Leveraging Dynamic ANN for Early Detection of Financial Distress | Chapter 2 | Mathematics and Computer Science: Contemporary Developments Vol. 10

Microfinance Institutions (MFIs) play a crucial role in Kenya, contributing approximately 7% to the economy according to the Central Bank of Kenya. Despite their success in promoting financial inclusion, MFIs face challenges such as poor management, competition, and unfavorable business conditions, which can lead to financial distress. In Kenya's economic landscape, financial hardship has been a growing concern since 2019 after the onset of Covid 19, leading to the closure of organizations as they are unable to meet their financial obligations and expectations. This study presents a financial distress prediction dynamic model using Artificial Neural Networks (ANN) using financial ratios as input features where each node represents a single financial metric utilizing their power to measure financial health, stability and standardization across periods of organizations. Data was normalized using a min-max scaling approach, setting values between 0 and 1 for better model convergence and split to training and testing set in a ratio 8:2. The perceived dynamic ANN model utilizes the Multilayer Perceptron Networks (MLP), which is modeled to be adaptable, scalable and adjustable with the ability to self-update its architecture and parameters achieving a 94% accuracy, with strong recall and precision of 92% showing predicted financially distressed instances that were actually distressed. Further, the model demonstrated a ROC-AUC score of 0.99, demonstrating its effectiveness in distinguishing between distressed and non-distressed instances. The model's balanced F1 score of 87% further highlights its value as an Early Warning System (EWS) for financial management, helping organizations make informed decisions early enough to avoid financial crises. To further improve the EWS, it is recommended to integrate a dynamic adjustment mechanism within the ANNGARCH framework. This enhancement aims to capture volatility in predictions and refine accuracy for risk assessment, which will be explored in future research.

Author (s) Details

Abel M. Nyabera

Department of Mathematics, Multimedia University of Kenya, Kenya.

 

Cynthia Ikamari
Department of Finance and Accounting, Multimedia University of Kenya, Kenya.

 

George Mocheche
Department of Mathematics, Multimedia University of Kenya, Kenya.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v10/3252  

Detecting Financial Statements’ Fraud Using Relational Trend Analysis (RTA)| Chapter 2 | Current Overview on Science and Technology Research Vol. 7

 Severe critiques trail the level and vertical study designs of detecting fiscal statements trickery (FSF) for inexact lack of accuracy. This paper presents the relational current study (RTA) method to correct this defect. The study selected the desk research procedure utilizing a rehashed five-old age financial affidavit dossier. The study working tables and simple MS Excel commands to act repeated related analysis equating the probabilities of the current ending incident of an item as one accompanying analogous probabilities of alike in the base period. The results presented that RTA caused indications which emphasize not only the question district of the financial charges but again the beginning(s) of the problem(s). The judgments again marked that RTA overcame the imperfections of allure forerunners accompanying better precision removing the need for state-of-the-art numerical modeling.

Author(s) Details:

Enyi, Patrick Enyi,
Department of Accounting, Babcock University, Nigeria.

Please see the link here: https://stm.bookpi.org/COSTR-V7/article/view/8504

Study on Target Financial Forecasting as an Instrument to Overcome Financial Difficulties: Concept, Technique and Practice

 The theoretical, methodological, and practical aspects of target financial forecasting at the micro level, i.e., at the company level, as a new tool for anti-crisis financial management that can help a company overcome financial difficulties and improve its financial position over virtually any time period are examined in this paper. Financial ratio analysis, corporate cash flows, and the Balanced Scorecard are all principles that the study approach and outcomes are built on (in terms of its financial score). The target financial forecasting procedure is described as consisting of an appropriate information-accounting support, a target financial forecasting of the company's financial position, a target forecasting of the company's cash flows, and the development of specific events (management decisions) aimed at overcoming the company's financial difficulties and improving its financial position. Target financial forecasting, according to the author, is an effective technique for financial forecasting in today's environment since it allows for the construction of a foundation for overcoming a company's financial challenges and improving its financial situation. The primary areas of the target financial forecasting application are anti-crisis bankrupt company management, management of a company on the verge of bankruptcy, and the growth of a firm's investment attractiveness (in terms of its financial aspect).

Author(s) Details:

Sergey Krylov,
Academic Department of Accounting, Analysis and Audit, Ural Federal University, Yekaterinburg, Russian Federation.

Please see the link here: https://stm.bookpi.org/NIEBM-V6/article/view/6194