TrustScore: Real-time AI-powered Network Intelligence

Fraud is constantly changing. Unfortunately, creating new models to tackle new threats takes a lot of time and effort. Thankfully, financial institutions can stay ahead of new fraud tactics with TrustScore, a core component of Feedzai’s federated learning solution, Feedzai IQ™. TrustScore is an AI-native risk score aggregated from network-level fraud intelligence to deliver immediate fraud prevention. 

In this article, we’ll outline how TrustScore delivers more precise risk decisions and adapts to known and emerging fraud typologies.

Key Takeaways

• TrustScore, a key element of Feedzai’s federated learning solution Feedzai IQ™, produces precise risk decisions based on known and new fraud typologies. 
• TrustScore’s decisions are based on insights from across the global Feedzai Community.
• Using rules-based legacy systems, financial institutions must wait months to realize the benefits because of the time required to collect and repair historical data for model training.
• TrustScore eliminates the need to wait on models or undergo intense data science training: the models are ready from day one.

What is TrustScore?

TrustScore is an out-of-the-box solution that adapts to new fraud typologies and immediately works from day one. The AI models are ready to use immediately, detecting complex fraud schemes more quickly than older rule-based systems.

The solution addresses critical fraud prevention challenges by providing real performance insights to enhance a model’s fraud detection and decisioning capabilities. It is trained using Feedzai’s broad industry intelligence, meaning financial institutions do not need customer training data or machine learning expertise. 

Many financial institutions use legacy rules-based systems that developers initially created to prevent fraud of known typologies. Organizations must wait months to realize the benefits of new models in the broader industry because of the lengthy process involved: collecting and preparing historical data and then training the models.

With TrustScore, you don’t need to wait or acquire intensive data science training. Financial institutions are ready to fight fraud immediately.

Fraud Prevention Challenges for Financial Institutions 

Fraud prevention is critical for financial institutions to protect their revenues and secure customer trust. However, many factors make getting started with fraud prevention challenging. These reasons include:

Limits of Rules-based Systems 

Legacy rules-based systems are programmed to detect and prevent some of the most common fraud schemes. However, once fraudsters realize that one scheme has lost its effectiveness, they move on to another method.

Because legacy systems are not dynamic, financial institutions must constantly update rules manually or struggle to keep up with new fraud methods.

AI Models Also Have Limitations

Organizations may already have their own AI models in place. But to be effective, AI models need data, and lots of it. 

This often means waiting for customer data, analyzing transactions, and labeling them with the fraud outcomes where appropriate. All told, organizations can take months or even up to a year to collect, prepare, label, and train their custom AI models.

“Instead of getting a truckload of raw data and having to figure out what it all means, banks gain actionable insights—ready to use right away to detect and prevent fraud.” – Anusha Parisutham, Senior Director of Product at Feedzai.

The process slows down model enablement and time-to-value. It also weakens organizations’ ability to respond to changing fraud tactics. Meanwhile, keeping these models working can be expensive due to constant retraining.

Data Science Label Delays

Proper data labeling is essential for AI models to assess whether or not a transaction is fraudulent. Unfortunately, you can only access labels after analyzing transactions. This further delays the effectiveness of AI models, creating inertia in model predictions.

Limited Insights into Fraud Landscape

Customer training data for models often delivers a limited view of current fraud threats. Without a clear understanding of threat intelligence unique to a financial institution’s industry and local region, their organization will be at a disadvantage.

TrustScore: Accessible AI Models Ready to Go

Due to these delays and limitations, banks and financial institutions need help deriving immediate value from AI models. Immediate insight into data is critical to improving fraud detection, minimizing false positives, and protecting customers from new fraud threats.

TrustScore enables financial institutions to fight fraud using AI models that fuse the latest AI framework with industry-wide learnings. This approach eliminates delays in model deployment and challenges that can emerge with processing historical data. Instead, financial institutions can fight fraud instantly.

Here’s how TrustScore works:

Federated AI Models

An innovative algorithm aggregates multiple TrustScore models based on fraud patterns and confirmed fraud outcomes across similar use cases and geographies from Feedzai IQ™.

Transparent, Explainable Results

The AI models deliver clear explanations for why fraud prevention decisions were reached. Financial institutions do not require manual intervention or an in-depth data science and machine learning background to use TrustScore.

Ongoing Maintenance

Innovative machine learning technology finely tunes TrustScore models to enhance fraud detection performance. A centralized model improves efficiency and gives access to an AI risk strategy. This way, financial institutions do not need to develop their own expertise.

AI Models Ready Out-of-the-box

Financial institutions do not need to wait to gather historical customer data to start using TrustScore. TrustScore already trains models, enabling organizations to deploy them immediately and benefit from a robust strategy. 

TrustScore provides federated AI models that users can deploy immediately. The models instantly detect fraud schemes faster than legacy rules-based systems can capture. 

It can be used as its own AI model or to complement existing models. Most importantly, it constantly evolves to help banks and financial institutions stay current with the latest fraud tactics and provides insights from new geographies.

How TrustScore Prevents Fraud

How does TrustScore change the fight against fraud? Let’s outline how these models enhance organizations’ fraud prevention efforts.

1. Enhanced Fraud Detection From Day One

AI models are dynamic, capturing evolving threats using Feedzai’s industry intelligence. The breadth and depth of this data on which models are trained means financial institutions can start making a positive impact on reducing fraud immediately.

2. Customer Protection from New Fraud Patterns

Based on patterns and risk signals, you can be alerted to potential fraud and scams, including authorized push payments and money mules. Banks can receive alerts on fraud patterns that may be prevalent in one region but only emerging in others. This intelligence, applied to malicious threats via the models, gives customers an unconstrained view of the fraud landscape. Scale across use cases and geographies.

3. Results for Today and the Future 

Analyzing data is not a check-box task that an organization does once. AI models must learn and adapt from the moment they are deployed into production. 

Feedzai’s industry-proven TrustScore addresses the challenges of shifting fraud behaviors, provides real model performance insights, and enhances fraud detection performance.

Feedzai provides the AI expertise to ensure the model is always current. We fine-tune the model using the latest machine learning techniques. Your organization can use the existing model’s current design to complement existing rules systems. It can also act as a springboard for a bespoke model tailored to your specific risk strategy. 

4. Explainable Fraud Decisions

Fairness, governance, and privacy are embedded in every TrustScore model. Customer data is never revealed when leveraging our industry intelligence across the models. 

In addition, explanations help users understand how the model reached its decisions. Users, customers, and auditors understand the reasons for blocking a transaction. Fairness checks in models act as guardrails for treating individuals equally.

Legacy rules-based systems can’t keep up with the pace of fraudsters’ innovations. With TrustScore, financial institutions can immediately implement strong fraud prevention measures without losing time cleaning, labeling, or analyzing historical data. Just as important, the TrustScore model can complement existing models and adjust for new use cases and geographies. 

Stay ahead of shifting fraud typologies with AI models out of the box. Ensure your AI models are accurate, current, fair, and sustainable. Feedzai’s industry-proven TrustScore models address the challenges of shifting fraud behaviors, provide real model performance insights, and enhance fraud detection performance.

Resources

• Blog: How Feedzai IQ™ Is Redefining Fraud Intelligence
• Report: 2025 AI Trends in Fraud and Financial Crime Prevention
• Commentary: Collaborative Data Exchange: The Next Frontier in Fraud Prevention
• Solution: Feedzai IQ™: The Power of Network Intelligence