Department of Statistics

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Academic Performance in a University with Senior High School Entry Grades
(University of Ghana, 2012) Kobina, F.
The objectives of this study are to determine if Senior High School entry grades used for university admission could predict final performance in Methodist University College Ghana (MUCG) and to establish the relationship that exist between entry grades E, D7 or E8 and final performance of students who entered MUCG with these grades. The study population comprises of students who entered MUCG with Senior Secondary School Certificate Examination (SSSCE)/West African Senior School Certificate Examination (WASSCE) results. Out of this population, 888 students were selected from the academic years 2000/2001 to 2007/2008 for the study. Multiple regression, chi-square test of independence and correlation were used to analyze the data. The findings revealed that there is a weak correlation between entry grades E, D7 or E8 and final cumulative grade point average in MUCG. Based on the findings, it is recommended that a standardized entrance examination should be conducted for Senior High School students who are not able to meet the initial admission requirement, especially those with at most two subjects being grades E/D7/E8.
Adopting Zero Inflated Models For Claim Counts And The Gamma Regression Model For Claims Cost In Determining Actuarial Premiums
(University Of Ghana, 2022-04) Amenu, F.M.
Insurance is the exchange of risk by an insured person through the payment of premiums for financial protection and economic benefit. The problem is how premiums should be charged so as to keep the industry alive to perform this basic function of insurance. Because of the Bonus-Malus system, or Hunger for Bonus system (also called No Claim Discount), and deductibles, most claims are not reported by policyholders, causing the number of claims to be dominated by zeros, which leads to over-dispersion in the data. In modeling the claim frequency, the Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models were adopted. The Gamma regression model was used to fit the claims cost data. The claim frequency regression model that best fits the claim frequency with the Gamma model for the claims cost was combined in determining the actuarial premium. These models were numerically illustrated with data obtained from a major non-life insurance company in Ghana and French Motor Third- Party Liability data from https://www.kaggle.com/datasets/karansarpal/ fremtpl2-french-motor-tpl-insurance-claims. The score test demonstrated the inability of the Poisson model to appropriately model the claims data due to the inflation of zeros in the data. The ZIP and ZINB were both found to be superior to their conventional equivalents based on the Vuong test statistics. The ZIP was chosen as an appropriate model for analyzing claim frequency data for both the French and Ghanaian data based on the values of the AIC and BIC. The risk factors that were found to influence claim frequency and claim cost were discovered to be different when both datasets were used. It is recommended that a separate analysis of claim frequency and claim cost be conducted with claim frequency receiving a high rating power.
Analysing The Effects Of Macroeconomic Variables On Inflation In Ghana Using Distributed Lag Models
(University of Ghana, 2016-06) Agbenorhevi, A
The study examines the relation between inflation and some key macroeconomic variables such as money supply, interest rate, exchange rate, and GDP in Ghana. These macroeconomic variables are obtained from the Bank of Ghana spanning through January 1990 to December 2014. Data obtained were on monthly basis. However, it is only the GDP which was an annual data but had been transformed into monthly data. We use the Augmented Dickey-Fuller (ADF) technique; the Granger Causality Test Technique, the Autoregressive Distributed Lag (ARDL) Cointegration Technique, and the Error Correction Model (ECM) of the ARDL model are used to test for the existence of the short and long run relationship between inflation and the other variables. Unit root test is performed using the ADF test, the ARDL model is used in establishing the long-run relation between inflation and money supply, interest rate, exchange rate and GDP while the ECM is used to establish the relation between the variables and the level of significance used throughout the study is 5%. From the study, it is established that there exists a significant long-run and short-run relationship between inflation rate and money supply; this confirms the monetarist theory which says „inflation is everywhere a monetary problem‟, interest rate, exchange rate and GDP. The Granger-causality test used lag two (2). The test results show there is a unidirectional causal relation running from inflation rate to money supply, a unidirectional causality from interest rate to inflation. The results also suggest a non-directional causality between exchange rate and inflation rate and a bidirectinal causality between GDP and inflation rate.
Analysis of Spatial Differentials of Income Inequality in Ghana: Application of Bayesian Estimation
(University of Ghana, 2015-07) Tutuani, B.; Mettle, F. O.; S. I.; University of Ghana, College of Basic and Applied Sciences, School of Physical and Mathematical Sciences, Department of Statistics
The prime objective of the study was to introduce the application of a Bayesian method of estimation in the computation of the Gini coefficient. The bootstrap estimation technique was also employed to obtain confidence intervals for the estimated Gini coefficients which were used to statistically analyze the significant change of inter-household income inequality between 2005/06 and 2012/13 in Ghana with emphasis on the sex of head of household. Most of the methods used in calculating the Gini coefficient are numerically determined and many rely on income. This study provides a more statistical way of calculating the Gini coefficient which depends on income and household size. The income gap between the rich and the poor in Ghana increased from 0.507 in 2005/06 to 0.647 in 2012/13. Meanwhile, the percentage change in the unequal distribution of income was higher among all male headed households than their female counterparts. It was also discovered that not only was the spatial differentials of inter-household income inequality between 2005/06 and 2012/13 alarming but also statistically significant. Income inequality remains a threat to the developmental strategies in Ghana. Government and policy makers need to formulate policies and programs targeted at bridging the income gap between the rich and the poor in Ghana.
Application of Bühlmanns-Straub Credibility Theory to Claim Histories of Non-Life Marine Insurers in Ghana
(University of Ghana, 2019-07) Adjorlolo, P.K.
The study sought to demonstrate how credibility claim costs without the consideration of claim frequency and claim severities underlined by different risk profiles underestimate claim costs or premiums charged policyholders by non-life insurance companies. We used secondary data of non-life marine insurers in Ghana, claim histories that range from the period of 2013 to 2018. The claim histories included claim sizes, claim counts and policy counts. Bühlmans-Straub Credibility theory model was used in estimating credibility weights, credibility claim costs, credibility claim frequencies and credibility claim severities and subsequently find the credibility frequency-severity claim cost as the product of credibility claim frequency and severity for the individual and respective risk classes. We compared the estimates of the claim costs or premiums and have observed that the credibility claim costs underestimates claim costs or the average claim costs compared to the credibility frequency-severity claim costs for most of the risk classes. This is an indication of how a lack of consideration for variability or unstableness of claim frequencies and severities with different risk profiles undermine claim costs estimated through credibility rate makings in insurance. The study recommends that credibility ratemaking by insurance companies based on inadequate claim history and or with enough class risk variation should include credibility risk frequency and severity for the determination of credibility risk premiums.
Application of Bühlmanns-Straub Credibility Theory to Claim Histories of Non-Life Marine Insurers in Ghana
(University of Ghana, 2019-07) Adjorlolo, P.K.
The study sought to demonstrate how credibility claim costs without the consideration of claim frequency and claim severities underlined by different risk profiles underestimate claim costs or premiums charged policyholders by non-life insurance companies. We used secondary data of non-life marine insurers in Ghana, claim histories that range from the period of 2013 to 2018. The claim histories included claim sizes, claim counts and policy counts. Bühlmans-Straub Credibility theory model was used in estimating credibility weights, credibility claim costs, credibility claim frequencies and credibility claim severities and subsequently find the credibility frequency-severity claim cost as the product of credibility claim frequency and severity for the individual and respective risk classes. We compared the estimates of the claim costs or premiums and have observed that the credibility claim costs underestimates claim costs or the average claim costs compared to the credibility frequency-severity claim costs for most of the risk classes. This is an indication of how a lack of consideration for variability or unstableness of claim frequencies and severities with different risk profiles undermine claim costs estimated through credibility rate makings in insurance. The study recommends that credibility ratemaking by insurance companies based on inadequate claim history and or with enough class risk variation should include credibility risk frequency and severity for the determination of credibility risk premiums.
An Application Of Markov Chains And Mixed Poisson Distribution In Modelling No-Claim Discount Systems For Motor Insurance Data
(University Of Ghana, 2022-05) Akuminge, C.N.
Most No-Claim Discount (NCD) systems are unfair to either one or both parties. Most systems are the simple random walk model, whereby in case of a claim, the policyholder moves down a discount level and vice versa. Modelling of data of claim amount is of paramount importance to manage risk reserve for payment of claims. Actuaries model uncertainty using probability distributions. The movements within the NCD systems are those of the in-between cases, and a fair NCD system, should take into consideration the frequency of claims and the non-homogeneity factor. In this study, the Markov chains have been employed to explain the movement between levels in the NCD system and mixed Poisson distribution to calculate the probabilities, with the mixing distributions been the exponential and the gamma, and Poisson models. The motor insurance claim data from Sweden was used in this study. The study found that the Geometric distribution model was better fitted to the observed claim frequencies in both the maximum likelihood estimation and method moments than the Poisson model. The results for the 3-Level NCD systems showed that the policyholders were rewarded with approximately 95% chances of moving to the next higher level towards attaining a no premium zone if a claim is not made in the cycle and were punished if a claim is made by dropping to the lower level with approximate probability of 0.048 and it was generally observed. The study found that due to the fairness of the system, policyholders who make claims are equally punished by dropping from their current level to the lower one or are made to stay in their current level in the next cycle. In conclusion, the multi-level NCD system was designed to reward policyholders who are extra vigilant on the road in terms of avoiding road accidents and penalized bad road users. The descriptive statistics of the claim frequency revealed that the number of claims by policyholders was right or positively skewed with a mean number of claims approximately zero (0.053).
Application of Numerical Integration to Stochastic Estimation of the Gini Coefficient
(University of Ghana, 2015-07) Darkwah, K.A.; Nortey, E.N.N.; Lotsi, A.; University of Ghana, College of Basic and Applied Sciences School of Physical and Mathematical Sciences Department of Statistics
Over the years, measuring inequality based on the distribution of income has been a major concern to economist. Inequality has had a broader concept than poverty in that it is defined over the entire population not just for the portion of the population below a certain poverty line. The Gini coefficient satisfy many desirable properties of a good measure of inequality such as mean independence, population size independence, symmetry, and Pigou-Dalton Transfer sensitivity. The empirical observation (income) distribution exhibit excess kurtosis and heavy tails. This research first described the probability distribution of income. The study presented a proposed numerical integration method to stochastic estimation of the Gini coefficient. The Proposed Numerical Integration Method showed a better estimate of functions as compared to the Newton’s cotes methods such as the Trapezium rule, Simpson’s 1=3 rule, Simpson’s 3=8 rule, Boole’s rule and Weddle’s rule. Diagnostic tests such as Q-Q plots and Kolmogorov-Smirnov test were graphically and quantitatively used to assess the fitness to the income data respectively. The study therefore concludes that the proposed method is superior to the Newton-Cotes methods of integration. Also, the Gini coefficient estimate using the proposed numerical integration method with k=3 was 0.48 which shows that there is disparity in income in Ghana and recommend to statisticians or mathematicians to use the proposed numerical integration method when computing functions that can’t be easily integrated.
The Application of Queuing Theory to Customer Service at Selected Branches of the Standard Chartered Bank Ghana Limited.
(2004-12) Thompson-Nunoofio, E.; Odoom, S. I. K.; University of Ghana, College of Basic and Applied Sciences, School of Physical and Mathematical Sciences, Department of Statistics
This thesis investigates the Application of Queuing theory to customer services at selected branches of the Standard Chartered Bank in Accra. The problem investigated was to determine among other things whether the customer service points of the bank functions well without bottlenecks or undue delays. That is this thesis intends to investigate, (a) If the bank current system of operation at the customer service points is satisfactory. (b) If not what changes are required to improve the mode of operation at the service points of the bank. The methodology employed was to collect data from the customers arriving at the service points of the bank. Customers arriving at the service points of the bank are given identification or trial numbers such as 1, 2, 3 etc in the order of their arrival. Their clock time of arrival, beginning of service and at the end of service are observed for each customer at a duration of at least three(3) hours for three days for the selected branches under study. The data was analyzed by preparing an elaborate worksheet using Microsoft Excel. This would depict the state changes for any arriving customer during the period of observation. A summarized relative frequency distribution was also depicted for the inter arrival and service times. This would enable the researcher to know the proportion of customers whose inter arrival and service times fall within a certain category for each day of observation. This would help determine in what ways the queues differ from day to day and, possibly from one branch to another. The results obtained indicate that a customer spends a minimum of thirty (30) minutes at the service points of the bank. Hence the conclusions that follow indicates that very few cash booths are opened to serve customers at the various hours of the day at the service points of the selected branches of the bank investigated. Consequently, excessive long queues are observed at the service points of the selected branches of the bank. This means that the current demand for service at the selected branches of the bank exceeds the current capacity to provide that service. Hence, cashiers at the cash booths of the selected branches of the Standard Bank, Ghana Limited spend most of their times serving customers at the service points of the bank. To improve the mode of operation, an additional teller window should be opened to serve customers at the service points of the bank at various hours of the day. This would reduce the burden on the cashiers and enhance efficient customer service delivery.
The Application of Queuing Theory to Customer Service at Selected Branches of the Standard Chartered Bank Ghana Limited.
(University of Ghana, 2004-12) Thompson-Nunoofio, E.; Odoom, S.I.K.; University of Ghana, College of Basic and Applied Sciences, School of Physical and Mathematical Sciences Department of Statistics
This thesis investigates the Application of Queuing theory to customer services at selected branches of the Standard Chartered Bank in Accra. The problem investigated was to determine among other things whether the customer service points of the bank functions well without bottlenecks or undue delays. That is this thesis intends to investigate, (a) If the bank current system of operation at the customer service points is satisfactory. (b) If not what changes are required to improve the mode of operation at the service points of the bank. The methodology employed was to collect data from the customers arriving at the service points of the bank. Customers arriving at the service points of the bank are given identification or trial numbers such as 1, 2, 3 etc in the order of their arrival. Their clock time of arrival, beginning of service and at the end of service are observed for each customer at a duration of at least three(3) hours for three days for the selected branches under study. The data was analyzed by preparing an elaborate worksheet using Microsoft Excel. This would depict the state changes for any arriving customer during the period of observation. A summarized relative frequency distribution was also depicted for the inter arrival and service times. This would enable the researcher to know the proportion of customers whose inter arrival and service times fall within a certain category for each day of observation. This would help determine in what ways the queues differ from day to day and, possibly from one branch to another. The results obtained indicate that a customer spends a minimum of thirty (30) minutes at the service points of the bank. Hence the conclusions that follow indicates that very few cash booths are opened to serve customers at the various hours of the day at the service points of the selected branches of the bank investigated. Consequently, excessive long queues are observed at the service points of the selected branches of the bank. This means that the current demand for service at the selected branches of the bank exceeds the current capacity to provide that service. Hence, cashiers at the cash booths of the selected branches of the Standard Bank, Ghana Limited spend most of their times serving customers at the service points of the bank. To improve the mode of operation, an additional teller window should be opened to serve customers at the service points of the bank at various hours of the day. This would reduce the burden on the cashiers and enhance efficient customer service delivery.
An Application of Survival Analysis in Auto Insurance Contracts in Ghana
(University Of Ghana, 2015-07) Opoku-Ameyaw, K.
Survival models for life-time data and other time-to-event data are widely used in many fields, including medicine, economics, agriculture and actuarial mathematics. In this study, survival analysis was applied to the Ghanaian insurance industry to model the time until first claim after policy inception and time until payment after reporting. The nonparametric Kaplan-Meier model is used in the analysis. Cumulative hazard functions for time until claim reporting and time until payment were calculated. Confidence intervals were also computed for the Kaplan-Meier estimates. The findings indicate that time until reporting claims and time until payment followed a polynomial of order 6. It was also observed that the log-transformed confidence interval is better than the linear confidence interval. The probability that claims will be reported within a shorter period (e.g. a week) was higher than that of a longer period (e.g. a month). It was concluded that survival analysis is an appropriate tool for studying the insurance industry.
Application of Time Series in Predicting the Water Levels of the Akosombo Dam
(University of Ghana, 2013-05) Mensah, D.; Doku-Amponsah, K.
Energy from hydro-electricity is the cheapest form of power generation in this country. The Volta River Authority can however generate power optimally if water levels within the dam is between 240ft and 280ft. This is not always the case, since the only source of water for the dam is rainfall, which is also random and dependent on weather conditions. Knowledge of the water level within any month of the year will therefore be very useful in the production, distribution and management of power from the dam.The study looked at how use of time series analysis could be used in predicting the average monthly water levels of the Akosombo dam. The study took a step-by-step approach of the Box-Jenkins ARIMA process and arrived at a seasonal model (1,1, 0) × (0,1,1) 12 . This model turned to be a good forecast for the average monthly water levels. Per the findings in this research work, it was recommended that, if data points were in the excess of 70, then the Box-Jenkins ARIMA model can be used to predict prices of utilities such as water and electricity. Fellow statisticians were also encouraged to look at other forecasting tools such as artificial neural networks since it had very good features as the Box-Jenkins ARIMA model.
Arbitrage Opportunity In The Ghanaian Stock Market An Arfima Approach
(2017-07) Armachie, J.
Most of the methodologies employed in analyzing stock time series data are based on the assumption of Efficient Market Hypothesis which does not assume long range memory or dependence in the data generating process. However empirical evidence from stock data fails to support the lack of dependence especially in developing countries. This study investigated the long range memory in some selected equities on the Ghanaian stock market using non-parametric and parametric methods. Using the fact that, markets that are described by fractional Brownian motion possesses an arbitrage opportunity, an ARFIMA model which is a discretized version of fractional Brownian motion was fitted to the selected equities to investigate the presence of memory. The study found long memory in most of the stock returns of the equities selected. The study also explored the presence of long memory in the absolute return and squared returns. The study found memory in the absolute and squared return which was in general, lager than the return series. The long range memory in the absolute return and the square return can be used by herd fund managers in forecasting of future returns.
Assessing the Impact of Risk Based Insurance Supervision Methodology on Non-Life Insurance Companies in Ghana
(University of Ghana, 2019-07) Abdul-Rahaman, R.
The purpose of this research is to evaluate how the inception of risk based supervisory approach has influenced the solvency positions of insurers in Ghana. The study considered 20 General Insurance Companies from 2008 – 2018. The main objectives of the study were to generate credibility risk premium values for individual claim experience to be used as an average premium regulator and develop Performance ratio thresholds for the Insurance Industry in Ghana using credibility theories and lastly determine whether the introduction of RBS approach has caused a shift in the solvency position of Non – Life Insurance. Credibility models such as the Buhlmann Straub‟s Credibility theory and its components were used to test the researcher‟s objectives and the results showed that, Claim Counts for General Insurers reveals that, Ghana Union Assurance recorded the highest average of 76.3 claim count and a maximum of 154 claim count. Prime Insurance Company Limited recorded as low as 3.0 average claim count. Ghana Union Assurance recorded the highest average of 937.36 policy count and a maximum of 1896 policy count. Priority Insurance Company Limited recorded as low as 25.00 average policy count. The results showed that expected process variance (EPV) was 0.049064 whiles the variance of the hypothetical mean (VHM) was 0.000000226. It was also realized that the expected process variance (EPV) was 901967.62 whiles the variance of the hypothetical mean (VHM) was 11489323773 and the variance of the hypothetical means (VHP).22 was . It is recommended that Gross Written Premium is the only performance ratio that recorded a significance credibility weight (54%) on the Ghanaian industry average.
Bayesian Hierarchical Model With Classification And Regression Tree In Predicting Loan Default
(University of Ghana, 2017-07) Anno-Kwakye, R.
Bayesian modelling as well as decision tree methods are some of the efficacious classification methods in credit scoring applications. Application of these methods to credit scoring provides several advantages, which are highlighted in the literature. In this research, a Bayesian hierarchical model using the latent variable approach coupled with the classification and regression tree ‘CART’ approach is applied to classify customers who applied for loans into potential defaulters and non-defaulters. The contribution of this paper is two-fold. First, we apply a Bayesian hierarchical model, it is assumed that there is a latent variable relationship between applicant’s credit amount, age, credit duration and credit history from which predictive posterior estimate is acquired. Subsequently the predictive posterior mean of each observation coupled with the CART is used to generate decisions. Secondly, we assess the effectiveness of Bayesian Hierarchical model with the Classification and regression tree approach in predicting loan defaulters. The finding from the research shows that the Bayesian Hierarchical model with the CART approach has a predictive accuracy of 91% as compared to the singular use of the CART model. The Bayesian Hierarchical model with classification and regression tree is therefore built to improve upon the credit granting decisions of financial institutions.
A Comparative Analysis of Forecast Performance between Sarima and Setar Models Using Macroeconomic Variables in Ghana
(University of Ghana, 2018-07) Ahmed, N.B.
Most macroeconomic variables such as; inflation, GDP and others have been described by most financial and economics time series analysts to exhibit nonlinear behaviour. Therefore, to cater for this behaviour, the nonlinear class of models have been largely adopted to model and forecast such time series. In this study, the Keenan and Tsay tests for linearity showed inflation and CIC rates follow threshold nonlinear processes. Hence, the two-regime SETAR model was adopted to accommodate these nonlinearities in the datasets. Using the linear SARIMA model as a benchmark for comparative analysis. Results from both in-sample and out- of- sample forecast performance using MAE and RMSE measures revealed that, the nonlinear SETAR model outperformed the linear SARIMA model for inflation. This was however different for CIC rates, since the Linear SARIMA model turned to outperform the nonlinear SETAR model. Further analysis of forecast accuracy using the Diebold- Mariano test showed there was no significant difference between the two models for inflation but, there was significant difference between both models for CIC rates. Nevertheless, it is recommended that, continuous monitoring of these models, review market conditions and necessary adjustments are vital to make realistic use of these models.
Comparative Analysis of Statistical models in Credit Assessment
(University of Ghana, 2013-06) Ansah, A.Y.; Nortey, E.N.N.; Baidoo, I.
With the emergence of the current financial crisis, important advances have been made in credit risk management. Inherent in this management process is the assessment of creditworthiness routine which subsequently leads to a credit granting decision. This study is aimed at developing a statistical model that can be used to ascertain credit assessment and to predict the probability of default of firms seeking credit from a Ghanaian commercial bank. Subsequently, an attempt was made to find financial ratios that can best be made used to successfully construct the model. To achieve these purposes, the study employed the Probit and logit models for comparative reasons in terms of their predictive abilities. Performance of the models was assessed using the percentage correctly classified (PCC) and the area under the receiver operating characteristics curved (AUC) where significant differences between the two models were observed. It was found that both the Probit and the logit classifiers yield very good performance rates but the logit model performed better for credit scoring. It was also found that ratios bordering on assets to liability ratios, account receivable to liability, Cash to Assets, current liability to total liabilities , Net current asset ,and total asset firm size are those that were significantly helpful in scoring credit applicant. Practically the model assist in reducing the time spent on evaluating credit applicant of each firm subject to the model and also serve as a difference between application serving and portfolio management . Indeed the multiplier effect will be a significant improvement in loan portfolio quality of the model user.
Comparison Of Imputation Methods For Missing Values In Longitudinal Data
(University of Ghana, 2017-06) Katsekpor, J.
Longitudinal data are common in various sectors where repeated measurements on a dependent variable are collected for all subjects. Missing data pattern are caused when most planned measurements are unavailable for some subjects. The dropout process may cause three missing values mechanism, namely: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). The missing values have influence on quantitative study that can be serious, leading to biased estimates of parameters, information loss, reduced statistical power, increased standard errors, and weakened generalization of findings. This thesis compared the performance of seven (7) techniques of imputing missing values under the assumptions of MCAR and MAR mechanisms. The study adopted the little’s test to check whether a dataset with missing values is MCAR or MAR. The techniques for solving missing values problems were compared using the Generalized Estimating Equation (GEE) model for the complete dataset, the coefficient of determination and root mean squared error (RMSE). The study discovered that when large (above 10%) or small (below 10%) values are missing at random (MAR), it is important to use multiple imputation or expectation maximization to replace missing values in the dataset. The pairwise deletion is the best under MCAR mechanism. Listwise deletion and the hot deck imputation methods performed poorly under the MCAR mechanism. It is recommended that researchers should understand the patterns of missing values in dataset and clearly recognize missing data problems and the situations under which they occurred. However, further research is needed to find a better method for imputing missing not at random (MNAR) with multiple imputation. This thesis focused on missing values in a longitudinal dataset. However, future research using categorical data is a step in right the direction.
Credit Card Fraud Detection; A Machine Learning Approach
(University Of Ghana, 2020-11) Glah, J.
In recent times, credit card usage has increased tremendously because it is convenient to use and also saves a lot of time. Credit cards are rectangular plastic cards issued by banks which allow a person to borrow funds from a pre - approved limit to pay for one’s purchases now and pay later. In the same manner, credit card frauds have also been on the increase causing huge sums of financial loss to credit card issuers. Credit card fraud is the use of a credit card by someone who is not the owner of the card and is not allowed to use it. In this study, three classification methods were used to do a deep analysis of credit card transactions history and the fraud detection models built. This study presents and demonstrates the advantages of support vector machine, artificial neural network and the k - nearest neighbor algorithms to the credit cards data for the purpose of reducing the bank’s losses. The results show that the linear support vector machine and k - nearest neighbor approaches outperform artificial neural network in solving the problem under investigation. This study allows for multiple algorithms to be integrated together as modules and their results combined to increase the accuracy of the final results.
Delinquency and Default Risk Modeling of Microfinance in Ghana
(University of Ghana, 2013-06) Arku, D.; Doku-Amponsah, K.; Baidoo, I.K.
The objective of this research was to identify the risk factors that influence loan default of customers in the microfinance sector and to develop model that links these factors to credit default for any customer in the sector. Data from a microfinance institution based in Accra was used. A binomial logistic regression analysis was fitted to a data of 472 customers who were granted credit from January 2011 to December 2012. Based on the Wald criterion, it was realized that among the variables that were considered only six out of the 16 predictor variables significantly influence the probability of loan default. One of the key findings of the study was the fact that the loan officer has a significant effect on loan default risk. Other factors include; Client‟s age, Assessment, Type of collateral, guarantor, and residential status. The findings suggest that, default rate is higher for trading and manufacturing sectors than for food vendors as well as those in the service sector. Clients in the service industry are relatively less risky. Clients with guarantors or security other than household items perform well in their obligations. The findings indicated that the responsibility of the loan officer have a tremendous impact on loan default. A test of the full model against a baseline model was statistically significant, indicating that the predictors, as a set, reliably distinguished defaulters and non- defaulters. The Receiver Operating Characteristics (ROC) that measures the sensitivity and specificity of the model was significant at 0.05 level. Using the hold out sample, the model is able to classify defaulters and non-defaulters with at least 80% accuracy. This means that for every four out of five clients, the model is able to predict correctly: default or otherwise. The model could serve as tool to manage and improve loan decision and ultimately enterprise profitability.

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