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

Broadridge Partners Tookitaki to Deliver AI Based Data Reconciliation Platform

Subscribe to our newsletter

Responding to the ongoing industry challenge of data reconciliation, Broadridge Financial Solutions has released Data Control Intelligent Automation, an artificial intelligence (AI) and machine learning (ML) platform built for deployment across reconciliation, matching and exception management applications. The solution has been developed with Singapore-based Tookitaki, a provider of AI and ML technology.

The platform will allow customers to license modules on the platform that provide intelligent automation applications. The initial modules are Break Management and Recon Perform. Both modules provide enterprise wide capability, working across not only Broadridge reconciliation solutions, but also in-house and third-party developed solutions.

Alastair McGill, general manager of Data Control Solutions at Broadridge, says: “Intelligent automation will drive performance and productivity gains from incumbent reconciliation systems, especially where organisations have multiple vendor solutions in place.”

The Broadridge platform uses a distributed computing framework to deliver a high-performance and scalable matching and exception process. It is agnostic to the underlying reconciliation system and can be deployed on premise, on Broadridge managed servers or in the cloud. The Break Management module accelerates the investigation process, reducing resolution times by continuously improving break classification according to client-defined business reasons. Recon Perform automates reconciliation builds with an automatic matching scheme that uses supervised ML models and continuous matching scheme improvement, saving time and cost for firms managing large volumes of reconciliations. 

The Tookitaki element of the solution includes the company’s patent-pending explainability framework, which offers a ‘glass-box’ approach to ML models that allows users to view decisions made by the platform’s engine through a simple interface.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: The challenges and potential of data marketplaces

Data is the lifeblood of capital markets. It is also a valuable commodity providing financial institutions with additional insight when gathered in an internal data marketplace, or packaged and sold externally to other institutions. While the theory is sound, the practice of setting up a data marketplace can be challenging. Internally, vast amounts of data...

BLOG

Bloomberg Employs ML and Industry-Implied Models to Increase Carbon Emissions Data

Bloomberg has increased its carbon emissions dataset to cover 100,000 companies. The dataset consists of company reported carbon data and estimates based on either a machine learning smart model or Bloomberg’s newly developed industry-implied model accompanied by a Partnership for Carbon Accounting Financials (PCAF) reliability score. “Greater precision in Scope 1, 2 and 3 carbon...

EVENT

RegTech Summit London

Now in its 6th year, the RegTech Summit in London will bring together the RegTech ecosystem to explore how the European capital markets financial industry can leverage technology to drive innovation, cut costs and support regulatory change.

GUIDE

Regulatory Data Handbook 2022/2023 – Tenth Edition

Welcome to the tenth edition of A-Team Group’s Regulatory Data Handbook, a publication that has tracked new regulations, amendments, implementation and data management requirements as regulatory change has impacted global capital markets participants over the past 10 years. This edition of the handbook includes new regulations and highlights some of the major regulatory interventions challenging...