Your models approve and deny loans. Regulators want to know why. Today, that answer lives in logs, Slack threads, and tribal knowledge. DecisionSOR captures the complete reasoning trace, including every input, policy, and exception, as a durable, auditable record.
A loan gets approved. The system logs: "Approved. $250,000. 7.2% APR."
But the reasoning disappears:
When the regulator asks (and they will), you're reconstructing from fragments.
Salesforce, your LOS
Store outcomes. Loan approved. Rate assigned. Collateral recorded.
Snowflake, Databricks
Aggregate history. Good for analytics. But they receive data after the decision. The reasoning context is already gone.
The missing layer
Captures decisions as they happen. The inputs. The model. The policy. The exception. The approval chain. The why.
When humans made every lending decision, the reasoning lived in their heads. Examiners could interview the underwriter. Now AI makes thousands of decisions per hour. There's no one to interview.
And regulators aren't waiting:
Mandates model risk documentation
Requires explainability for adverse actions
Is watching AI lending closely
Are adding their own requirements
The question isn't whether you'll need to explain your AI decisions. It's whether you'll be ready when asked.
DecisionSOR is the first system of record purpose-built for decisions, not just outcomes. The most valuable data in your organization isn't what happened. It's how and why decisions were made.
When you persist decision context as a first-class asset, you get:
Every input, every policy, every exception, every approval: captured automatically
Once recorded, decision traces can't be edited or deleted. Ever.
Answer any regulatory question with the actual record, not a reconstruction
Complete chain of custody for every decision
Precedents become searchable, not tribal knowledge
Guardrails with teeth, not just policies on paper
Stop reconstructing. Start recording.
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