About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

n-Tier Blockchain-Based Reference Data Consensus Solution Aims to Drive Down Errors and Costs

Subscribe to our newsletter

As reference data volumes continue to soar, bringing with them huge data cleansing, validation and management costs, financial institutions are beginning to consider collaborative solutions that can improve data accuracy while reducing cost. n-Tier, a New York headquartered company that helps firms ensure accuracy and completeness of reference data, has joined the party with a consensus-based reference data blockchain solution.

The solution uses a private blockchain to establish consensus across firms on key data elements and aims to reduce reference data errors and costs. The model is similar to that of the DANIE consortium that is bringing financial institutions together to improve the quality of their client reference data by benchmarking data with peers without revealing data sources and with encryption.

The key difference is that the n-Tier offer is integrated with the firm’s Compliance Workbench platform, which allows data differences between both data owners and data consumers to be identified on the blockchain, resolved on the platform, and integrated back into in-house reference data.

n-Tier founder and CEO, Peter Gargone, says: “The n-Tier consensus solution is driven by customer interest in leveraging technology across the industry. Everyone we talk to spends a tremendous amount of time trying to ensure they have accurate reference data, but they are all doing the same work, trying to keep the same key reference data elements up to date. Working with our customers it became clear that if we could connect firms through an anonymous and secure blockchain they could all benefit from each other’s efforts, saving everyone a lot of time and money.”

The company is currently talking to its customers about the reference data types they would initially like to compare on the blockchain, perhaps LEI, KYC or security data, and hopes to have the solution up and running in the next month or so. “The first users of the blockchain will probably be our current Tier 1 and Tier 2 customers that already use our infrastructure. For them, this is a plug in.”

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Unpacking Stablecoin Challenges for Financial Institutions

The stablecoin market is experiencing unprecedented growth, driven by emerging regulatory clarity, technological maturity, and rising global demand for a faster, more secure financial infrastructure. But with opportunity comes complexity, and a host of challenges that financial institutions need to address before they can unlock the promise of a more streamlined financial transaction ecosystem. These...

BLOG

Twelve Leading Data Lineage Solutions for Capital Markets

The ability to trace the journey of data from its origin to its final report is no longer a luxury but a regulatory and operational necessity. As firms grapple with the intensifying requirements of regulations such as BCBS 239, GDPR and the shifting landscape of MiFID II, the “black box” approach to data management has...

EVENT

ExchangeTech Summit London

A-Team Group, organisers of the TradingTech Summits, are pleased to announce the inaugural ExchangeTech Summit London on May 14th 2026. This dedicated forum brings together operators of exchanges, alternative execution venues and digital asset platforms with the ecosystem of vendors driving the future of matching engines, surveillance and market access.

GUIDE

Regulatory Data Handbook 2025 – Thirteenth Edition

Welcome to the thirteenth edition of A-Team Group’s Regulatory Data Handbook, a unique and practical guide to capital markets regulation, regulatory change, and the data and data management requirements of compliance across Europe, the UK, US and Asia-Pacific. This year’s edition lands at a moment of accelerating regulatory divergence and intensifying data focused supervision. Inside,...