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    Financial Planning & Analysis (FP&A)

    Overview

    Financial Planning & Analysis sits at the heart of every financial institution's strategic decision-making. Yet many organizations still rely on fragmented spreadsheets, manual consolidation, and static reporting — creating blind spots that lead to missed forecasts and delayed responses to market shifts.

    Root Cause Analysis

    Why Traditional FP&A Fails

    1. Data Silos: Financial data lives across ERP systems, CRMs, HR platforms, and trading systems with no unified view.
    2. Manual Consolidation: Teams spend 80% of their time gathering and reconciling data, leaving only 20% for actual analysis.
    3. Static Snapshots: Monthly or quarterly reporting cycles create decision lag in fast-moving markets.
    4. Lack of Scenario Modeling: Without real-time what-if capabilities, CFOs are flying blind during volatility.

    Use Cases

    1. Automated Budget Consolidation

    Challenge: A regional bank with 40+ branches manually consolidated budgets across Excel files, taking 3 weeks per cycle. Solution: We deployed a cloud-native data platform on Azure Synapse with Power BI dashboards, automating ingestion from their core banking system. Result: Budget consolidation reduced from 3 weeks to 2 days with 99.5% accuracy.

    2. Real-Time Revenue Forecasting

    Challenge: A fintech company couldn't forecast subscription revenue accurately due to complex pricing tiers and churn patterns. Solution: Built a Databricks-powered ML pipeline integrating billing, usage, and CRM data with automated rolling forecasts. Result: Forecast accuracy improved from 72% to 94%, enabling better capital allocation.

    3. Variance Analysis at Scale

    Challenge: A Fortune 500 financial services firm needed instant variance analysis across 200+ cost centers. Solution: Implemented a Snowflake data warehouse with semantic layer and self-service Tableau dashboards for real-time budget-vs-actual tracking. Result: Finance teams reduced month-end close analysis from 5 days to 8 hours.

    Cloud Technologies We Use

    • Azure Synapse Analytics — Unified data warehousing and big data analytics
    • Databricks — Advanced ML for predictive forecasting
    • Snowflake — Elastic cloud data warehouse for multi-source consolidation
    • Power BI / Tableau — Interactive dashboards and self-service analytics
    • dbt — Data transformation and governance for financial models
    • Apache Airflow — Orchestration of complex financial data pipelines