Prediction
Forecast loss, migration, severity, demand, liquidity, and disruption as distributions instead of single-point guesses.
Outcome distribution
Fibonacci + Risk
Quantify risk before it compounds.
Forecast loss, detect anomalies, score exposure, and stress decisions with evidence built for review.
Built for accountable risk teams
BankingInsuranceTradingSupply chain
Why FiboRisk
Sequence becomes structure. Structure becomes a decision.
Fibonacci is our operating metaphor for connected risk: small changes compound, dependencies repeat, and every outcome has a traceable path.
EvidenceSignalScoreScenarioAction
Quantitative scenarios
See how the model changes the decision.
Banking
Surface portfolio deterioration before a payment event.
Combine borrower, account, collateral, sector, and behavior signals into an explainable early-warning view.
0.82Illustrative validation AUC
14Days of modeled lead time
7.4Illustrative stability index
Illustrative sample data for product demonstration. Customer outcomes depend on data, scope, controls, and acceptance criteria.
Four quantitative jobs. One governed evidence chain.
Each capability can operate alone or share context inside a single private risk environment.
Anomaly detection
Find entity, network, sequence, and time-series behavior that breaks from accepted baselines.
Deviation and contextRisk scoring
Calibrate explainable scores, thresholds, overrides, and review policy for each operating decision.
Score and policyScenario simulation
Compare base, adverse, sensitivity, reverse stress, and custom scenarios with every assumption attached.
Impact and actionMove the shock. Watch the decision change.
This live sample connects scenario severity, model output, policy band, leading driver, and recommended action.
Composite risk
/100
ReviewBaselineSevere
Leading driverPayment velocity
Recommended actionTighten review threshold
Evidence retainedFeatures, model version, policy, reviewer
Illustrative sample data. It is not a customer result or a production recommendation.
What the risk committee needs to see.
Discrimination
Does the model separate the outcomes that matter across time, segments, and operating conditions?
Benchmark, challenger, error analysisStability
Does performance remain understandable when data, population, policy, or market conditions move?
Drift, sensitivity, backtestingGovernance
Can reviewers reproduce the score, evidence, model version, policy, override, and final action?
Lineage, approval, audit exportExplore the quantitative risk library.
Search representative engagement patterns by risk domain and model capability.
8 representative scenarios
Portfolio early warning
Forecast migration and loss while preserving the borrower, feature, model, and policy trail.
Credit decision consistency
Calibrate scores, thresholds, overrides, and review policy against accepted portfolio behavior.
Claims network anomalies
Prioritize unusual provider, claimant, timing, location, and relationship combinations.
Loss development stress
Test severity, inflation, event, concentration, and reserve assumptions in one evidence trail.
Intraday behavior breaks
Detect changes in price, volume, liquidity, spread, and conduct sequences against live baselines.
Counterparty correlation stress
Connect collateral, exposure, market, entity, and concentration shocks before limit decisions.
Supplier dependency scoring
Reveal financial, delivery, geographic, ownership, logistics, and single-source concentration.
Demand and disruption forecast
Model demand distributions and route disruption so inventory decisions include uncertainty.
From one risk decision to an accepted private deployment.
No self-service signup. Customer administrators invite authorized users after implementation and model acceptance.
Diagnose the decision
Map the loss event, available evidence, operating constraints, failure costs, and acceptance criteria.
Validate the model path
Test signal quality, explainability, stability, and the limits of automation on representative data.
Agree the controls
Document scope, environments, audit evidence, ownership, release gates, and delivery milestones.
Deploy and accept
Open the private environment, integrate required systems, and support formal model acceptance.