Client Name

Habib Metropolitan

Faculty Advisor

Dr. Nauman J Amin

SBS Thought Leadership Areas

Investment Decision Making

SBS Thought Leadership Area Justification

The project directly supports data-driven investment decisions by building a financial model for HabibMetro’s investment banking function. The model helps the bank simulate outcomes, evaluate strategies, and make informed recommendations on investment allocation and capital structure—critical aspects of financial decision-making. For instance, our model suggests optimized portfolio scenarios based on deposit inflows, market trends, and regulatory developments.

Aligned SDGs

GOAL 9: Industry, Innovation and Infrastructure

Aligned SDGs Justification

We proposed a data-driven investment decision-making framework that incorporates innovative modeling techniques. This aligns with the goal of fostering innovation in the financial sector and strengthening institutional infrastructure for investment services.

NDA

No

Abstract

This Experiential Learning Project (ELP) was conducted in collaboration with Habib Metropolitan Bank to design a dynamic, Excel-based financial model for evaluating the credit feasibility of syndicated loans across key sectors in Pakistan, with a specific focus on the Oil & Gas industry. The objective was to create a practical decision-making tool that supports investment banking functions by integrating company-level financial projections, industry benchmarks, and loan amortization schedules.

The Industry Syndicate Financial Model (ISFM) consolidates historical financial data (2020–2023) and uses forecasting techniques such as Excel's FORECAST.ETS function to project key metrics like revenue, net income, and cash flows through 2026. The model incorporates sector-level insights and enables side-by-side company comparisons, sensitivity analysis, and scenario testing to evaluate the debt-servicing capacity of firms under varying market conditions.

Key features include a loan amortization engine that adjusts for tenure, interest rates, and grace periods, enabling tailored credit simulations. The project also accounts for macroeconomic variables like interest rate changes, SBP policies, and commodity price fluctuations, which are particularly relevant to Oil & Gas financing. Findings suggest that while major upstream firms like OGDCL and PPL demonstrate strong creditworthiness, weaker downstream entities exhibit significant volatility, requiring stricter lending terms.

The ISFM serves as a practical tool for both banks and analysts, providing a structured framework to evaluate risk-adjusted returns and recommend optimal loan structures. This project bridges academic learning with real-world application, enhancing our understanding of credit risk, investment decision-making, and financial modeling in Pakistan’s corporate lending landscape.

Document Type

Restricted Access

Document Name for Citation

Experiential Learning Project

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