Customer 360 & Real-Time Risk Intelligence Platform

Who uses the platform, which systems of record feed it, and what consumes its output — the boundary of scope, with no internal components shown.

The end-to-end shape in one picture: a Kafka event backbone and batch landing feeding a medallion lakehouse, orchestrated by Airflow, serving BI, APIs and event consumers.

What depends on what. Dependencies point downward only; platform services are consumed by every layer above them.

The deployable units inside the platform boundary, the technology each one runs on, and the protocols between them.

How data is zoned by ownership, rebuildability and retention, from landing through to business-ready gold tables.

The path a high-volume event takes from producer to enriched topic, including contract validation, dead-lettering, deduplication and windowed feature computation.

Log-based change capture from core banking systems, the initial historical snapshot, and how both land in bronze and merge into slowly changing silver dimensions.

Four reusable templates — transactional events, database CDC, files and semi-structured payloads — so a new source is onboarded without touching existing pipelines.

Rule definition, structural and semantic checks, disposition of failures, and the escalation and replay path back into the pipeline.

How a schema change is proposed, checked for compatibility, registered, rolled out to producers and consumers, and eventually deprecated.

How a trigger event is joined with profile and feature context, scored against declarative rules, published for the risk domain, and fed back as labelled outcomes.

Every inbound and outbound interface, with its protocol, cadence and owner — the complete catalogue of how the platform touches other systems.

Trust boundaries from the internet through to the data plane, what crosses each one, and where an attacker is stopped.

Which control applies where — classification, PII protection, access, lineage, retention and quality, mapped across every data zone.

What runs where across three availability zones and a warm standby region, and what survives the loss of each failure domain.

How a pipeline change reaches production, which gates stop a bad one, and how a failed release is rolled back.

Signal types against pipeline stages — how each signal is emitted, collected, stored, turned into a detection, and acted on.

How a logic change, defect or regulatory restatement is reprocessed from immutable history without disturbing live pipelines.

The ordered message exchange for a single card authorisation, from switch to fraud decision, including the schema-rejection path.

The core curated entities, their keys, and the joins that produce the customer golden record and its risk and interaction history.