Speaker "Leah Mcfarland" Details Back



Challenges in using machine learning for financial monitoring


Shifting from rules-based to machine learning-based financial monitoring reduces false positive alerts and increases escalation rates (ie, alerts selected for further investigation)
In conjunction with entity resolution/network generation logic, it also enables enhanced visualizations in support of investigations
However, notwithstanding the above, there continue to be multiple challenges in making this important transition, including:
Supervised machine learning uses suspicious activity reports (SARs) as a target, but these may be of low quality (eg, “defensive” filing)
Changes in underlying data over time (ie, data drift) require model retraining, but regulatory requirements on model validation extend this process
To move toward real-time data processing and/or more continuous model retraining requires greater maturity in model lifecycle management
There remains much greater scope for reduction of false positives, given:
  • Rates are still high even after initial machine learning adoption
  • Historical approaches to monitoring models have been backward (eg, identify rare data pattern and impose blanket rules)
  • Regulations and their application are ambiguous in not significantly distinguishing between low- and high-risk activities
  • There is insufficient attention to how compliance costs serve as a barrier to access to financial services

Increasing adoption of blockchain involves a novel data processing/storage mechanism only beginning to be subjected to financial monitoring

There remains a perception that machine learning is “black box,” despite relying on well understood statistical techniques

Use of machine learning has been associated with perpetuation of gender, racial, and other biases (and for financial monitoring could be relevant for treatment of money services businesses)
Development of machine learning models depends on availability of data scientists, who are in relatively short supply and have high rates of staff turnover
There are continuing and new data-related issues:
  • Poor data quality from legacy/siloed systems persists within a given institution
  • Third-party proprietary and open source data need to be integrated for holistic understanding
  • Semi- and unstructured data should be leveraged going forward
  • There is growing expectation that data be evaluated in nearer to real-time
  • Cross-border data requirements are becoming stricter


Leah McFarland is Anti-Money Laundering (AML) Transaction Monitoring Solutions Head at State Street, responsible for defining and implementing the strategy to transition from a rules-based to a features-based monitoring approach using machine learning on a big data analytics platform. Before joining State Street, she served as AML Business Solutions Head at Citibank, where she led global implementation for big data, robotic process automation, and data visualization and pioneered the use of agile project delivery. Prior to that, Leah worked at FSVC, where she managed a $5 million portfolio delivering risk management assistance to financial institutions in the Middle East, North Africa, Sub-Saharan Africa, and Eastern Europe. She began her career as a diplomat for the U.S. Department of State, serving in Moscow, the Secretary of State’s operations center in Washington, DC, and Sao Paulo. Leah has an M.A. in International Affairs from the University of California at San Diego and speaks Russian, Portuguese, Spanish, and some Mandarin Chinese.