BANKiQ's enterprise fraud risk management architecture is designed to help banks, NBFCs, payment providers and financial institutions detect, assess and respond to fraud in real time.
The platform uses an event-driven, API-first architecture to connect transaction channels, customer data, behavioural signals, fraud rules, AI/ML models and external risk intelligence — enabling real-time fraud risk decisions across digital banking, payments, cards, lending and other financial services. A modular, scalable architecture supports high-volume transaction processing, low-latency decisioning, high availability and enterprise integration, while fraud events route into case management and investigation workflows and analytics deliver operational, management and regulatory visibility.
Four layers, working together in real time — from every channel and data source through to governance and reporting.
Digital banking, UPI, cards, payments, lending, core banking, customer and external intelligence data.
Rules, AI/ML, behaviour, device, session and network signals scored in milliseconds.
Approve, hold, decline or step-up — alerts route straight into case management.
Operational, management and regulatory visibility across the whole platform.
BANKiQ is built on a modern, event-driven and API-first architecture designed for high-volume financial transactions, real-time fraud detection and enterprise-grade risk decisioning.
Transactions, logins, beneficiary additions, limit changes and device events are evaluated inline through a high-performance decisioning architecture. Scoring, policy evaluation and action selection complete inside the payment or session window, so intervention happens before value leaves the bank.
In-memory profile lookups, pre-compiled rule graphs and parallel model execution keep decision latency predictable even under peak transaction volumes.
Every fraud-relevant event is published onto a durable event backbone. Detection, enrichment, alerting, case creation and analytics all subscribe independently, so heavy downstream work never blocks the real-time decision path.
Replay and back-pressure handling allow safe recovery, model back-testing and reprocessing of historical event streams without disrupting live traffic.
BANKiQ exposes versioned REST APIs and callbacks for scoring, decisioning, case actions and reference data, and consumes external intelligence sources the same way. Integration is configuration-led rather than bespoke code.
Mutual TLS, signed payloads, token-based authentication, throttling and full request/response audit logging apply to every interface.
Deterministic rules capture known fraud patterns and regulatory mandates, while supervised and unsupervised models surface emerging typologies. Behavioural, device, session and network-relationship features add the context a single transaction cannot provide.
Model governance covers versioning, champion/challenger execution, drift monitoring and explainability so every score can be justified to risk and audit teams.
Services are containerised and independently scalable, allowing capacity to be added to decisioning, enrichment or case management without redeploying the platform. Active-active deployment supports high availability across data centres.
Health probes, circuit breakers, graceful degradation and disaster-recovery topologies protect the decisioning path during partial failures.
Alerts are automatically triaged, prioritised and routed into analyst queues with the enriched context that produced them — customer, device, counterparty, network and prior case history.
Configurable workflows, SLA tracking, maker/checker controls and complete audit trails support both internal governance and regulatory examination.
Role-based access, least-privilege service identities, encryption in transit and at rest, and policy-driven decisioning protect sensitive financial data across the platform.
Segregation of duties, immutable audit logging and controlled change management align the architecture with enterprise security and regulatory expectations.
The architecture above is deliberately high-level. For teams evaluating BANKiQ in depth, we walk through it progressively.
How BANKiQ maps fraud prevention to business outcomes — channels protected, risk reduced, cost of fraud operations.
How data, intelligence and decisioning layers interact — the four-layer view shown above, in more depth.
Component-level design, shared with technical and engineering teams under NDA during solution design.
Reviewed directly with your security, infrastructure and compliance teams as part of onboarding.
Discover how BANKiQ can help your institution detect fraud earlier, make smarter decisions and respond in real time.