Participants at the International Anti-Money Laundering (AML) and Financial Crime TechSprint selected one of the below problem statements to try and solve.
Teams spent 2 days developing their solution, using new and emerging technology, and then pitched their ideas to a cross-industry panel of judges.
- How could the detection and reporting of suspicious activity within capital markets be improved?
- How can technology aid to look at transactions flows across jurisdictions when a centralised way of data sharing is not possible?
- How can the quality of feedback provided to firms from Suspicious Activity Reporting be improved without breaching data privacy and Anti-Money Laundering/tipping-off restrictions?
- How can financial crime patterns be efficiently and effectively identified and codified in such a form that the algorithms can be shared across borders or between institutions?
- How can Robotic Process Automation be applied to improve the efficiency and/or effectiveness of regulated institutions’ financial crime and money laundering detection and prevention activities?
- How can Natural Language Processing aid in the investigation, automation of production and analysis of suspicious transaction/activity reports?
- How can one institution’s suspicions of fraud be recorded in such a way as to reduce the risk of laundering those financial gains at another institution while managing the risk of tipping off and protecting personal data?
- How can appropriate sanctions, money laundering and fraud assessments and interventions be implemented in distributed ledger-backed payment systems?
- How might cryptographic tokenisation be used to aid identification and tracing of units of value through the international financial system?
- How new technologies, big data, etc can be leveraged to measure the incidence of financial crime/money laundering/terrorist financing etc - to aid firms and regulators/agencies in understanding the effectiveness and impact of compliance, control, prevention and prosecution activities.
- How can Network Analytics be used to analyse trends and patterns across the network to give a meaningful network risk score?
- How can Machine Learning and Behavioural Science be used to facilitate decision making for operational support systems?
- How might Technology (Machine Learning and AI) assist in the more effective identification of authorised payment fraud? i.e. where consumers are tricked into sending money to a fraudster.
- How meta-data around transactions (such as IP addresses, card authorizations addresses, unique identification details of mobile phones and laptop computers, browsing data) can be incorporated in the analytical process?
- How can technology be used to facilitate efficient on-boarding of a new customer whilst at the same time ensuring a robust KYC process and giving the customer the ability to protect their own data?