Gjensidige breaks down data silos and speeds AI model deployment with Databricks
Gjensidige migrated to the Databricks Data + AI Platform to unify data engineers, scientists and analysts on one tech stack, rebuilding a single-person-dependent commercial renewal rules engine and cutting AI model development and deployment time from days or weeks to a few hours.
Overview
Gjensidige migrated to the Databricks Data + AI Platform to unify data engineers, scientists and analysts on one tech stack, rebuilding a single-person-dependent commercial renewal rules engine and cutting AI model development and deployment time from days or weeks to a few hours.
This entry has 13 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Gjensidige's data infrastructure was fragmented and overly reliant on legacy, on-premises systems, with a fragmented analytics solution that made it hard for teams to know what others were working on, and a critical commercial renewal process relied on complex scripts managed by a single individual, risking loss of access to a vital business function if that person left.
The solution
Gjensidige migrated to the Databricks Data + AI Platform, unifying data engineers, scientists and analysts on the same tech stack, rebuilding the commercial renewal rules engine with a modern architecture that a broader team could maintain, and implementing generative AI applications including retrieval-augmented generation (RAG) for real-time marketing actions.
Reported business value
With Databricks, Gjensidige has reduced the time it takes to develop and deploy AI models from days or weeks to just a few hours, broken down data silos across the company, and improved its ability to share and reuse data products across teams, enhancing cross-selling and customer engagement.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other insurance entries in the register.
UNIQA SEE cuts health insurance claims processing times by 90% with Azure AI
UNIQA SEE's claims handlers faced fragmented documents, manual tasks, and heavy call-center reliance that slowed claims processing. Using Microsoft Azure AI alongside Microsoft Foundry, Azure Document Intelligence, and Azure OpenAI, UNIQA SEE built an end-to-end AI-driven medical referral solution that extracts, validates, and integrates referral letter data via APIs into the core claims system. The solution reduced claims processing time from 15-30 minutes to 2-3 minutes (an 85-90% reduction), lowered costs and call volumes, and freed staff for higher-value work. UNIQA SEE also introduced a self-service web and mobile app allowing customers to submit medical referral letters digitally. UNIQA SEE became the first insurer in Southeastern Europe to implement an AI-powered business process solution of this kind.
UNIQA Insurance Group· AustriaMicrosoft Azure AI · Microsoft Foundry · Azure Document Intelligence +1
How Mapfre Insurance modernized fraud claims with Amazon EMR Serverless
Mapfre Insurance, the number one auto and home insurer in Massachusetts, modernized fraud detection by combining graph-based features from Neo4j with machine learning models deployed on AWS, using Amazon EMR Serverless, Apache Iceberg tables on Amazon S3, AWS Glue Data Catalog, AWS Lake Formation, and Amazon MWAA for orchestration. Fraud predictions integrate directly with Guidewire Claims via AWS Lambda, automatically creating claim activities showing the top model drivers for adjusters. The initiative, covering Massachusetts Auto insurance and later expanded to Home insurance, has delivered more than $5 million in Net Present Value, with detection accuracy improved 50-135 percent compared to baseline methods.
Mapfre Insurance· United StatesAmazon EMR Serverless · Apache Iceberg · AWS Glue Data Catalog +5Efficiency meets data sovereignty: Lector automates document processing with AI
Lector, a platform for intelligent document processing used by more than 60 corporate customers including over 25 health insurers, migrated its productive cloud services from a major American cloud provider to OVHcloud's German data centre to meet GDPR and data-sovereignty requirements. Using OVHcloud Managed Kubernetes, S3-compatible Object Storage, and AI Training/AI Deploy/AI Endpoint services, Lector achieved over 80% time savings in document processing across more than 500 million pages processed to date, while reducing the operational burden of running Kafka and MongoDB themselves.
lector.ai GmbHKubernetes · S3-compatible Object Storage · Kafka +2Generali France Rides the Wave of Microsoft 365 Copilot and Agentic AI with Microsoft
Generali France rolled out Microsoft 365 Copilot, Copilot Studio, and Azure OpenAI as part of its 'Boost 2027' strategic plan. 100% of employees have access to Copilot Chat and 3,700 use Microsoft 365 Copilot, with 70% of the workforce adopting it and generating an average of fifteen prompts per user per week. About fifty agents built with Copilot Studio address specialized cases including helpdesk operations. A 24/7 voice assistant resolves nearly 1.3 million calls (30% of requests) directly, with no human intervention. Generative AI also supports home insurance subscription, compliance beneficiary searches, and claims management, where 2.1 million operations were processed by RPA robots in 2024, including automated analysis of wall cracks during drought episodes (10% needing no human intervention) and recognition of more than 15 document types. Implementation was supported by Microsoft partner Avanade.
Generali· FranceMicrosoft 365 Copilot · Copilot Studio · Azure OpenAI +1Was this helpful?
Your feedback helps us improve our use case database