Case Studies.

A PiAI Case Study

Transforming Reporting & Analytics for a Global Financial Institution

How PiAI modernised enterprise reporting, governance, and AI readiness using Microsoft Fabric and Copilot.

The Client

The client is a large global financial institution operating across retail banking, corporate banking, wealth management, credit, trading, and international markets. The organisation handles high‑volume transactional data, stringent regulatory oversight, and complex operational reporting needs. To operate effectively at scale, the institution relies on timely, accurate insights and must balance innovation with strict governance controls.

The Challenge

The institution’s reporting landscape had become fragmented over time. Daily operations relied on spreadsheets, manual extracts, and soiled data sources distributed across business units. These processes slowed decision‑making, created inconsistency, and exposed governance risks.

As we worked to understand their struggles and goals, we noticed the following pain points:

Heavy dependence on Excel exports from core systems, often exceeding row limits or requiring multiple files.
Repeated manual joins, high reconciliation effort, and limited data lineage.
Conflicting versions of truth generated across teams.
Lack of transparency and traceability required for internal audit and regulators.
Inability to safely adopt AI tools such as Copilot or machine learning due to fragmented data foundations.

Given these challenges, the institution sought a modern, scalable analytics foundation that would support governance, automation, AI‑driven insight, and enterprise‑grade reporting.

Our Solution

PiAI delivered a unified Microsoft Fabric–based data and analytics platform designed to eliminate manual processes, enforce governance, and enable AI‑assisted reporting across the organisation.

Key components of this transformation included:

Microsoft Fabric Architecture: Implemented a governed OneLake environment consolidating offline reports and real‑time data sources.
Automated Ingestion Pipelines: Replaced manual extracts with secure ingestion flows and metadata tagging.
Semantic Modeling: Built business‑aligned semantic layers to ensure consistency, performance, and governance.
Self‑Service Analytics: Delivered interactive Power BI dashboards enabling non‑technical teams to filter, explore, and compare KPIs.
Copilot‑Enabled Insights: Enabled natural‑language querying to support frontline teams without requiring specialist support.
Machine Learning Integration: Provided forecasting, anomaly detection, and proactive insight directly within Power BI.
Governance & Compliance: Embedded role‑based access controls, audit logs, lineage, and risk‑aligned approval workflows.

“PiAI enabled us to modernise reporting, strengthen governance, and confidently adopt AI. Their partnership and expertise ensured a smooth and impactful transformation.”

— Head of Loan Syndicate Applications

The Process

PiAI introduced a structured, step‑by‑step workflow enabling teams to transition from manual reporting to AI‑supported insight generation.

Data Consolidation: All relevant operational and performance datasets were ingested into OneLake with automated scheduling.
Fuzzy Logic Linking: Fabric’s matching capabilities enabled relationships across datasets lacking perfect identifiers.
Interactive Dashboards: Parameter‑driven Power BI dashboards allowed exploration by region, product, customer segment, year, and financial thresholds.
Copilot Querying: Users asked questions such as ‘Which transactions exceed threshold X this week?’ or ‘Show anomalies in Region Y for Q2’ and received auditable insights.
ML‑Driven Signals: Forecasts and anomaly alerts surfaced emerging trends and risks in real time.
Workflow Integration: Insights were distributed via Teams, Outlook, and SharePoint for collaboration and action.

AI Governance & Audit Readiness

Given the institution’s regulatory environment, governance was designed into every layer. PiAI ensured:

Full traceability of all data transformations.
Alignment with internal audit, risk, security, and model governance policies.
Created guardrails for Copilot access with logging and validation.
Certified datasets to prevent uncontrolled reporting.
Object‑level and row‑level security aligned with user roles across global regions.

Change Management & Adoption

A phased adoption model enabled teams to embrace automation and AI confidently:

Phase 1

Assisted Reporting: Excel users connected to Fabric directly, reducing manual extracts but retaining familiar tools.

Phase 2

Guided Visual Analytics: Workshops supported transition to Power BI with training on filtering, comparing, and interpreting data.

Phase 3

AI Introduction: Copilot training, safe prompting, validation procedures, and audit log awareness.

Phase 4

Reduced Intervention: Increased trust enabled automated alerts, ML‑driven reporting, and operational efficiency gains.

Outcomes & Impact

The transformation delivered both immediate and long-term enterprise value.

Immediate Outcomes

Significant reduction in manual data preparation and reconciliation.
Faster access to reliable insights aligned with a single version of truth.
Empowered business teams able to self‑serve insights.

Long-Term Outcomes

Scalable, cloud‑aligned analytics foundation supporting future growth.
AI‑ and ML‑ready architecture compliant with governance requirements.
Deep integration into Microsoft 365 enabled collaborative, workflow‑embedded analytics.
Blueprint for expansion across additional teams and functions globally.

Why PiAI?

PiAI was selected due to its expertise in regulated‑industry data transformation, deep Microsoft Fabric experience, rigorous governance design, and ability to bridge business objectives with technical execution. PiAI’s delivery approach emphasised rapid value, safety, transparency, and long‑term sustainability.

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