Feedzero: Day Zero Fraud Prevention AI Models

In this article, we’ll outline how Feedzero delivers enhanced fraud prevention, reduces false positives, responds to emerging threats, and adapts to known and emerging fraud typologies.

What is Feedzero?

Feedzero 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 exclusive industry intelligence, meaning financial institutions do not need customer training data or data science or machine learning expertise. 

Many financial institutions use legacy rules-based systems that developers initially created to prevent fraud using 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 Feedzero, there is no need to wait or need 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, changes in fraud patterns require constant updates to the rules. Financial institutions must update rules manually and will struggle to keep up with new fraud tactics.

AI Models Also Have Limitations

Your organization may already have its own AI models in place. But to be effective, AI models need data —and lots of it. 

Training these AI models often means waiting for customer data, analyzing transactions, and labeling these transactions as fraud outcomes. All told, organizations can take months or even up to a year to collect, prepare, label, and train their custom AI models. 

The process slows down model enablement and time-to-value. It also weakens organizations’ ability to respond to changing fraud tactics. Also, keeping these models working can be expensive because you need to retrain them repeatedly.

Data Science Label Delays

Proper data labeling is essential for AI models to assess whether or not a transaction is fraudulent. Unfortunately, you can access labels only 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.

Feedzero: 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.

Feedzero 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 Feedzero works:

Models Trained on Patent-pending Algorithms

An innovative algorithm backs the Feedzero models by using fraud patterns across multiple use cases and geographies from Feedzai’s Industry Intelligence.

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 Feedzero.

Ongoing Maintenance

Innovative machine learning technology finely tunes Feedzero 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.

Ready Out-of-the-box From Day Zero

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

Feedzero provides a suite of AI models that users can deploy immediately. The models instantly detect fraud schemes faster than legacy rules-based systems can capture.

You can use it as its own AI model or to complement existing models currently deployed within your organization. It can also address new use cases and provide insights from new geographies. Most importantly, it constantly evolves to tackle new fraud patterns. This helps banks and financial institutions stay updated on the latest fraud tactics.

How Feedzero Prevents Fraud

How does Feedzero 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 Feedzero models address the challenges of shifting fraud behaviors, provide real model performance insights, and enhance 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 or replace 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 Feedzero 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 Feedzero, financial institutions can immediately implement strong fraud prevention measures without losing time cleaning, labeling, or analyzing historical data. Just as important, the Feedzero model can complement existing models and adjust for new use cases and geographies. 

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