Smartflow

From a field to an operational decision

Credit-agreement onboarding connects complex documents to operational systems.

Smartflow's public material describes field-level lineage and human-in-the-loop review. The technical question is how a reviewer can inspect both a proposed value and its supporting clause, especially when amendments change the relevant terms. A well-formed field is not, by itself, evidence that it came from the controlling document.

Abstention is a useful design principle when evidence is missing or conflicting: route the unresolved value for a decision rather than fill it with a plausible guess.

Evaluate accepted-field precision, coverage, intervention effort and the final reviewed result separately. A final corrected result and the share of work completed automatically answer different operating questions. Confidence calibration is another distinct question: Guo and colleagues’ research provides a method for checking whether confidence estimates agree with observed correctness.

• Smartflow public product description
• Guo et al.: On Calibration of Modern Neural Networks — 2017

The operating responsibility

My responsibility covers the operating model around the product: what can pass through, what needs human review, how a bank adopts the system and how the business supports delivery. Those decisions connect product quality to commercial and operating responsibility.

I lead the Data & AI business unit’s P&L, product and sales, reporting to the CEO. The published Finastra partnership and Loan IQ demo provide the public account of the integration.

Evidence and outcomes

The published partnership connects Smartflow with Finastra’s Loan IQ. Finastra describes the potential to reduce credit-agreement onboarding time.

The economics of enterprise AI