Buyer's Guide: Machine Learning in Fraud Detection (Out-of-the-Box vs Customized Models)

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Buyer’s Guide: Machine Learning in Fraud Detection

Out-of-the-Box vs Customized Machine Learning Models

This comprehensive guide is designed to help fraud and risk decision-makers understand the key differences between OOTB and Customized ML models, identify the right fit for their unique business needs, and make an informed decision with confidence.

Download and learn about:

A detailed comparison of OOTB vs. Customized ML models, highlighting their advantages, limitations, and ideal use cases.
A practical checklist to guide you through the evaluation process, ensuring you consider critical factors such as data readiness, business needs, and implementation timelines.
A case study showcasing how choosing the right ML model led to a 5x reduction in fraud losses for a leading enterprise.

Download the Buyer’s Guide

As fraud threats continue to evolve, businesses are increasingly turning to machine learning (ML) to enhance their fraud prevention strategies. However, selecting the right ML model – whether an Out-of-the-Box (OOTB) solution or a Customized mode – can be a complex decision with significant implications for performance, scalability, and long-term success.

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About Datavisor

DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

DataVisor is the world’s leading fraud and risk management platform that enables organizations to respond to fast-evolving fraud attacks and mitigate risks as they happen in real time. Its comprehensive solution suite combines patented machine learning technology with native device intelligence and a powerful decision engine to provide protection for the entire customer lifecycle across industries and use cases.