.svg)
Buyer’s Guide: Machine Learning in Fraud Detection
Out-of-the-Box vs Custom Machine Learning Models
This comprehensive guide is designed to help fraud and risk decision-makers understand the key differences between OOTB and Custom 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. Custom ML models, highlighting their advantages, limitations, and ideal use cases.
- A needs assessment to guide you through the evaluation process of 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.
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 custom model – can be a complex decision with significant implications for performance, scalability, and long-term success.
About DataVisor
DataVisor is the AI-native real-time decisioning engine for fraud and financial crime prevention.
As AI transforms both fraud attacks and fraud defense, DataVisor helps financial institutions, payment providers, and digital businesses detect, investigate, and stop sophisticated and previously unseen threats in milliseconds across billions of transactions. Combining adaptive machine intelligence, consortium intelligence, and emerging agentic AI capabilities, DataVisor enables organizations to modernize fraud operations, improve customer experience, and stay ahead of rapidly evolving financial crime. DataVisor is trusted by leading financial institutions, payment innovators, Fortune 500 enterprises, and digital businesses worldwide.