A comparative analysis of the determinants of profitability of commercial and microfinance banks in Nigeria

Purpose: The study aims to examine the determinants of profitability of commercial and microfinance banks in Nigeria, in order to be able to highlight the possible effect of Central Bank of Nigeria policy actions in influencing the internal factors and subsequently the profitability of the banks in Nigeria. Research methodology: The study adopted the panel data research design. Out of the total number of 22 commercial banks and 898 microfinance banks the study sampled 4 commercial banks and 4 microfinance banks using random sampling technique, and based on the availability of data. Data were sourced from the annual balance sheets and income statement of banks from 2010 to 2018 and analysed using the Random Effect Panel Estimation Technique. Finding: Findings from the study show that liquidity ratio is not a strong determinant of banks profitability whether commercial or microfinance banks while capital adequacy is a significant determinants of the profit level in both banks with positive effect for microfinance and negative effect for commercial banks. The study also found that real GDP is a significant determinant of only commercial banks profitability. This by implication indicates that the recent policy action by the central bank which saw the increase of cash reserve ratio from 22.5% to 27.5% is expected to have an insignificant reduction on the profitability of the banks. Limitation: The major limitation of the study is the use of a single measure of profitability and a single measure of external factor. The study period as well as its sample size was also considered as limitation. Contribution: Findings from this study are useful to the management of the banks, the selected banks to be more specific, and shareholders. Also, this study provides insights on the possible effect of the recent policy of the Central Bank on the banking sector. Thus, the results of this study are useful to policy makers and regulators of the financial system in Nigeria. Keywords: Profitability, Commercial Bank, Microfinance Banks, Liquidity Ratio, Capital Adequacy, Return on Asset, Real GD

Bipol: A Novel Multi-Axes Bias Evaluation Metric with Explainability for NLP

We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to detect bias along multiple axes using SotA architectures, we evaluate two popular NLP datasets (COPA and SQUAD). As additional contribution, we created a large dataset (with almost 2 million labelled samples) for training models in bias detection and make it publicly available. We also make public our codes.Comment: 12 pages, 4 image

Adapting Pretrained ASR Models to Low-resource Clinical Speech using Epistemic Uncertainty-based Data Selection

While there has been significant progress in ASR, African-accented clinical ASR has been understudied due to a lack of training datasets. Building robust ASR systems in this domain requires large amounts of annotated or labeled data, for a wide variety of linguistically and morphologically rich accents, which are expensive to create. Our study aims to address this problem by reducing annotation expenses through informative uncertainty-based data selection. We show that incorporating epistemic uncertainty into our adaptation rounds outperforms several baseline results, established using state-of-the-art (SOTA) ASR models, while reducing the required amount of labeled data, and hence reducing annotation costs. Our approach also improves out-of-distribution generalization for very low-resource accents, demonstrating the viability of our approach for building generalizable ASR models in the context of accented African clinical ASR, where training datasets are predominantly scarce

Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages

AfriSenti-SemEval Shared Task 12 of SemEval-2023. The task aims to perform monolingual sentiment classification (sub-task A) for 12 African languages, multilingual sentiment classification (sub-task B), and zero-shot sentiment classification (task C). For sub-task A, we conducted experiments using classical machine learning classifiers, Afro-centric language models, and language-specific models. For task B, we fine-tuned multilingual pre-trained language models that support many of the languages in the task. For task C, we used we make use of a parameter-efficient Adapter approach that leverages monolingual texts in the target language for effective zero-shot transfer. Our findings suggest that using pre-trained Afro-centric language models improves performance for low-resource African languages. We also ran experiments using adapters for zero-shot tasks, and the results suggest that we can obtain promising results by using adapters with a limited amount of resources.Comment: SemEval 202