In sectors like banking, insurance, telecommunications, or public administration, fraud causes billions in losses every year. Organizations invest heavily in prevention, but traditional methods often fall short when facing increasingly sophisticated techniques. So how can detection and prevention capabilities be improved without compromising data privacy or competitive advantage?
This article explores current approaches to fraud detection, their limitations, and how technology can help organizations stay ahead of the threat.
The Current Challenge: More Complex, More Distributed Fraud
Digital fraud is not static. It constantly evolves and takes new forms: identity theft, fake transactions, bot-based manipulation, card fraud in marketplaces, insider fraud, and more.
What makes it hard to detect is that fraudulent behavior adapts quickly to controls, spreads across different actors and regions, and often only makes sense when analyzed in context.
In this scenario, isolated or centralized approaches fall short:
They miss the full picture when data is fragmented across different organizations.
Collaboration becomes difficult due to legal restrictions or competitive concerns.
AI models underperform when trained on narrow or incomplete datasets.
Traditional Strategies and Their Limitations
Over time, organizations have used various methods to fight fraud. Some are still useful, but many have important limitations:
1. Rule-based systems (if/then)
Based on static, predefined conditions (e.g., more than X transactions in Y minutes). While fast and easy to implement:
They fail to detect new or unknown fraud patterns.
They produce many false positives.
They don’t adapt or learn over time.
2. Centralized data analysis
All data is moved into a single system to train detection models. While it offers a broader view:
It requires transferring massive volumes of data—raising privacy, security, and compliance concerns.
It often conflicts with GDPR, HIPAA, and other data protection regulations.
It’s hard to apply when multiple organizations hold parts of the data.
3. Local, siloed models
Each organization trains its own model with its own data. This preserves data ownership, but:
Limits predictive power due to narrow datasets.
Misses distributed fraud patterns involving multiple entities.
What Are the Alternatives?
To overcome these limitations, new strategies are emerging that focus on intelligent, collaborative detection without exposing sensitive information. Some of the most effective include:
Collaborative learning without data sharing
Multiple entities can train models together without transferring original data. This enables:
Larger, more diverse datasets that improve model accuracy.
Strong privacy protections and regulatory compliance.
Detection of fraud patterns that span across organizations.
Secure distributed training
Each participant keeps full control over its data and only contributes aggregated or anonymized model updates. This ensures that no raw data is ever exposed, while still building stronger, smarter systems.
Differential privacy and auditability
Mathematical techniques are applied to model updates to prevent the reconstruction of personal data. The entire training process can also be traced and audited to ensure full compliance with GDPR and other regulations.
The Sherpa.ai Approach: Frictionless Collaboration
Sherpa.ai has developed a platform that significantly improves fraud detection without sharing personal or sensitive information.
Some of its key strengths in this area include:
The ability to train joint models across entities without centralizing data, enabling the detection of fraud that individual systems would miss.
Advanced privacy-preserving technologies, including differential privacy, secure aggregation, and auditability by design.
Flexible deployment options: the platform can integrate with existing systems, run in the cloud, on-premise, or on edge devices, and adapt to highly regulated sectors.
Proven performance in real-world projects with banks, insurers, and public institutions.
Illustrative Use Cases
Here are some examples where this technology has delivered real value:
Banking and payments: detecting card fraud, money laundering, and suspicious transactions across multiple institutions.
Telecom: identifying anomalies in call traffic, SIM swaps, or fraudulent activations.
Public sector: preventing fraud in social programs and unauthorized use of government services.
Insurance: flagging suspicious claims or repeated fraud attempts across insurers.
Improving fraud detection isn’t just about better algorithms. It requires access to rich, diverse data, the ability to collaborate securely across entities, and a strong commitment to privacy.
The most effective strategies today combine secure collaboration, advanced analytics, and full regulatory compliance. With this approach, organizations can build fraud prevention systems that are not only smarter, but also more resilient, ethical, and trustworthy.
Those who take this step won’t just reduce losses—they’ll strengthen customer trust and competitive positioning in the process.