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Effects Of Financial Restructuring On Financial Performance Of Deposit Taking Sacco's In Nairobi County
No full textSacco’s are the key microfinancing institutions for mobilization of financial resources for various development activities and where instability in the Sacco’s exist, financial restructuring is key in turning it round to stability and profitability. This study investigated the effects of financial restructuring on the financial performance of deposit taking Sacco’s in Kenya. The objectives included undertaking an in-depth study based on debt, equity, deposits restructuring. Loanable funds Theory, Liquidity Preference Theory and Pecking Order Theory informed the study. This study was based on 39 deposit taking Sacco’s in Nairobi County. This study covered a 10 year period from 2010 to 2019. A descriptive research design was adopted. The research adopted the use of secondary data. The secondary data was obtained from SASRA registry comprising of audited financial statements submitted by the deposit-taking SACCOs. This study used autocorrelation tests, heteroscedasticity tests, multicollinearity tests, normality assumptions and Hausman test to evaluate the data collected before the actual analysis. STATA software was used to conduct the analysis. The findings indicated that a positive and significant relationship between Debt restructuring and return on assets for the deposit taking Sacco’s in Nairobi County. There was a positive and significant relationship between Equity restructuring and return on assets for the deposit taking Sacco’s in Nairobi County. Deposit restructuring had a negative and significant relationship with return on assets for the deposit taking Sacco’s in Nairobi County. This implied that an increase in debt restructuring, equity restructuring and deposit restructuring leads to a significant increase on return on assets for the deposits taking Sacco’s in Nairobi County. The study concluded that there is a strong correlation between debt restructuring, equity restructuring and deposit restructuring on return on asset for the deposit taking Sacco’s. The study recommends that the Sacco management should raise their lending capacity by seeking controlled external borrowing to raise their immediate cash for lending when in distress. However the external borrowing should be controlled to avoid increasing in debts as compared to the total assets. The Sacco management should maintain their core capital as a Sacco reserve. The study recommends that the Sacco management should formulate a mechanism to raise their lending capacity by periodically increasing their minimum member deposits. This will increase the liquid cash for lending which is the core activity of the Sacco. However the minimum deposit should be structured to accommodate all classes of the depositors to avoid discouraging the members
Factors Influencing Social-environmental Responsibilities Disclosures In Financial Reports Of Kenyan Listed Firms
No full textThe objective of this study is to determine the factors influencing the social-environmental
responsibilities disclosures in Annual financial reports of Kenyan listed firms. Social
environmental responsibilities disclosures are voluntary therefore disclosed at the discretion of
management and has been identified by various studies to improve image, reputation, enhance
accountability, legitimacy and help manage stakeholders. Some studies have also shown that
financial factors, governance characteristics, ownership characteristics and stakeholders, influence
the extent of these disclosures, hence this study examined how the level of social environmental
responsibilities disclosures in financial reports of Kenya listed firms is influenced by their size,
profitability and leverage. Descriptive research design was used and secondary data was collected
from 2009 to 2018 annual reports of 45 out of 48 targeted companies listed prior to 2009. The
dependent variable is extent of disclosure is measured on total score from 39 disclosure items each
with a rating between ‘0’ to ‘3’ based on absence and the degree of specificity or detail. The
disclosure items was developed guided by Global Reporting Initiative index. STATA version 12
software was used to analyze the significance of the factors on level of Social environmental
responsibilities disclosures. Exploratory, descriptive, diagnostic analysis were performed and the
results showed that factors of firm’s size, leverage were positively significant and profitability is
negatively significant in influencing the disclosure of social environmental responsibilities
information on financial reports of Kenyan listed firms
Effects Of Corporate Social Responsibility On Financial Performance Of Commercial Banks In Kenya
No full textCorporate social responsibility (CSR) is a significant notion that is progressively being deliberated and adopted worldwide. Various stakeholders are demanding the incorporation of social activities in the organization’s daily practices. Some of the reasons why companies adopt CSR are; compliance with the law, to enhance a competitive advantage, others do it because it is the right thing to do for the society. This study thus sought to establish the effects of CSR activities on performance in financial terms of 42 commercial banks in Kenya with focus on economic social responsibility, discretional social responsibility and ethical social responsibility activities. A cross- sectional simple descriptive research design was used in this study. The study will exclusively used primary data by administering questionnaires to all the 42Commercial banks in Kenya. Financial performance was the dependent variable while economic, discretional and ethical social responsibility as the independent variables in the study
Model For Assessing Fraudulent Medical Claims: An Application Of Machine Learning Algorithms In Health Care
No full textThe insurance industry is experiencing challenges as claim verification has remained to be a manual process due to its nature that requires human observation. Health insurance cost has endlessly been increasing with time. In Kenya, the majority of citizens cannot afford healthcare services as most are not insured. The insurance cost is so high with low per capita income. The high cost is due to administrative cost that results from inefficient operational processes that are prone to errors and fraudulent claims. We propose a machine learning model that can be extended to a system which will create claim assessment automation that will greatly reduce the administrative resources required to process medical claims. The model will learn from previous data patterns and predict the correctness or review of the claim thus reducing errors that are prone to manual processes. Using machine learning techniques for classification like Logistic Regression and SVM we are able to classify a correct claim or a claim that will be called for review by the auditors. We will show the significance of our model and how it achieves better precision and accuracy than the manual process
The Influence Of Firm Internal Environment On The Innovative Capacity Of Commercial Banks In Kenya
Get PDFThe banking sector has of late been considered as one of the least competitive sectors in the
economy of Kenya considering the number of commercial banks in operation against the market
size. Introduction of new capital adequacy ratio by the CBK to be observed by commercial banks
brought about several mergers and acquisitions. To add on to this, the introduction of the interest
rate capping laws by the government in September 2016 saw a reduction of revenue sources by
banks since they were required to cover the cost of operation through thin margins. Commercial
banks have to device innovative ways to meet the ever-changing user needs by investing
improving their innovative capacity in order to beat competition and stay relevant in the market.
The study examined how variables in the firm internal environment comprising of firm networks,
human capital and absorptive capacity influenced the innovative capacity of commercial banks in
Kenya. The theories that informed the study included Social capital theory, Social Network
theory, the Knowledge based view theory of the firm and the open innovation paradigm theory.
The study adopted a descriptive research design to understand the happenings regarding
innovation in the banking industry in Kenya. The census sampling method was applied since the
target population of 43 was not huge and the target banks are all headquartered in Nairobi. The
study used primary data collected by use of a structured questionnaire. Data was analysed using
regression method and yielded descriptive statistics including frequency, percentage, mean and
standard deviation. Hypotheses were tested using ANOVA. The study established that whereas
both Firm networks, Human Capital and Firm absorptive capacity had a great influence on the
innovative capacity of commercial banks in Kenya, knowledge creation and sharing through
collaborative networks was the key driver of innovation in commercial banks. The study
concluded that Firm Networks, Human capital and Firm absorptive capacity had a great
influence on bank innovation since they both facilitated acquisition and sharing of new
knowledge which is the most vital resource in the innovation of new products, services,
processes and procedures. The study concluded that banks needed to intensify collaboration
through networks, motivate their workforce through creation of a knowledge culture and putting
in place effective reward and recognition systems and finally development and implementation
of knowledge management systems to facilitate knowledge creation, processing, sharing,
application and storage for use by future generations
An Integrated Information Systems Adoption Model for Businesses in Kajiado, Kenya
No full textBusiness organizations in Kenya are classified as
small, medium and large. Within these classifications, there
exist structural and operational factors that influence the
adoption of information systems (IS), and identifying these
factors is crucial to IS implementation. The study conducted a
survey of 50 such businesses within Kajiado, a County
characterized by under-developed IS infrastructure and a
resource scarcity that is typical of many peripheral counties in
Kenya. Results are presented in the form of descriptive
statistics and a hierarchical regression model. Some of the key
variables studies during adoption and diffusion included
characteristics of decision-makers, organization, the
environment and the IS infrastructure. The results of this
study can be used to identify the key variables that drive the
adoption of IS in small- and medium-sized businesses. The
study therefore provides parties involved with adoption
process a practical synopsis of the IS adoption process which is
believed to assist them with successful adoption
Hardware Sizing Model For Optimum Application Deployments A Focus On Small And Medium Enterprises In Kenya
No full textSizing is the measuring of system resources required to serve a deployed system and other running systems and processes in their working environments. The sizing model, like any theoretical model, is just an approximation. Research can be addressed differently depending on the system needs, task specifications, and sizing turnaround time. The most common method is to enter all load-related parameters into a modelling tool that is constructed on different hardware using load simulation results. The requirements for hardware and software are determined by the tool's mathematical model. No sizing can be a hundred percent accurate without performing a test on the actual hardware environment to be used. Nonetheless, there is a need to forecast capacity in real life while budgeting equipment, evaluating technological risk, validating technical design, predicting power requirements for production systems, and estimating project costs. These scenarios require a quick way to estimate the requirements of the system. There is a need to develop credible and accurate estimates of sizing when dealing with prospects without spending a lot of time. One of Kenya’s SMEs challenges is the amount of effort and time spent on sizing and also failure to meet the standards mostly due to sizing related issues as they can’t predict the performance and scalability needs of a system before deployment hence, they are rated poorly. This project attempts to explore alternative methods for rapid sizing. It is possible to establish a relationship between sizing factors and CPU/Memory usage by combining the empirical data collected from production system environment and statistical technique. This can be used when deploying new applications
Effect Of Youth Demographics On Financial Empowerment In Busia County, Kenya
No full textThe aim of the research was to investigate the effect of youth demographic on the financial empowerment of youth in Busia County. Specifically, the study sought to establish the effect of borrowing practices on financial empowerment in Busia County; establish how saving habit affects financial empowerment in the Busia County; assess the effect of age on financial empowerment in Busia County, and to establish the effect of level of education on financial empowerment in Busia County. The study was quantitative in nature and used questionnaires to collect data from a sample size of 60 youths randomly selected from a sampling list obtained from the Microfinance Association of Kenya in Busia County. Both descriptive and inferential statistics were used to analyze the collected data. Specifically, the following methods of analysis were used. Pearson’s correlation and independent samples t-test. Analyzed data was presented in charts, tables, and text formats. From the analytical model developed, the relationship coefficient acquired was 0.8197 which showed a strong positive correlation among youth entitlement, loan economic acquired from microfinance institutions, amount of savings, aging as well as training of the respondents. The coefficient of determination (R2) acquired was 0.6719 implied that the model developed could account for 67.19% of increase in level of youth entitlement. This implied that the model developed was necessary at 95% confidence level and hence could be applied in prediction as well as making policy. The research connives that when destitute borrowing practices provide them with start-up and working capital, education as well as savings habits, the youths are able to engage themselves in business practices where they experience increased efficiency and effectiveness which leads to a positive results on entitlement and their task in community as well as making policy. The conclusion was also drawn that microfinance services enable youth to retain more money, obtain assets, improve their profits, advance living standards, education, involvement in making policy, allowed movement, self-belief, gain selfrespect, advanced youth position to the community as well as decreased youth inhuman in the community. This research also concludes that most youth are in casual industry as well as not very trained. The study recommends that microfinance institutions review their services meant for the precise industry as well as repackage them to suit customers from the casual industry since the casual group forms the main part of their customers as results indicated. The research also recommends that microfinance institutions improve youth education mostly in rural areas to improve their experience of viable and justifiable assets ventures
Association between breakfast frequency and physical activity and sedentary time: a cross-sectional study in children from 12 countries
No full textBackground
Existing research has documented inconsistent findings for the associations among breakfast frequency, physical activity (PA), and sedentary time in children. The primary aim of this study was to examine the associations among breakfast frequency and objectively-measured PA and sedentary time in a sample of children from 12 countries representing a wide range of human development, economic development and inequality. The secondary aim was to examine interactions of these associations between study sites.
Methods
This multinational, cross-sectional study included 6228 children aged 9–11 years from the 12 International Study of Childhood Obesity, Lifestyle and the Environment sites. Multilevel statistical models were used to examine associations between self-reported habitual breakfast frequency defined using three categories (breakfast consumed 0 to 2 days/week [rare], 3 to 5 days/week [occasional] or 6 to 7 days/week [frequent]) or two categories (breakfast consumed less than daily or daily) and accelerometry-derived PA and sedentary time during the morning (wake time to 1200 h) and afternoon (1200 h to bed time) with study site included as an interaction term. Model covariates included age, sex, highest parental education, body mass index z-score, and accelerometer waking wear time.
Results
Participants averaged 60 (s.d. 25) min/day in moderate-to-vigorous PA (MVPA), 315 (s.d. 53) min/day in light PA and 513 (s.d. 69) min/day sedentary. Controlling for covariates, breakfast frequency was not significantly associated with total daily or afternoon PA and sedentary time. For the morning, frequent breakfast consumption was associated with a higher proportion of time in MVPA (0.3%), higher proportion of time in light PA (1.0%) and lower min/day and proportion of time sedentary (3.4 min/day and 1.3%) than rare breakfast consumption (all p ≤ 0.05). No significant associations were found when comparing occasional with rare or frequent breakfast consumption, or daily with less than daily breakfast consumption. Very few significant interactions with study site were found.
Conclusions
In this multinational sample of children, frequent breakfast consumption was associated with higher MVPA and light PA time and lower sedentary time in the morning when compared with rare breakfast consumption, although the small magnitude of the associations may lack clinical relevance
Application Of Machine Learning For Estimating Motor Vehicle Insurance Premium
No full textRisk models need to be estimated by insurance companies so as to predict the magnitude of claim and determine the premiums charged to the insured. This is intended to prevent losses in the future. Motor vehicle damage insurance is the most common type of insurance in the world, forming the largest sector of the insurance industry. It is also the type of insurance that generates the largest amount of loss for most insurance companies. In Kenya especially, the challenge faced by insurers is to balance the growth of the motor vehicle insurance business by increasing the customer base while also maintaining the profitability of this sector. It is therefore important to identify the main causes of problems associated with motor vehicle damage insurance, its impact on the revenue of the insurers and factors that contribute to the high motor claims to enable more accurate estimates of risk versus premium paid. In recent years the interest has increased in the use of information technology (IT) and statistical machine learning methods. This is supported by increasing computing capabilities, data availability and the trend towards automation of cumbersome or repetitive tasks. Statistical regression models have numerous applications, where they have been used in many contexts. Using a linear or generalized linear regression model in predicting insurance premiums is an area in which only a few pioneer studies have been carried out with promising results. This thesis explores applicability of new machine learning techniques such as tree-boosted models to optimize the proposed premium of prospective policy holders. It proposes two machine learning models for pricing motor vehicle damage insurance (decision trees and regression). The thesis is therefore aimed at identifying sources of risks in motor vehicles, identifying variable for motor vehicle premium determinants and then establishing a framework that will be used to regulate the premiums charged by motor vehicle insurers. Data from insurance companies has been used, which is made up of the premium rates and compensations, and other variables such as age, driver's experience, etc. Results of this thesis should be seen as successful for the use of generalized linear models in the making of car damage insurance premium rates. The established model will be used to advise the insurance companies on how to charge premiums dynamically