Comparative Analysis of the Profitability Aspects of Banks using Fuzzy Logic based Multi Criteria Decision Making method

Comparative Analysis of the Profitability Aspects of Banks using Fuzzy Logic based Multi Criteria Decision Making method

M. Muthu

B.E Mechanical department Thiagarajar College of Engineering

Madurai , India

S. Adhithyan

1. Mechanical department Thiagarajar College of Engineering

   Madurai , India

   Abstract Whether it is the manufacturing or service industry, data analytics plays a pivotal role in generating professional validated reports that aid in the smooth functioning with reduced operation costs. In fact, while retrieving the data for the customized reports generation, the application of an appropriate multi criteria decision making(MCDM) tool gives fruitful results which are of much useful in corporate decision making. Every organization has to accomplish its goal and banks are nowadays struggling and starving to make profit by effectively trying to utilize all the resources.Banks, being an important service sector, employ people , assign role and utilize properly to accomplish the overall objective.This paper has attempted to evaluate , analyze and rank the Indian banks using an MCDM technique named fuzzy logic decision making method with a specific focus/objective of maximizing the profit and considering many parameters/criteria and moreover a comparative study on the financial performance of the Indian banks is made between the year 2011 and 2015 as this seems out to be the period where there was a rapid growth in the banking sector of India and revolutionary change in the financial performance of the banks .

   Our paper has scientifically ranked the banks considering the profitability of the banks and using non interest income (NII) as it is one of the the major influencing factors for high profitability in all banks . The paper also highlights interesting findings regarding the profit maximization over the years and

   analysis about the current marketing challenges.

   KeywordsMulti-criteria decision making , Fuzzy logic, Non interest income

   1. INTRODUCTION

      In recent years , the banking industry is undergoing a rapid transformation in the global economy . Internationally accepted prudential norms have been adopted, with higher disclosures and transparency. Indian banking industry is gradually moving towards adopting the best practices in accounting, corporate governance and risk management. The deepening of information technology has facilitated better tracking and fulfilment of commitments with multiple delivery channels for online customers . The Indian banking has welcomed this change . The country witnessed branch expansion in public sector banks , private sector banks and foreign banks in absolute term during the period 2010-2011. The basic principle of branch expansion is to tap deposit and culminate saving habit among the community . Tapping of potential savings and uses them for a productive purpose in

      particular , is the main objective . Upgradation of technology

      , innovation and mordernization are the key factors of having excellence in banking sector . But still the banking sector faces the issues like consolidation , management of costs , credit delivery systems , and management of sticky assets .

   2. BANKING SECTOR

      The banking sector is the section of the economy devoted to the holding of financial assets for others, investing those financial assets as leverage to create more wealth, and the regulation of those activities by government agencies .

      Banking in India in the modern sense originated in the last decades of the 18th century. Among the first banks were the Bank of Hindustan, which was established in 1770 and liquidated in 1829-32; and the General Bank of India, established in 1786 but failed in 1791. In the early 1990s, the then government embarked on a policy of liberalisation, licensing a small number of private banks. These came to be known as New Generation tech-savvy banks, and included Global Trust Bank (the first of such new generation banks to be set up), which later amalgamated with Oriental Bank of Commerce, UTI Bank (since renamed Axis Bank), ICICI Bank and HDFC Bank. Since then the performance of the banks improved [5] . Initially pioneered by financial institutions during the 1970s as interest rates became increasingly volatile, asset and liability management (often abbreviated ALM) is the practice of managing risks that arise due to mismatches between the assets and liabilities.The process is at the crossroads between risk management and strategic planning[8,9]. It is not just about offering solutions to mitigate or hedge the risks arising from the interaction of assets and liabilities but is focused on a long-term perspective: success in the process of maximising assets to meet complex liabilities may increase profitability.

   3. FUZZY LOGIC

      The concept of fuzzy logic was coined by Lofti Zadeh in 1965. It helps us to unravel the quantum of uncertainity associated with events . It is essential to realize that fuzzy logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance.

   4. FUZZY DECISION MAKING METHOD Fuzzy Multicriteria Decision-Making addresses theoretical and practical gaps in considering uncertainty and multicriteria factors encountered in the design, planning, and control of complex systems. Fuzzy Multicriteria Decision-Making will appeal to a wide audience of researchers and practitioners in disciplines where decision-making is paramount, including various branches of engineering, operations research, economics and management. Most decisions that people make are logical decisions, they look at the situation and make a decision based on the situation. The generalized form of such a decision is called a generalized modus ponens. If we knew about the states of future with certainity there is no problem . But in most cases it is not so . So we use of classical Bayesian decision method .

      Fuzzy logic decision making is based on the following equation ,

      = Wi µi

      µ i Membership grade for each individual input

      µ – Output membership grade

   5. BANKING DATA AND ANALYSIS Banking data : The following data are collected from the IBA bulletin

      Table 1 : Public Sector Banks in ten millions ( 2015 )

      The Public Sector Banks in India as per IBA Bulletin is shown below .

      Analysis : The performance of the banks are evaluated[10] using fuzzy tool as follows

      As per the procedures of Fuzzy logic decision making ( Timothy J. Ross ) , goals and constraints are considered . The following table reveals the performance of various banks , comparison of Fuzzy scores of Public Sector Banks for the year 2011 and 2015 ( major revolution era ) is also shown

      where i = 1 to n Wi weightings

      td>

      Bank of Baroda

      S.No	Banks	Net Profit	Non interest Income	Total Expendit ure	Profit Per Employee	Fuzzy scores (2015)	Fuzzy scores (2011)
      I	NATIONALISED BANKS	2015	2015	2015	2015	2015	2011
      1	Allahabad Bank	621	1,996	17,252	2.56	0.040	0.0866
      2	Andhra Bank	638	1,500	14,570	3.00	0.041	0.0567
      3	3,398	4,402	37,450	6.88	0.191	0.1775
      4	Bank of India	1,709	4,233	40,175	7.00	0.113	0.1669
      5	Bank of Maharashtra	451	1,006	11,316	3.00	0.029	0.0336
      6	Canara Bank	2,703	4,550	41,350	5.00	0.179	0.1776
      7	Central Bank of India	606	1,894	24,744	1.53	0.039	0.0800
      8	Corporation Bank	584	1,482	18,011	3.25	0.038	0.0794
      9	Dena Bank	265	721	10,155	1.95	0.017	0.0338
      10	Indian Bank	1,005	1,363	14,203	4.95	0.058	0.0747
      11	Indian Overseas Bank	-454	2,139	22,755	negative	0.039	0.0774
      12	Oriental Bank of Commerce	778	2,121	17,856	2.46	0.051	0.0607
      13	Punjab & Sind Bank	121	429	8,242	1.00	0.0074	0.0276
      14	Punjab National Bank	3,062	5,891	40,251	5.00	0.203	0.2283
      15	Syndicate Bank	1,523	2,110	19,717	5.55	0.091	0.0578
      16	UCO Bank	1,138	2,004	16,452	4.82	0.075	0.0584
      17	Union Bank of India	1,782	3,523	29,783	5.00	0.118	0.1289
      18	United Bank of India	256	1,747	9,499	15.98	0.016	0.0403
      19	Vijaya Bank	439	879	11,893	3.00	0.028	0.0336
      II	State Bank of India (SBI)	13,102	22,576	136,059	NA	0.301	0.3219

      III	ASSOCIATES OF SBI
      1	State Bank of Bikaner & Jaipur	777	926	7,828	6.00	0.039	0.0404
      2	State Bank of Hyderabad	1,317	1,325	12,235	8.29	0.057	0.0622
      3	State Bank of Mysore	409	768	6,377	4.00	0.026	0.0288
      4	State Bank of Patiala	362	1,007	9,759	2.00	0.023	0.0477
      5	State Bank of Travancore	336	1,015	9,211	3.00	0.021	0.0367
      IV	Other Public Sector Banks
      1	IDBI Ltd.	873	4,008	26,434	NA	0.057	0.1354
      2	Bharatiya Mahila Bank	20	19	106	0.05	0.00004	N.A

      Table 2 : Private Sector Banks in ten millions ( 2015 )

      The Private Sector Banks in India as per IBA Bulletin , is shown below .

      S.No	Banks	Net Profit	Non interest Income	Total Expenditu re	Profit Per Employee	Fuzzy scores (2015)	Fuzzy scores (2011)
      		2015	2015	2015	2015
      1	City Union Bank Ltd.	395.02	404.10	2,410.31	9.00	0.28	0.2397
      2	ING Vysya Bank Ltd.	N.A.	N.A.	N.A.	N.A.	0.5	0.4571
      3	Tamilnad Mercantile Bank Ltd.	379.40	291.88	2,520.05	9.21	0.19	0.2870
      4	The Catholic Syrian Bank Ltd.	(53.17)	127.34	1,621.81	-1.78	0.002	0.0195
      5	Dhanlaxmi Bank Ltd	(241.47)	84.95	1,351.96	-10.60	0.11	0.0423
      6	The Federal Bank Ltd.	1,005.75	878.31	6,669.99	9.38	0.64	0.7326
      7	The Jammu & Kashmir Bank Ltd.	508.00	593.00	5,819.00	5.48	0.38	0.5572
      8	The Karnataka Bank Ltd.	451.00	507.00	4,432.00	6.00	0.333	0.3333
      9	The Karur Vysya Bank Ltd.	455.61	580.84	5,033.42	6.45	0.339	0.4031
      10	The Lakshmi Vilas Bank Ltd.	132.29	284.03	2,122.13	3.82	0.06	0.1642
      11	Nainital Bank Ltd.	67.18	49.20	451.35	9.00	0.01	0.0168
      12	RBL Bank	207.17	403.41	1,996.37	6.00	0.13	0.0244
      13	The South Indian Bank Ltd.	307.20	497.07	4,901.29	4.00	0.21	0.3007
      II	NEW PRIVATE SECTOR BANKS
      14	Axis Bank Ltd.	7,357.82	8,365.04	30,458.21	17.07	0.603	0.6577
      15	Development Credit Bank Ltd.	191.18	165.72	1,310.69	6.00	0.147	0.0046
      16	HDFC Bank Ltd.	10,215.92	8,996.34	40,061.78	10.00	0.144	0.3528
      17	ICICI Bank Ltd.	11,175.35	12,176.13	41,547.36	16.00	0.138	0.4787
      18	Indusind Bank Ltd.	1,793.72	2,403.87	8,997.62	–	0.047	0.1074
      19	Kotak Mahindra Bank Ltd.	1,866.00	2,028.00	8,750.00	11.00	0.011	0.0952
      20	YES Bank	2,005.36	2,046.46	10,368.88	20.96	0.0012	0.0937

   6. SUMMARY OF FINDINGS

      1. Inferring table 1 , State Bank of India stands at the top , based on the fact of high profitability compared to other public sector banks right from 2011 to 2015 .

      2. Inferring table 2 , Federal Bank Ltd occupies first position , on the fact of high profitability compared to other private sector banks from 2011 to 2015 .

      3. Inferring table 2 , Axis bank is the highly profitable among the new private sector banks identified from 2011 till 2015 .

   7. CURRENT SCENARIO OF BANKING SECTOR Currently the banking sector faces a lot of challenges day

by day , and moreover several factors lead to the increment as well as the decrement of the performance of the banks .

The current scenario of the banks are as follows ,

1. Banking sector is experiencing a shift from the traditional branch customer channel to more technology- centric channels.

2. Banking sector has undertaken several technology initiatives like internet banking, mobile banking, online fund transfer, passbook printing self service and financial supply chain management facility for corporate customers

3. For Public Sector Bank , high provision requirements due to their staff expenses (including pension liabilities) dented their profitability.

4. Private sector banks were able to maintain profitability in a tough operating environment as their commission, exchange and brokerage income increased

5. Fiscal indiscipline leading to fiscal deficit

6. High inflation leading to high interest rate

7. Rupee devaluation which further deteriorates the current account deficit

8. Non-interest income as a proportion of total income for all banks under study has declined , leading to declining profitability. However, private sector banks continued to fare better on this aspect as compared with Public sector banks .

9. Amid the challenging macroeconomic environment and increased credit cost, banks continued to employ cost control measures, such as salary optimization, negotiating on rentals and using technology

10. The banks witness mixed trends on their Cost to Income (C-I) ratios.

REFERENCES

1. Data source : IBA bulletin

2. Dr.Sundareswaran, K ( 2008 ), A Learners Guide to Fuzzy Logic, Jaico Books, Mumbai .

3. Rajasekaran,S(2008), Neural Networks , Fuzzy Logic and Genetic Algorithms(synthesis and applications ), PHI Learning Private Limited , New Delhi.

4. Timothy J. Ross (2010) , Fuzzy Logic with Engineering Applications , Wiley India , Delhi.

5. Zainab Dabo , (2012) The impact of financial liberalisation on the performance of banks in Nigeria , Procedia – Social and Behavioral Sciences 62 ( 2012 ) 548 554

6. Ovidiu Stoicaa, Seyed Mehdianb, Alina Sargua , (2013), The impact of internet banking on the performance of Romanian banks: DEA and PCA approach , 7th International Conference on Globalization and Higher Education in Economics and Business Administration, GEBA

7. Daniel Berkowitz , Mark Hoekstra , Koen Schoors,(2014) Bank privatization, finance, and growth , Journal of Development Economics 110 (2014) 93106

8. S.Saksonova, I.Solovjova , Some Quantitative Aspects of Stability Management Strategy in a Bank , 8th International Strategic Management Conference

9. Svetlana Saksonovaa , Approaches to Improving Asset Structure Management in Commercial Banks , 9th International Strategic Management Conference

10. Svetlana Saksonovaa , The Role of Net Interest Margin in Improving Banks Asset Structure and Assessing the Stability and Efficiency of their Operations, 10th International Strategic Management Conference.

CONCLUSION

Our paper has scientifically ranked and compared the performance of the banks considering the profitability and using non interest income (NII) and an attempt has been made to analyze the current marketing challenges in banking scenario .

And our future works will be based on the analysis of how do these influencing factors of banking performance lead to the improvement in the value of our currency .