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Measuring Factors Affecting Non-performing Assets Using Neuro Fuzzy Model
Intelligent Systems Conference, IntelliSys 2022 ; 543 LNNS:597-608, 2023.
Article in English | Scopus | ID: covidwho-2048143
ABSTRACT
COVID-19 affects the banking sector to its maximum and moreover the repayments of the loans have become very dicey. Financial Industries like SBI are one of the major elements of the economic development of India. In the pandemic the Government has formulated various monetary and fiscal policies to deal with crisis for commercial banks under the supervision of Reserve Bank of India. To pursue these policies forward ensuring economic, industrial, socio-political and methodical development, they need proper funds to support lending to various corporate and individual customers. If any of the loan facilities granted become bad debt or doubtful debts then the goal of the policies is not fulfilled and it will mount the record of bad debt in the books of commercial banking especially after demonetization and pandemic. The paper deals with the factors like Loan Portfolio Management, Term Loan, Monetary Policies, and Change in Tax Rates and Loan performances. It represents and compares the input variables and its relations to predict the upcoming low performer of the credits. It determines to what extent the factors are affecting an individual and a corporate customer of the State Bank of India. A model through fuzzy logic and neural network is being developed to predict the low performing creditors. The factors are evaluated on the platform of python and the results are satisfactory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Intelligent Systems Conference, IntelliSys 2022 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Intelligent Systems Conference, IntelliSys 2022 Year: 2023 Document Type: Article