Financial Services · Regulatory compliance monitoring
Regulatory violations get caught weeks after the fact — in the audit cycle, not at the point of transaction. By then, the fine is already accruing and the remediation is an emergency project.
Banao builds AI compliance-monitoring systems that evaluate transactions, communications, and reporting events against your regulatory ruleset in near-real time — flagging issues before they become findings.
A monitoring system is only as good as the rules it enforces and the data it has access to. We own all three: model, rules engine, and integration.
Every transaction is scored against your current regulatory ruleset — AML thresholds, reporting windows, exposure limits — in near-real time, not overnight batch.
NLP scans trader, advisor, and support communications for prohibited language, undisclosed conflicts, and policy breaches — closing the channel gap that most monitoring misses.
A parsing layer monitors official regulatory feeds and flags rule changes that require model or policy updates — so your monitoring does not go stale between audit cycles.
Risk-ranked alerts with an evidence bundle attached: the specific event, the rule triggered, and the relevant data lineage. Compliance analysts review cases, not queues.
Structured logs and exportable evidence trails that satisfy examiner requests without manual reconstruction — the artefact regulators expect, generated automatically.
Rules live in a governed layer your compliance officers can update without a developer. When the regulation changes, the monitoring changes with it.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
PhonePe
At payments volume, rule-based batch monitoring produced alert queues no team could clear. Banao built a risk-ranked monitoring layer that evaluates transactions against current regulatory thresholds and surfaces the cases that actually warrant a compliance analyst's time.
Banao is a 300-person engineering company that uses its own AI products in production every day. InterviewGod screens our own engineering hires. Vikaas runs our own demand-generation pipeline. We are not advising on AI from the outside.
That operating discipline is what we carry into a compliance deployment. We have built systems that need to stay accurate over time, against changing rules, with real consequences for errors. That is the same standard a regulatory-monitoring system demands.
We will tell you before you commission a build that will not pay back:
We do not quote a compliance platform off a spec sheet. We map your actual rule surface and data access first.
We audit your current monitoring coverage, map rule gaps against your regulatory obligations, assess data-access feasibility, and hand back a prioritised monitoring scope with build estimates — yours to keep. If you proceed, the Sprint fee is credited against the build.
Implement the monitoring rules engine, integrate with your transaction and communications data sources, configure alert triage, and deliver the audit-reporting layer.
Deployment with a governed rule-management layer, ongoing regulatory-change tracking, and a compliance-team handover so your analysts run the system — not depend on ours.
Which regulations can the monitoring cover?
We configure against the specific obligations that apply to your licence and jurisdiction — AML/CTF, MiFID II, RBI/SEBI requirements, FATF guidance, or others. The Discovery Sprint maps your full regulatory footprint before we commit to scope.
How does the system stay current when regulations change?
A regulatory-feed parsing layer monitors official sources and flags changes that affect your current rules. Compliance officers can update rules in a governed interface without developer involvement, and we provide a support model for material regulatory shifts.
How do you handle false-positive alerts?
Alert volume is controlled by risk-ranking: only cases that exceed calibrated thresholds surface for analyst review. Analyst feedback on dismissed cases feeds back into the model to reduce repeat false positives over time — without retraining cycles.
What data does the monitoring system need access to?
At minimum: the transaction event stream and relevant entity data. Communications surveillance adds a channel feed. The Discovery Sprint establishes what access is available, what needs governance approval, and whether any data-architecture work is needed first.
Can this integrate with our existing compliance platform?
Usually, yes. Most compliance platforms expose an API or webhook surface for alert injection. Where they do not, we can output to a shared data layer your platform reads. Integration scope is part of the Discovery Sprint deliverable.
In 45 minutes, we will tell you what your current monitoring misses and what it would take to close those gaps with AI. Bring your regulatory obligations and we will bring the questions.
Book a 45-min scoping call
