Brief Summary

Moving beyond the choice between simple GLMs and "black-box" AI, this paper introduces Tsetlin Machines (TMs). Using propositional logic, TMs capture complex, non-linear risk patterns in motor claims while remaining fully transparent for regulators.

What You’ll Read

• The Actuarial Challenge: Why traditional models struggle with imbalanced claim data.
• Hypervector Computing: Using biologically inspired encoding for risk profiles.
• Logic-Based Learning: Replacing complex math with transparent "If-Then" clauses.‍
• Validation: Evidence of superior risk identification in motor liability datasets.

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"Tsetlin Machines produce human-readable logic, preserving the transparency required for pricing and regulation."