List of Quantemplate Machine Learning Customers
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Since 2010, our global team of researchers has been studying Quantemplate Machine Learning customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Quantemplate Machine Learning for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Quantemplate Machine Learning for ML and Data Science Platforms include: Aegon, a Netherlands based Insurance organisation with 15700 employees and revenues of $32.50 billion, RenaissanceRe, a Bermuda based Insurance organisation with 945 employees and revenues of $11.70 billion, Sagesure, a United States based Insurance organisation with 1500 employees and revenues of $250.0 million and many others.
Contact us if you need a completed and verified list of companies using Quantemplate Machine Learning, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Quantemplate Machine Learning customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Aegon | Insurance | 15700 | $32.5B | Netherlands | Quantemplate | Quantemplate Machine Learning | ML and Data Science Platforms | 2019 | n/a | In 2019 Aegon Blue Square Re implemented Quantemplate Machine Learning to unify premium and claims data from multiple policy administration systems for underwriting, exposure and pricing analytics. The deployment used Quantemplate Machine Learning within the ML and Data Science Platforms category to automate mapping and validation of bordereaux and exposure feeds for regulatory and pricing purposes. The implementation leveraged Quantemplate Machine Learning capabilities, specifically ML driven mapping suggestions and validation features, to cleanse, harmonise and validate bordereaux and exposure data. Configuration focused on automated mapping suggestion workflows, rule based validation checkpoints, and data harmonisation pipelines to support underwriting and pricing analytics workloads. Integrations in the project centered on ingesting bordereaux and exposure extracts from multiple policy administration systems and consolidating premium and claims records into a unified analytical dataset. Operational coverage targeted underwriting, actuarial, pricing and risk functions within Aegon Blue Square Re, enabling those teams to consume harmonised datasets for exposure modeling and pricing analysis. Governance changes included standardised mapping and validation workflows and tighter controls on bordereaux ingestion to support regulatory and pricing use cases. The case study reports a roughly 33 percent improvement in data processing efficiency while handling substantially more data, reflecting the direct impact of the ML driven mapping and validation approach. | |
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RenaissanceRe | Insurance | 945 | $11.7B | Bermuda | Quantemplate | Quantemplate Machine Learning | ML and Data Science Platforms | 2019 | n/a | In 2019, RenaissanceRe implemented Quantemplate Machine Learning to automate normalization and cleansing of reinsurance bordereaux and exposure data. The deployment used the ML and Data Science Platforms capabilities to support underwriting and aggregation analysis across the companys global operations. The implementation centered on Quantemplate Machine Learning modules for ML based mapping, validation and data transformation, applying automated mapping of broker and cedant bordereaux to canonical schemas, rule based validation, and transformation pipelines for exposure consolidation. Functional workflows included ingestion, schema reconciliation, automated validation reporting and programmatic data transformation to prepare datasets for underwriting models and aggregation analysis. These capabilities align with typical ML and Data Science Platforms functionality for data preparation and feature engineering. Operational coverage extended to underwriting and portfolio aggregation teams across RenaissanceRes global sites, standardizing how bordereaux and exposure feeds were normalized before analytical consumption. Governance was organized through centralized data and analytics teams that enforced mapping rules and validation policies and orchestrated data transformation workflows using the Quantemplate Machine Learning platform. | |
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Sagesure | Insurance | 1500 | $250M | United States | Quantemplate | Quantemplate Machine Learning | ML and Data Science Platforms | 2019 | n/a | In 2019, Sagesure implemented Quantemplate Machine Learning, an ML and Data Science Platforms application, to centralize data preparation, storage and business intelligence for its US residential property business. The deployment targeted underwriting teams and program growth initiatives, consolidating disparate data streams into a single preparation and storage layer. Sagesure configured Quantemplate Machine Learning to apply AI-driven mapping and automated data preparation workflows, enabling consistent feature engineering and standardized datasets for downstream analytics. The implementation included model training orchestration and dataset versioning capabilities typical of ML and Data Science Platforms, configured to support underwriting analytics and pricing model inputs. These capabilities were used to democratize access to prepared data and accelerate reporting for actuarial and underwriting stakeholders. The unified data layer supported underwriting decision processes and program management workflows across the United States, focusing operational coverage on US residential property underwriting and program teams. Governance emphasized data stewardship, standardized preparation pipelines and self-service reporting to reduce manual ETL handoffs between actuarial and underwriting functions. The initiative aimed to support underwriting and program growth by delivering faster, more consistent data for business intelligence and model development. |
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