This webinar provides a comprehensive discussion of the recent enforcement trends in Anti-Money Laundering (AML) and Counter Terrorism Financing (CTF) and their implications in 2021.

As gatekeepers of the financial system, banks play a crucial role in reporting possible instances of financial crime. At the same time, criminals continuously reinvent their approaches to hide their activities among dense transaction data. In this talk, we describe the challenges of detecting money laundering and outline why employing machine learning via MLOps is critically important to identify complex and ever-changing patterns.

In anti-money-laundering, machine learning answers to a dire need for vigilance and efficiency where previous-generation systems fall short. We will demonstrate how our open platform facilitates a gradual migration towards a model-driven landscape, using the example of transaction-monitoring to showcase applications of supervised and unsupervised learning, human explainability, and model monitoring. This environment enables us to drive change from the ground up in how the bank understands its clients to detect financial crime.

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Can AI be used for fraud prevention? Yes! In this video, we’ll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We’ll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we’ll try it out on a transaction dataset.

Code for this video:
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https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
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https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://pythonprogramming.net/python-programming-finance-machine-learning-framework/
https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676
https://www.quantopian.com/posts/simple-machine-learning-example

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The rising value and frequency of Anti-Money Laundering (AML) fines has hit global headlines, with European and UK regulators sending a strong message on non-compliance and effective AML programmes. Even the largest global institutions in these markets, with major departments and resources dedicated to AML operations have fallen foul of the long arm of the law. If the giants of the banking and fintech scene cannot manage to avoid the fines and bad press, how can other institutions, such as national, savings, or high street banks and building societies remain compliant in this ever-changing regulatory landscape?

How to modernize AML systems without risky or costly rip and replace projects
Extracting more value from current investments
Layering new technologies for operational efficiency
Improving the efficacy of AML transaction monitoring
Taking advantage of machine learning
Embracing data science within AML teams to improve outcomes
Managing the costs of compliance and non-compliance

Speakers:

Annegret Funke joined Featurespace in 2019 as Senior Solutions Consultant and has since transitioned to Head of Financial Crime, advising institutions bringing advanced analytics into their AML transaction monitoring programmes. She has 7 years’ experience working in the Regtech space, working closely with tier-1 financial institutions to successfully deploy innovative AML and EDD solutions in commercial and product management roles. She holds a MSc in Security Studies from University College London and is a Certified Anti-Money Laundering Specialist (CAMS).

Mark Gregory is a Director in PwC’s forensics practice specialising in the design and implementation of technology and analytics solutions to detect and prevent fraud and financial crime. Mark has over 12 years’ experience working in both public and private sectors and supports clients in areas such as strategy, design, review, implementation and optimisation of controls.