




































Indian Journal of Finance and Banking 

 Vol. 9, No. 1; 2022 

                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

47 

COMPARATIVE ANALYSIS OF EMPLOYEES’ PERSPECTIVE ON 

ASSET-LIABILITY MANAGEMENT IN BANKS 
 

Jyoti Tanwar 

Senior Research Scholar 

Department of Economics and Finance 

BITS-Pilani, Pilani Campus 

Jhunjunu, Rajasthan- 333031, India 

E-mail: jyotanwar@gmail.com 

https://orcid.org/0000-0001-6929-9512 

 

Arun Kumar Vaish 

Assistant Professor 

Department of Economics and Finance 

BITS- Pilani, Pilani Campus, India 

E-mail: akvaish@pilani.bits-pilani.ac.in 

https://orcid.org/0000-0003-4881-2588 

 

NVM Rao 

Professor & Head of CPU 

Department of Economics and Finance 

BITS-Pilani, Pilani Campus, India 

E-mail: nvmrao@pilani.bits-pilani.ac.in 

https://orcid.org/0000-0001-6329-7663  

 

Received: October 21, 2021      Accepted: December 20, 2021        Online Published: January 19, 2022  

 

DOI: 10.46281/ijfb.v9i1.1552            URL: https://doi.org/10.46281/ijfb.v9i1.1552 

 

ABSTRACT 
Asset-Liability Management is critical for the successful working of banks. The Indian Financial 

system is very dynamic and growing rapidly. Banks focus on both the assets and liabilities due to 

liquidity risk, interest rate risk and foreign exchange risk. The implementation and understanding of 

ALM policies, and procedures will provide insight into the approach of banks towards ALM. A 

primary survey is conducted among employees of twelve public and twelve private banks to collect 

data related to employees' experience, understanding, and knowledge about the ALM. The 

questionnaire used in the study carries Yes/No type questions, multiple-choice questions, checklist 

questions, scaled questions. The data is analyzed using SPSS. The results suggest that public and 

private banks' employees have a similar understanding and knowledge of the ALM. Both types of 

banks follow identical practices and policies in the implementation of ALM with few deviations. The 

employees of the banks were hesitant to answer complex questions, and therefore questionnaire has to 

be kept generic and straightforward. This paper studies ALM in banks using primary data. All other 

studies have been conducted using secondary data or literature surveys. These studies did not feature 

the banks' approach in ALM policies, practices, and procedures. 

 

Keywords: Banking, Assets-Liabilities Management, Risk Management, Interest Rate risk, Liquidity 

Risk. 

 

JEL Classification Codes: E58, E43, G33, G32, D81. 

https://orcid.org/0000-0001-6929-9512
https://orcid.org/0000-0003-4881-2588
https://orcid.org/0000-0001-6329-7663


https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

48  

 

INTRODUCTION 
Most banks in international economies started strategic planning for asset and liabilities management 

in the 1970s (Goodman & Langer, 1983). At that time, Indian banks were free from interest rate risk as 

it was regulated and governed by the Reserve Bank of India (Chatterjee & Dutta, 2016). When other 

economies were planning for deregulation and Asset-Liability Management (ALM), Indian banks were 

getting nationalization (in 1969). The restructuring phase that started in 1991 brought a paradigm shift 

in the banking sector. The purpose of the reform was to make the banking system sensitive to the 

changes happening in the market environment (Tanwar et al., 2020). To achieve the purpose, the 

function of RBI as micromanagement of banks’ operation needs to be switched to macro governance 

(Das & Ghosh, 2001). 

Prior to liberalization, banks were required to manage their balance sheets in accordance with 

regulatory and government directives. Following liberalization in 1991, interest rates were 

deregulated, and banks were granted complete control over their balance sheets. Hence, the ALM rules 

became critical for banks to assist them in avoiding major losses by aligning asset and liability 

mismatches. In February 1999, the Reserve Bank of India issued the first ALM implementation 

directions, which took effect on April 1, 1999   (Singh & Tandon, 2012).  

The ALM procedure was carried out by banks in accordance with RBI rules. The successful 

adoption of ALM relied heavily on information technology and information system management. 

Banks had to rethink their tactics to address future difficulties. Asset Liability Management (ALM) is 

a monetary arrangement process in which strategies for maintaining the firm's assets and obligations 

are planned, implemented, monitored, and scrutinized. It aids in the achievement of organizational 

financial goals by analyzing risks and restrictions (Romanyuk, 2010).  

ALM is also regarded as an essential tool banks use to conduct risk management activities such 

as market risk, financial risk, interest rate risk, and others (Fabozzi & Konishi, 1991). It is responsible 

for performing economic activities such as risk management of liquidity, project planning, trading, 

growth projection, capital planning, funding, and market risks (Adebisi et al., 2020; Haddad et al., 

2019; Riyazahmed & Baranwal, 2021). The ALM practices are implemented through a three-tier 

structure: 

 ALM information system,  

 ALM organization (structure and responsibilities) and  

 ALM process (recognizing risks, estimation, administration, and setting of policies) 

(Chaturvedi, 2014; Jayanthi & Umarani, 2014; Joshi & Sontakay, 2017; Singh, 2013).  

This paper discusses the ALM policies and their implementation in Public and Private Banks in India. 

The paper presents an extensive literature review of ALM and finds gaps that are addressed here. The 

other sections describe the methodology, results, findings, and lastly, the conclusion. 

 

LITERATURE REVIEW ON ASSET LIABILITY MANAGEMENT IN BANKING 

Asset and liability management (ALM) is a practice used by financial institutions to mitigate financial 

risks resulting from a mismatch of assets and liabilities. ALM strategies employ a combination of risk 

management and financial planning and are often used by organizations to manage long-term risks that 

can arise due to changing circumstances. It helps in attaining organizational financial objectives by 

estimating the risks and constraints. (Romanyuk, 2010). ALM is also regarded as an essential tool 

banks use to conduct risk management activities such as market risk, financial risk, interest rate risk, 

and others (Fabozzi & Konishi, 1991).  

Efficient management of interest rate risk and liquidity risk are two main activities of banks 

managed by ALM.  Umarani and Jayanthi (2015)  analyzed ALM in SBI & Associate banks. The 

liquidity position of banks is determined through the maturity profiling method, and the maturity gap 

was analyzed and compared to measure the liquidity risk in banks.  Meena and Dhar (2014) and Vij 

(2001) studied few banks in India using traditional gap analysis to measure liquidity risk and interest 

rate risk. Mismatch in assets and liabilities exposes the balance sheet to liquidity risk. Dash et al. 

(2011) used maturity gap analysis to determine and compare the liquidity position of public, private, 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

49  

and foreign sector banks. Chattha et al. (2020) used duration gap analysis to comparative analyze 

ALM practices in Islamic Commercial Banks (ICBs) and Conventional Commercial Banks (CCBs). 

The findings report that ICBs have 2.41 times more variation than CCBs and are exposed to liquidity 

risk.  

 Antony And Manimegalai (2018) studied the impact of ALM on profitability using ratios such 

as the current asset to deposit ratio, credit-deposit ratio, debt-equity ratio, current ratio, and quick ratio. 

Nugroho et al. (2020) studied the effect of ALM on the financial performance of banks using the 

CAMEL approach. Chakraborty and Mohapatra (2009) studied ALM in banks and applied canonical 

correlation to explore the relationship and strength between assets and liabilities. Abou-el-sood and El-

ansary (2017) also determined the interdependencies between asset and liability portfolios in Islamic 

banking using canonical correlation. It was analyzed that decision for a funding source depends on an 

asset portfolio. Islamic banking depends less on equity to finance investment during an economic 

boom than in financial turmoil. Kosmidou et al. (2004) applied the statistical cost accounting method 

to investigate the correlation between profitability and asset-liability composition. It was found that 

high-profit banks manage to procure funds at lower cost, which helps manage losses from lower 

returns on assets. Jain et al. (2010) presented a fuzzy programming model for pensioners to control the 

risk of underfunding. It dealt with unusual uncertainty of return on investment and future liabilities. 

Black et al. (2003)  determined the essential factors for instigating ALM transformation, i.e., 

market risk management, amendments in accounting and regulatory rules, and lastly, technological 

advancements. Fiedler et al. (2002) described that due to advancement in software, earnings and value 

could be supported by a single, integrated, and analytical framework to determine earnings sensitivity 

and future market valuation across dynamically modeled balance sheets. Tanwar et al. (2021, 2020)  

used a goal programming model to optimize the asset and liabilities of the banks while compiling the 

various constraints. 

The 1991 banking system reorganization, together with various worldwide developments, 

exposes banks to liquidity risk, foreign exchange risk, credit risk, interest rate risk, and other risks, all 

of which have a direct impact on bank productivity and profitability. There is a need to evaluate 

interest rate exposure since loosening interest rate controls has exacerbated market volatility. Interest 

rate risk was quantified by Charumathi (2008) and Singh (2013) using re-pricing gap analysis and 

duration analysis. Seshadri et al. (1999) studied strategic asset-liability management by building a 

simulation model that can generate dividends, market value, and capital duration for arbitrarily 

generated interest rate scenarios. Asset-liability strategies can be developed, tested, and refined using 

this method.  

The ALM process has a direct impact on financial institutions' financial performance, thus 

having an effective ALM process that can closely monitor and manage both assets and liabilities is 

critical. The importance of the ALM manifold has grown because of the complexities of our economy 

and its growing scale. As a result, this topic is being studied (Vossen, 2010). Hence to effectively 

guarantee the highest growth, it is critical to investigate the interrelationship between the asset and 

liability sides of the balance sheet (Singh & Tandon, 2012). 

 

RESEARCH GAP  

Based on our exhaustive literature search, we have not found any study that conducts a primary survey 

of ALM. The breadth of the literature reviewed necessitates a better understanding of the extent to 

which banks are applying ALM strategies and policies. We also want to look into how bank personnel 

in various bank departments comprehend the ALM process. Previous research on ALM is based on 

secondary data. 

 

RESEARCH QUESTIONS 

 Do employees of banks in India understand ALM policies and implement those policies as 

prescribed by the RBI? 

 Is there a difference between public and private banks in understanding and implementing 

ALM strategies, function, and importance? 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

50  

RESEARCH OBJECTIVES 

The objectives related to the study are: 

 To study, understand, and analyze strategies employed by banks to manage their assets and 

liabilities.  

 To determine whether Public and Private Banks follow a similar approach towards ALM. 

 

RESEARCH METHODOLOGY 
RBI lays down ALM strategies, policies, and procedures in circular 'Asset Liability Management 

(ALM) System (1999)' and 'Guidelines on ALM System- Amendments (2007)'. These rules establish a 

standard of practice for banks to follow. The RBI, on the other hand, established a basic framework 

and procedure. Depending on their risk management techniques, banks have the freedom to improve 

and extend them. Hence, a questionnaire is issued to banks' risk departments, ALM cells, Treasury 

departments, and Balance Sheet management departments to learn about ALM techniques used by 

public and private banks. 

Yes/No questions, multiple-choice questions, checklist questions, and scaled questions are all 

included in the study's questionnaire. In multiple-choice questions, the respondent selects one response 

from a list of alternatives, but in checklist questions, the respondent has the option of selecting 

numerous options. Answers to scaled questions are rated on a scale, such as the Likert scale. 

Pretesting the questionnaire for Reliability and Validity was done using a pilot survey. 

Pretesting aids in ensuring that the questions are phrased correctly and in plain English. It specifies 

that the order of the questions is correct. Pretesting allows you to make sure the respondent 

understands the questions and that the directions for filling out the questionnaire are clear. During 

pretesting, unnecessary and repetitive questions can be removed, and extra required questions can be 

added to the final questionnaire. 

A statistical test called Cronbach's- [alpha] is applied to test internal Reliability. Most literature 

argues that the acceptable value of Cronbach's- alpha is 0.7. A value between 0.6-0.7 is an acceptable 

level of Reliability, and 0.8 or greater is considered a very good level (Hulin et al., 2001; Ursachi et 

al., 2015). Hinton et al. (2004) mentioned that alpha value from 0.5 to 0.7 shows moderate Reliability, 

whereas 0.5 and below is considered low. Cronbach's- alpha] value for Yes/No questions is 0.634, 

where inquiries related to the ALM, and risk management are covered. The ALM Significance and 

ALCO functions have scores of 0.790 and 0.701, respectively. The questionnaire is internally reliable, 

according to the literature.   

 

Sampling Techniques 

Judgmental non-probability sampling, also known as the purposive non-probability sampling 

technique, is used in the current study. Only relevant opinions/views are required for the study job; 

hence this sampling method involves the deliberate selection of candidates. Quota sampling is used in 

this study because samples are divided into private and public sector banking groups, and banks were 

chosen based on the ease of obtaining replies. Banks from both the public and private sectors are 

chosen as target banks.  

 

Data Collection and Sample 

The primary methods are used for the overall data collection process to examine the ALM and risk 

management strategies and policies followed by private and public banks. The data collecting tool for 

primary data gathering is a questionnaire-based survey method. Surveys of officials from the Risk 

Department, the Balance Sheet Management Department, and other bank divisions were used to gather 

primary data.  

Banks from both the public and private sectors are targeted. The questionnaire is delivered to 

the bank's headquarters, regional offices, and branches. The survey is given to 400 public and private 

sector bank workers who work in the risk department, balance sheet unit, treasury department, and 

other departments. The study takes into account all 264 responses. The questionnaire was distributed 

to 16 public banks before the merger in 2019-2020, and 12 public banks responded. 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

51  

The questionnaire was also sent to 17 private banks, and responses from 14 banks were 

received. Only 12 private banks were taken in the study as two banks declined to answer most of the 

questions in the questionnaire. A non-probability purposive sampling technique was employed to 

collect data of 264 employees for the study.  A sample of 264 was adequate considering one 

item/question to five responses ratio (Hair et al., 2014; Israel, 1992; Ruparel, 2020) for the 

questionnaires. 

The public sector banks included in the study are Allahabad Bank, Bank of India, Bank of 

Baroda, Canara Bank, Corporation Bank, Indian Bank, Indian Overseas Bank, Oriental Bank of 

Commerce, Punjab National bank, Punjab and Sind Bank, State Bank of India, and UCO Bank. The 

private sector banks included in this study are the Federal Bank, HDFC Bank, ICICI Bank, IndusInd 

Bank, Axis Bank, Yes Bank, Kotak Mahindra Bank, DCB Bank, IndusInd Bank, South Indian Bank, 

RBL Bank, and IDBI bank. The data collected is then analyzed with the help of IBM SPSS software. 

 

Data Analysis 

Data analysis holds vital importance in the overall interpretation of the collected data. Data analysis 

involves the use of several statistical tools that allows efficient computation of the data. The study is 

exploratory and descriptive as it attempts to study the strategies and procedures applied by banks for 

the asset-liability management practice. Herein data analysis would mainly be attempted with the help 

of a statistical tool like the Mann-Whitney U test, mean, rank, frequency, etc. 

When two independent sets of dependent variables are ordinal or continuous but not normally 

distributed, the Mann-Whitney U test is used to compare them. The Mann-Whitney U test is a 

nonparametric test. The test provides an opportunity to make conclusions about data based on 

assumptions about data distribution. It helps in determining whether there is a difference between the 

two compared groups or not. The shape of the distribution of data determines the results. 

 

The null and two-sided research hypotheses for the nonparametric test are stated as follows: 

 

H0: The two populations are equal versus 

H1: The two populations are not equal. 

 

Descriptive statistics describe the quantitative characteristics of the data set. It includes 

frequency analysis. The repetition of an event is referred to as frequency. Frequency analysis 

determines the number of occurrences of an item and calculates the central tendency, dispersion, 

percentile, etc. 

 

RESULTS AND ANALYSIS OF THE STUDY 

Respondents’ Profile 

The respondents are both male and female bank employees. Male respondents are 66.7%, and female 

respondents are 33.3% of the total sample size, i.e., 264 (as shown in figure 1). Most of the 

respondents are aged 31-40 years (52.7%), followed by 41-50 years (30.7%), 21-30 years and 51-60 

years as 8.3% each (figure 2). Most respondents have a master’s degree (66.7%) as the highest 

education. Few respondents also have professional qualifications (33.33%), i.e., Chartered 

Accountants, CAIIBs, etc. The respondents are well qualified and experienced in their field of 

banking. At present, given the educational level, employees are even eligible for a higher position at 

the age of 31-40 years. 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

52  

 
Figure 1. Gender of the Respondents 

 

 
 

Figure 2. Age of Respondents 

 

The data for the study was gathered from a number of top bank executives. Assistant General 

Managers, Assistant Vice Presidents, and Managers make up the majority of the respondents. 

Employees in roles such as Chief Manager, Branch Manager, Senior Trader, and Regional Manager 

are also eligible to participate in the survey. The respondents were chosen from a variety of 

characteristics to have a better understanding of the policy process from start to finish. The majority of 

respondents have worked at their present bank for at least five years. Respondents' terms range from 

one year to fifteen years. Twelve public sector banks and twelve private sector banks were chosen as 

respondents.  

 

Structure and Resources 

A Separate Risk Management Function 

As per RBI guidelines, banks must have separate risk management functions supervised by the board 

of directors. They meet with other executive-level committees to ensure that the bank has accepted the 

risk management principle and regulations. The Board of Directors of each of the banks chosen for the 

survey (i.e., 100 percent) has its own risk management function. 

 

Availability of Chief Risk Officer 

According to the RBI circular, all scheduled commercial banks (except Local Area Bank and Regional 

Rural Bank) are required to frame a board-approved policy that should state the roles and 

responsibilities of the Chief Risk Officer (CRO). CROs’ have professional qualifications or experience 

in risk management. 100% of the respondents said their bank has a chief risk officer. 

 

Independent ALM Function 

As a mandatory requirement from RBI, all Scheduled Commercial banks are advised to have 

independent ALM function. ALM function of bank keep check on whether ALCO adhere to the limits 

set by the Board, design the business strategy of the bank (on the assets and liabilities sides) after 

incorporating the bank's budget and risk management objectives. 100% of the respondents said their 

bank had a separate asset-liability management function.  

 

 

67%

33%
Male

Female

0

20

40

60

21-30 31-40 41-50 51-60



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

53  

Reporting of Independent Asset-Liability Management 

Table 1. Frequency table for reporting of independent asset-liability management 

 

Response Frequency Percent Cumulative Percent 

Balance sheet management unit 11 4.17 4.17 

Risk department or ALM cell in Risk 

department 

187 70.83 75 

Treasury and Risk department 11 4.16 79.16 

Treasury department, Treasury operations 55 20.84 100 

Total 264 100  

 

From table 1, it is evident that all the respondents are engaged in ALM-related work. However, 

as the question is open-ended and responses are captured from their replies, many of the respondents 

might be handling multiple tasks, and all of them are aware of the ALM process. 70.83% of the 

respondents said their bank’s independent asset-liability management reporting area is the Risk 

Management Department or ALM cell in Risk Management Department. Then 20.84% of the 

respondents said it is the treasury department or treasury operations, where they work on ALM-related 

issues. The rest of the respondents said it was the Balance Sheet Management Unit, Treasury and Risk 

department that looked after the independent asset-liability management. 

 

Formal Committee for Asset-Liability Management 

As per RBI guidelines, all commercial banks must form an Asset Liability Management Committee 

comprising senior management. The ALCO in banks is responsible for balance sheet planning from 

the risk-return perspective that undertakes interest rate risk management and liquidity risk 

management. The Board of Directors decides the role and responsibilities of ALCO. ALCO is 

responsible for the decisions taken by it. ALCO ensures that it incorporates the risk management 

practices and parameters set by the board while making decisions. ALCO works on product pricing of 

deposits and advances, and maturity profile of assets and liabilities, etc. ALCO also reviews the work 

of the ALM desk related to the progress and results of the implementation of decisions made in the 

previous meetings. The committee is headed by either the Managing Director or Chief Executive 

officer. Also, the Chiefs of other departments such as Investment, Credit, Funds, International 

Banking, Economic Research, and Information Technology Department are committee members. 

Depending upon the complexity of the bank hierarchy, some banks also have sub-committees to assist 

ALCO.  

All the respondents (i.e., 100 percent of respondents) said they have a formal committee for 

asset-liability management. 

 

Statement of Principles and Objectives Concerning Asset-Liability Management 

A member of the Risk Management Committee, which is made up of members of the bank's Board of 

Directors, sets the objectives and principles. Other executive committees are informed of these 

objectives, principles, boundaries, and targets. ALCO is also one such committee that follows the 

goals and targets set by the board of directors and incorporates them in planning. The ALCO 

recommends the action needed to stay within the bank's internal boundaries based on the risk profiles' 

analysis, monitoring, and reporting by the ALM cell.  

100% of the respondents said their banks had statements of principles and objectives 

concerning asset-liability management. 

 

Frequency of Asset-Liability Management Committee Meet 

When asked about the frequency of the asset-liability management committee meetings, 50% of the 

respondents said ALM committee members meet every quarter for an asset-liability management 

meeting, whereas 33.3% of the banks have monthly meetings. Further, only 16.7% of respondents 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

54  

ticked others which shows that these banks comply with the minimum requirements of 4 meetings in a 

year (see figure 3). However, such banks also conduct meetings as and when required to discuss 

important matters.  

 

 
Figure 3. ALCO meeting 

 

It should also be emphasized that all banks meet regularly to discuss the monitoring, progress, 

and execution of ALCO decisions. 

Indicate the measure that the ALM financial objectives are based on 

Table 2. Measurement of ALM Financial Objectives 

 

 Responses Percent Of Cases 

Frequency Percent 

Financial 

objectives are 

based on  

Economic 

Value 

209 51.4% 79.2% 

Accounting 

Earnings 

187 45.9% 70.8% 

Other 11 2.7% 4.2% 

Total 407 100.0% 154.2% 

 

The profit produced by the bank is subtracted from the cost of financing the company's capital. 

It is a widely used management technique for assessing a bank's performance. Economic value is built 

on ideas including maximizing the return on existing assets, investing in a portfolio with a return 

greater than the cost of capital, and freeing up cash flows. Economic value is defined in banks as free 

cash flow and its present value. Deducting the mark-to-market value of liabilities from the assets 

yields Economic Value. A risk premium is included in the funding cost. 

Respondents were given the option of selecting multiple options. According to the survey, 

table 2 shows that 79.2 percent of respondents indicated their bank used economic value to measure 

financial asset-liability management objectives.  

Accounting profit is net income after all explicit expenditures have been deducted. Accounting 

earnings were used as a metric of financial objectives in asset-liability management by 70.8 percent of 

respondents. Risk limit, ratio, and regulatory criteria are also taken into account when measuring ALM 

financial objectives, according to 4.2 percent of respondents.  

 

 

 

 

50%

33%

17%

ALCO MEETINGS

Quarterly Monthly Other*



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

55  

Level at which ALM is performed  

Table 3. Frequency table for level at which ALM is performed 

 

 Responses Percent of 

Cases 
Frequency Percent 

Indicate 

level at 

which ALM 

is 

performed 

For each financial product/asset 

segment separately 

187 60.7% 70.8% 

At the divisional level 33 10.7% 12.5% 

at the total organization company 

/business unit level 

88 28.6% 33.3% 

Total 308 100.0% 116.6% 

 

We need to understand the organizational structure of private and public sector banks before 

we can analyze the responses in Table 3. A vertical system governs the operation of private sector 

banks. Each vertical system has a product, or a function associated with it. Capital market or wholesale 

banking operations, for example, are examples of verticals. Public sector banks, on the other hand, 

follow a pyramid form (see Figure 4).  

 
Figure 4. Structure of Public Sector Banks 

 

ALCO is the apex committee, and product-wise (whether asset or liability) targets at the HO 

level are selected based on ALCO, Treasury, and finances department recommendations, and then 

disaggregated using a top-down or bottom-up strategy. Within limits, targets are disaggregated down 

to the branch office level. When the HO establishes boundaries, criteria, and aims, the other units are 

ordered to meet those goals. Each controlling level takes care of deviations. The branch office/regional 

office/Zonal Office, on the other hand, plays a minor role in determining ALM. 

Table 3 shows a similar result, with 70.8 percent of respondents stating that ALM is carried out 

independently for each product/asset. Only 10.7% of respondents indicate that ALM is performed at 

the divisional level, while 28.6% claim that it is performed at the organizational/business unit level. 

 

Significance of asset-liability management in the banking system 

Statistics acquired regarding the significance of asset-liability management in the banking system 

reveals, 75% of the respondents felt that all the reasons given in the questionnaire are important 

reasons for having ALM in banking and RBI rigorously monitor it. These reasons are: - 

  To minimize the volatility in interest income and economic value. 

  The reason for the growing importance of ALM is the rapid innovation taking place in the 

financial products of the bank. 

H
O

Zonal 
Offic

e

Regional 
Office

Branches



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

56  

 It provides a framework for banks to tackle the market risks that may arise due to rate 

fluctuations and excessive credit risk. 

 Recognizes the vision of the management.  

In contrast, 16.7% of respondents believed that the most important purpose for asset-liability 

management in the banking system is to reduce interest revenue and economic value volatility. 

Additionally, 8.3% of respondents stated that it provides a framework for banks to address market 

concerns such as rate fluctuations and excessive credit risk (see figure 5). 

 

 
Figure 5. Significance of ALM in banking 

 

Department Responsible for the Asset-Liability Management 

All other bank departments, such as the credit department, loan department, finances department, and 

so on, are linked to ALM. ALCO is a high-level executive body that reports to the Risk Management 

department. 66.7 percent of respondents indicated their bank's risk department is in charge of asset-

liability management. Furthermore, 33.3 percent of respondents claimed that ALM in their institutions 

is handled by the treasury department, which deals with market risk (see figure 6). 

 

Figure 6. Department Responsible for the Asset liability Management 

 

Key Driver for Change in Strategic Asset Allocation  

Asset allocation depends on goal factors, risk tolerance, age-based asset allocation, etc. According to 

the reasons described above, the most important motivator for change in strategic asset allocation is 

matching liabilities with assets, according to 66.7 percent of respondents. While 29.2 percent of those 

surveyed stated that the shift in strategic asset allocation was made to reduce risk. Furthermore, 4.2 

percent of respondents reported that a move in asset allocation was caused by an increase in return. 

 

0 10 20 30 40 50 60 70 80

To minimize the volatility in Interest

Income and Economic value

It provides a framework for banks to

tackle the risks.

All of the above

Treasu
ry
33%Risk

67%



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

57  

Other Drivers to Be Considered While Changing Asset Allocation 
Table 4. Frequency table showing other drivers for change in asset allocation 

 

Drivers For Change in Asset Allocation Responses Percent of 

Cases 
Frequency Percent 

Lowering risk 176 28.1% 66.7% 

Increasing return 220 35.1% 83.3% 

Matching liabilities 99 15.7% 37.5% 

Changes in perception of the market 33 5.3% 12.5% 

Changes caused by variations in market values 44 7.0% 16.7% 

Availability of new asset classes 55 8.8% 20.8% 

Total 627 100.0% 237.5% 

 

Respondents were given the option of selecting multiple options if they so desired. Table 4 

shows that 83.3% of the respondents considered increasing returns as other drivers for changing asset 

allocation. 66.7% of the respondents said it was for lowering the rate of risk. Furthermore, 37.5 

percent of respondents stated that it was for the purpose of matching the liabilities. Following that, 

20.8 percent of respondents stated that a change in asset allocation is prompted to access new asset 

classes. On the other hand, 16.7% of respondents stated it was because of changes in market 

valuations, while 12.5 percent said it was because of changes in market perception. 

 

The basic motive for change in asset allocation strategy in the bank 
91.7% of the respondents believed that the motive for strategic asset allocation was an asset-liability 

matching study. Even though 45.8% of respondents disagreed, the decision was made based on the 

board's knowledge and research. However, 12.5% and 8.3% of respondents said the shift in asset 

allocation strategy was attributable to corporate influence or actuarial value for employee benefits, 

respectively. 

 
Figure 7. The basic motive for change in asset allocation strategy in the bank 

 

Asset-Liability Management Functions 

The bank must comply with all the statements made in the questionnaire about ALCO functions. 

ALCO's functions are decided by the board of directors. All functions are determined in accordance 

with RBI directives. Even though all of ALCO's functions are essential and useful for balance sheet 

planning, respondents were asked about their opinions on the importance of ALCO services (see table 

5).  

 

 

0% 10% 20% 30% 40% 50% 60% 70%

Actuarial valuation to be done for

employee benefits

Asset liability matching study

It was the decision based on the board’s 

knowledge and research

The change in asset allocation strategy

was due to corporate influence



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

58  

Table 5. Significance of Asset Liability Management 

 

Statements Related to the Asset Liability Management                      

Responses 

Count Percentage 

% 

1. Asset liability management comprises of 

managing effectively both the assets and 

liabilities sides of the bank balance sheet 

Neutral 46 17.4% 

Agree 127 48.1% 

Strongly Agree 91 34.5% 

2. Asset liability management comprises of 

managing liquidity risk and market risks in an 

effective manner 

Neutral 57 21.6% 

Agree 112 42.4% 

Strongly Agree 95 36.0% 

3. Asset liability management consists of managing 

maturity gaps and mismatches 

Neutral 57 21.6% 

Agree 113 42.8% 

Strongly Agree 94 35.6% 

4. Asset liability management involves managing 

structural, static, and dynamic gap 

Neutral 46 17.4% 

Agree 115 43.6% 

Strongly Agree 103 39.0% 

5. Management of overall liquidity of the bank Neutral 29 11.0% 

Agree 87 33.0% 

Strongly Agree 148 56.1% 

6. Facilitates, coordinates, communicates and 

control balance sheet risk planning 

Neutral 44 16.7% 

Agree 109 41.3% 

Strongly Agree 111 42.0% 

7. Ensures bank’s risk lies within parameters set by 

the Board 

Neutral 50 18.9% 

Agree 118 44.7% 

Strongly Agree 96 36.4% 

8. Undertakes regular maturity analysis of assets 

and liabilities to identify liquidity gaps 

Neutral 42 15.9% 

Agree 101 38.3% 

Strongly Agree 121 45.8% 

 

Respondents' opinions about their practice, expertise, and experience are based on their 

responses. 34.5 percent of the total respondents strongly agreed that asset-liability management entails 

properly managing both the assets and liabilities sides of a bank's balance sheet, while 48.1 percent 

simply agreed (Table 5). It was already mentioned in the literature study before. 

When asked whether asset-liability management comprises managing liquidity risk and market 

risks effectively, 36% of the respondents strongly agreed to it, whereas 42.4% only agreed to it. 35.6% 

of the respondents strongly agreed that asset-liability management consists of managing maturity gaps 

and mismatches, whereas 42.8% agreed (Table 5) ALM is used to manage the risks that are faced by 

the banks in the form of a mismatch of the asset and liabilities.  

In addition, 39% of respondents strongly agreed that asset-liability management entails 

addressing structural, static, and dynamic gaps. The remaining 43.6 percent and 17.4 percent either 

agreed or were unsure. When asked if ALM oversees the bank's overall liquidity, 56.1 percent said 

yes, while 33 percent said no. Asset-liability management facilitates, coordinates, communicates, and 

manages balance sheet planning, according to 42 percent of respondents, while 41.3 percent agreed. 

16.7% of respondents were undecided, meaning they didn't agree or disagree (Table 5). 

According to 36.4 percent of respondents, asset-liability management guarantees that the 

bank's risk is within the board's guidelines, and 44.7 percent of respondents agree to it. The remaining 

18.9% of respondents, on the other hand, are split between agreeing and disagreeing. Furthermore, 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

59  

45.8% of respondents strongly agreed that ALM conducts regular asset and liability maturity analyses 

to identify liquidity gaps, whilst 38.3% just agreed (Table 5). 

To obtain ranks for the significance of ALM that primarily define ALM, the sum and mean are 

determined. ALM is defined as the management of the bank's overall liquidity, according to the 

significance that is rated first. ALM also conducts frequent maturity analyses of assets and liabilities to 

identify liquidity gaps, according to respondents. 

 

Table 6. Mann Whitney Test Results- Significance of ALM 

 

TEST STATISTICSa 

 1 2 3 4 5 6 7 8 

Mann-

Whitney 

U 

7939.5 8533.5 8688.0 8577.0 8216.0 8504.0 8200.0 7117.5 

Wilcoxon 

W 

16717.5 17311.5 17466 17355 16994 17282 16978 15895.

5 

Z -1.357 -.309 -.042 -.236 -.902 -.364 -.892 -2.798 

Asymp. 

Sig. (2-

tailed) 

.175 .757 .967 .814 .367 .716 .372 .005 

a.Grouping Variable: bank 

 

H0: There is no difference between public banks and private banks regarding the understanding and 

implementation of ALM. 

H1: There is a difference between public banks and private banks regarding the understanding and 

implementation of ALM. 

 

In Table 6, the p-value is less than 0.05, showing that public and private banks have a similar 

understanding and implementation of ALM and use the same ALM approach. The p-value for the last 

statement on ALM, however, is less than 0.05. Hence, it can be concluded that Public Banks and 

Private Banks hold contrary opinions on whether ALM should conduct regular maturity analyses of 

assets and liabilities to identify liquidity gaps. According to a private sector bank, asset-liability 

management is functional beyond maturity analysis. It is not limited to maturity analysis only. 

 

FINDINGS OF THE STUDY 

The findings of the research on ALM practice in banks in India are: 

 Both public and private banks adhere to all of the RBI's mandatory standards. All of the study's 

banks have well-documented ALM policies, separate risk management functions, autonomous 

ALM functions, a separate ALM goal, and a formal ALM committee to monitor, manage, and 

control risk. To manage risk, the banks have hired a chief risk officer. 

 With ALM-related issues, bank personnel report to the ALM Cell, Risk Management 

Department, Treasury Department, Market Risk Department, or Balance Sheet Management 

Unit. All banks' ALM committees meet at least once a quarter to address ALM policy, 

investment policy, derivatives policy, and other topics. The market risk management group, on 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

60  

the other hand, holds regular monthly meetings to discuss any changes to policies, methods, or 

techniques. 

 ALM's financial objectives are measured by economic value, as they are for most banks. 

According to BIS II, banks must have interest rate measuring systems in place to analyze the 

effects of rate changes on economic values and earnings. 

 Most banks do ALM individually for each product/asset division. The importance of ALM is to 

reduce interest income and economic value volatility. ALM is required due to rapid innovation 

in the bank's financial offerings. ALM sets the groundwork for banks to handle market risks 

such as interest rate volatility and excessive credit risk. It also acknowledges the management's 

goals. 

 To meet liabilities and increase profits, most banks change the strategic allocation of an asset. 

 The primary motivation for the bank's asset allocation strategy to alter is the asset-liability 

matching concept. 

 In most banks, interest rate, liquidity, market, and foreign exchange risks are all considered 

part of ALM.  

There is no significant difference between employees of private and public banks concerning 

the fundamental understanding of ALM and its functions. 

 

CONCLUSION 

When a primary survey was conducted, it was found that both public and private banks adhere to the 

RBI's rules. Regardless of their type or nature, every bank strives to optimize return while also 

focusing on their risk appetite. In terms of ALM, both private and public banks have formed a formal 

ALCO committee to examine asset and liability mismatches, rate-sensitive assets and liabilities, and 

other risks that could affect the bank's performance. The ALCO is regarded as the decision-making 

unit responsible for balance sheet planning from the risk-return perspectives. It also includes 

articulating and decision-making related to future business strategies based on current interest rates 

movement. The decisions regarding the funding policy are based on interest rate movement.  

The size of ALCO depends on each financial institution's size, organizational complexity, and 

business mix. All the executives that belong to the top levels of management are included in the 

committee. It has a CEO, Chiefs of Investment Fund Managers, Treasury (forex and domestic) head, 

Officials of International Banking and Economic Research, and managers from the Funds 

Management section that form an integral part of ALCO. It is advisable to include senior officials of 

the Information Technology Division in the composition of ALCO so that guidance provided by them 

will support in developing adequate information systems and computerization. Support groups and 

sub-committees are also formed depending on the organizational size and need.  

All banks have to decide the frequency for holding their ALCO meetings. Management of the 

bank holds formal and timely meetings to discuss any ALM issues. Banks hold at least four formal 

meetings every year, with additional meetings held as needed. Banks were discovered to have a proper 

communication channel, standard procedure, and well documented ALM objectives. Liquidity risk, 

market risk, foreign exchange risk, credit risk, interest rate risk, legal risk, and so on are all managed 

by formal policies and departments of banks. Interest rate risk and liquidity risk, on the other hand, are 

two types of risk that are part of ALM in any bank under investigation. It demonstrates that banks' 

ALM cells are primarily concerned about the interest rate and liquidity risk. The mitigation of all 

stated risks are crucial for banks and undertaken by ALM committee while framing policies yet most 

risks are handled through specific department.  



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

61  

AUTHOR CONTRIBUTIONS 

Conceptualization: Jyoti Tanwar, Arun Kumar Vaish 

Data Curation: Jyoti Tanwar 

Formal Analysis: Jyoti Tanwar 

Funding Acquisition: Jyoti Tanwar 

Investigation: Jyoti Tanwar 

Methodology: Jyoti Tanwar 

Project Administration: Jyoti Tanwar 

Resources: Jyoti Tanwar, Arun Kumar Vaish 

Software: Jyoti Tanwar  

Supervision: A K Vaish, N V Murlidhar Rao 

Validation: Jyoti Tanwar, Arun Kumar Vaish, N V Murlidhar Rao 

Visualization: Jyoti Tanwar 

Writing – Original Draft: Jyoti Tanwar 

Writing – Review & Editing: Jyoti Tanwar, Arun Kumar Vaish, N V Murlidhar Rao 

 

CONFLICT OF INTEREST STATEMENT 

The authors declare that they have no competing interests.  

 

ACKNOWLEDGEMENT 

All authors contributed equally to the conception and design of the study. 

 

REFERENCES 
Antony, K. P. P., & Manimegalai, J. A. (2018). A Study on Asset Liability Management in Indian 

Bank. International Journal of Business Administration and Management, 8(1), 1–9.  

 

Abou-el-sood, H., & El-ansary, O. (2017). Asset-liability management in Islamic banks : evidence 

from emerging markets. Pacific Accounting Review, 29(1), 55–78. 

https://doi.org/10.1108/PAR-04-2016-0050 

 

Adebisi, A. J., Alabi, A. W., & Fatimehin, K. (2020). Influence Of Risk Assets Impairment On 

Performance Of Nigerian Listed Deposit Money Banks. International Journal of Accounting 

& Finance Review, 5(3), 41–63. 

 

Black, R., Brown, K., & Moloney, J. (2003). Asset and liability management : what does the future 

have in store ? Balance Sheet, 11(2), 32–38. https://doi.org/10.1108/09657960310480651 

 

Chakraborty, S., & Mohapatra, S. (2009). An Empirical Study of Asset Liability Management 

Approach by the Indian Banks. The IUP Journal of Bank ManagementJournal of Bank 

Management, 8(3 & 4), 7–13. 

 

Charumathi, B. (2008). Asset Liability Management in Indian Banking Industry - with special 

reference to Interest Rate Risk Management in ICICI Bank. Proceeding of the World 

Congress on Engineering, II(September). 

 

Chatterjee, C., & Dutta, P. (2016). Exploring the Linkage between Profits and Asset–Liability 

Management: Evidence from Indian Commercial Banks. Paradigm, 20(2), 131–142. 

https://doi.org/10.1177/0971890716670707 

 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

62  

Chattha, J. A., Alhabshi, S. M., & Meera, A. K. M. (2020). Risk management with a duration gap 

approach. Journal of Islamic Accounting and Business Research, 11(6), 1257–1300. 

https://doi.org/10.1108/JIABR-10-2017-0152 

 

Chaturvedi, V. (2014). Asset Liability Management in Banks. International Journal of Engineering 

Technology, Management and Applied Sciences, 1(2), 2349–4476. Retrieved from 

http://www.ijetmas.com/admin/resources/project/paper/f201406281403951723.pdf 

 

Das, A., & Ghosh, S. (2001). The Relationship Between Risk and Capital: Evidence from Indian 

Public Sector Banks. Reserve Bank of India Occasional Papers, 22(1, 2 and 3), 1–20. 

https://doi.org/10.1111/j.1813-6982.1961.tb02383.x 

 

Dash, M., Venkatesh, K. A., & Bhargav, B. (2011). An Analysis of Asset-Liability Management in 

Indian Banks. SSRN Electronic Journal, 1–13. https://doi.org/10.2139/ssrn.1760786 

 

Fabozzi, F. J., & Konishi, A. (1991). Asset/liability Management: Investment Strategies, Liquidity 

Requirements, and Risk Controls for Banks and Thrifts. Probus Publishing Company. 

 

Fiedler, R., Brown, K., & Moloney, J. (2002). The dynamically projected balance sheet : implications 

for earnings , value and funds transfer pricing. Balance Sheet, 10(2), 17–21. 

 

Goodman, L. S., & Langer, M. J. (1983). Accounting for Interest Rate Futures in Bank Asset-Liability 

Management. The Journal of Futures Markets, 3(4), 415–427. 

https://doi.org/10.1002/fut.3990030407 

 

Haddad, A., Ammari, A. El, & Bouri, A. (2019). Impact of the Ownership Structure on the Financial 

Performance of Banks: Comparative Study between Conventional and Islamic Banks. 

International Journal of Accounting & Finance Review, 4(2), 50–63. 

https://doi.org/10.46281/ijafr.v4i2.442 

 

Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural 

equation modeling (PLS-SEM): An emerging tool in business research. European Business 

Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128 

 

Hinton, P. R., Brownlow, C., McMurray, I., & Cozens, B. (2004). SPSS Explained (first). Taylor & 

Francis. 

 

Hulin, C., Netermeyer, R., & Cudeck, R. (2001). Can a Reliability Coefficient Be Too High? Journal 

of Consumer Psychology, 10(1), 55–58. 

 

Israel, G. D. (1992). Determination of sample size. In Program Evaluation and Organizational 

Development (Vol. 6). 

 

Jain, M. K., Dalela, A. K., & Tiwari, S. K. (2010). Application of Fuzzy Mathematical Model in 

Assets-Liabilities. International Journal of Trade, Economics and Finance, 1(3), 247–253. 

https://doi.org/10.7763/ijtef.2010.v1.45 

 

Jayanthi, M., & Umarani, R. (2014). Asset-Liability Management in Banks. CKPIM Business Review, 

9(2), 10–25. 

 

Joshi, S. P., & Sontakay, R. V. (2017). Review Paper on Asset Liability Management in Banking 

System. Imperial Journal of Interdisciplinary Research, 3(6), 670–678. 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

63  

Kosmidou, K., Pasiouras, F., & Floropoulos, J. (2004). Linking profits to asset-liability management 

of domestic and foreign banks in the UK. Applied Financial Economics, 14(18), 1319–1324. 

https://doi.org/10.1080/0960310042000293146 

 

Meena, A. K., & Dhar, J. (2014). An Empirical Analysis and Comparative Study of Liquidity Ratios 

and Asset-Liability Management of Banks Operating in India. International Journal of Social, 

Human Science and Engineering, 8(1), 4968–4974.  

 

Nugroho, M., Halik, A., & Arif, D. (2020). Effect of CAMELS Ratio on Indonesia Banking Share 

Prices. Journal of Asian Finance, Economics and Business, 7(11), 101–106. 

https://doi.org/10.13106/jafeb.2020.vol7.no11.101 

 

Riyazahmed, K., & Baranwal, G. (2021). Determinants of Credit Risk. International Journal of 

Accounting & Finance Review, 6(1), 53–71. https://doi.org/10.46281/ijafr.v6i1.1005 

 

Romanyuk, Y. (2010). Asset-Liability Management : An Overview. In Bank of Canada Discussion 

Paper. 

 

Ruparel, N. (2020). ( Re ) Thinking Positive Workplace /s : A Multisectoral Cross-Sectional 

Investigation. Birla Institute of Technology & Sciences- Pilani. 

 

Seshadri, S., Khanna, A., Harche, F., & Wyle, R. (1999). Method for strategic asset-liability 

management with an application to the Federal Home Loan Bank of New York. Operations 

Research, 47(3), 345–360. https://doi.org/10.1287/opre.47.3.345 

 

Singh, A., & Tandon, P. (2012). Asset - Liabilty Management in Indian Banking Industry. Asia 

Pacific Journalof Marketing & Management Review, 1(3), 121–132. 

 

Singh, K. (2013). Asset-Liability Management in Banks: A Dynamic Approach. AIMA Journal of 

Management & Research, 7(2), 1–14. 

 

Tanwar, J., Seth, H., Vaish, A. K., & Rao, N. V. M. (2020). Revisiting the Efficiency of Indian 

Banking Sector: An Analysis of Comparative Models Through Data Envelopment Analysis. 

Indian Journal of Finance and Banking, 4(1), 92–108. https://doi.org/10.46281/ijfb.v4i1.585 

 

Tanwar, J., Vaish, A. K., & Rao, N. (2021). Optimizing Balance Sheet for Banks in India Using Goal 

Programming. International Journal of Accounting & Finance Review, 6(2), 81–101. 

https://doi.org/10.46281/ijafr.v6i2.1082 

 

Tanwar, J., Vaish, A. K., & Rao, N. V. M. (2020). Mathematical Modeling of Asset Liability 

Management in Banks Using Goal Programming and Ahp. Indian Journal of Finance and 

Banking, 4(4), 1–19. https://doi.org/10.46281/ijfb.v4i4.899 

 

Umarani, R., & Jayanthi, M. (2015). An Analysis of Asset-Liability Management in Indian Banks. 

International Journal of Business and Administration Research Review, 1(11), 179–183. 

https://doi.org/10.2139/ssrn.1760786 

 

Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How Reliable are Measurement Scales? External 

Factors with Indirect Influence on Reliability Estimators. Procedia Economics and Finance, 

20(15), 679–686. https://doi.org/10.1016/s2212-5671(15)00123-9 

 

Vij, M. (2001). Asset Liability Management in Banks and Financial Institutions: A case study of 



https://www.cribfb.com/journal/index.php/ijfb                       Indian Journal of Finance and Banking                            Vol. 9, No. 1; 2022 
 

64  

IDBI. Journal of Management Research, 1(1), 111–120. Retrieved from 

http://www.businessworld.in/article/Asset-Liability-Management-In-Financial-Institutions/04-

11-2018-163493/ 

 

Vossen, B. van der. (2010). Bank Liquidity Management. Busienss/Business Administration. 39. 

https://doi.org/10.1007/978-0-230-37048-7_8 

 

 

Copyrights 

Copyright for this article is retained by the author(s), with first publication rights granted to the 

journal. This is an open-access article distributed under the terms and conditions of the Creative 

Commons Attribution license (https://creativecommons.org/licenses/by/4.0). 

 

http://creativecommons.org/licenses/by/4.0/)

