Collaborative AI for Fraud Detection
Train collective intelligence without sharing your data and monetize your contributions
Advantages
We offer unique advantages
Train collective intelligence
Our collaborative approach ensures models are always getting updated. Other solutions remain static, potentially missing out on new fraud patterns.
Without sharing your data
Our federated approach ensures your data stays with you. Other solutions may request your data, posing privacy risks.
Monetize your contributions
Our fair approach ensures you are rewarded for your contributions. Other solutions may use your data without sharing profits, leaving you paying the bill.
Industries
Fraud affects many industries
Problems
Fighting fraud on your own is hard
Machine Learning is complicated
As models become more complex, transparency and interpretability in model decisions are crucial for enhancing the reliability of fraud detection.
Internal Data is insufficient
Datasets contain significantly more non-fraudulent data than fraudulent ones, making it difficult for standard methods to detect fraudulent patterns effectively.
External Data is Inaccessible
Companies dealing with fraud can only use their own data because of privacy concerns. This makes their fraud detection models limited and less effective.
Fraudsters are always evolving
Fraudsters keep changing how they operate. This makes it difficult for standard fraud detection methods to keep pace.
Product
Fraudulo facilitates collaborative fraud detection
Treat data to maintain differential privacy
Anonymize and de-identify sensitive information to secure the privacy of your data before training.
Generate data to augment your datasets
Expand your datasets by generating additional features and data to enrich your training set with more data.
Collaborate with other institutions to enhance intelligence
Work with other institutions on training shared models without sharing your data to enhance fraud detection capabilities.
Train models to increase accuracy
Leverage your data to train and fine-tune models, increasing fraud detection accuracy.
Evaluate models to ensure reliability
Assess the performance and interpretability of your models before you integrate them in your system.
Questions
Frequently asked questions
Overview
Advantages
Collaboration
Technology
Pricing
Monetization
Support
What is Fraudulo?
Why was Fraudulo developed?
How does Fraudulo work?
Who can use Fraudulo?
What are the advantages of using Fraudulo?
How does Fraudulo differ from other solutions?
What is meant by collaboration on Fraudulo?
Why should companies collaborate on Fraudulo?
What technologies does Fraudulo use?
What is machine learning?
Why is machine learning effective for fraud detection?
What costs are involved in using Fraudulo?
Can I monetize the models I develop on Fraudulo?
How do rewards work on Fraudulo?
How do I get started with Fraudulo?
What support options are available for Fraudulo users?