TL;DR Breakdown
- Federated Machine Learning (FML) enables global banks to strengthen their Anti-Money Laundering (AML) strategies while preserving data privacy.
- FML methodologies, such as Horizontal Federated Learning and Vertical Federated Learning, facilitate collaboration and knowledge transfer without exposing sensitive customer data.
- By leveraging FML, banks can enhance their ability to detect and prevent fund diversion, contributing to a more secure global financial system.
In response to the Anti-Money Laundering (AML) Act of 2020, the financial industry is embracing the transformative potential of artificial intelligence (AI) and machine learning (ML) to strengthen their AML strategies. However, the adoption of these technologies has encountered challenges due to the data-intensive nature of AI/ML and the need to comply with data privacy regulations. While the digital age has facilitated seamless transactions, it has also provided opportunities for illicit activities like money laundering.
Money launderers often employ the tactic of fund diversion, rapidly moving money across accounts in small amounts to evade detection. Detecting and preventing such intricate tactics is a significant challenge for global banking institutions, especially considering the strict data privacy laws such as the General Data Protection Regulation (GDPR). This is where federated machine learning (FML) can play a vital role in bolstering AML strategies.
Federated Machine Learning Overview
Federated Machine Learning (FML) is a decentralized path to machine learning that prioritizes data privacy by keeping sensitive information on local servers while allowing global insights. It encompasses three essential methodologies: Horizontal Federated Learning (HFL), Vertical Federated Learning (VFL), and Federated Transfer Learning (FTL). HFL enables banks with similar data structures but different data owners to train local machine learning models and share only the model parameters without exposing the sensitive data itself.
VFL facilitates collaboration between different departments within a bank that holds different types of data about the same set of customers. FTL involves leveraging a pre-trained model on one dataset and fine-tuning it on another, enabling the transfer of insights from one region to another. These FML methodologies offer powerful techniques for preserving data privacy while still harnessing the benefits of machine learning on a global scale.
The FML process: A step-by-step approach
Here’s an outline of the FML process:
1. Initialization: The server initializes a global model with random weights and distributes this model to participating clients.
2. Local Training: Each client performs local model updates based on their own data, using techniques like stochastic gradient descent (SGD) or other optimization algorithms.
3. Model Update: Each client sends their local model updates back to the server, which only includes the model parameters and excludes raw data, safeguarding client privacy.
4. Global Aggregation: The server aggregates the local model updates, typically by computing a weighted average, to create an updated global model.
5. Iteration: The server sends the updated global model back to the clients, and the process repeats for several rounds until the global model meets the desired performance criteria.
6. Evaluation: Once the global model is finalized, its performance can be evaluated on a separate validation dataset or using additional metrics as required.
AML and FML: A synergistic approach
FML holds enormous potential in enhancing AML strategies, particularly in detecting fund diversion. Let’s explore a use case for each FML methodology:
Horizontal Federated Learning (HFL) and AML
Suppose two regional business units of a global bank, both operating within the EU and subject to GDPR regulations, aim to improve their fund diversion detection capabilities while complying with privacy laws. With HFL, each unit can train a local ML model to identify suspicious patterns associated with fund diversion. They can then share the model parameters, rather than the customer data itself, with a central server. The server aggregates these parameters to create an enhanced global model, which is subsequently shared back with each regional unit. This approach ensures that customer data remains within the local bank, effectively addressing privacy concerns under GDPR.
Vertical federated learning (VFL) and AML
Consider a single regional unit within the EU consisting of two departments: the banking department with customer banking transaction data and the wire transfer department with data on international transfers made by the same customers. Both departments seek to collaborate to improve fund diversion detection while maintaining data privacy. VFL enables these departments to jointly train a model using their combined features, without sharing sensitive customer data. By doing so, patterns indicative of fund diversion, such as large deposits followed by rapid international transfers, can be identified. Importantly, customer data does not need to be consolidated into a single database, ensuring compliance with GDPR requirements.
Federated transfer learning (FTL) and AML
Imagine a global bank with operations in Region A (within the EU, governed by GDPR) and Region B (outside the EU with different privacy regulations). The bank possesses a well-performing AML model trained on Region A data but finds that this model performs poorly in Region B due to distinct economic conditions and transaction patterns. With FTL, the bank can take the model trained on Region A data and fine-tune it using Region B data.
This approach allows the bank to leverage insights from Region A while adapting to the specific conditions of Region B. Region B only receives the model parameters and not the specific customer data from Region A, ensuring GDPR compliance while benefiting from knowledge transfer. Consequently, the bank’s ability to detect potential fund diversion across both regions improves significantly.
FML is revolutionizing AML efforts by providing a powerful tool to combat money laundering while upholding privacy concerns addressed by GDPR. As we navigate an increasingly digital future, integrating cutting-edge technologies like FML can have a significant impact on ensuring the security of our financial systems. By incorporating FML into their AML strategies, banks can enhance their capacity to detect and prevent illicit activities such as fund diversion. This, in turn, creates safer financial environments for customers and contributes to a more secure global financial system.
