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Designing Fairness-Aware AI Algorithms to Profile Risk for Insurance Industry
Research Gap:
AI risk assessment models often bring preconceived biases from datasets, resulting in unfair pricing or claims refusals.
Objectives:
• Design and implement a fairness aware AI model for dynamic insurance pricing.
• Use bias detection metrics, and apply mitigation measures (i.e., pre-processing, in-processing, post-processing).
• Assess effectiveness of model, under trade-offs between fairness and accuracy.
Methodology:
• Use synthetic datasets, or open insurance datasets.
• Implement using Python (Scikit-learn, AIF360).
• Quantitatively assess biased vs. de-biased models using fairness metrics (Demographic Parity, Equal Opportunity).
Theoretical Support:
Key References:
• K. Kuppan, D. Bhaskar Acharya and D. B, “Foundational AI in Insurance and Real Estate: A Survey of Applications, Challenges, and Future Directions,” in IEEE Access, vol. 12, pp. 181282-181302, 2024, doi: 10.1109/ACCESS.2024.3509918. https://ieeexplore.ieee.org/abstract/document/10772203/authors#authors
• Mehrabi, F. Morstatter, N. Saxena, K. Lerman and A. Galstyan, “A survey on bias and fairness in machine learning,” ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1–35, 2021.
• R. Zemel et al., “Learning Fair Representations,” in Proc. ICML, 2013
Real-time insurance fraud detection with Edge AI and Federated Learning
Research Gap:
Objectives:
• To design an AI model that is compatible with edge-based systems for the intended purpose of detecting fraud in vehicle or health insurance.
• Investigate using federated learning which will allow organizations to learn on their own data without the direct sharing of raw data.
Methods:
• Building an edge-based environment (such as mobile telematics).
• Use frameworks like TensorFlow Federated and PySyft.
• Assess the model’s accuracy, latency and communications constraints.
Theoretical support:
Key References:
• K. Kuppan et al., 2024.
• Q. Yang, Y. Liu, T. Chen and Y. Tong, “Federated Machine Learning: Concept and Applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, 2019.
• Y. Kang, Y. Li and Z. Xu, “Edge Computing for Real-Time AI Applications,” IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1564–1574, Feb. 2020.
An NLP-Based Legal Compliance Review for Real Estate Contracts
Research Gap:
Goals:
• Build an NLP model to identify compliance risks in property leases or contracts.
• Automate clause detection and red-flagging using rules for regulations (e.g., tenancy, zoning).
Method:
• Fine-tune pre-trained models (BERT, LegalBERT) on labeled legal documents datasets.
• Apply rule-based post-processing for tagging by contract-specific.
Tools:
Theoretical support:
Key IEEE References:
• K. Kuppan et al., 2024.
• D. Chalkidis et al., “Legal-BERT: The Muppets straight out of law school,” in Proc. Findings of EMNLP, 2020.
• M. Zhong, H. Chen, C. Yu and H. Xie, “Smart Contract Semantic Analysis Based on NLP,” in IEEE Access, vol. 8, pp. 171448–171459, 2020.
Multimodal data for an AI-powered dynamic pricing system for real estate rentals
Research Gap:
Objectives:
- Develop a multimodal AI model that uses images of real estate rental properties, text descriptions and location to set a rental price at a point in time.
- Extend this pricing signal to account for demand signals in the market that will vary continuously over time.
Method:
• Generate CNN outputs as image features; NLP outputs as features for the property description; integrate GIS data on location.
• Employ either gradient boosting or transformer-based regression for the value ultimately predicted.
Tools:
Theoretical support:
Key IEEE References:
• K. Kuppan et al., 2024.
• C. Zhang, J. Sun, Y. Qi and X. Hu, “A survey of dynamic pricing: From the perspective of machine learning,” IEEE Access, vol. 8, pp. 187212–187228, 2020.
• L. Wei, H. Zhou and J. Huang, “Multimodal Learning for Property Value Estimation,” in Proc. IEEE International Conference on Big Data, 2019.
Explainable AI for Automated Property Valuation via Computer Vision
Research Gap:
Objectives:
• Develop a computer vision model for property valuation that uses visual traits.
• Develop a model that incorporates explainability (Grad-CAM, SHAP), that can substantiate their predictions.
Method:
• Using labeled interior/exterior images, with appraisal values.
• Train a regression-based CNN or ViT model.
• Provide a visualization of what areas of the image most contributed to the prediction for the price.
Tools:
Theoretical support:
Key IEEE References:
• K. Kuppan et al., 2024.
• R. R. Selvaraju et al., “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” in IEEE ICCV, 2017.
• S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing Systems (NeurIPS), 2017. (SHAP)
Constructing a Privacy-Preserving AI System for Smart Building Energy Management
Research Gap:
Objectives:
• Build a privacy preserving AI based solution for smart energy management.
• Use federated learning with differential privacy tech to protect the data.
Methodology:
• Simulate multi-zone building with sensory inputs (HVAC, Lighting).
• Application of FL (with local training updates) with DP-noise.
Tools:
Theoretical support:
Key IEEE References:
• K. Kuppan et al., 2024.
• C. Dwork, “Differential Privacy,” in Automata, Languages and Programming, Springer, 2006.
• T. Li, A. K. Sahu, A. Talwalkar and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, 2020
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