COMPARATIVE ANALYSIS OF EMPLOYEES’ PERSPECTIVE ON ASSET-LIABILITY MANAGEMENT IN BANKS

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.


INTRODUCTION
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 Elansary (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 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 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.
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. 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.  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 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. 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.

Indicate the measure that the ALM financial objectives are based on
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.

Quarterly
Monthly Other* https://www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 9, No. 1; Level at which ALM is performed 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.  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).

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 assetliability 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).

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. 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.

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  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 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. 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.