Financial Services
Data and AI architecture for regulated financial institutions
Risk, fraud, treasury and customer analytics depend on data that is timely, lineage-complete and defensible under examination. We architect for all three.
Industry challenges
The conditions we design around
Reporting under examination
Regulatory submissions must be reproducible months later, with every figure traceable to a source record.
Latency-sensitive decisions
Fraud and risk decisions are made in the request path, where batch pipelines cannot help.
Legacy core systems
Systems of record cannot be replaced quickly, so the data platform must integrate rather than assume greenfield.
Model scrutiny
Models used in credit or risk decisions attract governance requirements that ad-hoc data science does not satisfy.
Data challenges
Where the data work actually is
These are the problems that determine whether analytics and AI initiatives in this sector succeed.
- Lineage that survives audit, from report figure back to source record
- Reconciliation between the warehouse and the systems of record
- Sensitive-field classification and least-privilege access at scale
- Retention rules that differ by data category and jurisdiction
- Late-arriving and corrected transactions handled without silent restatement
AI opportunities
Where AI is worth the investment here
Fraud and anomaly detection
Models scoring transactions against behavioural features, with human review paths for contested outcomes.
Document processing
Extraction and classification across onboarding, claims and contract documents to reduce manual handling.
Analyst assistance
Grounded retrieval over internal policy and research so analysts get cited answers, not model recollection.
Cloud transformation
What moving to Google Cloud unlocks
Elastic risk compute
Batch risk and scenario workloads that scale for the window they need and release capacity afterwards.
Segmented landing zones
Environment separation, network controls and audit logging designed before regulated workloads move.
Streaming decision paths
Event-driven architecture for decisions that must complete inside the transaction.
Security & compliance
Considerations we design for
We architect towards your obligations and document the controls we implement. We do not claim regulatory certifications on your behalf or ours.
Auditability
Immutable audit logging, lineage capture and reproducible pipeline runs designed in from the start.
Access control
Least-privilege IAM, column and row-level policy, and separation of duties across environments.
Data residency
Region selection and data-movement boundaries agreed with your compliance function during design.
Architecture patterns
Patterns we apply in this sector
- Event-driven scoring
- Pub/Sub ingestion into a low-latency scoring service with asynchronous enrichment.
- Auditable warehouse
- Append-only fact tables with effective dating and full lineage capture in Dataplex.
- Segmented environments
- Project-level separation for development, testing and production with policy guardrails.
- Permission-aware retrieval
- Retrieval layers that honour the source system's entitlements at query time.
Example use cases
Engagements typically look like this
Illustrative scopes based on the patterns above. No client names or results are implied.
- Regulatory reporting rebuilt on a lineage-complete warehouse
- Real-time transaction scoring with reviewable outcomes
- Customer 360 for servicing and retention analytics
- Document extraction across onboarding workflows
- Scenario and stress-testing compute on elastic infrastructure
Start a conversation
Discuss a financial services engagement.
Bring the constraint you are working within — regulatory, operational or technical — and we will tell you what a workable architecture looks like.