Introduction to AI in Finance

Federated Learning is gaining significant momentum for financial applications as it offers a practical solution for training machine learning models collaboratively while sensitive data remains secure within each bank's local environment.

This allows financial institutions to optimize fraud detection and risk management across organizations and country borders, something previously thought impossible due to regulatory and competitive barriers.

Federated Learning (FL), at its core, is a distributed machine learning paradigm that enables AI models to be trained collaboratively across a network of decentralized institutions. Unlike traditional centralized models that require data to be pooled, FL keeps data local, thus preserving the integrity of proprietary customer information, confidential transaction data, and intricate financial relationships.

With data privacy being paramount in today's regulatory environment, governed by laws like GDPR and CCPA, FL is an essential tool in the financial industry's quest for AI-driven insights while safeguarding critical information.

This post presents an application of FL within financial services, demonstrating enhancements in fraud detection and anti-money laundering (AML) processes.

By using the Sherpa.ai Privacy-Preserving AI Platform, a consortium of banks can collaboratively train a superior AI model that detects sophisticated, cross-institutional fraud patterns.

The resulting FL model significantly reduces financial losses and the operational costs associated with high false-positive rates, all while ensuring full regulatory compliance.

How Does Federated Learning Work?

 Every time you use a digital banking app, it collects data. This data is highly sensitive, containing personal notes, private messages, and financial details. Storing this data in a central location would represent a significant business risk and be challenging to justify to customers and regulators.

For this reason, FL comes into play to train powerful AI models, such as for fraud detection, without ever sharing the raw data.

This is how it works in a banking consortium:

1. Distribute Initial AI Model: An initial model, designed to detect fraudulent transaction patterns, is distributed from the Sherpa.ai platform to each participating bank.
2. Prepare Data: As transactions occur, training data is collected within each bank's secure environment. FL allows to use raw data as it is never shared or exposed. However, an additional layer of privacy and security can be added, before this data is used, privacy-enhancing technologies like Differential Privacy are applied. This technique adds a mathematically calibrated amount of "noise" to the data, making it impossible to reverse-engineer individual data points without altering the overall patterns needed for training.
   
3. Train Locally: The AI model is trained asynchronously within each bank's secure infrastructure, using only their local transaction data. This training happens directly on the bank's servers, ensuring that sensitive customer data remains private and never leaves their control.
4. Create Model Update Locally: After local training, each bank generates a model update. This update contains summarized, encrypted changes to the model's parameters (weights) based on the patterns learned from its local data. It is a compact and anonymized representation of improvements.
5. Transmit Local Update: This compact representation of model changes is sent to the central Sherpa.ai server.
6. Learn Globally: The Sherpa.ai platform securely combines all the model updates from participating banks to create a new, more intelligent global model using algorithms like federated averaging.
   
7. Deploy Updated Global Model: Every participating bank receives the updated global model. As a result, every institution benefits from the collective intelligence, enjoying enhanced and more accurate fraud detection capabilities.

In this example, all banks are performing the identical task of "fraud detection" using the same input data type of "transaction records." This is known as Horizontal Federated Learning (HFL), where multiple parties have the same data features for different sets of customers.

How Does Federated Learning Help the Financial Industry?

 The transition of AI from research to real-world financial applications often fails for many reasons, from regulatory barriers to strategic concerns and cybersecurity risks. The Sherpa.ai Federated Learning platform is a compelling option for addressing these challenges.

• Regulatory Barriers: Financial data is subject to strict regulations like GDPR, which can prevent data from being moved across country borders. FL keeps the data local and processes it at the source, ensuring compliance while leveraging insights from data across borders and organizations.
• Strategic Concerns: Real-world fraud detection requires more data than a single bank can provide. However, banks are competitors and have concerns about sharing proprietary data and intellectual property. With a FL setup, data remains with its owner, and collaboration partners agree to share only specific insights represented by the AI model.
• Cost Concerns: Modern financial institutions generate enormous amounts of data. While data transfer costs to the cloud may seem minimal at first, the sheer volume drives costs up. FL reduces data transfer costs and the associated carbon footprint.
• Cybersecurity Risks: Central pools of combined financial data are an attractive target for malicious actors. With FL, a central data pool is not required, elegantly mitigating the associated risks.

How Can Federated Learning Work Across the Financial Value Chain?

Let us imagine two companies: a bank has data on financial habits, and a telecommunications company possesses data about mobile usage and travel patterns. Their customer base overlaps. While both companies have data on the same customer, the data attributes ("features") they possess are different.

With Vertical Federated Learning (VFL), they can collaboratively train a model using their combined features without directly exchanging raw data about their customers. For VFL, the model is essentially split into pieces, and each company trains its part of the model. VFL can be applied across entire value chains, where every participant trains a model part for their segment.

Applying this to finance could yield significant improvements in areas like enhanced credit scoring or highly personalized marketing campaigns.

What is Needed to Implement Federated Learning in Banking?

 FL is a deep-tech combination of mastering machine learning and distributed systems. The requirements for deploying industrial-grade FL in banking are significant:

1. Privacy and Data Security: Protecting data requires preventing reverse-engineering from the AI model. This is where advanced Privacy-Enhancing Technologies (PETs) are essential.
2. Heterogeneous Data: Every bank's setup is different, and data can vary in formats, intervals, and storage systems. The capability to train the same model with different data is essential.
3. Combining Entities and Products: When developing models, the combinations of different customer segments, financial products, and transaction types must be considered. Defining cohorts of comparable combinations is challenging when details are kept confidential.
4. Device and Setup Heterogeneity: The variety of IT infrastructures, security protocols, and operating conditions across banks presents a significant challenge.
5. Initial Model: Scenarios require an initial model to start, which implies that some data is available for development. This can be a blocker for many FL frameworks, especially in VFL cases.
6. Data and Model Monitoring: A FL setup requires both local monitoring for model performance and privacy-preserving federated monitoring for the overall training process.
7. Decentralized Compute Power: Locally performed model training requires decentralized AI compute resources at each participating institution.

Applications for Federated Learning in Financial Services

FL establishes trustworthy learning systems across multiple parties. The following application areas illustrate some of the potential that the Sherpa.ai platform can unlock today.

• Fraud Detection and Anti-Money Laundering (AML): This is a primary use case. By collaboratively analyzing transaction data across multiple banks, a federated model can identify sophisticated fraud rings and money laundering networks that are invisible to any single institution.
• Credit Scoring and Defaulter Prediction: A consortium of lenders can train a more accurate credit risk model without sharing sensitive customer financial data, leading to better lending decisions and reduced risk.
• Customer Churn Prediction: Banks can securely collaborate with partners, such as insurance companies or telcos, to build a more holistic view of the customer and more accurately predict and prevent high-value customer churn.
• Cybersecurity Threat Detection: Institutions can collaboratively train models to detect emerging cybersecurity threats, such as ransomware or phishing attacks, by sharing learnings from their network activity without exposing sensitive infrastructure details.
• Personalized Marketing and Cross-Selling: By partnering with retailers or airlines, banks can develop better AI models for cross-selling relevant products—like a premium travel credit card—without any data leakage between the organizations.

Federated Learning is not just a promising technology—it’s a necessity for any financial institution that wants to innovate while respecting data privacy.

With our AI Platform, banks can finally collaborate securely—with each other and with trusted partners—to fight fraud, retain customers, and unlock new revenue streams. No risky data transfers. No compliance headaches. Just smarter AI.

In a world where trust is the new currency, our solution helps banks protect both their customers and their competitive edge.