Machine learning models

Precise machine learning models

Our machine learning models are truly universal—they can be easily integrated into companies of all sizes. Boost your online payment security with an AI-powered fraud detection solution whether you choose context-specific, pre-trained, or custom-made models.

Try for free
Group 99281

Get essential machine learning models features:

allInOnePlace
Pre-trained and custom models
Close to real-time
Recommendations in less than a second
reputationScores
High precision in fraud detection
Reduced
Reduced manual review rates
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Pull more information thanks to our white-box ML

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1

Collect as much information as possible

Creating a complete customer profile requires collecting a tremendous amount of data. We obtain 5,000+ attributes about each user, including behavioral, device, and network data. We go beyond data made available by the user by digging deep into your customers' sessions to discover fraudsters.

2

Process all data

Machine learning adapts to constant changes in user traffic and fraud methods. Our ML models spot non-obvious patterns and automate decision-making faster than heuristics and manual rules sets, with fewer false positives.

3

Put our recommendations to use

Our machine learning models provide simple, actionable recommendations for your risk management team in less than a second. With less manual work needed, you can spend more time researching new features and focus on optimizing your business performance.

Improve your business with machine learning models

[02.03.00] Product Page - Features - Machine learing models - 03 Ready to take

Ready to use

Leverage our datasets and basic machine learning models to determine the reputation of card data, IP, e-mail, and physical address to increase user verification.

This option would fit best smaller companies that want to improve rule-based decisions with additional data. 

Choose Premium Plan
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Pre-trained

Use our ready-to-go machine learning model and enrich your fraud detection with access to 100+ risk factors to help you fight fraud more accurately. 

This is an excellent choice for companies that don't have much customer data or a dedicated fraud prevention team. 

Choose Standard Plan
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Custom made

Custom, premium ML models come with a Data Scientist and a dedicated Customer Success Manager who advise you on leveraging our solution. 

This is a perfect fit for companies looking for someone to take care of their data and finer details. 

Choose Premium Plan

Try it for free and see our machine learning models in action!

Try it for free and see our machine learning models in action!
Try for free Choose your plan

Nethone has built an advanced ML based models customized specifically for our fintech e-commerce platform which helps us to identify various types of fraud before confirming the transaction. Appreciate professional attitude, fast reaction to requests and perfect customer support from Nethone team!

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indre-arstikaityte
Indre Arstikaityte

Global Risk Product Manager

Zoodmall

Industry: eCommerce

indre-arstikaityte
Indre Arstikaityte

Global Risk Product Manager

Zoodmall

Industry: eCommerce

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Nethone has created a set of deep user profiling tools that together with heavy expertise in machine learning allow us to better understand our end customers.

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konrad-howard
Konrad Howard

CPO

Booksy

Industry: Digital goods & services

konrad-howard
Konrad Howard

CPO

Booksy

Industry: Digital goods & services

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Nethone allows us to remove applications with a high risk of fraud that we can't recognise while using other services. Their ML models detect fraudsters based on features like uncommon device resolution, unusual transaction amount, pasting texts.

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marek-biernacki
Marek Biernacki

CEO Mokka Poland

Mokka

Industry: Online lending & BNPL

marek-biernacki
Marek Biernacki

CEO Mokka Poland

Mokka

Industry: Online lending & BNPL

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Large fashion eCommerce player

Read how we tailor-made a fraud prevention solution that delivered increased conversion rates and lowered both the overall chargeback rate and the amount of manually-reviewed traffic.

60%

12%

89%

Read full case study
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