Monitoring and early warning.

Which customers are heading for trouble before they miss a repayment? Luminnovate watches each customer against its own normal and its peers, and says where a change happened before anyone explains why.

A quarterly review sees the quarter. Behaviour changes on a Tuesday.

Arrears arrive after the warning signs have been in the account for months. A system that alerts on every dip buries the team; one that waits for a missed repayment is too late.

MonitoringOne borrower against its own normal and its peersAlert

Overdraft fees in the last 90 days: 3. Repayments: on time so far.

Illustrative example.

What Luminnovate does

Judges each customer against its own normalAnd against peers that operate the same way, so seasonal patterns stay quiet.
Forecasts delinquencyDelinquency forecasting across the book, with the drivers behind each customer's change and a range, monitored once live.
Holds false alerts to a budgetSet in advance, so the team can act on what it sees.
Shows where a change happenedBefore anyone explains why, so a regional fix is not mistaken for a book wide shift.

Seeing missed repayments coming

Comparing each borrower with its own history and its peers flagged 63% of first missed repayments at 1.6% false alerts; the instalment rule alone reached 57% at 0.7%. Once the cost of each call and the team's capacity were counted, the existing rule was hard to beat. Read the full case

Impact Sandbox result for early warning: no change recommended, because no measured option gains enough over the instalment rule, with a chart of net benefit a year against outreach contacts a month and the team's capacity marked. — From Luminnovate's Impact Sandbox. Detection rates measured on real bank data from an open research dataset; dollar figures from illustrative assumptions, not a client's.

Two cases

Results from Luminnovate's work, labelled by source. Unlike the illustration above, these figures are not invented.

81%Real bank data from an open research dataset

Overdraft fees before missed repayments

of loans showing overdraft or penalty fees defaulted, against 7.3% of the rest

What was observed: Of 84 loans that missed a repayment, 33% had been overdrawn or charged penalty interest beforehand.
What a generic approach says: Wait for the first missed repayment.
What the engine read: Loans showing the fees defaulted 81% of the time, against 7.3% for the rest, and the flag caught all 3 defaults that never missed a repayment first.
What it meant for the decision: Fees in the account are an early sign worth watching. The numbers are small: this shows the mechanism, not a production signal.

Read the full case

3.65% to 0.04%Real invoice data, anonymised

The collections fix that looked like a model

invoices paid 30 or more days late

What was observed: Invoices paid 30 or more days late collapsed from 3.65% to 0.04%.
What a generic approach says: A book wide change in payment behaviour.
What the engine read: 65% of the fall sat in one region, where 39% of invoices had run late and then none did.
What it meant for the decision: This corrected our own earlier reading of a book wide switch. Monitoring should show where a change happened before anyone explains why.

Read the full case

Where it is used: Banks and lenders, super trustees, Energy and telco retailers
One case: Seeing missed repayments coming (Real bank data from an open research dataset)
Trust: How explanations are made and tested

Start with a backtest on your own history: would it have seen your last bad quarter coming?

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