AML TechSprint problem statements

Teams spent 2 days developing their solution, using new and emerging technology, and then pitched their ideas to a cross-industry panel of judges.

Problems statements

1. How could the detection and reporting of suspicious activity within capital markets be improved?
2. How can technology aid to look at transactions flows across jurisdictions when a centralised way of data sharing is not possible?
3. 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?
4. 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?
5. 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?
6. How can Natural Language Processing aid in the investigation, automation of production and analysis of suspicious transaction/activity reports?
7. 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?
8. How can appropriate sanctions, money laundering and fraud assessments and interventions be implemented in distributed ledger-backed payment systems?
9. How might cryptographic tokenisation be used to aid identification and tracing of units of value through the international financial system?
10. 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.
11. How can Network Analytics be used to analyse trends and patterns across the network to give a meaningful network risk score?
12. How can Machine Learning and Behavioural Science be used to facilitate decision making for operational support systems?
13. 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.
14. 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?
15. 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?