Fintech proofs-of-concept

We have engaged with a number of different fintech firms.

Current proofs-of-concept

Digital regulatory reporting

We want to make regulatory reporting easier for firms and improve the quality of the information they provide. We’re working with the Financial Conduct Authority (FCA) and other organisations to explore how digital technology can help.

During the first phase of this work, we developed a prototype reporting system to send data using a distributed ledger technology (DTL) network. 

In the second phase, we looked at the potential cost and logistics of implementing and using a digital approach to regulatory reporting as a whole. 

In January 2020, we published a discussion paper on Transforming data collection from the UK financial sector.

The next phase of this work will depend on the responses to this discussion paper and the work the FCA is now doing directly with firms. 

Firms we have worked with in the past

Regulatory reporting

Digital Regulatory Reporting Pilot Phase I

We worked with the FCA, and a number of other organisations, on a six-month pilot to develop a prototype using a DLT network. The prototype calculates and supplies data for two types of regulatory reporting: 

  • UK domestic mortgage reporting
  • calculation of the Common Equity Tier 1 (CET1) ratio. 

The report below is a summary of these activities and findings.

DRR Pilot Phase 1

Digital Regulatory Reporting Pilot Phase 2

The FCA, the Bank of England and six regulated firms have jointly published a viability assessment report  that outlines phase 2 of this pilot. The report looks at the technological and economic factors that may lead to more automation in regulatory reporting. 

We will continue to work together with the FCA to:

  • explore joint work on common data standards
  • commission a joint review of the legal implications of writing reporting instructions as code 
  • commission a joint independent review of some of technical solutions explored as part of the Digital Regulatory Reporting (DRR) pilot
  • collaborate closely while engaging with industry and planning future phases.

DRR Pilot Phase 2

Cyber security

BitSight

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

PwC

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

PDFPwC proof of concept

Ripple

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

Chain

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

Baton Systems, Clearmatics Technologies Ltd, R3 and Token

The Bank completed a proof of concept 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. 

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

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

MindBridge Analytics Inc

In this second phase, the Bank built on previous 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, including credit union datasets and a daily dataset of trades submitted for the calculation of the SONIA benchmark.

PDFMindBridge Analytics Inc proof of concept

Data analysis

Enforcd

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

Experimentus

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

Privitar

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 08 January 2020
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