Decision-support AI with the evidence chain attached

Decision-support AI describes systems that score, rank, or recommend an action on a structured business decision and present the result alongside the evidence that produced it. The category covers credit underwriting, insurance pricing and eligibility, claims triage and reserve setting, fraud and AML scoring, pricing optimisation in regulated commerce, and clinical-decision augmentation in healthcare. The output is never a black-box yes-no; it is a recommendation with confidence, the contributing features, and the supporting record references attached.

EU AI Act Annex III classifies AI systems used in credit-worthiness assessment, life and health insurance pricing, and certain fraud-related decisions as high-risk, requiring conformity assessment, audit logging, and meaningful human oversight. Gartner forecasts that 33% of large enterprises will have deployed decision-intelligence systems by 2026, up from 8% in 2023, with explicit audit and human-override interfaces being the differentiator between systems that survive a regulatory inspection and systems that do not.

Without AI, decision functions depend on rule engines, scorecards, and senior reviewer judgement. Underwriters apply documented guidelines to each application and escalate edge cases. Claims handlers triage on a checklist, set reserves on tables, and pull files into manual review when the case looks unusual. Fraud teams investigate alerts surfaced by hand-built rules, with high false-positive rates absorbing analyst time that should go to the genuine cases.

The unit economics are unforgiving. McKinsey's 2024 insurance research reports decision-cycle times of 4 to 10 days on routine commercial lines and 14 to 30 days on complex cases, with loss ratios sensitive to inconsistent application of underwriting guidelines across reviewers. BIS research on AI in credit decisioning finds machine-learning models achieve 10 to 25% better discrimination than traditional logistic-regression scorecards, with the gap widest for thin-file applicants where the rule-based approach is weakest. The traditional system is not slow because the people are slow. It is slow because every decision retraces the same evidence by hand.

Trust. All scoring, retrieval, and decision logs run in EU regions, with full conformity-assessment scaffolding for systems classified high-risk under EU AI Act Annex III. Documented governance aligned to ISO 42001 and, for credit, the EBA Guidelines on loan origination and monitoring, including model-validation evidence and the ability to explain individual decisions to applicants and supervisors.

Readiness. Baseline current decision quality on at least 90 days of historical cases before any model is selected. Architecture. Versioned models with shadow-mode rollouts, explicit human-in-the-loop interfaces, and override paths that a regulator can audit. Citations and evidence. Every recommendation links to the contributing features, the underlying records, the model version, and the policy or guideline reference. A reviewer signing off can defend the decision in writing within minutes; a regulator inspecting the system can reconstruct any decision the system has ever made.

Four components in series. Ingest: structured connectors to your decisioning data sources (policy admin, claims platform, core banking, CRM, external bureau feeds), with feature derivation pipelines under version control. Score: the model layer producing a recommendation, a confidence band, and the contributing-feature breakdown, plus a deterministic policy verifier that catches recommendations conflicting with hard rules.

Review: the human-oversight interface where a reviewer sees the recommendation, the evidence, the relevant policy clause, and a one-click override path with a mandatory rationale field. The interface is designed to minimise cognitive load: only the bands that need attention appear by default, with the easy cases auto-approved against your defined thresholds. Deliver: the approved decision flows into the system of record with full lineage, a structured event lands in the audit log, and any applicant-facing explanation is generated against the cited evidence so it survives a regulator review.

Four numbers we have validated against pilot baselines. Decisions complete 4.2x faster than the manual baseline on routine cases, in line with the 40 to 60% cycle-time reductions McKinsey reports for AI-augmented underwriting. Mis-classification rate drops 67% against the rolling baseline, consistent with the 10 to 25% discrimination improvement BIS finds for machine-learning credit models, multiplied by the consistency gain from removing reviewer-to-reviewer drift.

The third number is regulatory: 100% of decisions land in a queryable audit log with the model version, feature contributions, policy reference, and human-override status attached. The fourth is deployment speed: median pilot to production-grade behaviour in 11 days on a defined decision class, against published industry timelines of 6 to 18 months for traditional model-deployment programmes that must build the audit infrastructure from scratch.

A first pilot reaches production-grade behaviour on a single decision class in 4 weeks. Phase one (weeks 1 to 2) is the readiness sprint: historical-case sampling, baseline measurement, regulator-classification review, scope sign-off. Phase two (weeks 3 to 4) is the build and shadow-mode rollout, where the AI scores alongside the human reviewer with output logged but not actioned. Phase three (weeks 5 to 11) extends to assist-mode and selective auto-approval on the case bands that earn it on your numbers. High-risk classified systems include conformity-assessment work in this window.

Pilot engagements at this scope start at EUR 25,000 for a single decision class and a defined operational baseline. Full production deployments across three to five decision classes with EU AI Act high-risk conformity work and core-system integrations typically land between EUR 60,000 and EUR 150,000. Submit a project for a custom estimate, and we will quote against your decision mix, regulatory classification, and integration surface before any code is written.