Generic monitoring tools flag too much, explain too little, and price every transaction they check. Relevant builds custom transaction monitoring software around your actual transaction patterns, with detection logic you own, tune, and defend to any regulator.
End-to-end monitoring systems built around your risk model instead of generic typologies.
Machine learning models that baseline normal behavior per customer and flag genuine anomalies.
Rule engines your compliance team configures and deploys without engineering tickets.
Investigation tooling that puts the full transaction context on one screen.
Legacy detection engines rebuilt for current volumes and typologies through AI-driven legacy modernization.
Discovery maps your transaction flows, jurisdictions, and the typologies your risk assessment actually names, then fixes what the system must detect, score, and report. You receive a requirements document your compliance officer approves and your board can fund.
Data pipelines, storage, and processing take shape against your real throughput and peak load. The deliverable is an architecture and cost model you own before build spend begins.
Rules and ML models get built against your transaction history, with every scenario backtested and run in shadow mode on live traffic. Nothing reaches production on assumption.
Handover covers rule configuration, threshold tuning, backtesting, and reporting workflows, delivered as training and documentation. Your compliance team holds the keys from the first day in production.
Detection accuracy decays as typologies shift, so the system ships with MLOps built in: retraining pipelines, drift monitoring, and scenario updates on a support model you scale up or down.
13+ years of building software for banks, payment providers, and regulated platforms means your typologies and rails need no explaining.
92% of our team is senior-level, so the people designing your rule engine have shipped monitoring, payments, and data systems before.
AI-assisted delivery cuts timelines by up to 50% with up to 20% fewer errors, and senior engineers review everything before it ships.
ISO 27001:2022 certification and GDPR coverage govern how we handle your transaction data throughout development.
Roadmaps hold, and budgets hold with them. 99% of our projects land on time and on budget.
A 4.9 Clutch rating from clients who point to transparency and delivery quality in their own words.
See what our FinTech software development work returns when clients hand us the full build, from data pipeline to production.
If you don’t like the answer, the fix is detection tuned to your customers. Bring the alert stats, and you’ll leave with a clear read on what custom monitoring would change.
The proof comes from your own data. During the build, every detection scenario runs against your historical transactions, producing the numbers that matter before production: how many alerts each rule would have generated, what shares were true hits, and where thresholds need moving.
If you want to pressure-test this before engaging anyone, pull your last quarter’s alert data and calculate the ratio of alerts to filed reports. If fewer than five percent of alerts result in a filing, your current rules are calibrated for someone else’s customer base, and that gap is exactly what backtesting against your own history closes.
Yes, provided governance is engineered in rather than documented afterwards. In practice, that means every model ships with recorded training data lineage, versioned thresholds, feature-level explanations for each score, and a validation record your model risk function can review independently of us.
Deterministic rules run alongside the models, so no single decision rests on a score nobody can decompose. Before signing with any provider, confirm this structure exists: examiners increasingly ask not whether you use machine learning but whether you can reproduce and justify any individual decision it made.
Coverage never drops, because nothing gets switched off until its replacement has proven itself.
The custom system shadows your current tool on live traffic, and both alert streams get compared until the new detection matches or exceeds the old one on your own data. Only then does traffic cut over, with your historical alert and case records migrated so an examiner sees one continuous audit trail rather than a gap at the vendor boundary.
This parallel-run discipline applies whatever your migration path: if a provider proposes a hard cutover date instead of a proven-equivalence milestone, that proposal transfers the coverage risk to you.
No, but it does make the data work part of the project rather than an assumption. Discovery includes a data quality assessment that measures what monitoring actually depends on: completeness of counterparty fields, consistency of timestamps and currency codes, and whether customer records resolve to single entities across products.
The architecture stage then covers the data engineering that cleans, normalizes, and enriches transactions before detection runs on them. Most monitoring implementations that fail, custom or vendor, fail here, so treat any proposal that skips a data assessment as a risk signal. The assessment itself has standalone value: it tells you what your current tool is silently missing because the data feeding it is incomplete.
Jurisdiction logic is built as configuration, not code.
Reporting thresholds, currency rules, and scenario variants attach per jurisdiction, so entering a new market means enabling and tuning a rule set rather than commissioning a rebuild.
Two things deserve attention before any expansion, whatever system you run:
Both belong in your market-entry assessment, not in the post-launch surprises column.
Yes, and we build for that from the start. The foundation ships with documentation and tests that stay current, observability that your team can read, and pipelines designed to be maintained rather than babysat.
Many clients take it in-house and run it from there. Others keep Relevant on to extend it, and with 96% retention, the people who built it are the ones who stay with it. Nothing in the work locks you to us.
Fintech
Fintech
Fintech
Relevant builds transaction monitoring software for banks that screens wires, ACH, SEPA, and card transactions in real time against structuring, layering, and smurfing patterns: deposits split to stay under reporting thresholds, rapid movement through newly opened accounts, dormant accounts that suddenly activate. The system files directly into your SAR workflow and connects to your core through our banking software development practice.
For payment companies we build velocity and volume detection: card testing runs (bursts of small authorizations from one merchant or device), transaction laundering through miscoded merchants, and mule account patterns where funds arrive and exit within minutes.
Wealth platforms need monitoring that reads asset movements, not just payments: deposits inconsistent with a client’s declared source of funds, rapid in-and-out trades used to layer money through securities, and third-party funding of accounts. We build detection into onboarding and ongoing activity both, alongside our wealthtech software development services.
Recurring revenue attracts its own fraud: stolen cards tested against low-value subscriptions, refund abuse rings, and accounts created solely to cycle chargebacks. We build transaction monitoring that scores signup, payment, and refund behavior together, so the pattern surfaces before the chargeback ratio does.
For lenders we monitor the behavior that precedes the loss: loan stacking across products, synthetic identities that pass onboarding but transact like no real customer, and payout redirection right after disbursement. Detection runs at the account level, linking application data with transaction behavior so fraud shows up before the first missed payment.
