Fintech accelerator

Our fintech accelerator works in partnership with new technology firms to help us investigate how fintech innovations could be useful in central banking.

Our fintech accelerator helps us to improve our understanding of fintech first hand, and also allows us to support development of the sector. We carry out explorative proofs of concept on use cases that are relevant to our role as a central bank that could enable us to function more efficiently and effectively. The knowledge we gain through the proof of concept process and our engagement with a broad spectrum of firms allows us to track trends and developments in the sector.

In return, we offer firms the chance to demonstrate their solutions for real issues, and to work directly with some of our leading experts. Firms will get feedback on their applicability and usefulness their tools and products over the course of a proof of concept. We will also provide firms with valuable references for future clients, and the opportunity to join our fintech community.

How can fintech firms work with us?

Fintech firms can engage with us through submitting a response to one of our calls for applications, or through our fintech community.

PDFFintech accelerator process FAQ's

Fintech Community

We have created a community of fintech-related organisations with the aims of:

  • sharing developments, trends and insights so that firms across the sector can learn from each other, and we in turn can learn from them
  • making sure that we’re engaging with a variety of fintech firms from across the sector
  • increasing networking across firms that are interested in fintech, helping the sector to develop.

The community will not discuss or share any commercially sensitive material. It is free to join, but we are initially limiting membership to firms most relevant to the Bank’s remit and fintech objectives, and those that have made contact with us to share knowledge. Firms which have completed a proof of concept with us will automatically be invited to join. We’ll review community membership regularly to make sure members continue to meet these criteria, and that we have an appropriate balance of organisations. We will also continue to engage with fintech firms outside the community.

Members of the community will be invited to meet with us two to four times per year, to share updates on trends and developments in the sector. These meetings are confidential. Community members will also be invited to quarterly networking and knowledge-sharing events. We publish summaries of the topics discussed at these events:

Key discussion topics:

PDFFintech Community event 5 December 2017

PDFFintech Community event 13 June 2017

PDFFintech Community event 17 March 2017

Current members of the community are:

  • Anomali
  • BitSight
  • BMLL
  • BT
  • Deloitte LLP
  • Enforcd
  • Financial Conduct Authority (FCA)
  • H M Treasury
  • Illuminate Financial Management LLP
  • Innovate Finance
  • London and Partners
  • NEX/Euclid Opportunities
  • Omidyar Network
  • Privitar
  • PwC
  • Simmons and Simmons LLP
  • Thomson Reuters
  • UK Finance

Membership of the community does not entail approval or endorsement by the Bank.

Calls for applications

Our latest call for applications is now closed.

In the meantime, if you would like to stay in touch with us, you can follow us on LinkedIn. We will also be posting updates on our work on the Bank’s Twitter account.

If you have any questions about the accelerator, you can email

Which fintech firms have we worked with?

After our recent call for applications, the following firms have been selected to join the fourth cohort of the Bank of England’s FinTech Accelerator:


One of the Accelerator’s first PoCs raised questions about the confidentiality that could be maintained in a distributed ledger system and the possible trade-off between privacy, performance and resilience in the system. The technical issues around privacy were identified in the digital currencies research questions that the Bank published last year. This PoC will specifically explore how distributed ledgers can be configured to enable privacy amongst participants whilst keeping data shared across a network.

Digital Reasoning

Our call for applications included a request for a solution to ingest and analyse large quantities of weakly-structured data and detect patterns, anomalies and themes. We have decided to work with Digital Reasoning who, through their cognitive computing solution, will draw out sentiment and qualitative insights within a sample of publicly available information. The results of this PoC could support the Bank’s supervisory approach.

NTT DATA and Reportix

This PoC will investigate an innovative processing solution for XBRL based datasets, which account for around 80% of the regulatory reporting data collected by the Bank of England, to support the evolution of the One Bank Data Architecture initiative of the Bank’s Strategic Plan. It is anticipated such innovation could have a significant impact on the speed at which we import, store, analyse and visualise regulatory data.

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.

We have worked with the following firms 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.

PDFProof of concept

Anomali and ThreatConnect

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

PDFProof of concept

PDFProof of concept

Distributed ledger technology


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

PDFProof 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.

PDFProof 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.

PDFProof 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.

PDFProof 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.

PDFProof 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.

PDFProof 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.

PDFProof of concept

This page was last updated 16 January 2018
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