Shifting the balance of work from administration to investigation
Sponsored by: BAE Systems
- Financial Crime Prevention
Date: 30 April
Days to go: 23
Time: 9AM UK/ 10AM CEST/ 4PM SGT/ 6PM AEST
It’s time to fight back in financial crime.
Machine learning (ML), artificial intelligence (AI) and robotic process automation (RPA) can, when coupled with big data capabilities, take financial crime prevention to the next level. RPA was the fastest growing segment officially tracked by Gartner in 2018 with a year-on-year growth of more than 63%. However, progression only occurs if old, out- dated and static methods are replaced with new innovative and dynamic techniques. Forbes has reported that banks spent over $100 billion on regulatory compliance, and predicted that the regulatory costs will rise from 4% to 10% of revenue by 2021. Yet, the fact remains, at an estimated $3.8 trillion annually, if money laundering were an economy it would be the 5th largest in the world.
Historically in AML, manual intervention occurs very early in the detection process, leading to larger operational overheads and high false positives. Transactions are processed daily and contribute to customer profiles. Manual rules are created against these profiles and alerts are fired if rule thresholds are breached, for example when Peter Smith performs a suspiciously high transaction on his account or receives money from a suspect jurisdiction. It’s been proven that in the vast majority of instances customers are, in fact, not suspicious actors and here is where technology can begin to intervene. By learning from the behaviour of a skilled workforce over a period of time, alerts can be automatically routed, in effect auto-performing the first line of triage. The result of such automation, with alerts for example being hibernated or escalated, can be significant for operational efficiency and false positive rates.
But, how do we go about detecting those who continue to fly under the radar? The next, and vitally important, stage of the evolution in financial crime prevention includes removing the dependency on alerts to detect suspicion and introducing stages of evaluation. Combining ML, AI and RPA and filtering transactions based on perceived suspicion can lead to paradigm- shifting prevention methods. In reality what this means is, technology is capable of learning about what might be suspicious and then applying further levels of analytics to this sub-set of customer behaviour in order to further investigate and detect suspicious activities which would not be obvious to a manually- generated set of AML rules.
While highly-skilled human input remains as important as ever, the time is now to put technology to work!
During this Webinar, we will talk about the new techniques that can be applied to detect better and quicker suspicious transactions and how to use ML, AI and RPA to maximum benefit
Financial Crime Prevention Advisor
Samantha is a financial crime prevention professional with over 15 years of practical experience in compliance. Sam's previous work experience includes working as MLRO, Data Protection Officer, Chief Compliance Officer and Group Head of AML for various financial institutions in Europe.
Sam worked offshore for several years as the first legal counsel to the financial regulator in Guernsey, heading up enforcement actions and the creation of its dedicated financial crime division. Her role included working with regulators from other jurisdictions in the investigation of complex offshore financial crime. She continues to maintain ongoing engagement with other regulators on a variety of subjects matters, including the transposition of Pan-European regulations and guidance related to financial crime prevention.
Sam has extensive training experience in the field of financial crime prevention and corporate governance matters. She has most recently been involved in projects related to FinTechs, the use of RegTech and other innovative technology to mitigate financial crime. Sam’s focus in this area is on the operationalisation of regulatory requirements and their alignment to the control environment.
Sam is a regulator speaker, moderator and writer on various topics related to financial crime with a focus on tactics used by illicit actors to launder the proceeds of crime. Sam most recently worked with ACAMS Europe as its inaugural AML Director and was instrumental in the development of training content and raising awareness around financial crime prevention challenges in Europe.
Originally from Montreal, Quebec Canada, Sam holds a Bachelors of Public Administration, a Law Degree, qualified as a barrister and solicitor in Canada and as a solicitor in NSW Australia and holds her Masters in Business, specialising in risk management. She most recently completed a certificate with the London School of Economics on Data Analytics.
Global Product Manager, KYC and CDD, BAE Systems
Enda Shirley is the Global Product Manager for KYC and CDD as a discipline within AML Regulatory Compliance at BAE Systems Applied Intelligence. Enda has over 14 years’ of industry experience in market leading software organisations.
Enda joined BAE Systems Applied Intelligence (formerly Norkom) in 2011 and works with clients to understand their requirements and works with engineering teams to get solutions that meet their challenges. Enda is a Certified Anti-Money Laundering Specialist (CAMS).
Key Learning Objectives
- New techniques to be applied to detect better and quicker suspicious transactions
- How to use Machine Learning, Artificial Intelligence and RPA to maximum benefits
- How to remove dependency on alerts by introducing different stages of evaluation
- How to adjust current technologies
- Head of Internal Audit
- Head of Compliance
- Head of Group Compliance
- Head of AML
- Head of Compliance Group
- Global Head of Financial Crime
- Global Financial Crime Unit
- Head of Department Global Financial Crime Unit
- Global Head of Anti Financial Crime
- Chief Compliance Officer