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: Are you making the most of the business-critical structured data stored in your mainframes?

Fewer than 30% of companies think that they can fully tap into their mainframe data even though complete, accurate and real-time data is key to business decision-making, compliance, modernisation and innovation. For many in financial markets, integrating data across the enterprise and making it available and actionable to everyone who needs it is extremely difficult....

BLOG

Why your Technology Spend isn’t Delivering the Productivity you Expected

By Gareth Evans, Chief Product Officer, FINBOURNE. An uncomfortable truth: technology spend in asset management has surged 8.9% annually over the past five years across North America and Europe. But productivity? Flat. Cost as a share of assets under management (AUM)? No improvement. Operational expenses in other functions? Despite the promises that technology would create...

EVENT

Buy AND Build: The Future of Capital Markets Technology

Buy AND Build: The Future of Capital Markets Technology London examines the latest changes and innovations in trading technology and explores how technology is being deployed to create an edge in sell side and buy side capital markets financial institutions.

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,...