Fuzzy Logic Approach to Credit Scoring for Micro Finance in Ghana: A Case Study of KWIQPLUS Money Lending

FaroukIbnAbdulrahman, Umar; Panford, Joseph Kobina; Hayfron-Acquah, James Ben

doi:10.5120/16362-5772

Cited by 15 publications

(7 citation statements)

References 3 publications

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“…The study used inputs parameters such as ability to pay back, profitability, operational ability in order to predict the credit potential of the different clients. In his conclusion, he identifies fuzzy logic as one the most significant techniques in the field of machine learning especially since it's reliable, scalable and stable [10] The research found that, Fuzzy Logic is effective in modelling applications where human judgement is involved and could therefore be used in evaluating loan applications. However, the choice of variables, especially thresholds can make the model less useful if economic conditions change in the future unless the variables are adjusted.…”

Section: Literature Review 21 a Review Of Fuzzy Credit Score Modelsmentioning

confidence: 99%

Fuzzy Relational Model to Establish Credit Worthiness of Sacco Members in Kenya

Ahmed¹,

Njagi²,

Otieno³

2019

IJCA

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Credit scoring has provided a number of financial institutions like banks, Microfinance institutions the means of determining if a given client will default or repay their debt obligation. Credit defaulting has become a stubborn enemy to the financial sector globally. With numerous Saccos in Kenya today it is challenging to predict accurately the trust of its members hence there arise a need of models, which will determine Sacco members credit worthiness. Qualitative output variable (i.e. member credit worth) measured using factors (i.e. Credit Duration, Concurrent Credits, Repayment Amount, Most Valuable Asset and Account Balance with Sacco) are scaled using appropriate linguistic terms and fused using hierarchical sensory fusion to evaluate credit worth of Sacco members in Kenya. Similarly, the output variable member credit worthiness was assigned linguistic terms of Excellent, Good, Fair/ Average, Bad and Poor.

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Section: Literature Review 21 a Review Of Fuzzy Credit Score Modelsmentioning

confidence: 99%

Fuzzy Relational Model to Establish Credit Worthiness of Sacco Members in Kenya

Ahmed¹,

Njagi²,

Otieno³

2019

IJCA

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“…It was not until the 1970s that these credit scores became an important component of the lending industry. Many lenders, from banks to microfinance institutions, currently use credit scoring to measure a potential borrower's creditworthiness (Abdulrahman et al 2014).…”

Section: Credit Scoring In Related Industriesmentioning

confidence: 99%

Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

Dushimimana

Wambui

Lubega

et al. 2020

JRFM

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Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels of default and therefore allowing for larger volumes of customers.

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“…It was proposed by Prof. Lofti Zadeh in the year 1965. It is mostly used to deal with imprecision and vagueness of human subjective judgment and represent information from zero to one [9].…”

Section: Related Workmentioning

confidence: 99%

“…Five attributes were used in the evaluation criteria. [9] Applied fuzzy logic to credit scoring in Ghana to determine the credit worthiness of applicants.…”

Section: Related Workmentioning

confidence: 99%

Assessing Lecturers' Performance using Fuzzy Logic

Abdulrahman¹,

Shamsu-Deen²,

Abaidoo³

2017

IJCA

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Assessing lecturers is a routine work conducted at various universities to ascertain the academic performance of the lecturers. In literature, various methods and variables are being employed to do the evaluation. The objective of this research was to employ fuzzy logic in assessment of lecturers as against the traditional statistical methods. Four input variables, namely lecturer delivery, punctuality, interaction with students and assignment were used which produces a single output-Result, with its linguistic terms as Excellent, Fair, Good or Poor. The result shows that Fuzzy logic can be a simpler way of evaluating lecturers.

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Fuzzy Logic Approach to Credit Scoring for Micro Finance in Ghana: A Case Study of KWIQPLUS Money Lending

Cited by 15 publications

References 3 publications

Fuzzy Relational Model to Establish Credit Worthiness of Sacco Members in Kenya

Fuzzy Relational Model to Establish Credit Worthiness of Sacco Members in Kenya

Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

Assessing Lecturers' Performance using Fuzzy Logic

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