Old Mutual AI Model for Self-service Analytics Qorus-NTT DATA Innovation in Insurance Awards 2026

Concept and objectives Our project delivers an AI-driven self-service analytics platform built on the Universal Customer Data Portal (UCDP), a single, enterprise-wide repository that consolidates customer data from all business unit source systems. The primary objective is to democratize data access and analysis across the organization by:

Enabling non-technical users to ask natural-language questions and receive real-time, actionable insights. Allowing users to independently slice, dice, filter, and visualize datasets. Supporting advanced and predictive analytics (churn prediction, cross-sell modeling, lapse forecasting) to drive proactive decisions. Reducing analytics turnaround time and dependency on centralized BI/IT teams. Reasons behind the project Siloed data, long wait times for reports, and an overburdened analytics function were creating decision delays and missed opportunities. Leadership sought a scalable solution that would:

Improve speed-to-insight for frontline teams and business managers. Increase data literacy and accountability across departments. Translate consolidated customer intelligence into measurable business outcomes (retention, revenue growth, product uptake). State of competition The market includes established BI vendors with self-service offerings and specialist AI analytics startups. Competing solutions often require:

Heavy configuration or technical skills for advanced analysis. Separate data preparation pipelines or multiple tools to access enterprise data. Our differentiator is the tight integration of a verified, enterprise-grade UCDP with an AI model optimized for natural-language interaction, self-service exploration, and predictive analytics—reducing setup friction and eliminating the need for multiple point solutions. Sources of inspiration

Modern conversational AI and semantic-layer approaches that translate business language into analytical queries. Best practices from leading data democratization initiatives (embedded analytics, governed data marts, semantic models). Use-cases from insurers and financial services where rapid customer insight (churn, lapse, cross-sell) directly impacts revenue and risk management. Departments involved This is a cross-functional initiative involving:

Data Engineering: UCDP ingestion, data lineage, quality and governance. Data Science / ML Engineering: model development for question understanding, insight generation, and predictive models. Business Intelligence / Analytics: semantic layer design, visualization templates, KPI definitions. IT & Security: infrastructure, access control, compliance, and integration with core systems. Product & UX: user journeys, conversational interface, and self-service tooling. Business Units (Sales, Underwriting, Customer Service, Retention, Marketing): domain requirements, testing, and adoption. Main results so far

Consolidation: UCDP now ingests and harmonizes customer data from all key source systems, with established data lineage and basic governance controls. Adoption pilot: Successful pilot with frontline retention and sales teams that reduced time-to-insight for common queries from days to minutes. Self-service capability: Non-technical users can run ad-hoc explorations, filter cohorts, and generate visual reports without BI support. Predictive models: Production-ready churn prediction, cross-sell propensity, and lapse forecasting models integrated into the platform; early use shows improved targeting precision in pilot campaigns. Reduction in BI backlog: Measurable decline in routine reporting requests to central analytics teams, freeing specialists to focus on high-value projects. Positive feedback: Users report higher confidence in decision-making and an increased rate of data-driven actions (campaign launches, targeted outreach, pricing adjustments). Conclusion By combining a governed, enterprise-grade data foundation with conversational AI and embedded predictive analytics, our project transforms how teams access and act on customer intelligence. The platform delivers faster insights, empowered users, and measurable business impact—positioning the organization to scale data-driven decisions across all customer touchpoints.