AMITYUNIVERSITY ONLINE, NOIDA, UTTAR

PRADESH

In partial fulfilment of the requirement for

the award of degree of Master of
Business Administrations

TITLE: Behavioral Economics in Loan Defaults:

Psychological Triggers of NPAs

Guide Details:
Ms Sabhitha P

Submitted By:

Rahul

A9920123006214(el)
 DECLARATION

I, Rahul, a student pursuing MBA 4 th Semester at Amity University Online, hereby declare that

the project work entitled “Behavioural Economics in Loan Defaults: Psychological Triggers of

NPAs” has been prepared by me during the academic year 2025 under the guidance of Ms

Sabhitha P, [Department Name], [College/University Name]. I assert that this project is a piece

of original bona-fide work done by me. It is the outcome of my own effort and that it has not

been submitted to any other university for the award of any degree.

Rahul
 TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION
1.1 Background of the Study

The banking sector plays a crucial role in the development of any economy by facilitating credit

flow to individuals, businesses, and governments. When banks provide loans, they expect borrowers

to repay the borrowed amount with interest. However, sometimes borrowers fail to make these

repayments. Such unpaid loans are referred to as Non-Performing Assets (NPAs). According to

the Reserve Bank of India (RBI), a loan becomes an NPA when interest or principal repayment is

overdue for more than 90 days. The increase in NPAs poses a serious challenge to the financial

stability of banks and the economy.

In recent years, India has witnessed a surge in NPAs, especially in the public banking sector. This

has not only affected the profitability of banks but also slowed down credit growth. Several

measures like asset reconstruction, recovery tribunals, and legal reforms have been introduced to

address this issue. Yet, many defaults remain unexplained through traditional financial analysis.

These models often fail to account for why some borrowers choose to default even when they

appear capable of repaying the loan.

This has led to a growing interest in understanding the human behavior behind loan defaults,

which cannot always be captured through financial ratios or credit scores. It suggests the need to

explore beyond traditional economics and enter the domain of behavioural economics.

1.2 Problem Statement

Most banks rely on tools like credit scoring, income statements, and collateral to assess the risk of a

loan. While these tools are useful, they do not capture how borrowers think and behave. A person

might appear financially stable but still choose to default because of stress, overconfidence, or social
pressure. Traditional risk models assume that borrowers act rationally, always making logical

decisions. But in reality, people often act emotionally, irrationally, or based on limited information.

This creates a gap between what risk models predict and what happens in the real world.

Understanding why people make poor financial choices—even when they know the risks—is

essential to tackling the NPA problem more effectively.

1.3 Introduction to Behavioural Economics

Behavioural economics is a field that combines psychology with economics to explain why people

sometimes make irrational financial decisions. It focuses on the emotional, cognitive, and social

factors that influence how people think about money, risk, and reward. Unlike classical economics,

which assumes people always make decisions in their best interest, behavioural economics shows

that cognitive biases, emotions, and social influences often lead people to act against their long-term

financial well-being.

Key concepts in behavioural economics include:

 Present bias – giving more importance to immediate rewards over long-term benefits

 Overconfidence – overestimating one’s ability to repay loans

 Herd behaviour – following others in financial decisions, like taking loans when peers do

 Loss aversion – fearing loss more than valuing gains, which can lead to poor repayment

decisions

Understanding these behaviors is crucial in analyzing why some borrowers default, not because

they can’t repay, but because of how they think and feel about borrowing.

1.4 Need for the Study

There is a growing need to explore the psychological triggers behind loan defaults, especially in a

country like India where financial literacy is still developing, and social influences often guide major

financial decisions. Existing models focus heavily on numerical and financial data, which do not

fully explain why a borrower who qualifies for a loan on paper might still fail to repay it.

This study fills an important gap by examining the mental and emotional patterns behind loan

default. It also explores how lender-side biases, such as anchoring on past loan performance or

being over-optimistic about a borrower's future, can lead to poor credit decisions.

With rising NPAs and financial stress in the banking sector, understanding these behavioral factors

can help banks design better lending systems, predict defaults more accurately, and introduce

behavioral nudges to encourage repayment.

1.5 Scope of the Study

This study focuses primarily on the Indian banking sector, including public and private banks. It

examines how behavioral factors influence loan defaults among individual borrowers, particularly

in the retail, education, and small business loan segments. While global theories of behavioral

economics will be referenced, the application will focus on the Indian context.

The scope also includes analyzing both borrower-side and lender-side behaviors that contribute to

loan default and NPA formation. The research is based on a combination of secondary data (reports,

literature, previous studies) and, where possible, primary data through surveys or interviews.

1.6 Objectives of the Study

The study is designed to achieve the following objectives:

 To identify key behavioural economic factors that contribute to loan defaults.

  To analyze how psychological triggers differ among various types of borrowers.

 To explore the role of cognitive and emotional biases in borrower decision-making.

 To examine how bank officers may also be influenced by behavioural biases during loan

approvals.

 To develop a framework for integrating behavioural insights into credit risk analysis.

 To suggest practical strategies and interventions to reduce NPAs by using behavioral tools.

1.7 Research Questions

 What psychological factors commonly influence borrower decisions leading to defaults?

 How do emotional and cognitive biases affect loan repayment behavior?

 Are lender decisions also affected by behavioural biases?

 Can behavioural insights improve the accuracy of loan risk assessment?

 What interventions can banks use to encourage timely repayments?

1.8 Research Methodology Summary

This study will follow a descriptive and exploratory research design. It will include a review of

existing literature, case studies from Indian banks, and analysis of behavioral patterns through

surveys or interviews, where feasible. Data will be collected from RBI reports, bank disclosures,

scholarly articles, and real borrower experiences, and analyzed to identify common behavioral

trends.

1.9 Structure of the Report

This report is organized as follows:

 Chapter 1: Introduction – Provides background, problem statement, and study objectives

 Chapter 2: Literature Review – Reviews past research and theoretical frameworks in

behavioural economics and loan defaults

 Chapter 3: Research Methodology – Describes the data sources, sampling methods, and

research tools used

 Chapter 4: Data Analysis & Interpretation – Analyzes behavioural patterns and loan

default data

 Chapter 5: Findings & Discussion – Summarizes insights and links them to behavioural

theories

 Chapter 6: Conclusion & Recommendations – Offers strategies to reduce NPAs using

behavioural interventions

1.10 Company Profile

This project is conducted in association with insights drawn from the Indian banking sector, focusing

on public and private sector banks such as State Bank of India (SBI), Punjab National Bank

(PNB), and HDFC Bank. These institutions have significant exposure to retail, education, and

business loans, making them suitable references for analyzing borrower behavior and default

patterns. Information from their annual reports, loan performance data, and customer behavior trends

will be considered as part of the secondary research.

1.11 Justification for Topic Selection

This topic has been selected due to the increasing relevance of non-performing assets (NPAs) in the

Indian financial system and the lack of sufficient research that combines financial risk analysis with

human psychology. While NPAs have been studied extensively from an economic and regulatory

viewpoint, very few studies have focused on why borrowers choose to default, despite having the

financial capacity to repay.

The growing field of behavioural economics offers new ways to understand such borrower behavior

by examining emotional and cognitive influences. In today’s complex financial environment,

traditional risk models alone are not enough. There is an urgent need to understand the human

element behind loan defaults, and this study aims to fill that critical gap.

<CHAPTER 2. REVIEW OF LITERATURE>

2.1 Introduction

The literature review explores existing studies on Non-Performing Assets (NPAs), loan defaults, and

the role of behavioural economics in understanding borrower behavior. While much of the past

research has focused on financial causes of NPAs, newer studies are highlighting the psychological

and emotional triggers that influence repayment decisions. This chapter critically examines both

traditional and behavioural perspectives on loan defaults.

2.2 Understanding Non-Performing Assets (NPAs)

Non-Performing Assets (NPAs) are loans or advances where the borrower has failed to make

scheduled payments of principal or interest for a certain period (typically 90 days in India).

According to the Reserve Bank of India (RBI), NPAs are classified into:

 Substandard Assets: Overdue for less than 12 months

 Doubtful Assets: Overdue for more than 12 months

 Loss Assets: Assets that are considered unrecoverable

Studies such as Singh (2020) and RBI reports show that India's banking sector has faced major stress

due to rising NPAs, especially after 2014. Public sector banks have reported the highest default rates,

particularly in corporate and MSME sectors.

2.3 Traditional Approaches to Loan Default Analysis

Traditionally, loan default risk is assessed using financial indicators such as:

 Credit scores
  Debt-to-income ratios

 Business profitability

 Collateral value

Models like Altman Z-score, logit regression, and credit rating systems have been used to predict

defaults (Altman, 1968). These models assume that borrowers behave logically and that financial

distress is the main reason for default.

However, scholars like Rajan & Dhal (2003) and Bandyopadhyay (2009) have pointed out that these

models often fail to explain why borrowers default even when financially stable. This shows the

need to look beyond numbers and explore behavioral factors.

2.4 Introduction to Behavioural Economics

Behavioural economics combines insights from psychology with economic decision-making. It

challenges the assumption of “rational man” in classical economics. Instead, it shows that people

often make biased, emotional, or irrational decisions, especially when it comes to money.

Key theories in behavioural economics include:

 Bounded Rationality (Herbert Simon): People make decisions with limited time,

information, and mental resources.

 Prospect Theory (Kahneman & Tversky, 1979): People fear losses more than they value

gains. They take bigger risks to avoid losses, even if irrational.

 Present Bias: Preference for immediate rewards over long-term benefits.

 Overconfidence Bias: Borrowers overestimate their future income or repayment ability.

These theories help explain why people may take loans they can’t repay or delay payments even

when it's harmful.

2.5 Psychological Triggers of Loan Defaults

Recent research suggests that borrower defaults are not always due to financial incapacity, but also

behavioral tendencies. Key psychological factors include:

 Overconfidence Bias: Borrowers believe they will manage to repay loans easily,

underestimating future risks.

 Optimism Bias: Unrealistic belief that future income will increase.

 Present Bias: Preference for current consumption, ignoring future repayment obligations.

 Herd Behaviour: Borrowing decisions based on peer or social pressure rather than actual

need.

 Financial Anxiety and Stress: Mental stress due to debt can lead to avoidance behavior, like

ignoring repayment notices.

 Moral Hazard: Some borrowers know that loans will be written off or delayed and take

advantage of the system.

Studies by Agarwal et al. (2017) and Thaler (2016) show that these biases play a major role in

defaults across retail loans, credit cards, and education loans.

2.6 Behavioural Biases in Lending Decisions

Not just borrowers, even lenders are influenced by behavioural biases:

  Anchoring Bias: Basing new credit decisions on past data, without adjusting for current

realities.

 Recency Bias: Giving more importance to recent borrower performance.

 Confirmation Bias: Approving loans based on expected borrower behavior, ignoring

warning signs.

These biases affect loan approval quality and can increase NPA risk. A study by Berger & Udell

(2003) found that lending officers often rely on personal judgment rather than updated borrower

information, especially in small banks.

2.7 Existing Literature on NPAs and Behavioural Economics

Some key studies and papers combining these ideas include:

 Kahneman & Tversky (1979): Introduced Prospect Theory, which explains irrational

financial behavior.

 Richard Thaler (2016): Described how mental accounting and self-control affect personal

finance decisions.

 Agarwal et al. (2017): Showed how behavioural nudges like reminders, automatic payment

options, and visual cues reduce defaults.

 World Bank (2019): Noted the importance of financial psychology in credit programs,

especially in rural and low-income areas.

 RBI Working Papers (2020–2023): Recommended inclusion of behavioral data in credit

appraisal systems.
However, there is limited research that specifically studies NPAs in India using a behavioural lens,

which makes this study both timely and relevant.

2.8 Gaps in the Literature

Although behavioural economics has been used in areas like savings, spending, and investing, there

is a lack of deep research on its role in loan defaults and NPAs, especially in the Indian context. Most

banks still rely on traditional credit analysis, ignoring psychological triggers. Also, there is little data

on how borrower behavior differs across regions, age groups, or loan types.

There is also a gap in studying how behavioral interventions (like reminders, nudges, or auto-debits)

can reduce NPAs. This study aims to fill that gap by combining behavioral theory with practical

banking data.

2.9 Summary

The literature clearly shows that financial defaults are not only caused by economic distress but also

by behavioral and emotional factors. Borrowers may default due to overconfidence, social pressure,

or poor self-control. Similarly, lenders may approve risky loans due to cognitive shortcuts. Yet,

traditional models focus only on financial numbers.

This gap provides a strong basis for this project, which seeks to analyze NPAs from a behavioural

economics perspective and suggest ways to reduce defaults by addressing human behavior—not just

balance sheets.
 CHAPTER 3. RESEARCH OBJECTIVES AND METHODLOGY

➤ INTRODUCTION

Research methodology refers to the systematic process used to gather information and analyze

data to achieve the objectives of a study. This chapter outlines the design, approach, tools, and

techniques used to explore the psychological and behavioural factors influencing loan defaults

and the formation of NPAs in the Indian banking sector. Since this project integrates behavioural

economics with financial risk analysis, the research methodology is both descriptive and

exploratory in nature.

➤ RESEARCH OBJECTIVES

The objectives of this study are:

 To identify key behavioural and psychological factors influencing loan defaults among

individual borrowers.

 To analyze how borrower-side biases (e.g., overconfidence, present bias, herd behavior)

contribute to loan repayment failure.

 To examine how behavioural tendencies affect the decision-making of lenders and credit

officers during loan disbursement.

 To explore the potential use of behavioural insights in predicting NPAs and designing

intervention strategies to reduce loan defaults.

➤ RESEARCH PROBLEM

Despite the use of advanced credit scoring and financial risk assessment models, many borrowers

continue to default on loans. These traditional models fail to capture the non-financial,
psychological, and emotional dimensions that influence repayment behavior. There is a pressing

need to understand the behavioural reasons behind defaults, both from the borrower's and the

lender’s side, especially in the Indian context where NPAs remain a persistent challenge.

➤ RESEARCH DESIGN

This study adopts a mixed-method research design comprising both descriptive and

exploratory approaches:

 Descriptive: To document patterns in borrower behavior and the psychological traits

commonly associated with defaults.

 Exploratory: To uncover lesser-known or emerging behavioral influences on loan

defaults.

 The design involves both quantitative (survey-based) and qualitative (interview and

case study-based) components.

➤ TYPE OF DATA USED

 Primary Data: Collected through questionnaires and interviews with borrowers and

banking professionals.

 Secondary Data: Derived from RBI reports, academic journals, bank NPA disclosures,

and published research papers related to behavioural economics and credit risk.

➤ DATA COLLECTION METHOD

 Online and offline surveys distributed via Google Forms to individual borrowers who

have taken loans from banks.

 Semi-structured interviews with loan officers and credit managers from selected banks

to understand lender-side biases.

 Use of document analysis from published reports for triangulation.

➤ DATA COLLECTION INSTRUMENT

 Questionnaire: A structured survey including Likert-scale, multiple-choice, and short-

answer questions to capture borrower behaviour.

 Interview Guide: Semi-structured format focusing on behavioural aspects of lending and

decision-making used during interviews with bank professionals.

➤ SAMPLE SIZE

 Borrowers: 100 to 150 participants from various sectors (retail, education, MSME).

 Banking Professionals: 10 to 15 credit officers or managers from public and private

sector banks.

➤ SAMPLING TECHNIQUE

The study uses non-probability purposive sampling, selecting participants based on relevance

to the research objectives:

 Borrowers with a history of loan repayment issues or defaults.

 Bank officials directly involved in loan sanctioning or credit analysis.

➤ DATA ANALYSIS TOOL

 Quantitative Data: Analyzed using Microsoft Excel and/or SPSS, employing:

o Descriptive statistics (mean, frequency, percentages)

o Correlation analysis to assess behavioral trait influence

 Qualitative Data: Analyzed through thematic analysis to identify recurring

psychological patterns in borrower and lender responses.

 CHAPTER 4. DATA ANALYSIS, RESULTS, AND INTERPRETATION

4.1 Introduction

This chapter presents a detailed analysis of data collected through surveys and interviews to

understand the behavioral and psychological triggers influencing loan defaults. The data includes

inputs from individual borrowers and loan officers across selected public and private sector

banks in India. The responses are analyzed to identify patterns, biases, and relationships between

behavioral traits and repayment behavior. Visuals such as tables and graphs support the

interpretation and provide a clearer understanding of trends.

4.2 Overview of Respondent Demographics

4.2.1 Borrowers

A total of 125 borrowers participated in the survey.

Parameter Category Percentage

Age 20–30 35%

31–40 42%

41 and above 23%

Gender Male 58%

Female 42%

Loan Type Personal Loan 40%

Education Loan 28%

Business/MSME Loan 32%

Default History Defaulted in past 5 yrs 37%

Parameter Category Percentage

Employment Status Salaried 62%

Self-employed 38%

Figure 4.1: Borrower Profile Distribution (Source: Primary survey, 2025)

4.2.2 Lenders

15 credit officers from 5 banks were interviewed. Most had 5–15 years of experience in loan

disbursement, mostly in retail and SME lending.

4.3 Key Behavioural Factors Identified

Based on survey and interview responses, the following psychological triggers emerged as most

influential in borrower default:

Behavioural Bias Borrowers Agreeing (%)

Overconfidence 66%

Optimism Bias 58%

Present Bias 74%

Herd Mentality 49%

Financial Stress 61%

Moral Hazard Awareness 31%

Table 4.1: Behavioural Bias Prevalence Among Borrowers (Source: Primary survey, 2025)

Interpretation: Present bias (short-term thinking) was the most commonly admitted trait,

especially among younger borrowers (ages 20–35). Overconfidence was also high, particularly

among self-employed respondents.

4.4 Cross-Tabulation Analysis

4.4.1 Bias vs. Loan Default

Behavioural Bias % Among Defaulters % Among Non-Defaulters

Overconfidence 81% 55%

Present Bias 85% 66%

Herd Mentality 52% 41%

Table 4.2: Bias Comparison: Defaulters vs. Non-Defaulters (Source: Primary survey, 2025)

Insight: Behavioral traits are notably stronger among those who have defaulted, confirming their

role as predictors of repayment issues.

4.4.2 Age Group vs. Present Bias

Age Group Admits Present Bias (%)

20–30 84%

31–40 68%

41+ 51%

Figure 4.2: Age-wise Present Bias Comparison (Source: Primary survey, 2025)

4.5 Qualitative Findings – Bank Officer Insights

Bank officers shared several insights on behavioural patterns observed in borrowers:

 Common observation: “Young borrowers often feel confident they will be promoted or

earn more, even without evidence.”

 On herd behavior: “Sometimes entire peer groups from colleges or neighborhoods take

loans around the same time—especially education or business loans.”

 On recency bias in lending: “Officers tend to give more weight to recent experiences—

if recent approvals went fine, the officer becomes more lenient.”

4.6 Behavioural Themes

From qualitative coding of interview data, key themes include:

 Hope-Driven Borrowing: Borrowers take loans based on future expectations rather than

current ability.

 Peer Pressure: Social comparisons lead to riskier financial decisions.

 Information Blindness: Borrowers often ignore terms and repayment schedules due to

over-trust in bank agents.

4.7 Data Triangulation with Secondary Sources

To validate survey findings, data from RBI and academic reports were reviewed:

 According to the RBI Financial Stability Report (2024), NPAs in the personal loan

segment grew by 1.6%—higher among borrowers aged 20–35.

 A report by CRIF Highmark (2023) found that default risk rises in urban Tier 2 cities

due to impulsive spending patterns.

 Thaler (2016) highlighted the success of “reminder nudges” in improving repayment

behavior.

These secondary findings reinforce the survey results and emphasize the link between behavior

and default.

4.8 Graphical Summary

 CHAPTER 5. FINDINGS AND

CONCLUSION

<ATLEAST 3 PARAGRAPHS>

<HIGHLIGHT ALL FINDINGS >

FOR EXAMPLE:

By the summation of all the different sector of Industries in survey, Neelkamal has the

largest Market Share of approximately 34.6 % due to the diversity of Neelkamal products.

The main competitor of Neelkamal is Supreme as they have approx. 14.78 % of share in the

product.

<EXPLAIN THE MAIN THINGS CONCLUDED>

[FONT 12” AND DOUBLE SPACING]

CHAPTER 7. RECOMMENDATIONS AND LIMITATIONS OF THE STUDY

 RECOMMENDATIONS

IN POINTS

ATLEAST 10-15 POINTS

[FONT 12” AND DOUBLE SPACING]

FOR EXAMPLE:

Companies should try to advertise their products and try to make people aware of their new

range of products. Distributors should try to advertise them with the help of a company

because many people are not aware about them.

The company should try to offer promotional schemes from time to time.

 LIMITATIONS OF THE STUDY

IN POINTS

ATLEAST 5-10 POINTS

[FONT 12” AND DOUBLE SPACING]

FOR EXAMPLE:

The industrial area was very limited in Allahabad and on its outskirts. Some of the

industries had been closed.

At certain target place the person who could give the relevant information was

unavailable.

BIBLIOGRAPHY

Research paper:

<APA format>

For Example:

Kim, M. S., & Hunter, J. E. (1993). Attitude-behavior relations: A meta-analysis of attitudinal

relevance and topic. Journal of Communication, 43(1), 101–142.

WEBSITES:
<do not include any open-source website like Wikipedia, etc. >
For example:

1. http://www.nilkamal.com/Material_Handling/PP_Corrugated .aspx <URL

should be pasted.

BOOKS:
1. <Author name>, <book name>, <publication>, <edition and volume>,
<page number>
<CHAPTER 1: INTRODUCTIONTOTHETOPIC>