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Fanancial Inclusion Agriculture

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mba(2007-09)

mba(2007-09)

Published in: Technology, Education
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  • 1. Identification of Factors Influencing Financial Inclusion in Agriculture – An Application of Artificial Neural Networks Vishnuprasad Nagadevara Indian Institute of Management Bangalore [email_address]
  • 2. Introduction
    • Financial development leads to economic growth
    • Financial development creates enabling conditions for growth
    • access to credit has a significant impact on agricultural growth
  • 3. Introduction
    • Share of non-institutional sources of credit for cultivators had declined from 93% to 30% in recent years
    • Agricultural growth has slowed down, especially growth of food grains
    • Banks have been mainly focused on financing crop loans connected largely with food grains
    • Therefore, there is a reason to believe that financial exclusion may actually have increased in the rural areas over the last 10-15 years
  • 4. Share of Credit (percentage) 38.9 30.6 36.8 68.3 81.3 92.7 Non-Institutional Sources 12.1 13.1 20.7 32.2 32.1 23.0 Other Non-Institutional Sources 26.8 17.5 16.1 36.1 49.2 69.7 Money Lenders 61.1 69.4 63.2 31.7 18.7 7.3 Total Institutional Sources 4.6 4.2 4.6 7.3 15.5 3.1 Others 26.3 35.2 28.8 2.4 0.6 0.9 Commercial Banks 30.2 30.0 29.8 22.0 2.6 3.3 Cooperative Societies 2002 1991 1981 1971 1961 1951
  • 5. Ratio of Bank Assets to GDP
    • The ratio of bank assets to GDP is one of the indicators of financial deepening.
      • Indonesia : 101 per cent
      • Korea : 98 per cent
      • Philippines : 91 per cent
      • Malaysia : 166 per cent
      • UK : 311 per cent
      • France : 147 per cent
      • Germany : 313 per cent
    • India 80 per cent in 2005-06
  • 6. Objectives of the study
    • Identify factors that influence the sources of credit for the agriculturists
    • To rank these factors in order of importance with respect to different sources of credit
    • Suggest appropriate policy measures to enhance financial inclusion
  • 7. Methodology
    • Factors that influence financial inclusion such as gender, occupation, income groups, etc. are mostly either categorical or ordinal
    • Financial inclusion itself is a categorical variable.
    • Chi-square is one of the Best techniques
    • It is not amicable to determine the relative importance
    • Could not be used to prioritize the factors
    • An alternate approach is needed
    • One such technique is application of Artificial Neural Networks
  • 8. Artificial Neural Networks
    • Artificial Neural Networks (ANN)
    • Process of Machine Learning
    • Directed/Supervised Data Mining
    • Applications in “Prediction”
      • Fraud Detection
      • Customer Response
      • Credit Rating
  • 9. Neural Networks
    • Mimic neurons of the human brain
    • Links are the Processing Elements
    • Learn from experience
    • Good in detecting unknown relationships
    • PEs process data by summarizing and transforming it through mathematical functions
  • 10. Neural Networks
    • PEs are interconnected and trained and retrained repeatedly
    • PEs are linked to inputs and outputs
    • Training involves modifying the weight or connection
    • Uses “Learning Rules” to adjust weights
    • Training continues till desired accuracy level is reached
  • 11. Neural Network Model Age Region Call Rate Service Income Loyal Hopper Lost
  • 12. Data
    • The data is from the National Survey on Saving Patterns of Indians.
    • The sample covered both the rural and urban areas
    • Various demographic characteristics such gender, age, marital status, household size, education, profession, caste, asset ownership, media exposure
    • Information on coverage with respect to borrowings from different sources : financial institutions, money lenders, SHGs, relatives and friends etc.
    • The respondents belonging to the agricultural sector are selected
  • 13. Sample Profile 6.2 430 Female 93.8 6518 Male Gender 100 6948 Total 26.2 1821 51 & Above 54.4 3782 31-50 19.4 1345 Up to 30 Age Group Percent Frequency   Characteristic
  • 14. Sample Profile 0.4 30 Divorced / Separated/Deserted 4.5 316 Widow/Widower 5.9 413 Never Married 89.1 6189 Currently Married Marital Status 5.2 361 Graduation & Above 42.9 2984 Up to Intermediate Education Level 74.1 5148 Others 25.9 1800 SC/ST Social Category
  • 15. Sample Profile 4.8 331 No 95.2 6617 Yes Ownership of Occupied House 100 6948 Total 6.6 458 Large 19.1 1326 Medium 23.4 1626 Small 40.7 2829 Marginal 10.2 709 Landless Agricultural Landholding
  • 16. Savings Instruments 6.9 482 Recurring Deposits 4.8 332 Fixed Deposits 44.9 3118 Savings Account Banking Products 0.2 11 Gratuity Scheme 0.2 12 Government Provident Fund 0.2 11 Government Pension Scheme 0 3 Employees Pension Scheme 0.1 5 Employee Provident Fund Percent Frequency  
  • 17. 0.7 47 Non-Life General Insurance 0.2 15 Health Insurance 0.5 32 Personal Accident Insurance 2.4 166 Life Insurance (Non Endowment) 14.6 1013 Life Insurance (Endowment) Insurance Products 3.1 216 KVP 0.4 30 NSC 0.1 4 PPF Post Office Products 6.9 482 Recurring Deposits 4.8 332 Fixed Deposits 44.9 3118 Savings Account Banking Products
  • 18. Sources of Credit 100.00 2058 Total number of loans 1.18 82 More than 3 1.09 76 Three Sources 5.35 372 Two Sources 21.99 1528 Single Source 70.38 4890 No Credit Percent Frequency  
  • 19. Important Sources of Credit 5.64 1.7 116 SHG 30.27 9 623 Relatives/Friends 4.81 1.4 99 Govt. 14.72 4.4 303 Cooperative Society 16.38 4.9 337 Cooperative Bank 18.76 5.6 386 Nationalized Banks 4.18 1.2 86 Private Financial Institutions 46.02 13.6 947 Money Lender Percent Percent Frequency Source of Credit
  • 20. Prediction Accuracies 61.90% 37.99% Debt 96.13% 0.76% 99.24% No Debt Nationalized Banks 77.02% 22.77% Debt 99.21% 0.07% 99.93% No Debt Private Financial Institutions 82.98% 17.00% Debt 84.71% 13.93% 86.07% No Debt Money Lender   Debt No Debt Actual Source Overall Forecast
  • 21. Prediction Accuracies 59.83% 39.89% Debt 98.49% 0.16% 99.84% No Debt SHG 62.90% 37.03% Debt 93.80% 1.65% 98.35% No Debt Relatives/Friends 85.68% 14.26% Debt 98.34% 0.50% 99.50% No Debt Cooperative Society 53.18% 46.70% Debt 95.89% 0.58% 99.42% No Debt Cooperative Bank   Debt No Debt Actual Source Overall Forecast
  • 22. 2 3 8 3 2 1 1 Awareness of Alternative Investment Options     1     6   Annual Investable Surplus   1 5 10 9   5 Annual Income   2   1 3 8   Annual Expenditure           9   All Savings 5 4 9 2 1 3 6 Agricultural Landholding 6 10 7 7 7 5 8 Age Group SHG RF PI NB ML CS CB Variable
  • 23. 4             Marital Status 3   4 8   2   Insurance Products 8         4   Exposure to TV     10       10 Exposure to Radio   9           Exposure to Newspaper     2   4   3 Education Level 7 7       10   Banking Products SHG RF PI NB ML CS CB Variable
  • 24.       6 5   4 Language Proficiency [Local] 10 8   5 6   9 Language Proficiency [Hindi]     3   8     Language Proficiency [English]     6         Social Category 1 6   4 10   7 Primary Savings Need       9       Post Office Instruments   5       7   Owner of Other Real Estates 9           2 Media Exposure SHG RF PI NB ML CS CB Variable
  • 25.
    • Table 8 helps in identifying eh unique factors
    • There is a significant difference of the ranking
    • Three factors - age group, agricultural land holding and awareness of alternate investment options figure in all the sources
    • Social category (SC or ST ) only with respect to private financial institutions.
    • Annual expenditure is an important factor for credit from Cooperative societies.
    • Agricultural land holding is the most important factor with respect to money lenders,
  • 26.
    • Relative importance of these factors are likely to be different in different regions of the country.
    • The dataset used for the above analysis is an aggregate sample across the entire country
    • It is necessary to obtain different samples for different regions of the country so that the factors that are specific to different regions can be clearly identified
  • 27.
    • Questions?
    • Suggestion?
    • Comments?

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