Regimes
CFTC · MiFID II / MiFIR · EMIR REFIT
ACKurate™ · Trade & transaction reporting controls · CFTC · MiFIR · EMIR REFIT
Machine-learning controls that find what yours miss — and pay for themselves doing it.
For fifteen years, banks bridged the gap between growing reporting obligations and ageing operations technology with manual effort. That model has run out of road. ACKurate finds the reporting errors your rules-based controls cannot see — and removes the operational waste that stops your team acting on them.
Deployment
Bank-controlled · on-premise or private cloud
Data egress
None
01 · The economics
have expired
have expired
The efficiency strategy of the last fifteen years has run out.
Since 2008, reporting obligations have grown without pause — new regimes, then refits of the regimes. EMIR REFIT alone took reportable fields from 129 to 203. A Tier-1 firm now reports more than a billion fields a day. Over the same period, reporting operations were passed over in technology investment, and the widening gap between what regulators demanded and what systems could do was closed the only way available: manual work, relocated to low-cost centres.
Cheaper labour was the efficiency strategy. It was rational when it was built. It no longer works: headcount is now capped and shrinking in every jurisdiction, regardless of cost — while volumes and the regulatory change agenda keep compounding.
You cannot hire your way out. You cannot rule-write your way out — rules only test what you already know to check. The remaining lever is intelligence applied to the operational process itself.
Obligations since
2008
New regimes, then refits of the regimes.
EMIR REFIT reportable fields
129→203
One regime, one revision.
Fields reported daily, Tier-1 firm
1bn+
Volume the manual model was never built for.
02 · The control gap
Your reports are well-formed. Are they true?
Deterministic controls test format: the field is populated, the value is valid, the schema passes, the acknowledgement comes back clean. None of that tests whether the report is right — whether the values reflect what actually happened to the trade.
That is the gap where reporting errors live: records that satisfy every rule and are still wrong. A trade can carry a valid, correctly formatted product identifier and be systematically mis-categorised. It passes every check. It does not fit the population. Traditional controls provide the appearance of assurance while validating structure, not truth.
Your ACKs tell you your reports are well-formed. They don't tell you they're true.
03 · The capacity
paradox
paradox
Finding more issues doesn't help if no one can look at them.
Most reporting-analytics tools solve half the problem — and worsen the other half. They surface more findings into review queues that are already beyond capacity. An unreviewed finding is not control. It is exposure, sitting in a backlog.
Detection only creates value when it arrives with the capacity to act on it. That is why ACKurate does both, and why we do not sell them separately.
A tool that only finds anomalies isn't the answer. A tool that finds them and creates the capacity to handle them is.
04 · One product,
two capabilities
two capabilities
One machine-learning layer. Both problems.
Capability 01 · the efficiency engine
Alert Prioritisation Intelligence
Classifies and ranks findings by confidence and materiality, distinguishing genuine reporting errors from booking artefacts and noise. Review queues arrive ordered by importance, not volume, with an explanation attached to every item. Analyst decisions feed back into the model, so prioritisation sharpens with use.
What it removes
- Review hours spent on false positives
- Duplicated effort across overlapping checks
- Legacy verification steps kept out of habit
- Manual spreadsheet triage
Capability 02 · the control engine
Structural Anomaly Detection
Identifies records that pass rules-based validation but do not fit the deeper structural patterns of your reporting population — across field distributions, cross-field relationships, record structure, and time-based behaviour.
What it analyses
- Field distributions
- Cross-field relationships
- Record structure
- Time-based behaviour
Under the hood
An ensemble of unsupervised models scores every record inside your environment; findings arrive ranked, explained, and linked to the relevant rules; analyst feedback retrains the system.
Inside the product · click through it
Demo environment · lab data
Coverage
Tuned to the field-level patterns of CFTC (Parts 43/45), MiFID II / MiFIR (RTS 22) and EMIR REFIT — including where the regimes diverge. Further regimes added as engagements require.
05 · Built for a bank,
not a demo
not a demo
Everything about ACKurate assumes your environment, your governance, your teams.
01
Your data never leaves.
Deployed on-premise or in your private cloud. Models are trained and run on your data, inside your boundary. Nothing is transmitted to us or any third party. We plan the route to production with your technology function from the outset — not as a late surprise.
02
Machine learning your model-risk function can approve.
ACKurate uses machine learning — deliberately, not generative AI. Outputs are deterministic, explainable and repeatable; every finding says why it was flagged. The product is designed for review under the current model-risk regimes — SR 26-2 in the US, SS1/23 in the UK, and equivalent frameworks — and each engagement produces the development, validation and monitoring documentation your second line expects. Classical ML sits inside a settled supervisory perimeter; we built there on purpose.
03
Humans decide. Always.
ACKurate prioritises; it never silences. No finding is auto-resolved. Analysts review and disposition every item the model surfaces — the system directs attention, people exercise judgement. That is both good operations and the design choice that keeps the product's governance footprint light.
06 · Proven in
production conditions
production conditions
Issues that passed every rule — confirmed by the bank's own experts.
Working inside a Tier-1 bank's own environment, on its own historical CFTC and MiFID reporting data, ACKurate's detection layer surfaced a substantial volume of structural reporting issues in a single quarter of data — including product misclassifications and lifecycle-sequencing breaks. Every finding passed the bank's existing deterministic controls. Each was verified by the bank's own subject-matter experts and escalated for remediation, and a number were serious enough to require action.
The engagement also proved the other half of the thesis: findings arrived faster than existing review processes could absorb them. Detection without capacity is not control — which is why prioritisation and detection are now inseparable in how ACKurate is built and sold.
In lab testing on labelled datasets, alert-prioritisation classification reached approximately 90% accuracy; production output is always subject to analyst review.
07 · Run it on
your data
your data
Register interest. We will arrange the data transfer when you are ready.
An anonymised extract is enough — one regime, one asset class, a single day of records. Datasets are not uploaded through this page: the ACKurate team agrees a secure transfer route with your firm, then returns a findings pack over your population — what does not fit, why it was flagged, and which items your deterministic controls passed clean.
We will not quote you a savings number before we have measured your process — and you should not trust a vendor who will. The sample establishes your baseline; the value case is then built in your numbers, against your operation, and tested rather than asserted.