Fraud-detection papers regularly report near-perfect accuracy on public datasets. Almazroi and Ayub’s 2023 IEEE Access paper is a recent example: a ResNeXt-embedded GRU …
The next phase of AI in payments is not faster fraud scoring. It is AI moving from an advisory layer that observes the transaction to an actor that initiates payments and operates …
This is part 3 of a series on building grounded AI for payment systems. Part 1 made the case that payments need grounded AI, and part 2 Part 2covered the retrieval pattern that …
This is part 2 of a series on building grounded AI for payment systems. Part 1 made the case that payments need grounded AI, not a generic LLM guessing from training data. This …
This is part 1 of a series on building grounded AI for payment systems. This post sets up the problem. Part 2 covers the retrieval pattern, and part 3 covers the practical use …
This post looks at where machine learning actually fits in a payment system: not inside the authorization path that EMV, tokenization, and the networks already own, but in the risk …
This post continues the ideas explored in Part I: Super Agents and Multi-Agent Communication and Part II: Swarm Intelligence. Those posts covered how agents coordinate within a …
This is Part II of a two-part series on multi-agent AI architecture. Part I covered the super agent pattern: centralized orchestration, structured communication, and a single …
This is Part I of a two-part series on multi-agent AI architecture. This post covers centralized orchestration. Part II explores the opposite approach: swarm intelligence. …
In a few short years, Large Language Models (LLMs) have moved from research labs into the core of how we work. They are no longer experimental novelties; they shape how we search, …