How Artificial Intelligence can transform Insurance in Tunisia

Bys: Ben Nasr, Mehrez and Anderson, John

Tunisia's insurance sector faces operational inefficiencies, high fraud rates in motor third-party liability (MTPL) with loss ratios exceeding 100%, and outdated mortality tables like TD 99 creating profitability and mispricing risks. An explanatory sequential mixed-methods design was employed: a survey of 56 insurance professionals using TAM, TOE, and DOI frameworks, followed by quantitative modeling with GLM for non-life pricing, Kaplan-Meier for mortality/lapses logistic regression for lapse prediction, Random Forest for fraud detection, and a claims-handling chatbot prototype. GLM outperforms manual tariffing for MTPL premiums. Observed mortality is 17-18% below TD 99 (Chapter 6). Lapse prediction achieves AUC ≈ 0.96 (95% CI: 0.942-0.978) Table 16]. Fraud detection yields AUC ≈ 0.805 (95% CI: 0.801-0.898) Table 24]. Chatbot reduces claims cycle times by 40% Table 18], generating estimated gains of TND 2,029,750 ( USD 648,880) Table 43].

• Format: Paperback
• Language: English
• ISBN: 6209531180
• ISBN13: 9786209531187
• Release Date: February 2026
• Publisher: LAP Lambert Academic Publishing
• Length: 244 Pages
• Weight: 0.73 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"