Inputs
Applications, behavioural data, bureau, exposure, transactions or other relevant context.
LuminoEvidence
Build the context layer that turns portfolio data into governed decision signals.
LuminoEvidence engineers candidate signals from historical data and tests them against a specific outcome. It shows what adds reliable lift beyond the current baseline, for which cohorts, and under which model specification.
Discuss LuminoEvidence →Who it is for
Decision evidence for credit risk, portfolio, analytics and data teams.
LuminoEvidence serves repeatable decisions where representative historical inputs, the decision or treatment taken, and an observed downstream outcome are available.
01
Risk models
Default, loss and recovery models, scorecards and risk segmentation.
02
Decision strategies
Automated approval, referral, decline, thresholds, rules and decision-engine optimisation.
03
Growth decisions
Pricing, pre-approved offers, propensity and next-best-action modelling.
04
Portfolio actions
Monitoring, optimisation, early collections, treatment and recovery strategies.
Required evidence
Historical context, the decision taken and what happened next.
Not every use case requires every field. The target, population, observation window, baseline, holdout and commercial success measure are defined before modelling begins.
Decision
Approval, decline, price, limit, offer, referral or treatment actually applied.
Outcome
Repayment, delinquency, default, loss, recovery, acceptance, renewal or response.
Baseline
Current model, scorecard, rules or operational approach.
Holdout
Representative time, cohort and out-of-sample evaluation.
Economics
Approval quality, loss, margin, recovery, response or operational value.
THE BRIDGE
Connect statistical risk with underwriting language.
Risk teams work with models, scores and thresholds. Underwriters work with ability, stability, conduct, trajectory and concentration.
LuminoEvidence can test a mathematical candidate or an underwriter's behavioural hypothesis, prove where it carries risk or commercial value, and document a metric people can interpret and challenge.
Discover
Generate candidate statistical signals and formalise practitioner hypotheses.
Validate
Measure incremental value, stability, coverage, fairness, economics and limitations.
Translate
Convert supported evidence into a model, scorecard, strategy or interpretable behavioural definition.
Monitor
Track drift, outcomes, thresholds, overrides and performance after use.
What you receive
Engineered signals with evidence of what they add.
The output is a reproducible evidence package your data science, risk and validation teams can inspect, challenge and use.
01
Engineered signal library
Definitions, source fields, observation windows, transformations, coverage and lineage.
02
Outcome validation
Performance, stability and limitations for the specific default, loss, recovery, response or commercial outcome.
03
Incremental lift
Measured value beyond the current model, scorecard, rules or operational baseline, including cohort and economic effects.
04
Experiment record
Feature sets, model specifications, parameters, data splits and results showing which combinations produced the strongest reliable lift.
Start with one decision
What could your historical outcomes prove?
Define the decision, target, baseline, available history and evidence bar.