--- title: "Decision-support AI for European enterprises - Impetora" description: "AI-augmented underwriting, claims triage, loan eligibility scoring, and fraud detection with the evidence chain attached. 4.2x faster decisions, 67% fewer mis-classifications, full audit trail." url: https://impetora.com/use-cases/decision-support-ai locale: en dateModified: 2026-04-27 author: Impetora alternates: en: https://impetora.com/use-cases/decision-support-ai lt: https://impetora.com/lt/naudojimo-atvejai/sprendimu-palaikymo-di --- # Decision-support AI with the evidence chain attached > Decision-support AI is the practice of augmenting underwriting, claims triage, loan eligibility scoring, fraud detection, and pricing decisions with AI-generated recommendations that carry the evidence chain on every output. Impetora ships these systems aligned to EU AI Act high-risk requirements, delivering 4.2x faster decisions and 67% fewer mis-classifications. *Updated 2026-04-27. By Impetora.* ## Key metrics - **4.2x** — Faster decisions vs manual baseline - **67%** — Reduction in mis-classifications - **100%** — Decisions with audit trail - **11d** — Median pilot deployment ## What is decision-support AI? 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 (https://artificialintelligenceact.eu/annex/3/) classifies AI systems used in credit-worthiness assessment, life and health insurance pricing, and certain fraud-related decisions as high-risk. Gartner forecasts (https://www.gartner.com/en/information-technology/insights/decision-intelligence) that 33% of large enterprises will have deployed decision-intelligence systems by 2026, up from 8% in 2023. ## How does it traditionally work? 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 and pull files into manual review when the case looks unusual. McKinsey's 2024 insurance research (https://www.mckinsey.com/industries/financial-services/our-insights/the-state-of-insurance-2024) reports decision-cycle times of 4 to 10 days on routine commercial lines. BIS research on AI in credit decisioning (https://www.bis.org/publ/work1037.htm) finds machine-learning models achieve 10 to 25% better discrimination than traditional logistic-regression scorecards. ## How does Impetora's TRACE methodology solve it? 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 (https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring). 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. ## What does the system architecture look like? 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. 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. ## What measurable outcomes can you expect? Four numbers we have validated against pilot baselines. Decisions complete 4.2x faster than the manual baseline on routine cases. 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. ## How long does a deployment take? 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. Phase three (weeks 5 to 11) extends to assist-mode and selective auto-approval on the case bands that earn it. High-risk classified systems include conformity-assessment work in this window. ## What does it cost? 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 before any code is written. ## Frequently asked questions ### Is this compliant with the EU AI Act? Yes by design. Decision-support systems for credit-worthiness, insurance pricing, and certain fraud-related decisions are classified as high-risk under EU AI Act Annex III, requiring conformity assessment, technical documentation, automated event logging, human oversight, and post-market monitoring. We build to those requirements from day one. ### How explainable is the output? Two layers of explanation. First, every recommendation carries the contributing-feature breakdown with attributions visible to the reviewer at decision time. Second, for adverse decisions affecting natural persons, the system generates an applicant-facing explanation grounded in the cited features and the relevant policy clause, aligned to GDPR Article 22 and EU AI Act transparency obligations. ### Will it replace our underwriters or claims handlers? No. Production-grade decision-support shifts senior reviewers from routine application of guidelines to exception handling, model oversight, and the cases where their judgement actually matters. The override path is mandatory for the case bands that touch high-risk classification: a human signs the decision, the AI provides the evidence pack. ### How is model risk managed? Model-validation evidence is generated as part of the build, not retrofitted. We run discrimination, calibration, and stability metrics against held-out historical cases before any production deployment, and re-run them quarterly. For credit applications under EBA Guidelines, we ship the documentation pack required for the supervisory model-risk-management function. ### What happens when the AI is uncertain? Uncertainty bands are designed in, not bolted on. For each decision class, we set confidence thresholds during scoping: cases above the threshold may auto-approve, cases in the middle band route to human review, cases below the threshold are flagged as out-of-distribution and escalated to a senior reviewer. ### Can it work with our existing core systems? Yes. The architecture is designed around your system of record (policy admin, claims platform, core banking, loan origination). We ship integrations for the major commercial platforms and a queue-based bridge for in-house systems. We do not require a rip-and-replace. ## About this service **Decision-support AI** — AI-augmented underwriting, claims triage, loan eligibility scoring, fraud detection, and pricing decisions with full evidence chain. EU-resident, EU AI Act high-risk-aligned, audit-traceable. Pilot in 4 weeks, production in 11 weeks. Engagements from EUR 25,000.