To get started: Data is the New Gold, but Privacy is the Vault!
The data, and more specifically, customer data, is everything in finance.
Whether you’ve seen it at a point-of-sale transaction with card swipes, in the granting of repayment with loans, or in transactions that are deemed potentially suspicious, every fraction of a transaction contributes to massive outcomes that are stable and
supported with risk or fraud, as well as meeting a client, or customer’s need.
The reality is – while data has and always will be valued more than anything else, and the assessing of ordinary consumer behaviours is massive today, privacy – is a lesser recent phenomena even this despite regulations such as the
European GDPR; the US counterpart HIPPA (for healthcare – finance overlap); and India’s Digital Personal Data Protection (DPDP)
Act being increasingly applied; and the growing unacceptable level of cyberthreats, reputational risks, and contributing to customer distrust surrounding data use and handling.
Pulling everything to one space is feeling less and less historical.
So, how are banks, insurance, or fintech supposed to exploit and harness the power of artificial intelligence and trust models, without facing compliance risk and client trust loss?
This is where Federated Learning (FL) comes in
The Issue Today – Centralized AI is against the Wall
The majority of machine learning models being operated today in finance are being created in a centralized way – a bank’s data warehouse or cloud system for a fintech – by simply capturing that data into a central location of some sort and training algorithms
that can learn and then be developed to assess for fraud, credit worthiness, or even recommend financial products.
From the perspective of how a centralized model creates a mess, with centralized models, a small number of variables complicate models:
• Regulations: GDPR prohibits cross-border data sharing, and India’s DPDP Act mandates close handling of personal data, determining how banks that operate in so many countries are going to be able to do things, without treating hundreds
of regulations to comply with, seems a daunting prospect.
• Business Competition: No financial institution wants to “hand over” personal customer data to either a direct competitor or to their group’s consortium. • Cybersecurity Threats: Centralized warehouses are a preferable target for hackers.
One breach means millions of records may have been compromised.
• Operational Expenses: Moving petabytes of data from one system to another is expensive, slow, and fragile.
In a nutshell, centralization creates risk, cost, and compliance blockers that slow down innovation.
What is Federated Learning? (Analogy for Finance)
Federated Learning is a method of training AI models by NOT centralizing raw data.
Here is one way to think about it: if five banks want to improve fraud detection, they could send all of their customer transaction logs to one server (risk and potentially illegal), or each bank could train a local model using its own customer transaction
data.
Each bank then sends just the learned updates (i.e., mathematical patterns, not raw data) to a central aggregator.
The aggregator takes all these updates and creates a stronger global model. The global model is then sent back to the banks, so everyone benefits from the collective intelligence while no bank ever shares customer records.
In terms of the banks, it is like comparing “lessons learned” without opening the books.
Why Federated Learning is Important for Finance
Federated Learning offers more than a neat technical trick: it directly speaks to the fundamentals of what the financial industry needs:
AI Compliance – Data stays in the jurisdiction where it is permitted (on-premise, in-country). Banks can comply with GDPR, the DPDP Act, and requirements for sector-specific governance, risk, and compliance.
Stronger fraud detection – Fraud detection patterns often cross several institutions. FL allows banks to “learn together”, without exposing sensitive transaction logs or other sensitive data.
Cross-border Collaboration – Global banks are able to collaborate in a financial sense without having to share or transfer raw data across borders. Trust and Reputation
Customers are more likely to trust banks that will keep their information local and target their cutting-edge services at their customers.
Cost Efficiency
Less movement of data minimizes bandwidth and storage costs.
In other words, FL is about trust and being competitive.
Federated Learning Use Case Examples in Finance
1. Fraud Detection Among Banks
Fraudsters will leverage multiple institutions. One institution may detect pieces of the pattern, but with institutions working together to proactively combat fraud, they can limit the fraudsters and detect fraud faster. FL can allow the participants to
create joint fraud detection models without exposing the transaction-level data that led to that model.
2. Anti-Money Laundering
Money laundering cannot be tracked without views across countries and institutions. FL can allow consortia members to work towards developing joint (AML) Anti-money laundering models, and leave the final customer details masked.
3. Credit Risk Scoring
A financial institution may not have a long relationship with a borrower, hence the historical context may not be long enough for the institution to build risk models.
By using FL, researchers may be able to leverage the context curated as insights from other lenders to produce risk models that involve history, without providing the credit history
4. Insurance Underwriting
Insurance companies can leverage FL to improve insurance underwriting processes, while at the same time learning from historical claims across geographies, notwithstanding an individual’s demands for data privacy.
5. Personal Finance Offers to Customers
Banks/Fintechs can utilize Federated Learning to provide tailored finance offers (for example, credit cards or investments) by learning/clustering on broader datasets based on the representative behaviours of a customer cohort/demographic, whilst assuring
the individuals’ financial information never leaves that institution.
6. Researching Consortia
Universities, regulators, and consortia can use Federated Learning to research the financial risks as research clusters and to build better systemic risk models, while leveraging the competitive angle of their banking consortium members.
Advantages for Financial Institutions
• Compliance-first innovation – Innovate in ways that allow institutions to stay ahead of regulators while building enhanced AI.
• Reduced breach risk – There is no central honeypot of data to take.
• Faster model training – Training occurs locally with limited data transfer costs.
• Improved accuracy – Learning collaboratively means greater accuracy in detecting fraud and scoring risk.
• Customer trust – Marketing privacy-preserving AI could become a brand differentiator.
Barriers and Limitations
FL has clear benefits, but isn’t a panacea. Financial institutions have to work through:
• Data heterogeneity – banks have different data. Harmonizing models would be a challenge.
• Trust levels among participants – banks may want to worry about fellow competitors colluding to tweak models (i.e., Byzantine attack).
• Infrastructure costs – combined with inclusion of secure aggregation, encryption, and MLOPs, could complicate matters.
• Explainability – regulators want models to be explainable, and this is particularly difficult to do if updates come from multiple institutions.
Research and technologies are addressing these challenges very quickly.
Case Studies and Examples
• Fraud Detection Collaboratives: Some banks in Europe have been piloting fraud schemes using FL-based fraud models to identify suspicious transactions.
• Medical imaging to financial transfer: FL has been a success in medical imaging (with hospitals collaborating without sharing patient data), and showcases many of the same models applicable in finance.
• Federated XGBoost: Tested already in cross-bank credit scoring. Performance increased with privacy guarantees maintained.
Implementation Guide for Banks
Banks can take a phased approach:
• Phase 0: Define Success – Example: “fraud detection improvement of 5% without data transfer”.
• Phase 1: Pilot – A few banks pilot with anonymized/synthetic data.
• Phase 2: Stabilize – Add secure aggregation, compression, and adaptive optimizers to the collaboration’s latter stages.
• Phase 3: Privacy Hardening – Differentiate privacy combined with robust aggregation.
• Phase 4: Put into production – Administer with real clients, including audit-ready governance for the data used.
This gradual, curated approach ensures both compliance and trust.
The Future of Federated Learning in Finance
In summary, this appears to indicate that FL may become a pillar of AI in the financial services space.
There are several ways in which it could evolve:
• Integration with Central Bank Digital Currencies (CBDCs) – where learning fraud or funding patterns is valuable to users without sharing transaction-level details.
• Open Banking & APIs – FL allows banks to learn from learning from broader ecosystems of banks and clients while protecting sensitive datasets.
• AI Regulation – As regulations are tightened across Europe and India for AI use, FL would/should be recognized as a compliance-friendly approach.
• Digital Public Infrastructure – In countries (e.g., India), FL could act to help UPI, Aadhaar-linked services, and lending ecosystems securely enable scale.
Conclusion
In finance, data privacy and AI performance do not need to be at odds with each other any longer. FL provides financially regulated institutions with the means to collaboratively learn, detect fraud more efficiently, and manage risk directly without handing
over raw customer data.
Overall, FL is more than purely a technical innovation; it is a strategic choice for compliance, trust, and to remain relevant in the competitive financial services marketplace.
The financial institutions that take on FL first will be the ones leading the way to improve the standards of privacy-preserving intelligence for the next decade.