Joyllene Vivian
Jomo Kenyatta University of Agriculture and Technology
Kenya
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Assistant Accountant at Apstar Sacco
I am a detail-oriented financial analyst with a Bachelor of Science in Financial Engineering from Jomo Kenyatta University of Agriculture and Technology. I have a strong background in financial modeling, accounting, data analysis, and forecasting, combined with hands-on experience in credit management and customer engagement. In my role as an Assistant Accountant at Apstar Sacco, I successfully managed loans recovery and debt collection processes, while my time as a Salesperson at Chamte Patisserie enhanced my communication and problem-solving skills. I am proficient in financial software, possess a deep understanding of financial markets and trends, and excel in generating insightful reports to support strategic decision-making. Currently pursuing CPA certification and a Data Analytics course at Amdari.io, I am passionate about financial management, analytics, and project leadership. I thrive in fast-paced environments and am committed to delivering value by applying my expertise to drive organizational success.
Experience
Apstar Sacco
Assistant Accountant
January 2023 - April 2023
Recognizing and making payments by posting to the general ledger.
Preparation of Financial statements and monthly management reports including accounts and ledger reconciliations.
Payroll processing and management.
Timely filing and submission of statutory returns ie. VAT, PAYE, NSSF NHIF.
Education
Certificates & Badges
No certificates or badges added
Projects
MODELLING FINANCIAL DISTRESS OF NSE LISTED FIRMS : A COMPARATIVE ANALYSIS OF ALTMAN Z SCORE AND LOGISTIC REGRESSION MODEL
•Team member
This study compares the performance of Altman Z Score and Lasso Logistic Regression models in predicting financial distress in Kenya from 2018 to 2022. Lasso Logistic Regression emphasizes liquidity and profitability ratios, showing improvement in 2022. Evaluation metrics like confusion matrices reveal fluctuating performance, with Altman Z Score consistently outperforming Lasso Logistic Regression in accuracy and recall. The study addresses the need for predictive models in Kenya's context and highlights key variables. Recommendations include proactive measures for flagged companies, improving data quality, tailoring models to industries, incorporating economic indicators, and refining models through cross-validation.
Languages
English
Professional
German
Beginner
Skills
Data Analytics
SQL
Financial Analysis
Financial Accounting
R Statistical
Accounting Practices