As one of the world’s largest insurers, Allianz has long been a bellwether for how heavily regulated financial institutions modernise their technology foundations.
Operating across more than 70 countries and serving over 100 million customers, the group runs a vast digital estate spanning underwriting, claims, payments and risk modelling. Its scale means even incremental improvements in data quality, software testing or platform engineering have significant operational impact.
Against this backdrop, Allianz is now pushing ahead with one of the insurance sector’s most structured data-and-AI transformations, pairing enterprise-wide governance with large-scale digital engineering to strengthen resilience, software testing and online service delivery across its global business.
For QA and testing teams, the insurer’s 2024–2025 programme offers a real-world view of how a major organisation is shifting from AI pilots to production-grade systems at scale, underpinned by data quality, lineage, accessibility and responsible-AI controls aligned with the EU AI Act.
Data quality
Allianz has placed data at the core of its strategy for decades, but the latest company-wide Data & AI framework aims to make that foundation fully measurable and operationally embedded.
Its operating entities are now led by Chief Data Officers who oversee governance, architecture and data-quality standards across the group.
The programme includes formal assessments of ‘Data Maturity’ and ‘Data Fitness’, annual reviews that evaluate consumption, production, accessibility and consistency across 16 dimensions.
Allianz said high-performing units such as Allianz Brazil and Allianz German Life are setting internal benchmarks for others to follow.
A central pillar is the ‘Data Value’ metric, which quantifies the financial impact of data initiatives. By 2024, 39 entities participated in the assessment, revealing that nearly one in three AI or data assets is directly linked to quantifiable financial outcomes.
This focus on measurability is already being reflected in real-world resilience. Allianz RE maintains more than 125 million catastrophe-related data points, while in late 2024, the dataset allowed the company to send early warnings to customers in Valencia, Andalusia and nearby regions hours before severe flooding occurred.
“The most impactful decision was to be stubborn about the outcome, and to never waver on what good looks like.”
– Philipp Kroetz
For QA and engineering teams, this is a vivid example of production-quality data pipelines supporting predictive services at scale.
To bring structure and traceability to its ecosystem, Allianz has created three major internal standards: the Global Business Glossary, the Global Data Model and the Data Catalogue.
Together they define common business terms, map relationships between attributes and track data lineage from source to target systems. That traceability supports both regulatory compliance and high-integrity AI training, two key requirements for modern testing environments.
Controlled, scalable access
The Allianz Data Platform (ADP) is emerging as the group’s core engine for secure data integration and discovery. It allows staff across the business to access governed, contextualised data, crucial for test engineers building cross-market or cross-product automation frameworks.
One of the AI initiatives running on ADP is the Underwriter Guidance Tool BRIAN, which relies on consistent, high-quality data inputs.
Allianz said its long-term goal is to create a system where employees can “simply ask for the data they need” and receive immediate access to reliable, privacy-compliant datasets.
For software-quality teams, this vision, if and once realised, would radically simplify test-data provisioning, reduce manual preparation work and enable more dynamic regression and performance testing.
The insurer’s Data & AI strategy is also anchored in responsible-AI governance, transparency and workforce training.
Allianz has aligned its practices with the EU AI Act and joined the AI Pact, committing to principles of privacy, accountability and human oversight.
Technical safeguards are paired with workforce controls: every employee completes annual training focused on social-engineering risks and data-handling discipline.
For testing functions, this reinforces the organisational mandate that model behaviour must be explainable, traceable and auditable across the lifecycle.
The cultural shift is equally deliberate. Allianz emphasises that the biggest vulnerabilities in digital systems tend to come from human error rather than software failures alone. Its programme therefore stresses awareness, data literacy and shared responsibility, an approach increasingly mirrored in QA and DevSecOps operating models.
Engineering-led transformation at scale
Alongside the group-wide data agenda, Allianz is also advancing platform-driven innovation through Allianz Direct, its European digital insurer.
A multi-year transformation, built with support from McKinsey, centred on a state-of-the-art digital platform designed for rapid scaling, cross-market reuse and high levels of self-service.
For customers, that architecture enables services such as the ’60-second claim’, powered by AI-based loss assessment from uploaded photos and documents.
Christoph Weber
For internal testing teams, it represents a shift towards reusable components, plug-and-play software and shared learnings across markets.
Christoph Weber, Chief Transformation Officer at Allianz Direct, said the success came from “the combination of technical excellence, sophisticated IT infrastructure, and advanced digital marketing capabilities, along with robust execution and global delivery in a compliant way.”
He added: “We dedicated utmost attention, allocating 150% of our focus to launch and establish our platform as a solid foundation.”
Philipp Kroetz, CEO of Allianz Direct, highlighted the strategic discipline required for this shift.
“The most impactful decision was to be stubborn about the outcome, and to never waver on what good looks like,” he explained, adding “that means you need to invest in the best technology and in the best people, and be really stubborn about it.”
The engineering-focused restructuring, where a third of staff now work in technology or data roles, has delivered year-over-year revenue growth of 15% in selected markets, 30–50% cost reductions through the scalable platform model, and customer-satisfaction levels above 90%.
Allianz’s approach offers a clear blueprint for QA organisations navigating AI adoption: high-quality, well-governed data; model transparency and traceability; platform consolidation; and an engineering culture grounded in measurable outcomes.
As financial firms race to prepare for the EU AI Act, modernise test-data provisioning, and industrialise AI-augmented testing, Allianz’s transformation shows how disciplined data strategy, responsible-AI principles and platform-first digital engineering can translate directly into resilience and operational advantage.
Looking for more news on regulations and compliance requirements driving developments in software quality engineering at financial firms? Visit our dedicated Regulation & Compliance page here.
