Banks and fintechs now let models decide who gets credit, which payments are blocked, and which customers are flagged. Each of those is a decision you're accountable for, to the customer, the RBI, the Fed, and potentially a court. AskLedger makes each one provable.
When an AI credit or fraud decision is disputed, it's rarely disputed the same day. It's disputed months later, after a complaint, an ombudsman referral, or a discrimination claim. By then the logs have rotated, the model has been retrained, and nothing ties that decision to that model version on that date.
The same receipt AskLedger signs to prove your AI savings seals each decision the instant it's made: the exact input, output, model version, and timestamp, verifiable later by an auditor, a regulator, or a court with only a public key.
Counsel asks the bank to show exactly what the model produced on that date.
Logs rotated, the model retrained twice; nothing ties that decision to that version.
The precise input, output, and model, signed and timestamped, defensible before a court or the RBI.
A valued customer is blocked and escalates; the team must explain the decision fast.
The score that triggered the block is gone; the team is left guessing after the fact.
Whatever the decision recorded, sealed at the moment it was made: the score, the threshold it crossed, the model version, and a hash of the feature snapshot. An evidenced answer instead of a guess. Note the honest shape of this: the drop-in adapters wrap LLM calls, so for a fraud engine or a scorecard you pass these fields in at the point of decision. That is a few lines, not an integration project, but it is not automatic.
See how a banking-grade receipt works, or talk to us about your credit, fraud, and model-risk workflows.