Global AML and Financial Crime TechSprint 2018

In May 2018, we held a 3-day TechSprint, focusing on how 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.

Megan Butler, executive director of supervision – investment, wholesale and specialist, highlighted how data and technology can help detect and disrupt criminal activity. 

Read Megan's speech.

Christopher Woolard, executive director of strategy and competition, also spoke on the importance of greater international collaboration in fighting financial crime.

Read Christopher's speech.

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 3 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.

Awards

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

Observing proofs of concepts

We also acted as an observer for several consortia 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 using blockchain to allow the cross-referencing of KYC/ KYB data on small and medium-sized 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 3 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.

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 more information, email [email protected].