Fintech proofs-of-concept

We have engaged with a number of different fintech firms.

Current proofs-of-concept

Baton Systems, Clearmatics Technologies Ltd, R3 and Token

The Bank is undertaking a Proof of Concept (PoC) to understand how a renewed RTGS service could be capable of supporting settlement in systems operating on innovative payment technologies, such as those built on DLT. The Bank is partnering with a range of firms developing payment arrangements using innovative technologies: Baton Systems, Clearmatics Technologies Ltd, R3 and Token. These firms are engaging with the PoC in a range of ways.

PDFRTGS Renewal proof of concept

Firms selected to join the last cohort of our Fintech Accelerator

MindBridge™ Analytics Inc

The Bank worked with the firm in its previous cohort to help identify anomalies in anonymised credit union datasets. In this second phase, the Bank will build on those learnings to look at the versatility of the MindBridge Ai Auditor™ tool to provide data visualisation and data preparation techniques for larger numeric and transaction-level datasets. We are interested in the potential of machine learning to assist the way the Bank conducts plausibility and validation checks on different types of datasets. One of the datasets our in-house experts will run through the MindBridge artificial intelligence tool will be the transaction data that will go towards calculating the Bank’s reformed SONIA benchmark.

Firms we have worked with in the past

Cyber security


In the BitSight proof of concept, we tested BitSight’s tool that assesses a firm’s cyber resilience based on publicly available data.

PDFBitSight proof of concept

Anomali and ThreatConnect

We asked these companies to create a searchable database where intelligence on cybersecurity threats can be optimised and stored.

PDFAnomali and ThreatConnect proof of concept

Distributed ledger technology


Our project with PwC looked at possible applications of blockchain and distributed ledger technology.

PDFPwC proof of concept


We carried out a proof of concept with Ripple to explore the synchronised movement of two different currencies across two different real-time gross settlement systems linked using Ripple Connect and the Interledger protocol. We wanted to demonstrate how this kind of synchronisation might lower settlement risk and improve the speed and efficiency of cross-border payments.

PDFRipple proof of concept


This proof of concept explored how distributed ledgers could be configured to enable privacy amongst participants whilst keeping data shared across a network. 

PDFChain proof of concept

Machine learning

MindBridge Analytics Inc

MindBridge’s artificial intelligence (AI) auditor detects anomalies in financial transactions and reports using data science, machine learning and other AI techniques. In this proof of concept we asked the firm to prove the analytical value of the tool for detecting anomalies in anonymised credit union datasets.

PDFMindBridge Analytics Inc proof of concept


BMLL’s machine-learning platform provides access to historic limit order book data – trading exchanges’ records of buyer and seller interest in particular trades – with the aim of making it easier to analyse and check anomalies in the data. We tested the alpha version for the BMLL proof of concept.

PDFBMLL proof of concept

Data analysis


Enforcd’s enforcement database holds publicly available UK regulatory enforcement actions and news, along with commentary written by Enforcd’s own regulatory lawyers, and insights from City law firms and chambers. In this proof of concept we wanted to understand the benefits and the influence on decision-making of viewing publically available regulatory enforcement action from different perspectives.

PDFEnforcd proof of concept


This proof of concept applied Experimentus’ ORB tool to analyse historic Bank of England projects to visualise how they had performed against a range of standard key performance indicators.

PDFExperimentus proof of concept


For this proof of concept, we tested Privitar’s software on a manufactured dataset to examine the analytical value of the desensitised data. We did this to establish whether we could provide Bank researchers with wider access to data.

PDFPrivitar proof of concept

NTT DATA and Reportix

This proof of concept investigated an innovative processing solution for XBRL based datasets to support the evolution of the One Bank Data Architecture initiative of the Bank’s Strategic plan.

PDFNTT DATA and Reportix proof of concept

Digital Reasoning

This proof of concept involved using a machine learning solution to ingest and classify large amounts of weakly-structured data. The aim was to assess the effectiveness of the software in drawing out sentiment and qualitative insights from publicly available information to support of the Bank’s supervisory approach.

PDFDigital Reasoning proof of concept

This page was last updated 12 July 2018
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