TL;DR:
- Several major banks, including Danske Bank, Credit Suisse, Santander Bank UK, USAA Federal Savings Bank, and Wells Fargo, have been fined a total of $2.2 billion for financial regulatory infractions.
- The financial industry is exploring the use of artificial intelligence (AI) and machine learning (ML) for Anti-Money Laundering (AML) strategies.
- Federated Machine Learning (FML) offers a decentralized approach to ML, enabling data privacy preservation while allowing for global insights.
- FML consists of three 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 ML models and share only the model parameters.
- VFL allows collaboration between different departments within a bank to train models on combined features without sharing sensitive customer data.
- FTL enables fine-tuning of models trained on one dataset using data from another region, ensuring compliance with privacy regulations.
- FML provides a powerful synergy with AML efforts, facilitating the detection of fund diversion while respecting data privacy regulations.
Main AI News:
In recent years, esteemed financial institutions including Danske Bank, Credit Suisse, Santander Bank UK, USAA Federal Savings Bank, and Wells Fargo have faced substantial fines, collectively amounting to a staggering $2.2 billion, for various infractions in the realm of financial regulation. Such penalties shed light on the pressing need for proactive measures to combat illicit activities, such as money laundering, which have thrived in the digital age. As the financial industry grapples with the challenges posed by the data-intensive nature of artificial intelligence (AI) and machine learning (ML) applications, they are also presented with a transformative solution—federated machine learning (FML).
With the advent of the Anti-Money Laundering (AML) Act of 2020, the financial landscape has been compelled to explore innovative approaches that harness the potential of AI and ML while adhering to data privacy regulations. However, the integration of these technologies has encountered hurdles due to the intricate nature of financial data and the stringent data privacy laws, such as the General Data Protection Regulation (GDPR).
The diversification of funds, a technique employed to swiftly transfer money across multiple accounts in small increments to evade detection, stands as a common method employed by money launderers. Tackling such sophisticated and evasive tactics proves to be a formidable challenge for global banking institutions, necessitating innovative solutions to fortify their AML strategies. It is in this context that federated machine learning emerges as a powerful ally.
Fundamentals of Federated Machine Learning
Federated machine learning (FML) presents a decentralized approach that preserves data privacy while fostering global insights. This paradigm encompasses three primary methodologies:
- Horizontal Federated Learning (HFL): HFL enables banks with similar data structures but different data owners to train local ML models and share only the model parameters, without compromising the sensitive data itself.
- Vertical Federated Learning (VFL): VFL facilitates collaboration between different departments within a bank that possesses distinct types of data related to the same pool of customers.
- Federated Transfer Learning (FTL): FTL leverages a pre-trained model on one dataset and fine-tunes it using another, enabling the transfer of valuable insights from one region to another.
A Glimpse into the FML Process
To grasp the mechanics of FML, it is crucial to understand the core steps involved:
- Initialization: The server initiates a global model with random weights, dispatching it to participating clients.
- Local Training: Each client leverages its own data to compute a local model update, employing stochastic gradient descent (SGD) or another optimization algorithm.
- Model Update: Clients transmit their local model updates to the server, ensuring that these updates are solely based on model parameters and devoid of any raw data, thereby safeguarding client privacy.
- Global Aggregation: The server aggregates the local model updates, usually by calculating a weighted average, culminating in an updated global model.
- Iteration: The server transmits the updated global model back to the clients, repeating the process for numerous rounds until the global model meets the desired performance criteria.
- Evaluation: Once the final global model is established, its performance is evaluated using a distinct validation dataset or other pertinent performance metrics.
AML and FML: A Powerful Synergy
It is imperative to recognize the tremendous potential of FML in enhancing AML strategies, particularly in detecting the diversion of funds. To provide a comprehensive perspective, let us explore a use case for each FML methodology:
HFL and AML
Consider two regional business units of a global bank, both operating within the European Union and thus adhering to GDPR regulations. Although they cater to different sets of customers, both units gather similar transaction-related data, encompassing transaction amounts, types, and account balances.
Use Case: These units aspire to enhance their capability to identify fund diversion patterns while upholding GDPR compliance. By employing HFL, each unit can train a local ML model, isolating suspicious patterns indicative of fund diversion. Subsequently, they share the model parameters—rather than the customer data itself—with a centralized server. The server amalgamates these parameters to create an improved global model, which is then reciprocated to each regional unit. This approach guarantees that customer data remains within the confines of the local bank, thereby addressing privacy concerns stipulated by GDPR.
VFL and AML
Imagine a singular regional unit within the European Union encompassing two departments: the banking department, equipped with data pertaining to customer banking transactions, and the wire transfer department, armed with data pertaining to international transfers conducted by the same customers.
Use Case: Both departments strive to collaborate to enhance fund diversion detection while upholding data privacy. VFL enables these departments to collectively train a model utilizing their combined features, all without divulging sensitive customer data. This facilitates the identification of patterns—such as significant deposits followed by rapid international transfers—that serve as red flags for fund diversion. Importantly, customer data need not be consolidated into a singular database, ensuring compliance with GDPR requirements.
FTL and AML
Let us consider a global bank operating in both Region A (within the EU, thus bound by GDPR regulations) and Region B (outside the EU, governed by distinct privacy regulations). The bank possesses a well-performing AML model trained using data from Region A. However, this model yields subpar results in Region B due to divergent economic conditions and transactional patterns.
Use Case: Employing FTL, the bank can capitalize on the model trained using Region A data and fine-tune it using Region B data. This approach enables the bank to leverage insights garnered from Region A while adapting to the distinctive conditions prevalent in Region B. Crucially, Region B solely receives the model parameters without gaining access to specific customer data from Region A, thus ensuring GDPR compliance while benefiting from knowledge transfer. This seamless integration empowers the bank to bolster its ability to detect potential fund diversion across both regions.
A Stride Towards a Safer Future
FML presents a remarkable opportunity to fortify AML efforts by providing a formidable weapon to combat money laundering while simultaneously addressing the privacy concerns outlined by GDPR. As the world hurtles forward into an increasingly digital era, harnessing cutting-edge technologies such as FML can indubitably yield a substantial impact in safeguarding the security of our financial systems. By seamlessly integrating FML into their AML strategies, banks can augment their proficiency in detecting and preventing illicit activities like fund diversion, ultimately fostering secure financial environments for their customers and bolstering the integrity of the global financial ecosystem.
Conclusion:
The adoption of Federated Machine Learning (FML) in the financial industry has the potential to significantly enhance Anti-Money Laundering (AML) strategies. By leveraging decentralized approaches such as Horizontal Federated Learning (HFL), Vertical Federated Learning (VFL), and Federated Transfer Learning (FTL), banks can improve their ability to detect fund diversion while preserving data privacy in compliance with regulations like the General Data Protection Regulation (GDPR).
This integration of cutting-edge technologies not only strengthens AML efforts but also contributes to a safer financial environment. As the market embraces FML, we can expect increased security and improved detection of illicit activities, ultimately fostering trust and confidence in the global financial system.
