Luminnovate connects two parts of the credit decision system: LuminoUnderwrite turns application data into review-ready underwriting intelligence, while LuminoEvidence engineers and validates signals against historical outcomes and measures their incremental value.
Built by Parisa Milne, PhD, with 20+ years applying signal processing and pattern recognition across healthcare, retail and financial services.
Illustrative interface. Evidence remains traceable to source data, decisions and observed outcomes.
Underwriters need clear, contextual evidence for the file in front of them. Risk and data teams need historical evidence that shows which metrics, models and strategies actually improve outcomes across the book.
Luminnovate connects those languages without replacing policy, governance or human judgment.
LuminoUnderwrite is the operational product for assessing an application or renewal. LuminoEvidence uses historical decisions and realised outcomes to engineer signals, validate them for a specific decision and prove what they add beyond the current baseline.
Turn each SME application into review-ready credit intelligence.
Accept forwarded broker packages, PDFs, spreadsheets, screenshots, API feeds or consented open-banking data. Verify the case, classify transactions, calculate contextual behavioural metrics and return a source-backed credit memo.
Turn historical outcomes into better decision systems.
Build, validate and optimise default models, scorecards, automated approvals, decision engines, pricing, pre-approved offers, propensity models, next-best actions, early collections and portfolio strategies.
LuminoUnderwrite serves the individual application or renewal. LuminoEvidence serves the historical portfolio. Together, they connect statistical evidence with underwriter language.
KYB, classification, behavioural metrics, risk flags and a source-backed credit memo for each case.
Engineered signals, outcome-specific validation, incremental lift and a reproducible record of the strongest experiments.
Translate mathematical risk patterns into interpretable ability, stability, conduct and trajectory metrics.
Keep definitions, evidence, limitations and outcomes connected as the decision system improves.
Each stage has a defined input, output and evidence trail. The workflow can run through API, batch delivery or an initial analyst-assisted pilot.
Ingest the application, documents, bank transactions and prior relationship history.
Orchestrate business and applicant checks, ownership, registrations, account control and data completeness.
Categorise transactions, identify transfers and reconcile activity across supplied accounts.
Build behavioural metrics across ability, stability, conduct, trajectory, concentration and confidence.
Apply industry context and credit policy, then surface risk flags, exceptions and questions.
Return a source-backed credit memo and structured JSON for underwriter review.
LuminoUnderwrite extracts and reconciles legal names, trading names, identifiers, applicants, owners, guarantors and account-holder evidence. It cross-checks what the application declares against bank-statement evidence and authoritative business-registry or KYB data.
The resulting business profile identifies the declared and verified structure, such as a company, sole trader, partnership, trust or trustee arrangement, and surfaces name, ownership, address, identifier and bank-account mismatches for review.
Declared entity, applicants, owners and guarantors
Account-holder names, trading names and counterparties
Legal status, identifiers, entity type, names and addresses
Structure, relationships, confidence and inconsistencies
LuminoEvidence starts with a lending decision and its realised outcomes, then determines which signals add reliable and commercially meaningful information beyond the current process.
Set the workflow, target outcome, current baseline and commercial success measure.
Connect applications, transactions, repayments, prior decisions and realised outcomes.
Test candidate signals across time, cohorts and economic outcomes to establish incremental value.
Use approved evidence in models, scorecards, pricing, portfolio actions and collections strategies.
Reliability is not a single accuracy number. It is the evidence that a signal is well defined, observed with sufficient coverage, stable where it will be used and controlled when it changes.
Each candidate carries its definition, source, observation window, version and lineage back to observed business activity.
Held-out performance, time and cohort stability, data coverage and sensitivity checks show where the evidence does and does not generalise.
Intended use, exclusions, approval status, monitoring thresholds and material changes remain explicit and reviewable.
Configure the inputs, behavioural definitions, policy context and output format against representative historical cases before committing to production integration.
Discuss your pilotChoose new applications or renewals, required inputs, policy checks, evidence needs and review criteria.
Reconcile accounts, calculate behavioural metrics, generate draft assessments and compare them with underwriter judgment.
Receive the agreed metric definitions, assessment schema, reliability record and API or batch integration plan.
Luminnovate was founded by Parisa Milne, PhD, who brings more than 20 years of experience in signal processing, pattern recognition and decision science across healthcare, retail and financial services. Her recent work in Australian SME credit covered cashflow analytics, portfolio allocation and credit risk.
LuminoUnderwrite is designed around the evidence an underwriter needs: context-aware behavioural metrics, clear source lineage and a review-ready assessment that supports professional judgment.
Bring representative new applications or renewals. We'll define the workflow, evidence and output worth testing.
