Banks Team Up to Catch Fraud Without Sharing Your Data

Financial fraud is a moving target, and the defenses built to stop it are struggling to keep pace. As digital payments, e-commerce, mobile banking, and credit card use have exploded worldwide, so too have the losses. According to figures cited in a new study, global payment card volume reached 57.08 trillion dollars in 2023, while gross card fraud was anticipated to hit 35.67 billion dollars, with losses projected to exceed 40 billion dollars by 2027. The Nilson Report recorded 18.39 billion dollars in fraud losses across nations outside the United States in 2018 alone, up from 14.99 billion dollars the year before. Against this backdrop, researchers at Manipal University Jaipur have unveiled a system designed to catch fraud earlier and more accurately than existing methods, while ensuring that no raw customer data ever leaves the bank that collected it.

The new framework, called FL-EF²DP for Federated Learning-based Early Financial Fraud Detection and Prevention, was published in the journal Discover Artificial Intelligence by Mudit Chaturvedi, Shilpa Sharma, and Gulrej Ahmed. Its central premise addresses a long-standing tension in the financial industry: the machine learning models that detect fraud best are trained on enormous volumes of transaction data, yet that data is among the most sensitive information banks hold, governed by stringent privacy regulations that make large-scale sharing impractical and frequently legally prohibitive. Centralized data aggregation, the traditional route to building powerful models, introduces risks of data breaches, single points of failure, and regulatory non-compliance that restrict both real-world deployment and cross-institutional collaboration.

Federated learning offers a way out of this impasse. Under the federated paradigm, a central server initializes a global fraud detection model and distributes its parameters to participating institutions, such as banks. Each institution trains the model locally on its own private transaction records, never transmitting a single raw transaction. Instead, only the updated model weights are sent back to the server, which merges them using the FedAvg averaging algorithm to produce an improved global model. This cycle repeats over multiple rounds until the model converges, at which point the final version is redeployed at every participating institution for real-time inference. The result is a model that has effectively learned from the collective experience of many organizations without any of them ever exposing their customers’ data.

At the heart of the framework sits a convolutional neural network, a deep learning architecture more commonly associated with image recognition. The authors acknowledge that financial transaction data is fundamentally tabular, but they transform the feature vectors into structured two-dimensional matrices before processing. This allows convolutional filters to capture local feature interactions and hierarchical patterns across transaction characteristics that manual feature engineering might miss. The choice of a CNN was also pragmatic: compared with recurrent and attention-based architectures such as LSTM, GRU, or Transformer models, convolutional networks require fewer trainable parameters and less communication overhead, a significant advantage in federated environments where model updates must be shuttled between clients and a server over many rounds. The specific network used in the experiments comprises four convolutional layers and two dense layers, with batch normalization before each pooling operation, a dropout rate of 0.20 to curb overfitting, ReLU activations throughout, a softmax output layer, categorical cross-entropy loss, and the Adamax optimizer.

What distinguishes FL-EF²DP from earlier federated fraud detectors is not a novel optimization technique but the integration of several complementary components into a single operational pipeline. Beyond the federated CNN, the system includes guideline-based control verification, which assesses each institution’s security posture against machine-readable rules drawn from frameworks such as ISO security controls, NIST cybersecurity recommendations, and RBI financial security guidelines. The module collects device- and system-level signals, including firewall status, antivirus operation, access control policies, encryption, and authentication methods, then computes a compliance score and a control severity score reflecting any gaps. An institution that fails to implement multi-factor authentication, real-time transaction monitoring, or periodic access-control reviews sees its compliance score fall and its severity score rise.

These infrastructure assessments are then fused with the behavioral fraud probability produced by the CNN. The risk fusion module computes a consolidated score using the formula Z-risk equals phi times P-F plus beta times U-gap plus gamma times C-risk, where P-F is the fraud probability, U-gap is the normalized guideline violation severity, and C-risk is a contextual risk score derived from transaction-specific and operational characteristics. In the current implementation the weights are fixed at 0.5, 0.3, and 0.2 respectively, summing to one, with a threshold of 0.7 triggering automated mitigation. This design means that a transaction conducted on a vulnerable device can be flagged even when its behavioral fraud likelihood is only moderate, capturing the reality that fraudulent operations are shaped by multiple interacting elements rather than transaction behavior alone.

When the fused risk score crosses the threshold, the system acts immediately. The action and response component, running locally on bank systems to minimize latency, can suspend transactions, require enhanced two-factor authentication, terminate sessions, block clients, or notify a security operations unit. A recommendation report generator then compiles compliance documentation covering system parameters, identified guideline deficiencies with severity classifications, relevant regulatory citations, and actionable remedial steps drawn from a knowledge base. The authors emphasize that this shifts the paradigm from passive detection to proactive prevention, allowing institutions to intervene during the transaction evaluation phase, before further fraudulent transactions, financial losses, or account breaches occur.

The experimental results are striking. Testing was conducted on two widely used benchmarks: the Credit Card Fraud Detection dataset, containing 284,807 real-world European credit card transactions from 2013 with only 492 frauds, a fraud ratio of 0.172 percent, and the PaySim Synthetic Financial Dataset, comprising roughly 6.36 million mobile money transactions with about 8,213 fraud instances, a ratio of approximately 0.13 percent. Both are severely imbalanced, mirroring real-world conditions. The researchers simulated 100 federated clients with a participation ratio of 0.3, meaning 30 clients joined each communication round, and distributed data using a Dirichlet distribution with a parameter of 0.5 to create moderate statistical heterogeneity. On the PaySim dataset, FL-EF²DP achieved 94.12 percent accuracy, 93.88 percent precision, 92.36 percent recall, and an F1-score of 93.11 percent, outperforming competing methods including FED-SPFD at 91.90 percent accuracy and Transformer-LOF-RF at 89.97 percent, while JNBO-SpinalNet fared worst. On the credit card dataset, the full framework reached 96.25 percent accuracy against 91.77 percent for a centralized CNN baseline. An ablation study confirmed that each added component, from federated learning to risk fusion to the complete pipeline, contributed measurable gains, and the framework also achieved the lowest loss values and fastest convergence over 100 epochs.

The authors are candid about the system’s limitations, and these reveal where the field must go next. The current implementation assumes an honest-but-curious server that follows the protocol faithfully but might attempt to infer information from model updates, and it does not yet incorporate formal privacy-enhancing technologies such as differential privacy, cryptographic secure aggregation, homomorphic encryption, or secure multi-party computation. Privacy protections currently stem from data localization and decentralized learning rather than explicit cryptographic guarantees, leaving the system potentially vulnerable to gradient inversion, model reconstruction, and membership inference attacks documented in the federated learning literature. The fixed weighting scheme in the risk fusion module, chosen for simplicity and interpretability, may also benefit from adaptive methods using reinforcement learning or attention mechanisms. Future work will additionally explore graph neural networks and temporal graph learning to capture relationships among users, merchants, devices, and accounts that a CNN cannot represent, along with time-aware evaluation protocols based on chronological transaction sequences.

Even with those caveats, the study offers a compelling glimpse of how financial institutions might collaborate against fraud without surrendering the data their regulators and customers demand they protect. The framework’s computational footprint is modest, with each client storing only its local dataset and a single copy of the CNN, and communication costs reduced by the partial participation scheme. As fraud losses climb toward 40 billion dollars a year and criminals adapt ever faster to static defenses, systems that combine collaborative intelligence with rigorous privacy preservation may prove not just technically elegant but operationally essential for the future of secure digital finance.

Subject of Research: Privacy-preserving federated deep learning for early financial fraud detection and prevention

Article Title: A privacy preserving federated deep learning system for early financial fraud detection and prevention

Article References: Chaturvedi, M., Sharma, S., & Ahmed, G. (2026). A privacy preserving federated deep learning system for early financial fraud detection and prevention. Discover Artificial Intelligence, 6(1), Article 1393. https://doi.org/10.1007/s44163-026-02099-x

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02099-x

Keywords: federated learning, financial fraud detection, deep learning, convolutional neural networks, data privacy, FedAvg, risk fusion, credit card fraud, PaySim, regulatory compliance, automated mitigation, distributed machine learning