SFS InsureMind Qorus-NTT DATA Innovation in Insurance Awards 2026

InsureMind was developed to address a structural challenge within modern insurance systems: the growing complexity of deterministic rule-based architectures. Over decades, insurance cores have accumulated thousands of variables, formulas, and interdependencies embedded within scripting layers. While flexible, these systems were not designed for structural visibility, explainability, or scalable knowledge reuse.

The objective of InsureMind is to convert deterministic rule logic and institutional expertise into structured, explainable, and reusable intelligence embedded within operational systems. Rather than replacing existing cores, InsureMind operates as a structural intelligence layer above them.

The competitive landscape is largely dominated by AI overlays focused on data-driven predictions, chatbots, or automation tools. Few solutions address the structural modeling of rule logic itself. InsureMind differentiates by focusing on deterministic architecture modeling instead of probabilistic overlays.

Inspired by graph-based modeling approaches and the need for regulatory transparency, the project integrates parsing, structural dependency modeling, and explainable intelligence design.

The initiative involved core software engineering teams, insurance product experts, business analysts, and AI specialists.

So far, over 50,000 rule variables and their interdependencies have been structurally modeled across operational systems, enabling traceability, dependency visibility, and scalable intelligence integration.