Why finance & banking companies work with Nodus Datas AI
Finance and banking leaders choose Nodus DataAI to turn fragmented data into a single, trusted source of truth. We design resilient, compliant data platforms that support real-time risk, regulatory and profitability reporting at scale.
Our teams blend deep data engineering expertise with hands-on knowledge of financial services, so we can move fast without adding technical debt.
Data Platforms Built for Modern Finance
Industry: Finance, Brokers, Banks
Engagement model: Architecture, data engineering, ML engineering, IoT pipelines
Cloud stack: Microsoft Azure (Databricks, CosmosDB, Event Hubs)
Core technologies: Apache Beam, Apache Spark, Apache Airflow, Delta Lake
Finance & Banking Data Use Cases
Use Case 1: Enterprise Financial Data & Audit Analytics Platform
The business problem
A large audit and advisory firm needed to industrialize the way it ingests, validates, and analyzes the financial data of its client engagements — replacing fragmented workbooks and ad-hoc scripts with a single, governed platform that engagement teams across the firm could trust.
Specifically, the firm needed to answer questions like:
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For a given client engagement, can we guarantee that every File3 transaction reconciles to File1 and maps to a valid File2 entry — automatically, on every refresh?
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When new client data is uploaded mid-engagement, how do we know which downstream tables, dashboards, and risk analytics need to be re-computed, and which don't?
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How do we run journal-entry risk analytics, ratio analysis, and trend reporting consistently across thousands of engagements, with the same definitions and the same audit trail?
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When something fails in the pipeline, how do we tell the engagement team exactly which file, which step, and why — without engineers having to dig through cluster logs?
Answering any of these requires unifying data that traditionally lives in disconnected places: client-supplied workbooks, internal engagement metadata, validation rule libraries, and the analytics surface used by auditors and reviewers.
What we built
We designed and delivered a unified financial data platform on Microsoft Azure / Databricks, anchored around the engagement (firm + client + fiscal year) as the universal join key, organized as a strict Bronze → Silver → Gold → Analytics medallion:
Workbook ingestion & status tracking:
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JSON exports of client workbooks (File1, File2, File3, File4) ingested in bulk from centralized ADLS with schema validation
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Per-file lifecycle (`Table1`, `Table2`, `Table3`, 0–100% progress) tracked in Azure Table Storage, with real-time progress pushed to the upstream application via an async notification API
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Built-in retry logic, "copy settings from parent engagement," and a circuit breaker that prevents transaction (Entity 3) processing until the foundation (Entity 1) is validated
Two-phase validation pipeline:
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Phase 1 — Foundation Import: File1, File2, and File4 land in Bronze, are deduplicated and quality-checked into Silver, and register themselves in a `Table4` table that drives downstream Gold refreshes
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Phase 2 — Transaction Validation: File3 transactions go through a 5-step validation — schema/CDM mapping, broadcast cross-reference against File2, typed DataFrame construction, enrichment (`Column1`, `Column2`, `Column3`, debit/credit indicator, ingestion timestamp), and final status write
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A reusable `Module1` enforces consistent rules — data types, date ranges, account existence, format compliance — across every engagement
Curated analytical layer:
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Gold tables partitioned and Z-ordered by `Column4` / `Column3` for fast engagement-scoped queries
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Analytics views shipped out of the box: monthly value trends, transaction summaries, journal-poster analysis, risk-based journal analytics, and filter dimensions
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Versioned ratio-analysis tables for File1 and Entity 1 metrics, plus a dedicated analytics execution log
Operational outputs:
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Engagement-team-facing status that tells reviewers exactly which files succeeded, failed validation, or are still in progress
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Centralized configuration (50+ table names, ADLS paths, API endpoints, vacuum policies) so new environments and engagements can be onboarded without code changes
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Proactive Notebook1 that fails fast if Key Vault, compute, ADLS, Azure Table Storage, or the catalog is unhealthy — surfacing infrastructure issues before they corrupt a run
What this kind of platform unlocks
Once client financial data sits in one place under a unified engagement key, with validated lineage from raw workbook to analytics view, the analytical surface area expands significantly. Beyond what shipped during our engagement, this kind of platform is the foundation for:
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Cross-engagement benchmarking — comparing ratios, journal-entry patterns, and risk indicators across clients in the same industry or size band
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Anomaly and fraud-risk detection — ML models scoring journal entries for unusual posters, timing, round-number patterns, or weekend activity, with feature-level explainability for the audit team
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Continuous auditing — moving from point-in-time fiscal-year reviews to always-on monitoring of client ledgers as data is refreshed
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Audit productivity analytics — measuring how long each validation step takes per engagement, where rework happens most, and which clients consistently submit the cleanest data
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Regulatory & retention compliance — Delta Lake commit logs and `Policy1` giving precise control over what is kept, what is purged, and who changed what
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Self-service analytics for engagement teams — a curated Gold/Analytics layer that auditors can query directly through dashboards without needing engineering involvement
Technologies: Azure Databricks (PySpark, Delta Lake) · Azure Data Lake Storage (ADLS Gen2) · Azure Table Storage · Azure Key Vault · Datadog (distributed tracing, statsd, log forwarding) · Python · Delta Lake `MERGE` / Z-ordering / vacuum · Medallion (Bronze/Silver/Gold/Analytics) architecture · scheduled ingestion & notification pipelines · Power BI / downstream reporting tools