Financial Risk & Revenue Leakage Automated Pipeline

Author: Indraneel Adak | Target Market: UK/Ireland Financial Services Tech Stack: Python (Pandas), SQL (CTEs, Window Functions), Power BI

📌 Executive Summary

Drawing from my experience as an Analyst at Danske Bank (UK), I developed this automated pipeline to identify dual-threat scenarios: Revenue Leakage and Anomalous Transaction Behavior. This tool analyzes over 10,000 transactions, classifying risk tiers and isolating system pricing failures.

⚙️ The Problem

Financial institutions often face "silent" revenue loss due to fee application failures across different transaction gateways (Online, ATM, POS). When combined with high device/IP risk scores, these transactions present both a financial and compliance threat.

🚀 The Solution

1. Data Engineering (Python): Built a pipeline to ingest transaction data and apply baseline expected fee rules against actual fees collected.
2. Business Logic (SQL): Utilized Common Table Expressions (CTEs) to create a 'Composite Risk Tier' and automatically flag transactions requiring immediate audit.
3. Visualization (Power BI): Designed an interactive, executive-ready dashboard enabling near real-time monitoring of pricing performance and control gaps.

📊 Business Impact

• Identified simulated revenue leakage across 15% of transactions.
• Automated the audit-flagging process, reducing manual reporting effort by an estimated 25% (replicating efficiencies I achieved at Concentrix).
• Grouped critical risk vectors into a single 100% traceable data model.

🛠️ Technical Implementation: DAX Logic

To bridge the gap between static reporting and active risk management, I developed a suite of DAX measures. These formulas allow stakeholders to interactively adjust risk sensitivity and visualize financial impact in real-time.

1. Dynamic Sensitivity Logic

This measure is the core of the "What-If" analysis, allowing the dashboard to react to the user-controlled slider. Dynamic Risk Flag = VAR SelectedThreshold = [Risk Threshold Value] RETURN IF( MAX('power_bi_risk_data'[device_risk_score]) >= SelectedThreshold, 1, 0 )

2. Financial Impact Metrics

Calculates the total value of identified revenue leakage across the filtered dataset. Total Leakage = SUM('power_bi_risk_data'[revenue_leakage])

3. Efficiency Ratios

Expresses the leakage as a percentage of total transaction volume to highlight systemic risk levels. Leakage % = DIVIDE( [Total Leakage], SUM('power_bi_risk_data'[amount]), 0 )

4. Operational Audit Count

A count of high-priority cases that require immediate intervention based on the dynamic threshold. Audit Cases = CALCULATE( COUNT('power_bi_risk_data'[transaction_id]), FILTER('power_bi_risk_data', [Dynamic Risk Flag] = 1) )

Screenshots:

1. Dashboard Overview

2. Risk Threshold Interaction

3. Technical Documentation Page