Enterprise Platforms × Financial Services
Power BI semantic modeling, regulatory reporting, and DAX optimization for banks and financial-services firms: auditor-grade governance, row-level / object-level security, and the report-performance discipline that compounds across the analytics estate rather than fragmenting into 400 personal dashboards.
The problem
Banks adopt Power BI for the same reasons everyone else does: it's in the Microsoft estate, it's cheap, and the dashboards look good in a demo. The pattern that fails them is the one nobody warns about: every team builds its own dashboards on its own model, the same metric returns three different numbers depending on which dashboard you open, and the auditor asks where the regulatory-reporting numbers come from. By month 18 the analytics estate is a liability.
The fix is semantic-model-first discipline. We build governed semantic models that own the metric definitions, RLS / OLS that enforces banking-grade access controls, regulatory-reporting datasets that match the source of truth, and the lineage / certification frame that satisfies auditors. Reports stop disagreeing because they all read from the same source.
Where it ships
Concrete applications where enterprise platforms unlocks measurable value inside financial services delivery constraints.
+62%
report performance vs. prior estate
Power BI semantic models matched to the regulatory-reporting source of truth, with explicit calculation-group logic for the variants regulators ask for. Audit-grade lineage from raw source through the report cell.
Single customer view across deposit / loan / wealth product lines, with privacy-preserving aggregation rules. RLS that enforces branch / region / role-based access; OLS for sensitive PII fields.
Portfolio analytics with explicit risk-tier modeling, vintage curves, and stress-testing scenarios. Composite models combining warehouse fact tables with operational data sources.
Daily branch performance, digital channel adoption, and call-center analytics in a single governed model. Replaces 30+ team-built dashboards with one certified dataset.
Investigative analytics for AML and fraud teams with tight access control, audit logging, and the ability to drill from aggregated trends into individual events for case work.
Migration to Fabric where the tenant's data architecture justifies it. Lakehouse + semantic-model promotion patterns, with honest fit assessment about whether Fabric is right yet.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to financial services delivery.
Three-week audit: existing Power BI workspaces, dataset count, model duplication, governance posture, and regulatory-report origin tracing. Output is a semantic-model strategy with prioritized consolidation roadmap.
Build the certified semantic model before consolidating reports. Star-schema discipline, calculation groups, RLS / OLS, and date-table consistency. Reports get rebuilt against the certified model, not the other way around.
Map regulatory-report definitions to the semantic model with explicit lineage. Auditor-acceptable evidence package: source query, transformation logic, model measure, report cell. Quarterly regression suite.
Tenant-wide governance frame: certified dataset policy, workspace structure, deployment pipelines, and the orphan-dataset cleanup cadence. Power BI estates that aren't actively governed regress to chaos within months.
Capabilities
Stack
Compliance overlay
Every financial services engagement carries the evidence collection that procurement and audit teams expect on day one.
Tokenization at the source where PCI data sits; OLS to restrict sensitive fields; aggregation rules that prevent re-identification. Tenant audit logs retained per regulatory requirements.
Lineage from source through report cell. Quarterly regression suite for regulatory reports. Change-management evidence on every certified-dataset modification.
Certified dataset policy, workspace structure, sharing controls, and orphan cleanup cadence. Service principals for automated deployment; named owners on every certified asset.
Selected work
Where this fits
Common questions
Because the alternative, letting every team build its own model, produces report disagreement that costs the auditors' trust and the executives' patience. Semantic-model-first gets the metric definitions, RLS, lineage, and governance right once; reports rebuild on top in days. Most Power BI estate cleanups we run are reverse-engineering the semantic-model that should have been there from the start.
Yes, with discipline. The semantic model has to match the regulatory-report source of truth (the GL, the loan accounting system, the deposit system) with explicit lineage. Calculation groups handle the variants regulators ask for. Quarterly regression suite proves report integrity. Auditors accept this: we've shipped Call Report, FFIEC, and FR Y-9C estate runs from Power BI.
Depends on the data architecture. Fabric wins when there's appetite for a unified lakehouse + semantic model with Direct Lake mode for performance. It loses when the existing data warehouse (Snowflake / Databricks) is mature and well-governed, wrapping Fabric around it adds complexity. Honest fit assessment is part of every engagement.
Sub-3-second initial load; sub-1-second slicer interactions on production-shape data. Most slow Power BI reports run on inefficient DAX, missing aggregations, or composite-model boundaries that force unnecessary refresh. The 7-pattern DAX playbook routinely cuts report time 50–80% without changing the visual.
Yes. RLS for branch / region / role-based row filtering; OLS for sensitive-column hiding (SSN, account numbers). Designed against the bank's identity model (Entra ID groups, security roles, dynamic role membership). Tested with explicit access-control matrices before launch.
Estate audit + semantic strategy: 3 weeks, $50K–$100K. Semantic-model build (single domain): 3–5 months, $300K–$700K. Tenant-wide consolidation: 6–12 months, $700K–$2M. Managed services post-launch: $20K–$80K monthly retainer. Brackets published honestly so visitors self-qualify before the first call.
Talk to us
A senior engineer plus the ATLAS department lead joins the first call, both with prior financial services delivery experience.