Client Name

IZAK10

Faculty Advisor

Ms. Mahwish Baasit Hussain

SBS Thought Leadership Areas

Investment Decision Making

SBS Thought Leadership Area Justification

While the "Investment Decision Making" area at IBA-SBS primarily focuses on "empirical asset pricing" and "market depth and efficiency," the core underlying principle is the generation and dissemination of knowledge to inform strategic financial decisions and enhance efficiency.

Our project directly contributes to this principle by:

* Informing Resource Allocation Decisions (Internal Investment):

* Risk Segmentation: By categorizing customers into 'Low', 'Medium', and 'High' risk based on payment probability, your project helps IZAK 10 make informed "investment" decisions on where to allocate their collection resources (e.g., tele-callers, field agents, specific strategies). They can strategically "invest" more effort in high-potential or critical risk segments, maximizing recovery from their existing "asset" (dues owed).

* Optimal Attempts Analysis: Identifying the most cost-effective number of attempts for maximum return on investment (ROI) directly guides IZAK 10 in optimizing their operational expenditure and effort. This is a crucial "investment decision" in their collection process.

*Enhancing Operational Efficiency and Financial Performance:

* The project aims to improve IZAK 10's ability to manage overdue payments efficiently and effectively. By predicting payment behaviors and optimizing collection strategies, it directly impacts the company's financial recovery rates and overall profitability, which can be seen as contributing to the "efficiency" of their financial operations.

* Generating and Disseminating Actionable Knowledge:

* Just like academic research adds to market depth, our project generates actionable insights and recommendations (e.g., optimal number of attempts, specific strategies for different risk groups) that can be "disseminated" within IZAK 10 to improve their "investment decision making" in credit management.

Examples from our project that demonstrate this alignment:

* Predictive Models for On-Time Payment Probability: These models provide a data-driven basis for IZAK 10 to "decide" which accounts are more likely to pay, guiding their resource allocation, much like an investor decides where to put capital.

* Customer Risk Categorization (Low, Medium, High Risk): This segmentation directly informs how IZAK 10 "invests" its collection efforts—whether to use soft reminders for low-risk accounts or more intensive strategies for high-risk ones.

* Analysis of Optimal Attempts and ROI: Providing a quantitative measure of the return on each additional collection attempt helps IZAK 10 make data-backed "investment decisions" regarding the intensity of their follow-up actions.

In essence, while not directly focused on market-level investment or asset pricing, our project provides the analytical tools and knowledge necessary for IZAK 10 to make smarter, data-driven "investment decisions" regarding their credit portfolio and operational resources, thereby enhancing their financial performance and efficiency in credit management.

Aligned SDGs

GOAL 8: Decent Work and Economic Growth

Aligned SDGs Justification

Goal Description: "Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all."

● Alignment: Our project directly contributes to the healthy functioning and efficiency of the financial sector. By optimizing credit management processes, it helps financial institutions (like IZAK 10's clients) recover dues more effectively. This reduces financial risk for lenders, which can in turn enable more stable and responsible lending practices. A robust financial system supports overall economic growth and job creation within the financial services industry and beyond. Your project's focus on efficiency and optimization helps IZAK 10 achieve sustainable economic growth.

● Examples from Project:

● Predictive Analytics and Optimal Attempts Analysis: By identifying the most effective strategies and optimal number of attempts for payment recovery, the project helps IZAK 10 and its clients use their resources efficiently, directly contributing to their economic growth and sustainability.

● Enhanced Financial Recovery: Increased recovery rates for overdue payments strengthen the financial health of the businesses involved, supporting their capacity to continue providing "decent work" and contributing to the economy.

NDA

No

Abstract

Abstract This Experiential Learning Project (ELP) addresses the critical challenge of credit recovery for a major utility company in Karachi, Pakistan, through a partnership with IZAK 10, a prominent customer engagement and credit management services provider. The project's primary objectives include analyzing customer payment behavior, assessing the effectiveness of past collection actions, developing machine learning models for risk categorization, and forecasting future payment trends. Utilizing a longitudinal dataset encompassing customer transaction history, payment delays, and recovery attempts, the methodology involved robust data preprocessing, extensive feature engineering, and the application of supervised machine learning models (Logistic Regression, Random Forest, XGBoost) for predicting payment behavior and segmenting customers into various risk categories (Low, Medium, Medium-Low, High, Defaulter). Time-series forecasting with Prophet was employed to predict macro-level recovery trends and segment-specific recovery rates. Major findings indicate that historical payment consistency, outstanding dues, and the type of billing significantly influence payment behavior and risk profiles. The project developed a dynamic risk segmentation framework that identifies customers requiring targeted interventions. Based on these insights, the report proposes a suite of cost-effective, data-driven recovery strategies tailored to each risk segment, including optimized communication protocols, flexible payment plans, and strategic escalation pathways. These recommendations aim to maximize recovery rates, reduce operational costs, and enhance customer relationships, providing IZAK 10 with actionable insights to strengthen their credit management services in a developing country context. Keywords: Credit Management, Debt Recovery, Machine Learning, Risk Segmentation, Predictive Analytics, Utility Company, Karachi, Pakistan, Developing Country, Behavioral Economics.

Document Type

Restricted Access

Document Name for Citation

Experiential Learning Project

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