Repayment deterioration without fraud concern
Falling cash-flow surplus and missed payments can point to a need for affordability support, without treating hardship as fraud.
Financial risk modelling
Dynamic causal financial risk modelling for credit and fraud
ByteGenie connects customer, repayment, transaction, device, and external evidence to forecast risk, explain what drives it, and compare possible interventions.
Credit and fraud risk do not develop in separate silos
Different behaviours need different explanations and responses. The demo follows customers with these distinct patterns.
Account takeover without credit deterioration
Device, session, and payment anomalies can call for account protection while leaving the customer’s credit treatment separate.
Financial stress and scam exposure together
A customer can face both risks. The model keeps the reasons and possible actions distinct while assessing their combined impact.
How ByteGenie works
One customer model connects evidence, forecasts, intervention choices, and observed outcomes.
Policy and regulatory constraintsCausal assumptions and relationship testing↓ Feed the customer model
Customer evidenceAccounts, repayments, transactions, devices, bureau, and economic data
Behavioural signalsCash-flow changes, delinquency, transaction velocity, and authentication anomalies
Dynamic customer modelRelationships tested against evidence and bounded by policy constraints
ForecastsCredit and fraud loss, cash flow, transaction, and device outcomes
ForecastsCompare baseline and interventionObserve customer and loss outcomesUpdate signals, relationships, and invalid assumptions↶ Feed new evidence into behavioural signals and causal testing
Explore the financial risk demo
Follow customer behaviour, inspect model relationships and forecasts, then see how ByteGenie compares possible actions.
What changes with affordability outreach?
ByteGenie compares two forecasts for the same cash-flow-constrained customer segment.
Selected scenario: 1,420 eligible customers · same starting evidence · same forecast horizon
No intervention
Forecast trajectory without targeted affordability outreach.
- Expected credit loss
- $1.82m
- 30+ days past due
- 4.7%
- Utilisation above 80%
- 9.2%
Affordability outreach
Early support for 72% of the eligible segment before customers reach 30+ days past due.
- Expected credit loss
- $1.34m
- 30+ days past due
- 3.4%
- Utilisation above 80%
- 7.4%
$482kExpected credit loss avoided
$164kOperating cost
$318kNet expected benefit before customer-friction cost
Conditional scenario estimates from the ByteGenie demo. They are not measured customer results.
The model learns from what happens next
ByteGenie compares forecasts with observed repayment, fraud, transaction, and loss outcomes. New evidence can change the signals, relationships, or assumptions used in the next assessment.
See how ByteGenie would model your risk decisions
Bring together your customer evidence, policy constraints, and observed outcomes in a model you can inspect and update.