In EMV chip transactions, the card does not simply return “approved” or “declined” as plain text. It returns an application cryptogram: an 8-byte MAC bound to that transaction’s …
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 …
Manual capture is not about typing a card number by hand. It is the deliberate split of a card transaction into authorization and capture, managed as a backend state machine. Why …
A duplicate charge is rarely a coding bug. It is a distributed system losing certainty about whether an authorization already happened — and then guessing wrong. Why blind retries …
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 …
In the POS systems I have worked on, “is this terminal secure?” almost never has a hardware-only answer. The card read is one boundary. The PIN entry is another. So are …
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 …
A partial approval is not a decline and not a completed sale. ISO 8583 response codes and amount fields must be read together — and the terminal must not show a green APPROVED …