Global AML and Financial Crime TechSprint

In May 2018, we held a 3-day Global Anti-Money Laundering and Financial Crime TechSprint. Our fifth and largest TechSprint, the event focused on how new technology can be used to more effectively combat money laundering and financial crime.

The United Nations (UN) estimates that at least US$1.6 trillion is laundered through the global financial system each year. This is the proceeds of human, drug, arms and endangered wildlife trafficking, slavery, corruption, fraud and other crimes. It is also estimated that less than 1% of illicit financial flows are intercepted globally.

The purpose of the TechSprint was to investigate how new technologies and greater international collaboration could help to improve prevention and detection rates.

TechSprint participants were welcomed by Megan Butler, Executive Director of Supervision – Investment, Wholesale and Specialist, who highlighted how data and technology can help detect and disrupt criminal activity. Christopher Woolard, Executive Director of Strategy & Competition, also spoke on the key theme of the TechSprint – the importance of greater international collaboration in fighting financial crime.

Video: TechSprint 2018 opening film

This video from our May 2018 TechSprint shows the human cost of money laundering and financial crime and highlights the need for regulators, industry and law enforcement to explore how technology can help increase prevention and detection rates.

Developing solutions

Over the three days, 260 participants from 105 firms spanning 16 countries – together with regulators and law enforcement agencies from the United States, Europe, Middle East and Asia Pacific – worked in teams to develop solutions to various problem statements.

The teams then demonstrated their solutions to a cross-industry judging panel and a sizeable audience of senior executives from regulated firms, technology providers, start-ups and academic institutions.

Some of the technologies and prototype solutions that were produced and showcased by the teams included:

  • A shared database of ‘bad actors’, secured and distributed using distributed ledger technology. The database would allow a financial institution to query whether a new customer had been rejected by another financial institution due to financial crime activities or concerns.
  • Natural language processing, topic modelling and text analytics to enhance financial crime-focused transaction monitoring solutions within financial institutions.
  • Graph/network analytics to more readily identify relationships between entities to aid in due diligence and ongoing monitoring of potentially suspicious entities and activities.

The teams also developed various methods to enable:

  • Greater sharing of crime typologies/patterns between institutions to aid detection and intervention capabilities.
  • Querying by a financial institution of the confidential/encrypted data of another financial institution using homomorphic encryption and/or zero-knowledge proof technologies. These technologies could enable financial institutions to verify certain types of information with each other, without compromising the security or confidentiality of the underlying data.
  • Centralisation of data from multiple institutions into a shared utility, with the data then being analysed for fraud, money laundering and sanctions monitoring purposes.


Prizes were awarded for the following categories. The winning teams will receive support to progress their solutions from Level 39, RegTech Associates and The Disruption House.

  • Fast award - the solutions that could be implemented most quickly. The winner – 'Connected Picture' – developed a machine learning system that uses the transaction and network features of known ‘bad actors’ to prevent authorised payment fraud.
  • Eureka award – the most original idea. The winner – 'Catch the Chameleon' – developed blockchain technology to share questions and answers between nodes in the financial system to identify matches between different profiles for the same entity without sharing data.
  • Jump award -  has the potential to provide the biggest jump forward. The winner – 'Red Flag' – developed a Natural Language Processing system to provide additional meaning and context to transactions. Artificial intelligence systems search for suspicious phrases within the transaction notes and analyse how common it is for certain industries to be transacting with each other to determine a risk score for transactions.

Observing proofs of concepts

We are also acting as an observer for several consortia who are developing anti-money laundering related proofs of concept resulting from the TechSprint. These include:

  • Catch the Chameleon: a collaboration between Santander and other financial and technology industry partners that uses blockchain to allow the cross-referencing of KYC/ KYB data on Small and Medium Enterprises (SMEs) with suspicious characteristics.
  • Webs of Suspicion: a collaboration between Solidatus, Grant Thornton UK LLP, Ashurst LLP, Solace and Privitar, to explore ways to help firms safely detect and share data on financial crime, using machine learning AI, encryption and smart contract protocols, underpinned by open standards.
  • Pooled Transactional Data: a collaboration between Deloitte and three large UK retail banks to assess whether using advanced analytics techniques on pooled transactional data from multiple financial institutions may identify potential money laundering activity which would not have been identified by conventional practices.

Please contact [email protected] if you are interested in discussing any potential proofs of concepts you may be working on in this area. 

Find out more

A podcast recorded at the TechSprint with Christopher Woolard and Nick Cook, provides more detail on the background and ambitions of the TechSprint.

For further information, contact [email protected], or search for #FCASprint on social media to see posts from various attendees and participants of the TechSprint.

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