Statistical modelling for detection of fraudulent activity on banking cards
- Authors: Nasila, Mark Wopicho
- Date: 2014
- Subjects: Mathematical statistics Mathematical models , Statistics Bank fraud
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/45887 , vital:39314
- Description: The current global recession has highlighted the fragile banking and related systems exposure to risks and acts of fraud. As a result of the ever changing information technology environment, where the internet has become an important retail sector channel, new fraud challenges are being encountered. The rapid growth in credit and cheque card transactions as a payment mechanism has led to an increase in card fraud. Approximately 70% of consumers utilising credit and cheque cards, as payment mechanisms, are significantly concerned about fraud (McAlearney, 2008). Additionally, credit card fraud has broader negative implications, such as funding organised crime, international narcotics trafficking and even the financing of terrorist activities. The first section of this study develops classification models that will improve on existing methods used to detect fraud and, as a result thereof, reduce the number of fraudulent transactions. Using confidential data obtained from a South African Bank, logistic regression and scoring techniques have been combined to develop a classification model that improves on the existing fraudulent identification methods. Using the methods developed in this study, a higher percentage of fraudulent transactions are classified correctly when compared to discriminant analysis, a method often used to identify fraudulent transactions. These models enable the banking business to identify demographic, socio-economic and banking-specific determinants which contribute significantly towards fraudulent transactions. The early detection methods will allow banks to put in place measures that will reduce the occurrence of fraudulent transactions on customer’s cards. The second section involves understanding how card holders and merchants contribute towards the occurrence of fraudulent incidents. This was achieved through two surveys which were carried out in the Johannesburg metropolitan area. These surveys aimed at understanding the perceptions of card holders and merchants with regard to aspects pertaining to card fraud contributed towards the occurrence of card fraud. Multinomial logistic regression (MLR) is used to classify card holders and merchants according to their likelihood of experiencing card fraud incidents. These results are based on their perceptions of certain aspects related to card fraud as obtained from the survey instruments.
- Full Text:
- Date Issued: 2014
- Authors: Nasila, Mark Wopicho
- Date: 2014
- Subjects: Mathematical statistics Mathematical models , Statistics Bank fraud
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/45887 , vital:39314
- Description: The current global recession has highlighted the fragile banking and related systems exposure to risks and acts of fraud. As a result of the ever changing information technology environment, where the internet has become an important retail sector channel, new fraud challenges are being encountered. The rapid growth in credit and cheque card transactions as a payment mechanism has led to an increase in card fraud. Approximately 70% of consumers utilising credit and cheque cards, as payment mechanisms, are significantly concerned about fraud (McAlearney, 2008). Additionally, credit card fraud has broader negative implications, such as funding organised crime, international narcotics trafficking and even the financing of terrorist activities. The first section of this study develops classification models that will improve on existing methods used to detect fraud and, as a result thereof, reduce the number of fraudulent transactions. Using confidential data obtained from a South African Bank, logistic regression and scoring techniques have been combined to develop a classification model that improves on the existing fraudulent identification methods. Using the methods developed in this study, a higher percentage of fraudulent transactions are classified correctly when compared to discriminant analysis, a method often used to identify fraudulent transactions. These models enable the banking business to identify demographic, socio-economic and banking-specific determinants which contribute significantly towards fraudulent transactions. The early detection methods will allow banks to put in place measures that will reduce the occurrence of fraudulent transactions on customer’s cards. The second section involves understanding how card holders and merchants contribute towards the occurrence of fraudulent incidents. This was achieved through two surveys which were carried out in the Johannesburg metropolitan area. These surveys aimed at understanding the perceptions of card holders and merchants with regard to aspects pertaining to card fraud contributed towards the occurrence of card fraud. Multinomial logistic regression (MLR) is used to classify card holders and merchants according to their likelihood of experiencing card fraud incidents. These results are based on their perceptions of certain aspects related to card fraud as obtained from the survey instruments.
- Full Text:
- Date Issued: 2014
Statistical models for pricing weather derivatives for Port Elizabeth
- Authors: Nasila, Mark Wopicho
- Date: 2009
- Subjects: Weather derivatives -- Prices -- South Africa -- Port Elizabeth
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10567 , http://hdl.handle.net/10948/1018 , Weather derivatives -- Prices -- South Africa -- Port Elizabeth
- Description: Weather has a significant impact on business activities of many kinds. The list of economic activities subjected to the risk of the weather include: the energy producers and consumers, the industry of leisure, the insurance industry, the food industry and the agricultural industries but the primary industry, namely the energy industry, has given rise to the demand for weather derivatives and has caused the weather risk management industry to evolve actively. A derivative is a contract or security, whose payoffs depend upon the price of an underlying asset price, and is used to control the risks of naturally-arising exposures to such an asset price. Therefore weather derivatives are financial contracts with payouts that depend on weather in some form. It is a contract that provides a payoff in response to an index level based on weather phenomena (West, 2002).The underlying variable can be for example humidity, rain, snowfall, temperature, or even sunshine. The main players who take part in the weather derivatives markets industry can be grouped in to five main categories, namely: 1) End users who are also referred to as hedgers 2) Speculators 3) Market makers 4) Brokers 5) Insurance and re-insurance companies. Since the late 90’s when the first weather derivatives transactions were recorded, the underlying market has witnessed the development of a new derivative market in the United States, which is gradually expanding across Europe. However, the newly developed market for weather derivatives is not liquid in Africa and specifically South Africa mainly due to the following factors: 1) Many companies and business organisations have not yet established a hedging policy or even figured out how their businesses or industries are exposed to weather risks. 2 2) “Since many companies and industries depend on insurance companies to cover their risks, it is possible that the solutions suggested by these companies or industries looking for protection from weather risks differ according to the cover provided by these insurance organisations “(Micali, 2008). The main aim of this study is to review available statistical models for pricing derivatives, with temperature as the underlying which could enable industries, businesses and other organisations in South Africa to protect themselves against losses due to fluctuations in the weather and therefore hedge their risks.
- Full Text:
- Date Issued: 2009
- Authors: Nasila, Mark Wopicho
- Date: 2009
- Subjects: Weather derivatives -- Prices -- South Africa -- Port Elizabeth
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10567 , http://hdl.handle.net/10948/1018 , Weather derivatives -- Prices -- South Africa -- Port Elizabeth
- Description: Weather has a significant impact on business activities of many kinds. The list of economic activities subjected to the risk of the weather include: the energy producers and consumers, the industry of leisure, the insurance industry, the food industry and the agricultural industries but the primary industry, namely the energy industry, has given rise to the demand for weather derivatives and has caused the weather risk management industry to evolve actively. A derivative is a contract or security, whose payoffs depend upon the price of an underlying asset price, and is used to control the risks of naturally-arising exposures to such an asset price. Therefore weather derivatives are financial contracts with payouts that depend on weather in some form. It is a contract that provides a payoff in response to an index level based on weather phenomena (West, 2002).The underlying variable can be for example humidity, rain, snowfall, temperature, or even sunshine. The main players who take part in the weather derivatives markets industry can be grouped in to five main categories, namely: 1) End users who are also referred to as hedgers 2) Speculators 3) Market makers 4) Brokers 5) Insurance and re-insurance companies. Since the late 90’s when the first weather derivatives transactions were recorded, the underlying market has witnessed the development of a new derivative market in the United States, which is gradually expanding across Europe. However, the newly developed market for weather derivatives is not liquid in Africa and specifically South Africa mainly due to the following factors: 1) Many companies and business organisations have not yet established a hedging policy or even figured out how their businesses or industries are exposed to weather risks. 2 2) “Since many companies and industries depend on insurance companies to cover their risks, it is possible that the solutions suggested by these companies or industries looking for protection from weather risks differ according to the cover provided by these insurance organisations “(Micali, 2008). The main aim of this study is to review available statistical models for pricing derivatives, with temperature as the underlying which could enable industries, businesses and other organisations in South Africa to protect themselves against losses due to fluctuations in the weather and therefore hedge their risks.
- Full Text:
- Date Issued: 2009
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