Supervision at the speed of AI.

Modern SupTech for financial regulators. AI does the reading. Your supervisors make the call.

A supervisor stands still in a busy office as colleagues and city traffic stream past

A lone supervisor watches the airfield at dusk from inside the control tower

Firms now move at AI speed. What took a year takes a month. Supervision has to see change as it happens, and stay predictable.

3 in 4supervisors worldwide now use AI, up from 1 in 4 a year earlier.Central Banking, Jul 2026 →

1The difference

Most supervisory AI writes summaries. Ours checks the returns, and your people decide.

Supervisors adopted AI fast, mostly to summarise and draft, beside the work. Line4 puts AI inside the work, with your people making every decision.

The usual approachLine4

Where the AI sitsBeside the work, summarising documents after they arrive.Inside the work, reading every return as it lands.

How calls are madeGenerated answers that can change from run to run.Deterministic rules make every call that must be repeatable.

EvidenceHard to show how a conclusion was reached.Every finding traces to the data point and the rule behind it.

Who decidesUnclear where the model stops and the person starts.Your supervisors, always. AI earns more responsibility only as evals and evidence prove it.

Supervisors' most common use of AI is summarisation and document preparation (Central Banking, Supervision Benchmarks 2026). Only 4% of central banks rate their ability to monitor AI governance as high (BIS, Oct 2026).

Three-minute readiness checkHow ready is your supervision for AI-speed firms?Eight questions. An instant score, benchmarked against 2026 data, with three next steps.Take the check →

2How it works

Line4 AI suggests

Liquidity ratio fell 38% on Q2. The narrative mentions a new funding line that is not in the template.

Senior supervisor decides

Agreed. Send the firm a query and attach both pages.

Decision recorded · evidence trail updated

AI reads. Rules check.
People decide.

The full supervisory data lifecycle on one core: the foundation for real-time supervision, with the final say always human.

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3Earned trust

AI earns trust the way people do.

Nobody hands a new analyst the final say on day one. AI starts by suggesting, and takes on more only when the evidence shows it matches your experts.

01
Assist
AI reads and suggests. Supervisors decide.
02
Prove
Suggestions are scored against your experts’ decisions.
03
Earn
Where evidence holds, routine checks move to rules or AI, audited.
04
Always
Outcomes stay with people, with a full evidence trail.

More supervision, not fewer supervisors.

When a return is checked in seconds and read in minutes, the work reaches further: key measures in view as they change, earlier conversations, deeper reviews and new sectors brought into scope.

4Proof

Proven at an international financial regulator

4systems in production

6months, start to live

2.5xproven ROI

AML data out of spreadsheets, enforcement onto case management, submissions onto a validated portal, and an incumbent platform replaced. Others quoted eighteen months.

Read the case study

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