Every failed payment is a decision. Make it the right one.**

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1. The problem

Payment decline codes are cryptic, inconsistent across payment methods, and often don't tell a merchant what to do next.

A code like 05 – Do Not Honour is a good example. Depending on the payment method, attempt history, and other transaction context, the right action could be to retry, ask the customer to contact their bank, try another payment method, or stop retrying altogether. Today, support teams often have to interpret these codes manually, while automated systems may rely on simple retry rules that treat very different failures in the same way.

Getting this decision wrong has a cost either way - retrying a hard decline can waste processing costs and lead to excessive retries, while giving up on a recoverable decline means losing revenue that could have been recovered.

The scale is significant. UPI AutoPay alone processed 808 million debits in July 2025, and around 74% of rejections were business declines.

Having worked with payment failures for years, I wanted to explore whether AI could turn these messy signals into a clear decision: what happened, what should the merchant do next, and what should they tell the customer?

2. What I built

Paste a decline code and a few details—payment method, amount, one-off or recurring, attempt number, and gateway message. Decline Decoder gives you:

• Root cause — what went wrong, in plain English
• Retry guidance — whether to retry, when to retry, or wait for customer action
• Customer recovery message — a message tailored to the failure type, the customer's payment history, the channel, and the attempt number, rather than a generic message
• Prevention step — what the merchant can do to reduce the same failure in the future

Upload a batch of failures and it does the same at scale: how much is recoverable now, and which repeated problems to fix and insights to handle them both reactively and proactively.

It’s built for merchant support and payment-ops teams—use it for one-off troubleshooting or in bulk to improve retry rules and prevent repeat failures.

3. Why an LLM instead of a lookup table?

For mapping a code to its cause, a lookup table is better: it is more accurate, deterministic, and auditable. On my test set, the lookup-based approach got 88% of retry decisions right, compared with 70–74% for the LLM alone (v1). The LLM also invented answers for 4 of 6 unknown codes.

But a lookup table can only tell us what the code means. It cannot use the full payment context—such as attempt number, amount, recurring vs. one-off, or whether the customer was already debited—to determine the right recovery action.

The table provides the trusted facts; the LLM applies those facts to the payment context. In v2, this combination increased retry-decision accuracy to 98% and turned the decoder from a code-lookup tool into a recovery assistant.