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AVAILABLE FOR WORK • AI & MACHINE LEARNING • FULL STACK DEVELOPMENT • OPEN SOURCE • DEEP LEARNING • COMPUTER VISION • NLP • AVAILABLE FOR WORK • AI & MACHINE LEARNING • FULL STACK DEVELOPMENT • OPEN SOURCE • DEEP LEARNING • COMPUTER VISION • NLP • AVAILABLE FOR WORK • AI & MACHINE LEARNING • FULL STACK DEVELOPMENT • OPEN SOURCE • DEEP LEARNING • COMPUTER VISION • NLP •
Nikhil Shah.

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Reliance Communications (RCom) Diagnostic Report
Case Study // Active Record

Reliance Communications (RCom) Diagnostic Report.

Strategy & Vision

"Diagnosing corporate collapse using financial signals Analyzed profitability, leverage, and liquidity trends to identify early warning signs of failure."

Technical Stack

ExcelPythonLooker Studio

External Assets

Launch Deployment
Technical Source

Overview

This project is a corporate crisis diagnostic of Reliance Communications (RCom), analyzing its rise and collapse using financial data, strategic timelines, and system-level evaluation.

The objective was to identify early warning signals of failure and understand how strategic, financial, and technological decisions led to collapse.

Problem

Corporate failures are often visible only after collapse, while early warning signals remain unnoticed.

RCom’s case presents a critical question:

  • How does a market leader collapse within a few years?

Approach

The analysis was conducted using a multi-layer diagnostic framework:

  1. Financial Ratio Analysis
  2. Strategic Timeline Evaluation
  3. Crisis Pattern Identification

Key dimensions analyzed:

  • profitability trends
  • leverage and debt structure
  • liquidity and survival indicators

An interactive dashboard and structured report were created to present findings.

Diagnostic Framework

The crisis was analyzed using three core indicators:

  • Profitability collapse
  • Unsustainable leverage growth
  • Liquidity breakdown

These combined signals formed a clear early-warning system for corporate failure.

Key Insights

  • Revenue declined sharply after 2016 due to market disruption
  • Profitability turned negative post-2017, indicating operational breakdown
  • Debt-to-equity crossed safe thresholds (>1.0), signaling high financial risk
  • Interest coverage dropped significantly, showing inability to service debt
  • Liquidity ratios fell below sustainable levels, leading to insolvency

The collapse was not sudden — it was predictable through financial signals. :contentReference[oaicite:0]{index=0}

Root Cause Analysis

The failure resulted from a combination of:

  • excessive debt accumulation (~₹45,000 crore)
  • failure to adapt to technological disruption (4G shift)
  • delayed strategic response to market competition
  • unsuccessful asset monetization attempts

This created a reinforcing cycle of decline.

Outcome

This project demonstrates how financial data and structured analysis can be used to detect corporate crises before collapse.

It highlights the importance of:

  • early signal detection
  • capital discipline
  • strategic adaptability

Recognition

This project was awarded Winner at Money Matrix 2025 (IIT Madras), recognizing its analytical depth, structured thinking, and business relevance.

Strategic Insight

Corporate failure is rarely sudden — it is a system of accumulating signals.

Organizations that fail to respond to early indicators enter a downward spiral that becomes irreversible.

Process Gallery

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