An Empirical Analysis of the Impact of Globalisation on Performance of Nigerian Commercial Banks in Post-Consolidation Period

The study examined the impact of globalization on performance of Nigerian commercial banks between 2005 and 2010. It specifically determined the effects of policies of foreign private investment, foreign trade and exchange rate on performance of Nigerian banks. The study utilized panel data econometrics in a pooled regression, where time series and cross-sectional observations were combined and estimated. The results of econometric panel regression analysis confirmed that globalization, i.e. foreign private investment, foreign trade and exchange rate have positive effects on the profit after tax of banks but the magnitude of such effects remains indeterminable because we discovered that there are variations in the data for performance of banks understudied. Based on these findings, the study recommends that banks in Nigeria should not relent in their interaction with their foreign counterpart in doing business in order to increase their foreign earnings. Banks should also spend more on information and communication technology since this has the capacity of increasing their profit. This information technology (IT) should be used to localize all the branches to a single branch networking. In spending more on information and communication, they should make use of satellite communication and very small aperture terminal (VSAT) technology and internet banking VSAT technology apart from making possible voice and video banking. We further recommend that more banks should be opened in foreign countries in order to increase foreign participation in home country’s banking

Engineering a Ruled-Based Software Solution for Credit Rating and Worthiness Assessment in Financial Operations

In loan provision, the central worry is whether the borrower will default or payback. A good number of institutions world-wide have gone into distress owing to bad debt arising from inability to recover borrowed funds. Credit Rating is a technique that is widely used to evaluate applications tendered for credit, identify prospective borrowers, and manage existing credit accounts. This work is aimed at the development of a system capable of evaluating the credit worthiness of fund-seeking bank customers and other borrowers towards repayment capabilities of loan facility availed to them in due time. The method carefully examines who qualifies for a loan based on certain rules consisting of Payment History, Credit Owed, Credit Available, Age of Account, Crime Records, Medical Records, Amount to be borrowed. and other factors. Percentage weights for assessment of each of these factors were proposed including threshold percentage above which credit is predicted adequate to be given. This factor creates a sort of satisfaction and level-playing field for correct assessment of lending risk

Reducing the Time Requirement of k-Means Algorithm

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in ddimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the quality is good (ARIHA.0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA.0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data