. Predictive analytics involves a range of statistical techniques from data mining, predictive modeling, and machine learning. These statistical techniques isolate trends that businesses want to act on immediately, as well as predictions that identify risks and opportunities which businesses want to act on in the future. In the insurance industry, predictive analytics is typically used in sales, marketing, or underwriting. Used in this way, it ensures companies do not underprice their products by insuring high risks at lower premiums, and it reduces fraud by using algorithms to discover unusual patterns in claims data that are typical of fraud.

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References

S. Kumari, “AI-Driven Cloud Transformation for Product Management: Optimizing Resource Allocation, Cost Management, and Market Adaptation in Digital Products ”, IoT and Edge Comp. J, vol. 2, no. 1, pp. 29–54, Jun. 2022

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Machireddy, Jeshwanth Reddy. "Data-Driven Insights: Analyzing the Effects of Underutilized HRAs and HSAs on Healthcare Spending and Insurance Efficiency." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 450-470.

J. Singh, “The Future of Autonomous Driving: Vision-Based Systems vs. LiDAR and the Benefits of Combining Both for Fully Autonomous Vehicles ”, J. of Artificial Int. Research and App., vol. 1, no. 2, pp. 333–376, Jul. 2021

Tamanampudi, Venkata Mohit. "AI and DevOps: Enhancing Pipeline Automation with Deep Learning Models for Predictive Resource Scaling and Fault Tolerance." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 38-77.

Ahmed Qureshi, Hamza, et al. “The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis.” International Journal for Multidisciplinary Research, vol. 6, no. 4, 14 Aug. 2024, pp. 1–21.

Singh, Jaswinder. "Deepfakes: The Threat to Data Authenticity and Public Trust in the Age of AI-Driven Manipulation of Visual and Audio Content." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 428-467.

Tamanampudi, Venkata Mohit. "Autonomous AI Agents for Continuous Deployment Pipelines: Using Machine Learning for Automated Code Testing and Release Management in DevOps." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 557-600.

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