May 29, 2024

Frauds Risk Analysis

Monnai’s AI-powered Insights Platform Gets $6.5m

Fintechs can now benefit from Monnai’s consumer insights platform thanks to the company’s $6.5 million Series A funding round. Tiger Global has led an investment round where current investors like Better Future Ventures, Emphasis Ventures, and 500 Global have also participated (EMVC). The funds will improve the company’s decision-making capacity using artificial intelligence. With this new funding, Monnai has raised nearly $10 million to date.

Financial institutions that need to make educated decisions throughout the customer’s lifetime can rely on Monnai’s platform, which integrates data from different global sources. Customer perspective, faith and fraudulent risk, collections optimization, and artificial intelligence-driven assessment for credit risk analysis are the four decision-making modules made available by its flexible architecture through a single API. Reduced time spent on ingesting, modeling, and implementing alternatives is made possible by the platform’s minimal code/no adaptive code aggregation, standardization, and contextual understanding of data sets.

Monnai has spent the past six months building out its AI (artificial intelligence) and customer experience (CX) offerings, most notably with the introduction of its first artificial intelligence-driven assessment engine. Using supervised models, the engine can recognize the synthetic identities used in fraud, making it easier for customers to spot fraud attempts that would have otherwise gone undetected. Monnai is working on new explainable XAI capabilities to improve the platform’s functionality, such as unique reason codes for each transaction and other complicated modeling methodologies.


Monnai wants to use the newly acquired capital to expand its operations into emerging countries and advance its progress in building its own research and data acquisition capabilities. Ravish Patel, the co-founder at Monnai, says the company is working to simplify the manual investigative process for fraudulent activity and credit risk analysts by introducing a graph-based user dashboard.

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