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Predictive analytics in harnessing financial efficacy of banks using camel model
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In the present study, we have taken 17 District Central Cooperative Banks (DCCBs) of Odisha and attempted to measure their efficacy of finance flow.

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  1. International Journal of Management (IJM)
    Volume 10, Issue 6, November-December 2019, pp. 177–190, Article ID: IJM_10_06_018
    Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=10&IType=6
    Journal Impact Factor (2019): 9.6780 (Calculated by GISI) www.jifactor.com
    ISSN Print: 0976-6502 and ISSN Online: 0976-6510
    © IAEME Publication

    PREDICTIVE ANALYTICS IN HARNESSING
    FINANCIAL EFFICACY OF BANKS USING
    CAMEL MODEL
    Chinmaya Kumar Rout
    Research Scholar, Faculty of Management Sciences,
    Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India

    Dr. Prafulla Kumar Swain
    Professor, Faculty of Management Sciences,
    Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India

    Dr. Manoranjan Dash*
    Associate Professor, Faculty of Management Sciences,
    Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India
    *Corresponding Author Email: manoranjandash@soa.ac.in

    ABSTRACT
    In India, Cooperative Banking has an idiosyncratic position in the rural credit
    delivery system. Cooperative Banks are providing timely and easy credit to rural
    people. The financial efficacy of Cooperative Banks is of immense importance for
    smooth credit disbursement. In the present study, we have taken 17 District Central
    Cooperative Banks (DCCBs) of Odisha and attempted to measure their efficacy of
    finance flow. For this purpose we have used the CAMEL model which is based on five
    parameters like Capital Adequacy, Asset Quality, Management Quality, Earning Ability
    and Liquidity. Under each parameter two ratios, are calculated for 10 years and
    DCCBs are ranked according to their score. Synthesized Index Table is developed by
    taking the average ranks of each parameter and DCCBs are ranked accordingly.
    Keywords: Cooperative Banking, Financial Efficacy, District Central Cooperative
    Banks, CAMEL Model.
    Cite this Article: Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and
    Dr. Manoranjan Dash, Predictive Analytics in Harnessing Financial Efficacy of Banks
    Using CAMEL Model, International Journal of Management (IJM), 10 (6), 2019, pp.
    177–190.
    http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=10&IType=6

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  2. Predictive Analytics in Harnessing Financial Efficacy of Banks Using CAMEL Model

    1. INTRODUCTION
    Indian economy is an agrarian economy. Most of the people are staying in a rural area and
    dependent on agriculture. Agricultural credit plays a pivotal role in the development of
    agriculture. Cooperative banks in India are providing agricultural credit to rural farmers since
    1912. The Short Term Cooperative Credit Structure (STCCS) in Odisha comprises of 2709
    Primary Agriculture Cooperative Societies (PACS) at the village level, 17 District Central
    Cooperative Banks (DCCBs) at the middle district level and Odisha State Cooperative Bank
    ( StCB) at the state level. DCCBs are connecting link between State Cooperative Banks and
    Primary Agriculture Cooperative Societies.
    DCCBs are taking funds from State Cooperative Bank, NABARD and State Government
    and disburse the funds to agriculture and other allied activities through PACS. As DCCBs are
    present at a middle level in Short Term Cooperative Credit Structure (STCCS), the efficacy of
    finance flows should be studied. To analyze the efficacy of finance flow through DCCBs, we
    have used the CAMEL model. CAMEL model is a supervisory rating system originally
    developed in the U.S. to classify a bank’s overall condition. CAMEL model derives from the
    five main components of a bank i.e. like Capital Adequacy, Asset Quality, Management
    Quality, Earning Ability and Liquidity. Under each parameter two ratios, are calculated for 10
    years and DCCBs are ranked according to their score. Synthesized Index Table is developed by
    taking the average ranks of each parameter and DCCBs are ranked accordingly.

    2. REVIEW OF LITERATURE
    As our study is related to the efficacy of finance flow through banks, So we have selected some
    literature relating to efficiency measurement and performance evaluation of banking sectors.
    DeYoung, R. (1998) studied the Management Quality and X-Inefficiency of National Banks
    with help of CAMEL ratings and the result of their study indicated that the well-managed banks
    had significantly lower estimated unit costs in comparison poorly managed banks,
    Shanmugam.R.K and Das. A (2004) studied the efficiency of Indian commercial bank by using
    stochastic frontier function and found that the state bank group and foreign banks are more
    efficient than their counterparts, Derviz. A and Podpiera.J (2008) conducted an analysis to
    identify the determinants of commercial bank ratings in the Czech Republic by using long-term
    Standard & Poor ’s rating and CAMELS model and the result indicated that S&P rating
    demonstrates a predictive accuracy of 70%. and CAMELS model explains 84% of the
    variability in the actual data, Ikhide.S.I (2008) studied the operational efficiency of commercial
    banks in Namibia by using Thick frontier approach (TFA) and the distribution-free
    approach (DFA) and the result indicated that to reduce operating cost and increase
    efficiency, the bank should increase its size.Kaur.H.V (2010) make an analysis of banks in
    India by using the CAMELS model. The study reveals that among public sector banks, the best
    bank ranking has been shared by Andhra Bank and State Bank of Patiala. Among the private
    sector banks, Jammu And Kashmir Bank secured the first rank followed by HDFC bank and
    among the foreign sector banks, Antwerp Bank has been ranked the best followed by JP Morgan
    Chase Bank, Banker, R. D., Chang, H., & Lee, S. Y. (2010) examined the Differential impact
    of Korean banking system reforms on bank productivity by using Data Envelopment Analysis
    (DEA). The findings of the study state that the capital adequacy ratio is positively associated
    with banks’ technical efficiency and non-performing loans ratio is negatively associated with
    technical efficiency, Dincer, H., Gencer, G., Orhan, N., & Sahinbas, K. (2011) evaluate the
    performance of the Turkish Banking Sector by using CAMELS Ratios and found that Turkey
    banking sector performance is satisfactory, Gardener, E., Molyneux, P., & Nguyen-Linh, H.
    (2011) studied the determinants of the efficiency of banks in South East Asian Countries
    using Data Envelopment Analysis and Tobit Regression. The results indicate that efficiency

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  3. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    has significantly declined during the study period. Shahhoseini, M. A., Khassehkhan, S., &
    Shanyani, N. (2012) identify the key performance indicators of an Iranian Islamic Bank i.e. K
    bank on the basis of Balance Scorecard and Analytic Hierarchy Process. Findings of the study
    indicated that bank policymakers should make the profit through customer’s satisfaction,
    Kumar, M. A., Harsha, G. S., Anand, S., & Dhruva, N. R. (2012) studied soundness of Indian
    banks through CAMEL Approach found find that private sector banks showing more soundness
    than public sector banks and suggested the Government to focus more on public sector banks,
    Fiordelisi, F., & Mare, D. S. (2013) examined the probability of default and efficiency of
    cooperative banks in Italy by using the Discrete-Time Survival Model. The result states that the
    capital adequacy reduces the probability of default which means higher capital protect the banks
    against additional loss, Trivedi, K. R. (2013) conducted a study on Surat People’s Co-operative
    bank with CAMEL model and found that the performance of the bank is satisfactory and good
    except liquidity which is unsatisfactory. So Researcher suggests that the bank should maintain
    adequate liquidity., Anand, M. B. (2013) studied the performance of banks in India Based on
    Camel model and found that there’s a significant difference between the public sector banks,
    private sector banks, and foreign banks, Waraich.S, and Dhawan. A (2013) evaluate the
    performance of Cooperative banks in Punjab by using Camel and found that banks are showing
    moderate performance on the basis CAMEL parameters, Roman, A., & Şargu, A. C. (2013)
    studied the financial Soundness of the Commercial Banks in Romania by using the CAMEL
    model and banks showing different performance on various parameters of CAMELS model,
    Makkar, A., & Singh, S. (2013) analyzed the financial performance of Indian Commercial
    Banks by using the CAMELS model and the study reveals that there is no significant difference
    in the financial performance of the public and private sector banks in India but public sector
    bank should improve their performance to sustain in the competitive market, Vadivel.M.S and
    Ayyappan.S,(2013) studied the financial efficacy of selected public and private sector banks in
    India found that due to financial intermediation, by using advanced technology the banks will
    be able to compete globally. Popovici.M.C (2014) conducted a study to measure the banking
    efficiency of the European Union on the basis of Return on Average Assets (ROAA) and Return
    on Average Equity (ROAE). Findings of the study reveal that Bank is affected differently by
    the international financial crisis and the European Union should take steps towards convergence
    of ROAA and ROAE to achieve optimum efficiency. Gupta, P. K. (2014) analyzed the financial
    position and performance of Indian public sector banks using CAMELS model and found that
    banks got the least rank according to CAMELS model need to improve their performance to
    maintain the desired level with other banks.Srinivasan, & Saminathan, Y. P. (2016) made an
    analysis on Public, Private and Foreign Sector banks in India by using the CAMELS model and
    the result signifies that the overall performance of within and between Public, Private and
    foreign Banks are different, so bank secure lower ranks should improve their performance to
    come into the desired level, Wanke, P., Azad, M. A. K., & Barros, C. P. (2016) studied
    efficiency Factors in OECD ( Organisation for Economic Co-operation and Development) banks
    by using CAMELS model and TOPSIS ( Technique for Order of Preference by Similarity to Ideal
    Solution) approach. Results reveal that the effects of ownership, trend, and origin of the bank may
    vary with respect to efficiency levels, whether high or low.Da Silva, T. P., Leite, M., Guse, J. C.,
    & Gollo, V. (2017) studied on Financial and economic performance of major Brazilian credit
    cooperatives by using both CAMEL model and Data Envelopment Analysis. They found that
    there is a positive relationship between the use of variables used in the model and the
    measurement of the financial performance of credit unions, Alqahtani, F., Mayes, D. G., &
    Brown, K. (2017) investigated about the efficiency of Islamic and conventional banks in the
    Gulf Cooperation Council region by using Data Envelopment Analysis and Stochastic Frontier
    Analysis before, during and after the global financial crisis (GFC). The result of the study
    indicates that Islamic banks suffered more in comparison to Conventional banks in terms of

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  4. Predictive Analytics in Harnessing Financial Efficacy of Banks Using CAMEL Model

    profit efficiency and cost superiority, Doumpos, M., Hasan, I., & Pasiouras, F. (2017) studied
    overall financial strength between Islamic banks and conventional banks by using Bank Overall
    Financial Strength Index (BSI) which is an improvement over CAMELS model and the study
    reveals that Islamic banks are the best ones in terms of capital strength, conventional banks are
    the best ones in terms of expenses management and liquidity, and banks with an Islamic window
    are the best ones in terms of profitability, Robin.I, Salim.R, and Bloch. H (2018) examined the
    financial performance of the commercial banks in Bangladesh and found that capital strength
    and asset quality are the main drivers of profitability. Therefore, an appropriate banking policy
    aimed at raising capital base and asset quality are important for ensuring a viable banking sector
    in Bangladesh.From the above literature, we have observed that for measuring the efficiency of
    bank different models and techniques are used by different researchers all over the world.
    Odisha states in India which have taken a leading role in cooperative movement No researchers
    have tried to measure the efficiency of cooperative banks in Odisha. Therefore we have
    attempted to measure the financial efficacy of District Central Cooperative Banks (DCCBs)
    banks in Odisha.

    3. OBJECTIVES OF STUDY
     To measure the efficacy of finance flows through District Central Cooperative Banks of Odisha.

     To make a comparative study on the performance of DCCBs in Odisha.

     To rank the DCCBs on the basis CAMEL model.

     To suggest the various ways of improvement of financial performance banks.

    4. RESEARCH METHODOLOGY
    For our study, we have taken 17 District Central Cooperative Banks (DCCBs) operating in
    Odisha. Mainly our study based on secondary data. Data in our research are derived from the
    annual report of OSCB (Odisha State Cooperative Bank) and NAFSCOB (National Federation
    of State Cooperative Bank). For the study, 10 years (2007-08 to 2016-17) data are taken into
    consideration. For analyzing the efficacy of finance flow through DCCBs CAMEL model has
    been used. CAMEL stands for Capital Adequacy, Asset Quality, Management Efficiency,
    Earning Ability and Liquidity position. The items used in our research and the formula for their
    calculation is presented in Table.1. Under each parameter of CAMEL two ratios are calculated
    for 10 years and an average is taken. Basing upon average score DCCBs are ranked. At the end
    of the analysis, a Synthesized Index Table is developed by taking average ranks of each
    parameter and DCCBs are ranked accordingly.

    4.1. CAMEL Model
    Table 1 CAMEL parameters and their calculation method
    CAMEL variables Ratios Calculation method
    Capital Adequacy 1) Capital Adequacy ratio (Capital tier I +tier II)/RWA
    2) Debt-Equity ratio (Borrowings+ Deposits)/Net Worth
    Asset Quality 1) Net Non-Performing Asset (NPA) by Net NPA/ Total Assets
    total assets
    2) Net NPA by net advances. Net NPA/Net Advances
    Management Quality 1) Cost of Management per Employee Cost of Management/ No .of
    2) Business Per Employee Employees
    Total Business/ No .of
    Employees

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  5. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    Earnings Ability 1)Return on Assets (ROA) Net Income/Total Assets
    2)Return on Equity (ROE) Net Income / Shareholders’ equity
    Liquidity Position 1)Liquid Assets/Total Deposits Liquid Assets / Total Deposits
    2)Liquid Assets/Total Assets Liquid Assets / Total Assets
    Authors design
    Note: RWA-Risk Weighted Assets
    NPA-Non Performing Assets
    Capital Adequacy (C) parameter is a vital measure for the overall financial health of a bank
    because it promises to soak up the losses arises due to by the manifestation of certain risks or
    certain indispensable macroeconomic imbalances. For the measurement of the capital
    adequacy, we have used two ratios i.e. Total capital to total assets ratio and Debt to Equity ratio
    Asset Quality (A) is a crucial parameter that measures the strength of a bank. In banks, the
    quality of the assets is determined by the quality of the loan and advances because this category
    of assets represents a major share in the overall balance sheet of a bank. To know the asset
    quality, we have used two ratios i.e. Net Non-Performing Asset (NPA) by Total Assets and Net
    NPA by Net Advance.
    Management Quality (M) is of great importance for the health and stability of a bank.
    Management is the most pioneering indicator of condition and a major determinant of whether
    a bank is having the ability to correctly diagnose and respond to financial stress or not. To
    analyze the capability of management, we have calculated two ratios i.e. Cost of Management
    per Employee and Business Per Employee
    Earning Ability (E) of banks is analyzed by calculating two ratios i.e. Return on assets and
    Return on equity. Return on Assets denotes how bank assets are capable of earning revenue
    whereas Return on Equity reflects the profit-generating capacity of the bank’s own capital.
    Liquidity Position (L) is the crucial ingredient for a bank. It acts as important elements that
    evaluate the operational efficiency of a bank because it indicates the capacity of a bank to pay
    its short term loans and face unexpected withdrawal of depositors. We have allotted higher rank
    for banks with higher liquidity ratio. Liquidity has been compared based on the two parameters,
    i.e. Liquid Assets/Total Deposits and Liquid Assets/Total Assets.

    5. RESULT & DISCUSSION
    In this section of our research, we have analyzed the efficacy of finance flow of 17 DCCBs in
    Odisha based on CAMEL model

    5.1. Capital Adequacy
    To analyze the Capital Adequacy, we have selected two parameters. The first parameter is
    Capital Adequacy ratio and the second parameter is the Debt-Equity ratio. The Capital
    Adequacy ratio is popularly known as CRAR which signifies Capital to Risk Weighted Assets
    Ratio and Debt-Equity Ratio signifies Total Borrowings plus deposits to Equity capital. We
    have assigned the first rank to that DCCB who is having the highest score on basis of Capital
    Adequacy and vice versa. This indicator determines the bank’s capacity to meet the time
    liabilities and other risks such as credit risk, operational risk, etc. On the basis of Debt-Equity
    ratio, those DCCB is having the lowest score assigned the highest rank and vice versa. Banks
    carry greater debt amounts because the money they borrow is also the money they lend. To put
    it another way, the major product that banks sell is debt. Therefore, it is logical that they have
    more of that product on hand that is common in other industries. The Capital Adequacy detail
    is given in Table-2.

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  6. Predictive Analytics in Harnessing Financial Efficacy of Banks Using CAMEL Model

    On the basis of first parameter Capital Adequacy ratio Bolangir DCCB secured 21.95% and
    obtained the first rank, Puri-Nimapara DCCB secured 20.80% and obtained the second rank,
    Aska DCCB secured 19.12% and obtained the third rank, Nayagarh DCCB secured 18.19% and
    obtained the fourth rank. Sundergarh DCCB secured 8.62% and obtained the last rank among
    the DCCBs. During our study period, all the DCCBs of Odisha are satisfying the Capital
    Adequacy norm i.e. 9% CRAR which is being fixed by Reserve Bank of India for all banks
    operating in India except Sundergarh DCCB. So on the basis of the first parameter, the position
    of all the DCCBs in Odisha are quite good.
    Coming to the second parameter i.e. Debt-Equity ratio, the Puri-Nimapara DCCB is having
    a score of 6.69% and obtained the first rank, Nayagarh DCCB secured 7.95% and obtained the
    second rank and so on. Sundergarh DCCB secured 17.43% and secured the last rank among all
    the DCCBs. All the DCCBs of Odisha are having excessive borrowings than standard banking
    norms. Central Government through NABARD and State Government through State
    Cooperative Bank injecting more credit to agriculture and allied sectors with the help of
    DCCBs. Members contribution as equity capital is less in comparison to borrowings in DCCBs
    of Odisha.
    Looking at the average rank, Puri-Nimapara DCCB secured first average score, Aska DCCB
    and Nayagarh DCCB both secured second and third average score and so on. All the DCCBs
    of Odisha are satisfying the Capital Adequacy Requirement.
    Table 2 Capital Adequacy
    Name of the DCCBs Capital Adequacy Ratio Debt-Equity ratio
    Average % Rank Average % Rank Average Rank
    Angul DCCB 13.65 9 13.42 13 11
    Aska DCCB 19.12 3 9.04 3 3
    Balasore DCCB 11.47 13 12.84 11 12
    Banki DCCB 11.44 14 9.45 4 9
    Berhampur DCCB 18.08 5 13.02 12 8.5
    Bhawanipatna DCCB 15.55 6 10.97 5 5.5
    Bolangir DCCB 21.95 1 11.82 8 4.5
    Boudh DCCB 13.73 8 11.27 6 7
    Cuttack DCCB 15.43 7 12.39 9 8
    Keonjhar DCCB 10.46 16 16.07 16 16
    Khurda DCCB 11.23 15 11.44 7 11
    Koraput DCCB 12.76 11 13.75 15 13
    Mayurbhanj DCCB 12.36 12 13.58 14 13
    Nayagarh DCCB 18.19 4 7.95 2 3
    Puri-Nimapara DCCB 20.80 2 6.69 1 1.5
    Sambalpur DCCB 12.81 10 12.69 10 10
    Sundergarh DCCB 8.62 17 17.43 17 17
    Source: Authors calculations based annual report of StCB and NAFSCOB

    5.2. Asset Quality
    To know about the Asset Quality of the DCCBs in Odisha, we have used two parameters i.e.
    Net NPA to Total Assets and Net NPA to Net Advance. The detail of the calculation is given
    in Table-3. The major proportion of the bank’s asset is loans and Advances. Non-Performing
    Assets (NPA) is unrecovered part of loan and advances to the customers during the period. In
    our analysis for Asset Quality, the first parameter is Net NPA to Total Assets. Basing upon this
    parameter Sundergarh DCCB is having 5.01% and secured the first position, Koraput DCCB is
    having a score of 5.25% and obtained the second position, Keonjhar DCCB is having 5.29%
    score and obtained the third position etc.Bolangir DCCB is having a score of 21.11% and
    secured the last position on basis of Net NPA to Total Assets parameter. Among the 17 DCCBs
    of Odisha 9 DCCBs have less than 10% score and 8 DCCBs have more than 10 % score basing

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  7. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    upon the ratio of Non-Performing Assets as a percentage of Total Assets during the study
    period.
    On the basis of the second parameter under Asset Quality perusal .i.e. Net NPA to Net
    Advance Puri-Nimapara DCCB secured 6.54% and obtained the first rank, Balasore DCCB
    secured 9.08% and obtained the second rank, Sundergarh DCCB secured 9.39% and get third
    rank, etc. Bolangir DCCB secured 33.81% and get the last rank among all the DCCBs of
    Odisha. Here one thing we have observed that basing upon both the parameters of Asset Quality
    investigation Bolangir DCCB secured lowest rank means more Non-Performing Assets as a
    percentage of Total Assets as well as Net Advance because Bolangir district in Odisha is a tribal
    populated district. Most of the people belong to Schedule Tribe (ST) categories. Due to their
    backwardness and lack of education, they are unable to repay the loan. So in Bolangir DCCB
    Non-Performing Asset percentage is more in comparison to other DCCBs.
    Table 3 Asset Quality
    Name of the NetNPA/ Total Assets Net NPA/ Net Advances
    DCCBs Average % Rank Average % Rank Average Rank
    Angul DCCB 6.02 5 10.87 8 6.5
    Aska DCCB 12.56 15 12.21 12 13.5
    Balasore DCCB 6.08 6 9.08 2 4
    Banki DCCB 16.98 16 23.18 16 16
    Berhampur DCCB 6.68 9 15.72 15 12
    Bhawanipatna 6.21 7 9.72 4 5.5
    DCCB
    Bolangir DCCB 21.11 17 33.81 17 17
    Boudh DCCB 9.46 14 12.71 13 13.5
    Cuttack DCCB 6.50 8 11.04 9 8.5
    Keonjhar DCCB 5.29 3 10.44 7 5
    Khurda DCCB 9.10 13 12.06 11 12
    Koraput DCCB 5.25 2 9.97 6 4
    Mayurbhanj DCCB 8.41 12 14.39 14 13
    Nayagarh DCCB 7.03 10 9.90 5 7.5
    Puri-Nimapara 5.51 4 6.54 1 2.5
    DCCB
    Sambalpur DCCB 7.71 11 11.76 10 10.5
    Sundergarh DCCB 5.01 1 9.39 3 2
    Source: Authors calculations based annual report of StCB and NAFSCOB
    Looking at the average rank on Asset Quality parameter Sundergarh DCCB secured the first
    position, Puri-Nimapara DCCB secured the second position, Balasore DCCB and Koraput
    DCCB secured the third position jointly and so on. On the basis of Asset Quality parameter,
    DCCBs in Odisha is having moderate performance level.

    5.3. Management Quality
    To know the management quality of DCCBs, we have used two parameters i.e. Cost of
    management per Employee and Business Per Employee. Details about Management quality are
    given in Table.4.
    Here the analysis about the expenditure incurred for an employee by a bank and Income
    earned by an employee in that bank, which otherwise is the outflow and inflow analysis of
    finance during a particular period. In the first parameter, Cost of Management per Employee
    that DCCB secured the lowest score assigned the highest rank and those DCCB secured the
    highest score assigned lowest rank because it costs part. Other Parameter i.e. Business per
    Employee that DCCB secured the highest score assigned the highest rank and those DCCB
    secured the lowest score assigned lowest rank.
    Basing upon the Cost of Management per Employee, Boudh DCCB incurred the lowest cost
    which is 2.68% and secured the first rank whereas Balasore DCCB incurred the highest cost of

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  8. Predictive Analytics in Harnessing Financial Efficacy of Banks Using CAMEL Model

    management per employee i.e 7.35% and secured the lowest rank among the 17 DCCBs of
    Odisha. Other DCCBs like Koraput DCCB scored 3.09%, Puri-Nimapara DCCB scored 3.18
    %, Mayurbhanj DCCB scored 4.06%, Sundergarh DCCB scored 4.12 %, secured second, third,
    fourth and fifth rank respectively.

    Table 4 Management Quality
    Name of the DCCBs Cost of Management per Business Per Employee
    Employee Average Rank
    Average % Rank Average % Rank
    Angul DCCB 5.07 11 6.69 3 7
    Aska DCCB 4.90 10 7.14 2 6
    Balasore DCCB 7.35 17 7.40 1 9
    Banki DCCB 4.15 6 3.21 14 10
    Berhampur DCCB 5.63 12 4.21 9 10.5
    Bhawanipatna DCCB 6.49 15 1.93 16 15.5
    Bolangir DCCB 5.98 14 5.17 6 10
    Boudh DCCB 2.68 1 4.71 8 4.5
    Cuttack DCCB 5.82 13 5.04 7 10
    Keonjhar DCCB 4.46 7 3.80 12 9.5
    Khurda DCCB 6.54 16 4.06 10 13
    Koraput DCCB 3.09 2 3.84 11 6.5
    Mayurbhanj DCCB 4.06 4 3.19 15 9.5
    Nayagarh DCCB 4.57 8 3.34 13 10.5
    Puri-Nimapara 3.18 3 1.91 17 10
    DCCB
    Sambalpur DCCB 4.61 9 5.39 5 7
    Sundergarh DCCB 4.12 5 5.69 4 4.5
    Basing upon the Business per Employee parameter Balasore DCCB shown the highest score
    i.e 7.40% and secured the first rank whereas Puri-Nimapara DCCB recorded 1.91% score and
    secured the lowest rank among the DCCBs of Odisha. Aska DCCB 7.14% score, Angul DCCB
    6.69% score, Sundergarh DCCB 5.69% score and secured second, third, fourth, fifth rank
    respectively.
    On the basis of the average rank of Management Quality parameter, none of the DCCB
    noticed less cost and more revenue per employee. Balasore DCCB showed the highest cost and
    highest revenue per employee. Here management of DCCBs in Odisha fails to catch the
    efficiency line i.e. Less Cost More Earning. Looking to average rank column Sundergarh DCCB
    and Cuttack DCCB both secured 4.5 and starts with the first rank and Aska DCCB secured 6
    average ranks and so on.

    5.4. Earning Ability
    To analyze the Earning Ability of the DCCBs we have used two parameters i.e. Return on
    Assets and Return on Equity which can be calculated through the ratio of Net Income to Total
    Asset and Net Income to Shareholder’s Equity respectively. The Earning ability is given in
    Table-3.
    On the basis of first parameter i.e. return on Assets, Sambalpur DCCB secured the first
    position, Bolangir DCCB secured the second position, Bhawanipatna DCCB secured the third
    position and Banki DCCB secured the last position. No DCCBs of Odisha are able to generate
    a 1% return on the total asset during the study period.
    Coming to the second parameter i.e. Return on Equity, Sambalpur DCCB secured the first
    position which is 11.69% and Koraput DCCB secured the last position which is 0.68%.
    Bhawanipatna DCCB is having 9.52% score, the secured second position and Puri-Nimapara
    DCCB secured the third position by having a score of 6.86%.

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  9. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    Table 5 Earnings Ability
    Name of the DCCBs Return on Assets Return on Equity
    Average % Rank Average % Rank Average Rank
    Angul DCCB 0.08854 15 1.4940 14 14.5
    Aska DCCB 0.26303 8 3.1758 9 8.5
    Balasore DCCB 0.51816 5 4.4444 6 5.5
    Banki DCCB 0.06731 17 1.9395 11 14
    Berhampur DCCB 0.32109 6 3.0799 10 8
    Bhawanipatna DCCB 0.70854 3 9.5281 2 2.5
    Bolangir DCCB 0.84198 2 5.1633 5 3.5
    Boudh DCCB 0.10548 11 1.8759 12 11.5
    Cuttack DCCB 0.29741 7 5.2142 4 5.5
    Keonjhar DCCB 0.16493 10 3.5531 8 9
    Khurda DCCB 0.08875 14 1.5533 13 13.5
    Koraput DCCB 0.10179 12 0.6820 17 14.5
    Mayurbhanj DCCB 0.09172 13 1.1520 15 14
    Nayagarh DCCB 0.06788 16 0.8473 16 16
    Puri-Nimapara DCCB 0.63080 4 6.8669 3 3.5
    Sambalpur DCCB 0.99030 1 11.699 1 1
    Sundergarh DCCB 0.22355 9 3.9544 7 8
    Source: Authors calculations based annual report of StCB and NAFSCOB
    On the basis of average rank of Earning Ability parameter Sambalpur DCCB obtained the
    first rank Bhawanipatna DCCB second rank, Puri-Nimapara DCCB and Bolangir DCCB both
    have secured third and fourth rank mutually, Cuttack DCCB and Balasore DCCB both have
    obtained fifth and sixth position and so on (Table.5). The DCCBs of Odisha have not noticed
    good performance on the basis of Return on Asset but on the basis of Return on Equity, a few
    DCCBs are secured satisfied rank. So overall Earning Ability all the DCCBs of Odisha are not
    satisfactory.

    5.5. Liquidity Position
    The focal aim behind the liquidity parameter is to analyze the capacity of a bank to meet the
    unexpected demand of deposit holders at a particular time. The liquidity position of the banks
    from our sample is determined by two ratios namely Liquid Assets to Total Deposits and Liquid
    Assets to Total Assets. Aggregate liquidity position has been presented in Table.6

    Table 6 Liquidity Position
    Name of the DCCBs Liquid Assets/Total Liquid Asset/Total
    Deposit Asset Average Rank
    Average Rank Average % Rank
    %
    Angul DCCB 6.71 14 3.50 13 13.5
    Aska DCCB 10.93 6 3.97 11 8.5
    Balasore DCCB 0.91 17 0.67 17 17
    Banki DCCB 7.12 13 2.22 16 14.5
    Berhampur DCCB 6.54 15 4.67 7 11
    Bhawanipatna DCCB 15.16 3 8.76 1 2
    Bolangir DCCB 8.92 10 7.18 3 6.5
    Boudh DCCB 9.53 9 3.94 12 10.5
    Cuttack DCCB 11.67 4 4.64 8 6
    Keonjhar DCCB 6.09 16 3.20 14 15
    Khurda DCCB 10.69 7 4.32 9 8
    Koraput DCCB 7.56 12 2.83 15 13.5

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  10. Predictive Analytics in Harnessing Financial Efficacy of Banks Using CAMEL Model

    Mayurbhanj DCCB 9.66 8 5.13 4 6
    Nayagarh DCCB 20.40 1 7.23 2 1.5
    Puri-Nimapara DCCB 18.77 2 4.74 5 3.5
    Sambalpur DCCB 11.16 5 4.68 6 5.5
    Sundergarh DCCB 7.96 11 4.11 10 10.5
    Source: Authors calculations based annual report of StCB and NAFSCOB
    Nayagarh DCCB secured the highest position on the basis of Liquid Assets to Total
    deposit ratio whereas Bhawanipatna DCCB secured the highest position in terms of Liquid
    Assets to Total Asset Ratio. But on the basis of average rank Nayagarh DCCB came first,
    Bhawanipatna DCCB is having the second rank, Puri-Nimapara DCCB secured the third rank
    and so on, based upon the average score of two ratios. Balasore DCCB secured lowest rank i.e
    17th among all other DCCBs. On the basis of first parameter Liquid Assets to Total deposit, the
    position of liquidity ranges from 0.91% to 20.40% which are of Balasore DCCB and Nayagarh
    DCCB respectively. Except for Balasore DCCB, all other 16 DCCBs have recorded more than
    6 %. It signifies all the DCCBs in Odisha are capable enough to meet the immediate withdrawal
    demand of depositors. In terms of another parameter i.e Liquid Assets to Total Asset
    Bhawanipatna DCCB is having 8.76% as the highest range and Balasore DCCB is having
    0.67% as the lowest range. Except for Balasore DCCB, all other DCCBs in Odisha are having
    a score of more than 2%. So overall liquidity position of all the DCCBs in Odisha are
    satisfactory.

    5.6. Synthesized Index
    The synthesized index is developed by taking the average of each parameter of the CAMEL
    model. Basing upon the synthesized index the overall ranking of DCCBs in are made in Table.7

    Table 7 Synthesized Index Table
    Name Of Capital Asset Management Earning Liquidity Synthesized Rank
    DCCB Adequacy Quality Efficiency Ability Position Index

    Angul DCCB 11 6.5 7 14.5 13.5 10.5 13
    Aska DCCB 3 13.5 6 8.5 8.5 7.9 6
    Balasore DCCB 12 4 9 5.5 17 9.5 10
    Banki DCCB 9 16 10 14 14.5 12.7 17
    Berhampur 8.5 12 10.5 8 11 10 11
    DCCB
    Bhawanipatna 5.5 5.5 15.5 2.5 2 6.2 2
    DCCB
    Bolangir DCCB 4.5 17 10 3.5 6.5 8.3 7
    Boudh DCCB 7 13.5 4.5 11.5 10.5 9.4 9
    Cuttack DCCB 8 8.5 10 5.5 6 7.6 4
    Keonjhar DCCB 16 5 9.5 9 15 10.9 14
    Khurda DCCB 11 12 13 13.5 8 11.5 16
    Koraput DCCB 13 4 6.5 14.5 13.5 10.3 12
    Mayurbhanj 13 13 9.5 14 6 11.1 15
    DCCB
    Nayagarh 3 7.5 10.5 16 1.5 7.7 5
    DCCB
    Puri-Nimapara 1.5 2.5 10 3.5 3.5 4.2 1
    DCCB
    Sambalpur 10 10.5 7 1 5.5 6.8 3
    DCCB
    Sundergarh 17 2 4.5 8 10.5 8.4 8
    DCCB

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  11. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    Source: Authors calculations based annual report of StCB and NAFSCOB
    From the above Table.7, it clearly indicates that Puri-Nimapara DCCB secured the first
    position on the average ranking. Bhawanpatna DCCB, Sambalpur DCCB, Cuttack DCCB,
    Nayagarh DCCB, Aska DCCB have secured second, third, fourth, fifth and sixth position
    respectively. Banki DCCB secured the last position based on average rank.Table.8 reflects the
    name of DCCBs and their average ranking based on rank 1 to rank 17.

    5.7. Ranking on CAMEL Model
    Table 8
    Name of DCCB Average rank
    Puri -Nimapara DCCB 1
    Bhawanipatna DCCB 2
    Sambalpur DCCB 3
    Cuttack DCCB 4
    Nayagarh DCCB 5
    Aska DCCB 6
    Bolangir DCCB 7
    Sundergarh DCCB 8
    Boudh DCCB 9
    Balasore DCCB 10
    Berhampur DCCB 11
    Koraput DCCB 12
    Angul DCCB 13
    Keonjhar DCCB 14
    Mayurbhanj DCCB 15
    Khurda DCCB 16
    Banki DCCB 17
    Source: Authors ranking based on Synthesized Index Table

    6. FINDINGS & SUGGESTIONS
    17 District Central Cooperative Banks (DCCBs) in Odisha are showing better performance on
    the basis of Liquidity and Capital Adequacy and have recorded a poor performance on the basis
    of Earning Ability, Management Quality and on the basis of Asset Quality all the DCCBs are
    showing moderate performance. Ranking on the basis of the average score of five parameters
    is shown in Table.8.
    The major problems that DCCBs are facing in Odisha are lack of advance technological to
    cope up with the growing need of customers, poor infrastructural facilities, excessive political
    interference, stiff competition from commercial banks, lack of awareness among the customers
    regarding repayment of loans which leads to NPA, lack of professional expertise for
    management, high Credit Deposit ratio in comparison to commercial banks, frequent natural
    calamities at the time of harvesting season, lack of awareness for crop insurance and many
    more. In spite of the above problems, cooperative banks are providing timely and easy credit to
    rural people. Government through cooperative banks injecting the funds to rural farmers those
    are continuously engaged in agriculture and other allied activities.
    From the above study, we are suggesting the following measures to be taken
     Supervisory measures which are applicable to commercial banks, should not be applicable to
    cooperative banks, hence RBI should develop separate norms for cooperative banks because
    they are mainly working in rural area.

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  12. Predictive Analytics in Harnessing Financial Efficacy of Banks Using CAMEL Model

     These banks should be given sufficient time period to cope up with RBI conditions.
     State government should contribute more capital to cooperative banks because these banks are
    under its control.
     Public sector undertakings should be allowed to subscribe to the share capital of these
    cooperative banks.
     Awareness should be created for repayment of loans and crop insurance should be essential for
    each farmer which will make strong the recovery status.
     Staffs should be work freely with less political interference.

    7. CONCLUSION
    In India, cooperative banks are serving the real credit need from the last 106 years. Without
    cooperative banks rural and agricultural development is impossible. In Odisha, the role of the
    cooperative structure is of prime importance. Again the importance of DCCBs is more. In this
    paper, we have tried to measure the efficacy of finance flow through DCCBs in Odisha by using
    the CAMEL model. On the basis of Liquidity and Capital, Adequacy DCCBs are showing better
    performance whereas on the basis of Earning Ability, Management Quality DCCBs are
    showing poor performance and on the basis of Asset Quality, all the DCCBs are showing
    moderate performance.
    Here efficacy of finance flow is not 100% satisfactory, it is moderate. To achieve the desired
    result suggestions should be implemented, Reserve Bank of India and Government should put
    forward to strengthen cooperative banks.

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