10decodersYour alert queue is full.
That isn't the same as being covered.
Most monitoring problems are not detection problems. The rules fire, the alerts land, and the queue grows faster than the team can clear it — so real typologies sit behind a wall of noise nobody has time to sort. We build and operate the monitoring stack that fixes the whole path: ingestion, detection logic, triage, case management, regulatory filing and the model evidence an examiner will ask for.
- Streaming + batch detection
- Rules & behavioural models
- Alert triage & case management
- STR / SAR workflow
- Model governance evidence
- Platform-neutral
- 200+Engineers
- 37+Clients served
- ISO 27001
& 9001Certified delivery - 4Global delivery centres
If any of this is being said out loud in your compliance stand-up, this page is for you
These are the sentences that precede an engagement. They come from heads of financial crime, MLROs, and the engineering leads who own the pipes underneath them.
We close alerts to hit the SLA, not because we finished the investigation.
Queue pressure has quietly become the real detection policy. Disposition quality is the first thing an examiner samples.
Nobody has touched the thresholds since the system went live.
Rules that were calibrated to a smaller, different book. Volume has tripled; the logic hasn't moved.
Our monitoring runs end-of-day. Our payments are instant.
A detection cycle slower than the settlement cycle can report, but it cannot prevent.
Fraud and AML each have their own engine and neither sees the other's signals.
Mule networks and account takeover live exactly in the gap between the two.
The vendor platform does what it does. We need what it doesn't.
Custom typologies, local regulator formats and product-specific behaviour rarely fit a packaged rule library.
We passed the audit, but we couldn't evidence the tuning.
Above-the-line and below-the-line testing, threshold rationale and model change history are now table stakes.
Work out whether your queue is a tuning problem or a headcount problem
Put your own numbers in. Everything below is arithmetic on what you enter — no benchmarks, no assumptions of ours. Most teams find the gap is bigger than the monthly report suggests, because the report only counts alerts that were closed.
- Alerts generated per month
- 16,000
- Analyst hours required
- 3,200Against 960 hours of available capacity.
- Coverage
- 30%Roughly 11,200 alerts a month go unreviewed or get closed without real investigation.
Tuning problem. Closing this gap with people would mean about 19 additional level-1 analysts. Precision work on segmentation and thresholds is almost always the cheaper lever.
Arithmetic only — your inputs, no industry benchmarks applied. A detection review replaces these estimates with measured numbers from your own alert history.
Seven layers, one accountable owner
Monitoring fails at the seams — between the payment rail and the feature store, between the model and the case file, between the case file and the filing. We take the whole path, so nobody gets to point at the layer next door.
Ingestion & event fabric
Streaming + batchCore banking, card switch, UPI/ACH/SEPA rails, wallet and ledger events landed on a durable stream with replay. Batch retained for lookback and retuning; streaming carries the decision path.
Entity resolution
Customer · account · device · counterpartyOne resolved identity across products and channels, with a counterparty graph behind it. Without this layer, mule rings look like unrelated small accounts behaving normally.
Feature & behaviour store
Velocity · peer group · baselineRolling velocity, structuring windows, dormancy breaks, peer-group baselines and device/geo drift — computed once, served identically to rules, models and investigators.
Detection logic
Rules + models, versionedDeterministic scenarios for regulator-mapped typologies, behavioural and anomaly models for the ones you can't write down. Every rule and model versioned, back-testable and tied to a documented rationale.
Alert triage
Scoring · grouping · suppressionAlerts scored, deduplicated and grouped into a single subject-level narrative rather than a row per transaction. Suppression rules are documented and reviewable, never silent.
Case management
Investigation to filingInvestigator workspace with evidence attached, four-eyes review, escalation paths, and STR/SAR generation in the format your regulator accepts. Full audit trail on every disposition.
Model governance
The layer that survives the examThreshold rationale, above- and below-the-line testing, tuning cadence, drift monitoring, change control and independent validation packs — produced as a by-product of running the system, not assembled the week before an inspection.
Typologies we implement, and how each one is caught
Scenario libraries are the starting point, not the deliverable. Each of these is implemented against your products, your rails and your customer segments — because a threshold that works for a salaried retail book is meaningless for a merchant-acquiring one.
Structuring & smurfing
Deposits and transfers deliberately kept under reporting thresholds, spread across accounts, days or channels.
AMLRapid movement of funds
Credit in, debit out within a short window, leaving little or no balance behind — pass-through behaviour.
AMLMule networks
Fan-in / fan-out patterns across newly opened or dormant accounts, resolved through the counterparty graph.
AML + fraudAccount takeover
Device, geo, session and behavioural drift preceding a beneficiary change or a high-value transfer.
FraudAuthorised push payment scams
First-time beneficiary, atypical value, urgency signals and out-of-pattern session behaviour on the payer side.
FraudSanctions & high-risk exposure
Post-transaction exposure to sanctioned or high-risk jurisdictions and counterparties, reconciled against screening hits.
AMLTrade-based laundering
Invoice, value and shipment mismatches on trade flows, and round-tripping between related counterparties.
AMLMerchant & acquiring abuse
Transaction laundering, bust-out patterns, refund and chargeback abuse across a merchant portfolio.
AML + fraudCrypto on/off-ramp exposure
Fiat rails connecting to VASPs and exchange counterparties, with on-chain attribution joined to the fiat leg.
AML + fraud
Cutting noise without cutting coverage
Every false-positive reduction exercise carries the same risk: you turn the dial and quietly stop seeing something real. So tuning is run as a controlled, evidenced process — never as a quiet threshold edit in production.
- Segment first, threshold second. Retail salaried, self-employed, SME, merchant and corporate books get their own baselines rather than one global cut-off.
- Above-the-line testing. Sample productive and non-productive alerts at current settings to establish what the rule is actually buying you.
- Below-the-line testing. Sample what sits just under the threshold to prove nothing material is being missed before anything is loosened.
- Shadow deployment. New logic runs in parallel against live traffic and historic windows before it ever touches the production queue.
- Documented rationale. Every threshold change carries the test evidence, the approver and the date. That file is the deliverable, not a side effect.
What you hand the examiner
Detection quality and defensibility are different problems. Teams that solve only the first still fail inspections. Everything below is generated by the platform as it runs.
- Scenario inventory — every rule and model, its typology mapping, owner, and current parameters.
- Change control log — what changed, why, who approved it, what was tested and when it went live.
- Tuning evidence pack — ATL/BTL results, sample sizes, methodology and conclusions per cycle.
- Model performance monitoring — precision, productivity and drift tracked per scenario, not just in aggregate.
- Data lineage — from source rail to feature to alert to filed report, traceable end to end.
- Disposition audit trail — who reviewed what, on what evidence, with what reasoning.
Four phases, each with something you can hold
We do not disappear for a quarter and return with a platform. Each phase ends in an artefact your compliance and engineering leads can review, challenge and use — whether or not you continue to the next one.
Baseline
We take your alert history, disposition data and current scenario inventory and measure what the programme is actually producing — productivity per rule, queue ageing, duplication and coverage gaps.
Out: detection baseline report · coverage gap map · prioritised remediation list
Typical: 3–4 weeks
Instrument
Event fabric, entity resolution and the feature store go in. Detection logic gets versioned and moved into a testable pipeline, with shadow running against live traffic.
Out: streaming pipeline · resolved entity graph · scenario library under version control
Typical: 8–12 weeks
Tune
Segmentation, threshold optimisation, ATL/BTL testing and alert grouping — run as evidenced cycles with the governance pack produced alongside, not afterwards.
Out: tuned scenario set · ATL/BTL evidence pack · updated threshold rationale
Typical: 6–8 weeks
Operate
We run it — or we hand it over with the runbooks, tests and documentation that make handover real. Your call, made at this point rather than at signature.
Out: managed detection ops, or full source and runbook handover
Ongoing
Three entry points. Start at the one that matches your evidence, not your ambition.
If you don't yet know how your current rules are performing, start with the review — the build scope will be wrong without it.
Detection Review
For teams who suspect the queue is noise but can't prove it
Fixed fee3–4 weeks · credited in full against a Monitoring Build
- Scenario-by-scenario productivity analysis on your own alert history
- Coverage gap map against your products, rails and customer segments
- Queue ageing and disposition quality assessment
- Prioritised remediation list with effort sizing
Monitoring Build
For teams replacing or rebuilding the detection path end to end
Fixed-scope programmePhased milestones · IP and source assigned to you
- Everything in Detection Review
- Streaming event fabric, entity resolution and feature store
- Versioned rules and behavioural models with shadow deployment
- Alert triage, grouping and investigator case management
- STR/SAR generation in your regulator's format
- Full model governance and tuning evidence pack
- Runbooks, tests and documentation built for handover
Managed Detection Ops
For teams who want the outcome operated, not the platform owned
Monthly retainerExit at any renewal with full source and runbooks
- Everything in Monitoring Build
- Continuous tuning cycles with ATL/BTL evidence each period
- New typology development as products and rails change
- Level-1 triage capacity from our delivery centres
- Named engineering owner and quarterly governance review
- Examination and audit support
You may not need us for this
There is a real category of established transaction monitoring vendors with mature scenario libraries, regulator-familiar reporting and their own validation history. If your situation looks like the list below, buying one is usually faster and cheaper than anything we would build, and we will say so on the first call.
- You are a single-product institution on standard rails with no unusual typologies.
- You need a monitoring capability in place within weeks to satisfy a licence condition.
- You have no engineering capacity to own anything after go-live, and no plan to build one.
- Your volumes are low enough that a per-transaction pricing model stays comfortably cheap.
We get called when the packaged answer has already been tried and the gap is in the parts it doesn't cover: custom typologies, your own product behaviour, local filing formats, integration into systems the vendor doesn't support, or tuning that nobody at the vendor will own on your behalf. That is engineering work, and it is what we do.
Built to the regime you actually report into
Detection logic is only half of it. Filing formats, timelines, record-keeping periods and evidence expectations differ by jurisdiction, and a monitoring programme that ignores that difference creates work rather than removing it. We build to the one you file under.
BSA obligations, SAR filing workflow and FinCEN submission formats, with FFIEC-aligned model risk documentation.
MLR-driven monitoring obligations, SAR submission to the NCA, and JMLSG-aligned risk-based tuning rationale.
AML package obligations and national FIU reporting formats, with documentation built for the AMLA supervisory model.
PMLA obligations, FIU-IND STR/CTR reporting formats, and RBI-aligned record-keeping and audit expectations.
MAS notice and guideline obligations, STRO reporting, and the ongoing-monitoring evidence expected on inspection.
One detection platform, multiple filing regimes — shared logic with jurisdiction-specific thresholds, formats and retention rules.
Regulated financial workflows are not a new line of business for us
We have delivered KYC/KYB, AML screening, lending, payments and card platform engineering for financial services clients across the US, UK, India and South East Asia — under ISO 27001 and ISO 9001 certified delivery, with a blended US and India model.
Reference calls with existing clients are available under NDA once scope is on the table. We do not publish outcome metrics we cannot evidence.
- MerchantradeM2PIBMSojern
How we handle your transaction data
- ISO 27001 and ISO 9001 certified delivery organisation.
- Data residency respected — development against synthetic or masked data by default; production access only where your policy allows it and under your controls.
- Least privilege by role, with access reviewed on a defined cadence and revoked on rotation.
- Model and platform neutral — no hyperscaler or vendor subsidy shaping the recommendation you get.
- Source and IP assigned to you under the MSA.
