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The Impact of Behavioural Biases in Personal Banking: A Study on

Sri Lankan Customers

Virajinie Dilhani Bandara*

Charisma University, 1321 Discovery Drive, Billings, MT 59102, USA

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150300018

Received: 13 March 2026; Accepted: 18 March 2026; Published: 02 April 2026

ABSTRACT

Behavioral finance shows that bias influences personal finance decisions. This study focuses on Sri Lankan

customers during the turbulent economic situation in the country and the biases that come with it. It samples all

present biases, loss aversion, anchoring, mental accounting, overconfidence, herding, status quo bias, and a

further, valence-biased, psychologically motivated bias. A structured questionnaire with demographic, financial

literacy, and behavioral-based banking questions, and behavioral bias on a Likert scale, was distributed to 400

personal banking customers selected through stratified random sampling, with 323 valid responses processed.

Descriptive statistics, chi-square, independent samples t, and logit regression were the analytical methods used.

Status quo bias and mental accounting were the leading ones with the highest mean scores. Present bias,

overconfidence, and psychology bias significantly predicted the respondents reporting that their savings were

always increasing. Bivariate tests also revealed age, education, and income association with gains in savings

perception. Age and market condition attention were positive predictors of perceived success in savings, while

higher present bias and emotion-driven tendencies were negative predictors. The study also shows strong

dependence on fixed deposits, considerable use of online banking, and ongoing reliance on personal judgement

and family advice when making financial decisions. The research offers the results of an under researched South

Asian environment and recommends the creation of banking products with a perception of bias, the establishment

of more comprehensible communication with the consumers, the creation of specific financial literacy programs,

and the increased inclusion of digitally reluctant and low-income clients in the banking strategies.

Keywords: behavioural bias; personal banking; Sri Lanka; savings behaviour; logistic regression

INTRODUCTION

Classical economic theory presupposes that people are rational decision-makers, who objectively process the

available information and choose the options that will maximize utility. Behavioral finance refutes this premise

and demonstrates that financial decisions are frequently influenced by systematic thinking biases, emotional

responses, as well as judgemental biases that depend on the context (Anuradha et al., 2024; Pompian, 2012).

These effects do not affect investing alone in a banking context, as they influence saving, borrowing, product

choice and adoption of digital-services as well (Pompian, 2012).

The majority of empirical research on behavioral finance has focused on Western or developed markets, and

stock-market involvement and retirement investments are of more interest than ordinary personal choices about

banking (Gomes et al., 2021; Kaustia et al., 2023). Consequently, there is limited information on how the biases

of behavior influence the household banking decisions in the emerging economies where the financial literacy

levels, economic volatility, institutional trust, and social norms can be less favorable than in the developed setting

(Badarinza et al., 2019).

Sri Lanka presents a very critical area of investigation. The macroeconomic instability of the country,

inflationary pressure, and currency depreciation, as well as uncertainty about household finances, can increase

short-termism, apprehension, and banking inertia (Samarakoon, 2024; DCS, 2023). Simultaneously, the rise of

digitalization of financial services opens new possibilities of convenience and simultaneously raises the level of

concern regarding the security, trust, and financial ability (Aboobacker and Bao, 2018; CBSL, 2021).

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It is against this background that the current study examines behavioral biases that shape individual banking

choices among the Sri Lankan customers. More particularly, the research objectives are: (1) to establish the

greatest salience biasing behaviors influencing individual banking decision making; (2) to determine the

connection between these biases and significant banking outcomes, particularly, savings outcomes; (3) to

determine the connection between demographic factors and financial behavior and the perceived gains on

savings. The paper addresses these objectives by providing empirical findings on an under-studied South Asian

context and provides its implications to banks, regulators, and financial educators to enhance customer results

in a challenging financial context.

LITERATURE REVIEW

Behavioral bias is a mode of thinking that makes a person arrive at something other than a rational decision and

is affected by cognitive boundaries, emotions, psychological shortcuts, or social culture (Ricciardi and Simon,

2000; Gethe et al., 2022). Behavioral biases can be applied in personal banking to influence the way consumers

interpret the description of products, the evaluation of risks, the responses to uncertainties, and to make decisions

between short-term gratification and long-term financial benefits (Rau & Bromiley, 2025).

Present bias

Present bias is a behavioral tendency that involves placing a higher value on rewards that are received

immediately versus those that are to be received in the future (Laibson, 1997). In retail banking, present bias can

result in inadequate savings, an unwillingness to lock up funds for long time periods, overuse of short-term

credit, and delays in making sound financial decisions. In an economically challenging situation, present bias is

likely, because the need for immediate cash overshadows long-term financial considerations (Maji & Prasad,

2024).

Loss aversion

Loss aversion is the tendency of people to consider losses more than equal gains (Kahneman & Tversky, 1979).

In banking, this may create a strong tendency of consumers to prefer investment products that offer guaranteed

returns, avoid taking risks in the stock market, be reluctant to change banks, and be overly conservative in taking

up new savings and credit offers. Although this type of behavior may be protective, it may lead to a reduction in

potential wealth in the long-term if economically protective consumers avoid growth opportunities (Hwang,

2024).

Anchoring bias

This phenomenon refers to the tendency to use the first piece of information given when making the following

decisions (Tversky & Kahneman, 1974). When marketing banking products in Sri Lanka, customers may refer

to pre-crisis exchange rates, previous returns on deposits, or previous costs of loans. Such phenomenon can

decrease customers’ ability to assess banking products appropriately, as they refer to out-of-date benchmarks

(Pompian, 2012).

Mental accounting

This phenomenon is the tendency to think of money in separate “buckets” instead of in aggregate (Thaler, 1999).

Bank customers may think of their salary, savings, emergency funds, bonuses, and borrowed money as different

“accounts” and they may use even sub-optimal, i.e. economically inefficient, strategies. This can impact

customers’ deposit behaviors, decisions on debt repayments, and their propensity to shift funds across different

banking products (Dan, 2025).

Overconfidence and herding

Overconfidence is a misplaced belief in one’s understanding of financial issues and/or decision-making skills.

Herding, meanwhile, is the tendency to follow other people’s behavior without assessing the situation

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independently (Adielyani & Mawardi, 2020). The overconfident consumer may believe that he or she can pick

the best products or control the risk optimally. By contrast, herding causes consumers to choose popular banking

products without assessing if the products suit their needs (Inghelbrecht & Tedde, 2024).

Status quo and psychological bias

Status quo bias is the tendency to stick to what one is accustomed to, even in the presence of more favorable

options (Samuelson & Zeckhauser, 1988). The survey in this study sought to capture breadth around the

psychological bias, bias of emotion, such as fear, uncertainty, and trust in the context of financial decision-

making. In a post-crisis situation, these responses may reinforce the inertia and deepen the hesitation, fostering

the tendency of consumers to stay with their old routine products and avoid new options even if they are better

(Godefroid et al., 2022).

The aforementioned biases are widely documented in behavioral finance, but the literature is scarce on how the

biases collectively impact everyday personal banking decisions, particularly in Sri Lanka. Hence, this study

attempts to evaluate these concepts in the context of local banking in an environment of economic instability,

disparate levels of financial literacy, and incomplete but increasing levels of digital adoption.

METHODS

Research design and sample

The research used a cross-sectional design and a structured questionnaire focused on behavioral biases, financial

literacy, and banking behaviors in personal banking customers in Sri Lanka. The stratified random sampling

method was employed to better represent the population on the main variables of demography - age, gender,

income, education, and occupation.

Among all questionnaires sent (n=400), 323 questionnaires were identified as appropriate to be included in the

study. The survey questionnaires were drafted in English since the study involved customers with access to

formal banking and online banking systems that are accessed in the English language in the data collection

exercise. This design decision traded consistency in the instruments with the restriction introduced by biases

towards more educated and English competent respondents.

Instrument and measures

The measure was divided into four sections: (1) screening and demographic data; (2) the question of financial

literacy and banking behavior; (3) self-reported savings, consultations, and digital banking behavior; (4) Likert

scale question on the strength of behavioral biases.

On the behavioral-bias scale, respondents have a scale of 1 to 5 with one being strong disagreement and five

strong agreements. Timely examples were loss avoidance, self-confidence in financial literacy, and need of

immediate gratification over delayed gratification.

The analysis maintained the same category of biases in the experiment, including present bias, loss aversion,

anchoring, mental accounting, overconfidence, herding, status quo bias, and a more generalized psychological

bias of the questionnaire. Because the source manuscript just enumerated summary statistics, the revision should

be aimed at the more comprehensible display of the given analytic findings, as opposed to the reconstitution of

the estimated values.

Analytical approach

Respondent profiles and the distributions of behavioral biases were described using descriptive statistics.

Associations between demographic factors and perceived savings gains were assessed using chi-square tests.

Bias scores were compared using independent-samples t-tests between respondents who stated their savings

always resulted in gains and those who stated otherwise.

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Behavioral and demographic predictors of reporting savings that always resulted in gains were identified using

a logistic regression model.

RESULTS

Respondent profile

Table 1 provides a demographic summary of the 323 respondents. The sample was evenly split by gender.

However, there was an overrepresentation of young and middle-aged adults, high levels of education, high

income groups, and participants in occupations.

Table 1. Frequency distribution of demographic variables

Variable

Category

Frequency (%)

Consultation

On my own

58.51

Consultation

Consult family

45.82

Consultation

Consult external experts

10.22

Consultation

Consider public view

5.57

Savings behaviour

Not interested

13.6

Savings behaviour

Regular savings

50.5

Savings behaviour

FDs with high yield

35.9

Considers market situation

Yes

73.68

Considers market situation

26.32

Savings instruments

FDs

77.09

Savings instruments

Unit trusts

6.19

Savings instruments

Share market

33.13

Savings instruments

Government securities

15.79

Online banking

Do not use

7.43

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Online banking

Use online banking

84.52

Online banking

Use for bill payments

38.08

Online banking

Use mobile wallets

17.34

Gain by saving

Never

4.34

Gain by saving

Rarely

51.08

Gain by saving

Always

44.58

Note. Some percentages reflect multiple-response items and therefore do not necessarily sum to 100 within a

construct.

The pattern suggests a careful, behavior-driven strategy towards money management. Saving regularly was

commonplace, but respondents demonstrated a clear inclination towards more predictable, less risky options,

and a rather narrow interest in more alternative options like unit trusts or government securities. Although digital

banking was adopted highly, practical use remained limited to general online banking and bill payment services

rather than more novel or complex offerings.

Distribution of behavioural biases

In Table 3, status quo bias received the highest average value, followed by mental accounting. Present bias, loss

aversion, anchoring, and overconfidence were of moderate prevalence.

Table 3. Distribution of behavioural biases

Bias

Mean +/- SD

95% confidence interval

Present bias

5.23 +/- 1.97

(5.01, 5.44)

Loss aversion

6.97 +/- 1.52

(6.80, 7.13)

Anchoring bias

6.78 +/- 1.67

(6.60, 6.97)

Mental accounting

10.04 +/- 2.02

(9.82, 10.26)

Overconfidence bias

6.65 +/- 1.45

(6.49, 6.81)

Psychological bias

2.18 +/- 0.98

(2.08, 2.28)

Herding bias

2.20 +/- 0.99

(2.09, 2.30)

Status quo bias

16.44 +/- 3.09

(16.10, 16.78)

Note. The status quo confidence interval contains an obvious formatting error in the source table; it has been

standardised here as (16.10, 16.78) for consistency with the reported mean.

The data indicates that the participants preferred to keep their financial habits the same, and mentally categorize

their money. While the emotion-driven and herding tendencies were below average, they were still present in

subsequent comparative and regression analyses as significant concerning perceived savings outcomes.

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Savings outcomes and behavioural predictors

Examining perceived savings success entails considering whether respondents had ever stated that their savings

always produced positive returns. The bivariate relationship between some demographic factors and this

outcome is shown in Table 4.

Table 4. Association between demographic factors and savings always generating gains

Factor

Categories examined

Chi-square

value

Age

18-29; 30-39; 40-49; 50-59; 60+

27.596

<.001

Gender

Male; Female

0.001

.982

Education

School; Diploma; Degree; Postgraduate

18.776

<.001

Income

<50,000; 50,000-99,999; 100,000-199,999; 200,000+

26.516

<.001

Note. p-values are reported as presented in the source analysis, with values reported as 0.000 standardised to

<.001.

At the bivariate level, age, education, and income were positively associated with perceived savings gains, while

the association with gender was not significant. Respondents with stronger socioeconomic resources, particularly

older respondents, were more likely to report savings gains.

Table 5 analyses the average bias scores of participants who indicated that they always benefited from savings

and of those that did not.

Table 5. Comparison of behavioural biases by savings outcome

Bias

Mean (Yes)

Mean (No)

|t|

p-value

Present bias

4.43

5.87

6.958

<.001

Loss aversion

6.96

6.97

0.081

.936

Anchoring bias

6.76

6.80

0.187

.851

Mental accounting

9.91

10.15

1.040

.299

Overconfidence bias

6.94

6.41

3.356

.001

Psychological bias

1.88

2.43

5.295

<.001

Herding bias

2.01

2.35

3.118

.002

Status quo bias

16.5

16.4

0.299

.765

Note. Absolute t statistics are reported because the source table contained sign inconsistencies relative to group

means.

Consistent gainer respondents were found to have lower present bias, psychological bias, herding bias, and

higher overconfidence. In the case of loss aversion, and the biases of anchoring, mental accounting, and status

quo, the two groups showed no material differences. This response pattern suggests that greater emotional

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control, and greater independence from the influence of the crowd, are of greater importance to perceived success

in saving than are more general fears, preferences, or biases toward caution or the status quo.

Table 6 shows the estimated coefficients of the logistic regression model for the probability of reporting savings

always generated gains.

Table 6. Logistic regression model predicting savings always generating gains

Variable

Wald statistic

p-value

Odds ratio

Age

4.754

.029

1.291

Education

1.083

.298

1.163

Income

0.311

.577

1.085

Consultation for saving

1.725

.189

1.305

Savings habits

0.003

.957

0.987

Considers market situation

6.826

.009

2.435

Number of savings modes

1.537

.215

1.369

Online banking

2.683

.101

2.801

Present bias

16.480

<.001

0.724

Overconfidence bias

9.070

.003

1.358

Psychological bias

9.151

.002

0.613

Herding bias

0.001

.975

0.995

Note. The dependent variable is whether respondents reported that savings always generated gains. Online

banking shows a positive but conventionally non-significant association at p = .101.

After adjusting for several covariates, age, and focus on market conditions continued to be positive predictors of

the outcome. Present bias and psychological bias decreased the likelihood of consistent gain reports, while

overconfidence increased the odds. There was a positive relationship, though not statistically significant,

between online banking. Education, income, saving behavioral tendencies and herding bias did not play a role

in the multivariate framework implying that behavioral traits and situation relevance perhaps explain more of

the variation than demographics.

DISCUSSION

Results show that technical financial reasoning can be a factor in individual banking behavior in Sri Lanka, but

the factor is largely affected by a combination of caution, comfort, and selective irrational confidence. The means

of mental accounting and status quo bias are high indicating that respondents are likely to mentally separate

money and maintain the current financial status quo. That is logical given the recent economic turmoil. Familiar

products and routine present some sense of control during periods of uncertainty.

At the same time, the savings models stress the importance of the short-term orientation and the impactful

responses. Increased present bias was correlated with reduced instances of reporting of savings that resulted in

positive consequences. This aligns with the premise that the desire to spend now may prevent the capacity to

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save and plan in the future (Laibson, 1997; Banerjee et al., 2025). Since the psychological-bias measure is

important in forecasting the results, it would be reasonable to conclude that emotionally motivated processes of

fear and uncertainty still play a crucial role in preventing successful results in a regular fashion.

Confidence was seen as a favorable predictor of reported financial gains. This requires careful questioning.

Compensated respondents are more likely to engage more with savings tools or market figures, yet it can also

reflect the tendency to overstate their financial prosperity. This, however, is a chance to banks. Self-confident

customers might need directions, explicit product presentation, and purposefully designed knowledge more than

make assumptions about the risk involved (Adielyani and Mawardi, 2020; Adkisson, 2008).

It is also about digital behavior. Although online banking was not a robust predictor among conventional levels,

the association was positive and the descriptive statistics indicated that digital channels were highly used. It

means that more frequent monitoring of account balances, occurrence of transactions, and availability of

information may occur with the help of the usage of digital channels. Nevertheless, behavioral bias cannot be

replaced by the utilization of digital channels. Digital banking ecosystem continues to experience fraud, digital

literacy, and trust challenges (Gargouri, 2023).

Another area of customer experience emphasized in the research is beyond technical financial expertise. The

reasons appreciated by respondents are trust, transparency, quality of service and product familiarity. These traits

are in line with previous findings that the combination of customer orientation with the quality of communication

boosts satisfaction and involvement in banking (Gonu et al., 2023). From an operational standpoint, banks cannot

focus only on the financial return of products or their technical attributes. They must also respond to how

customers perceive and experience risk, effort, trust, and ease.

Study limitations

This paper builds upon behavioral-finance results in a South Asian context that has been under-studied, though

the results need to be put into perspective considering a number of limitations. First, the sample attained is biased

towards the educated, higher-income, professionally-employed, English-skilled, and digitally-engaged

respondents. Though stratified sampling was planned, the ultimate sample of respondents does not perfectly

reflect the diversity of the customers of the Sri Lankan banking industry, particularly low-income, rural, older,

offline, and linguistically marginalized users. This is important since financial behavior in households remains

heterogeneous and financial inclusion in emerging markets remains varying with digital capability, education,

and socioeconomic position (Gomes et al., 2021; Adel, 2024; Jose and Ghosh, 2025).

Second, the research is based on self-reporting perceptions, especially on the dependent variable that

demonstrated that savings always yielded a gain. These are analytically helpful but lack the same evidences of

strength as checked balances, deposit history, and transaction history. Recent matched survey-administrative

finance datasets indicate that self-disclosed financial data may differ with the reported values, and some of the

differences are associated with financial literacy, household complexity, and reporting environments (Madeira

et al., 2022).

Third, the cross-sectional design is incapable of a robust causal inference. The reported regressions can establish

the relationship between behavioral characteristics and perceived savings results but cannot establish whether

biases explained savings performance, whether previous savings success modified confidence and market focus

or whether both were explained by factors related to the crisis but not observed.

Fourth, it would be advantageous to refine some of the constructs more. The general category of psychological

bias in general seems to extend emotional responses, including fear, uncertainty, and trust, into one residual

measure, which can lower construct clarity. The recent literature emphasizes that behavioral constructs,

including status quo bias, subjective financial knowledge, and confidence, should be operationalized with

caution since weak or over-aggregated measures can make interpretation difficult and comparison across studies

challenging (Godefroid et al., 2023; Xin et al., 2024; Lee et al., 2025).

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Future research directions

Future studies thus need to shift to a nationally inclusive, multilingual study where the instrument is administered

in Sinhalese, Tamil, and English and intentionally oversampling low-income, rural, and digitally reluctant

populations. Higher external validity would also be achieved through including those customers whose main

channel is based on branch-based as opposed to digital channels. Gender-age cohort-digital engagement

comparative subgroup analysis would particularly be useful as current studies reveal that there remains gender-

based and generation-based digital financial inclusion disparities in developing-country contexts (Özsuca, 2025).

A better design would also integrate survey data with qualitative interviews, focus groups, or financial diaries

such that the analysis reflects the effects of trust, crisis memory, family influence and perceived safety on

financial choice. Mixed-methods designs are becoming more and more suggested when quantitative models can

explain patterns but fail at explaining the mechanisms of their occurrence (Kurtaliqi et al., 2024). Lastly, self-

reports ought to be supplemented in future research with objective or quasi-objective measures including deposit

growth, frequency of transactions, product holding, arrears history or consent-based administrative data. By so

doing it would be simpler to isolate actual financial performance and confidence, optimism or recall bias and

would allow more accurate testing of the behavioral tendencies with respect to real banking outcome.

CONCLUSION AND RECOMMENDATIONS

This study reveals how behavioral biases affect decision-making in personal banking amongst customers in Sri

Lanka. Biases in present thinking, overconfidence, and psychological thinking were identified as the most

significant predictors of perceived savings from present thinking. On average, status quo and mental accounting

biases were the most significant. The mixture of habit, emotional shorts, and selective confidence, as opposed

to pure rational calculation, describes the mental banking behavior best.

This consideration provides a framework for the development of banking services. When banks and

policymakers account for behavioral biases in the design of their products in the areas of customer

communication, savings incentive tools, digital banking services, and financial literacy, they provide the greatest

opportunities. In this way, behavioral finance becomes the way to build improved customer outcomes and foster

banking system resilience.

Recommendations

1. Construct savings and investment products that are mindful of behavioral biases and provide customers

with the security necessary to eventually develop the behavioral ability to stretch their savings beyond

fixed deposits (Gargano and Rossi, 2024).

2. Apply present bias, self-control savings via simple behavioral nudges, reminders, and goal-setting

(Thaler, 1999).

3. Improve transparency, and advisory communication especially to confident customers who may be

underestimating the complexity of the products, or overestimating their financial literacy (Inghelbrecht

& Tedde, 2024).

4. The scope of innovation in banking should be widened to avoid deepening exclusion, particularly in the

case of digitally hesitant, marginalized, and low-income individuals, who should be the target of digital

financial literacy and support programs (Jose and Ghosh, 2025).

Ethical Approval

This study received ethical approval from the AIMS Campus Ethics Committee, No. 7, Rajakeeya Mawatha,

Colombo 07, Sri Lanka. Approval reference was granted by Professor Sirimevan Widyasekera

(profw@aimscollege.edu.lk) and Dr. Kithsiri Manchanayakke (kithsiri@aimscollege.lk). Participation

was voluntary, and all respondents provided informed consent prior to completing the survey.

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Conflict of Interest

The author declares no conflict of interest in relation to this study.

Data Availability

The de-identified survey dataset generated and analyzed during the current study is available from the

corresponding author upon reasonable request. Due to confidentiality and privacy obligations, the dataset

is not publicly accessible.

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