Real-time supervision in one minute
Most supervision still runs on periodic returns. A firm closes its quarter, assembles a return, and sends it weeks later. The supervisor validates it, reads it if there is time, and updates its view of the firm. By then the firm has moved on.
Real-time supervision closes that gap. Data comes straight from firms’ systems as it is produced, is validated the moment it lands, and feeds a small set of measures for each firm that update as the data arrives. The supervisor sees a change when it happens and decides what to do about it.
What it is not
It is not watching every transaction, and it is not a dashboard of live charts that nobody has time to look at. It is a few well-chosen measures per firm, refreshed as data arrives, with thresholds that tell a supervisor when to look closer. Most days nothing needs to happen. When something moves, the supervisor knows within days, not a quarter later.
Nor does it replace judgement. The Financial Stability Board described the aim in 2020: SupTech could “generate real-time indicators of risk to support forward looking, judgement based, supervision” (FSB, October 2020). The indicators are real time. The judgement stays with supervisors.
Why now
Three things have changed. Firms change faster: AI is speeding up how banks build and ship software, so a firm seen once a quarter is understood a quarter late. Supervisory data is becoming granular: the ECB has collected loan-by-loan data through AnaCredit since 2018 and plans one granular collection for the euro area through the Integrated Reporting Framework, live in 2031. And the formats have caught up: the EBA has moved its reporting to xBRL-CSV, the XBRL format built for large datasets.
Supervisors are asking the questions underneath too. The Bank of England's Future Banking Data discussion paper, published in February 2026, reviews what data the PRA collects and how. And the BIS Innovation Hub and the Bank of England have shown it can be done: Project Pyxtrial built a prototype pipeline that could give supervisors near real time data on stablecoins' liabilities and backing assets.
What it needs
The analytics are the easy part. What decides how close to real time a supervisor can get is the path the data takes to arrive.
- 01Data that arrives straight throughReturns and granular data sent by firms’ own systems, by API or portal, as they are produced, instead of spreadsheets assembled by hand at quarter end.
- 02Validation in secondsEvery submission checked against the supervisor’s rules the moment it lands, so errors go straight back to the firm and only clean data reaches the desk.
- 03A few measures that matter, per firmIndicators chosen for each firm and sector, refreshed as data arrives, with thresholds that tell a supervisor when to look.
- 04Decisions with their evidenceWhen a supervisor acts on a signal, the data, the rule and the judgement are recorded together, so the decision can be explained later.
Validation speed is the one most often underestimated. If a large return takes hours to validate, a firm submits, waits, fixes and resubmits, and real time is impossible. If it takes seconds, firms fix errors while they are still submitting, and a new rule can be tested against a year of filings overnight. In our own benchmark, a large FINREP return validated in 16 seconds, with 99.95% agreement with the open-source reference processor across 417,531 checks.
How supervisors get there
Nobody switches to real-time supervision in one step. It is a path, and each step is useful on its own.
- 01CollectA portal and API that validate every return as it lands. Firms fix errors while they are still submitting.
- 02ConnectFirms’ regulatory reporting systems call the same published rules before they send, and send straight through.
- 03WatchThe measures that matter for each firm update as data arrives, with alerts when one moves outside its range.
- 04DecideSupervisors act on signals early, with the evidence attached, and every decision feeds the next rating.
The first step is the one most supervisors can take this year: a collection portal that validates every return at submission. The second is straight-through reporting, where firms' RegTech systems call the supervisor's published rules before they send. That is where RegTech and SupTech meet, and where we expect most supervisors to want to be by 2030.
Where AI fits, and where people decide
More frequent data makes AI more useful, not less, because nobody can read it all by hand. AI can read what arrives, including narratives and attachments, and point a supervisor at what has changed, with sources. Deterministic rules decide whether data passes, so the same submission gets the same answer every time. Supervisors decide what to do, and every decision is recorded with its evidence. AI reads. Rules check. People decide.
- 1How many days pass between a firm’s reporting date and someone in your team reading the return?
- 2How long does your largest return take to validate?
- 3Can firms check their data against your rules before they submit?
- 4Which three measures would tell you a firm’s risk has changed, and how often do you see them?
- 5For your last supervisory action, could you show the data and the rule behind it?
What a real-time supervision platform does
A platform for real-time supervision runs the data path end to end: it collects returns by portal or API in XBRL, Excel or CSV, validates each one as it lands, reads it with AI, keeps the measures that matter for each firm current, and records every decision with its evidence. Line4 builds that as SupTech software for financial regulators, deployed by engineers inside the regulator's own team and cloud, starting with one return and growing from there.
Questions
What is real-time supervision?
Real-time supervision is supervision in which the supervisor sees the measures that matter for each firm as they change, using validated data sent straight through from firms’ systems, instead of waiting weeks or months for periodic returns. It does not mean watching every transaction.
Is real-time supervision the same as watching every transaction?
No. It means a small number of well-chosen indicators per firm, refreshed as data arrives, with thresholds that tell a supervisor when to look closer. Most of the time nothing needs to happen. When something moves, the supervisor knows within days rather than a quarter later.
Who first described SupTech producing real-time indicators?
The Financial Stability Board, in its October 2020 report on supervisory and regulatory technology, said SupTech could “generate real-time indicators of risk to support forward looking, judgement based, supervision”.
Has a supervisor tested near real time supervision?
Yes. Project Pyxtrial, run by the BIS Innovation Hub and the Bank of England, built a prototype data pipeline that could give supervisors near real time data on stablecoins’ liabilities and backing assets. The project has concluded and stayed a prototype.
What stops supervisors working in real time today?
Mostly the data path, not the analytics: returns assembled by hand, sent periodically, and validated slowly. If validating a large return takes hours, real time is impossible. If it takes seconds, firms can fix errors as they submit and supervisors receive clean data as it is produced.
What is the difference between real-time supervision and straight-through reporting?
Straight-through reporting is how the data travels: from a firm’s systems to the supervisor without manual steps, checked against the same rules on both sides. Real-time supervision is what the supervisor does with it: watching the measures that matter as they change. The first makes the second possible.
Where does AI fit in real-time supervision?
AI reads what arrives, including narratives and attachments, and points supervisors at what has changed, with sources. Deterministic rules decide whether data passes. Supervisors decide what to do. More frequent data makes AI more useful, because nobody can read it all by hand.
How does Line4 support real-time supervision?
Line4 builds SupTech software for financial regulators: a portal and API that validate returns in XBRL, Excel or CSV as they land, AI review of returns and attachments, and casework with an evidence trail. Our XBRL engine validated a large FINREP return in 16 seconds, against 4.2 hours for the open-source reference processor, which is the speed real-time supervision needs.