Analysis of the granting of micro-credits to SMEs using the artificial neural network (ANN) method: the case of 29 SMEs of TREICHVILLE (Ivory coast)

Abstract— The purpose of this article is to propose the advantage of using ANRs as a risk classification and prediction tool in the financing of SMEs in Ivory coast. The credit scoring is not optimal in terms of forecasting and classification "Steve Ambler & Alain Paquet (1997), Abdou et al (2007)" [1,2]. Indeed, the RNAs consist of a better classification-client for the granting of microcredit, in terms of their performance compared to traditional and traditional statistical methods "Marcus Odom & Ramesh Sharda (1990)" [3]. A two-stage survey (qualitative survey of the target population to identify important variables and collected by questionnaires and interview guides developed on the basis of selected variables) allowed us to select a sample of 29 tertiary sector firms. in the field of services with a margin of error of 5% and 2%, representing the rate of small firms in the tertiary sector holding normal accounts in Ivory Coast during the 2013 financial year.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-27
DOI
https://doi.org/10.5281/zenodo.18384053
Primary Topic
Financial Distress and Bankruptcy Prediction
Type
article
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article

Analysis of the granting of micro-credits to SMEs using the artificial neural network (ANN) method: the case of 29 SMEs of TREICHVILLE (Ivory coast)

Cerrou Serge Roland JUNO, Jean-Claude POKOU, Koffi Pacôme N'ZI
Zenodo (CERN European Organization for Nuclear Research)
Financial Distress and Bankruptcy Prediction
article

Analysis of the granting of micro-credits to SMEs using the artificial neural network (ANN) method: the case of 29 SMEs of TREICHVILLE (Ivory coast)

Cerrou Serge Roland JUNO, Jean-Claude POKOU, Koffi Pacôme N'ZI
article en

Abstract

Abstract— The purpose of this article is to propose the advantage of using ANRs as a risk classification and prediction tool in the financing of SMEs in Ivory coast. The credit scoring is not optimal in terms of forecasting and classification "Steve Ambler & Alain Paquet (1997), Abdou et al (2007)" [1,2]. Indeed, the RNAs consist of a better classification-client for the granting of microcredit, in terms of their performance compared to traditional and traditional statistical methods "Marcus Odom & Ramesh Sharda (1990)" [3]. A two-stage survey (qualitative survey of the target population to identify important variables and collected by questionnaires and interview guides developed on the basis of selected variables) allowed us to select a sample of 29 tertiary sector firms. in the field of services with a margin of error of 5% and 2%, representing the rate of small firms in the tertiary sector holding normal accounts in Ivory Coast during the 2013 financial year.

Zenodo (CERN European Organization for Nuclear Research)
African Virtual University (KE)
Openalex Percentile: Top 77%
Financial Distress and Bankruptcy Prediction
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