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Research Article
Credit Risk Modelling Using RNN-LSTM Hybrid Model for Digital Financial Institutions
Gabriel King’auwi Musyoka*
,
Antony Gichuhi Waititu,
Herbert Imboga
Issue:
Volume 10, Issue 2, June 2024
Pages:
16-24
Received:
11 March 2024
Accepted:
26 March 2024
Published:
11 April 2024
Abstract: In response to the rapidly evolving financial market and the escalating concern surrounding credit risk in digital financial institutions, this project addresses the urgency for accurate credit risk prediction models. Traditional methods such as Neural network models, kernel-based virtual machines, Z-score, and Logit (logistic regression model) have all been used, but their results have proven less than satisfactory. The project focuses on developing a credit scoring model specifically tailored for digital financial institutions, by leveraging a hybrid model that combines long short-term memory (LSTM) networks with recurrent neural networks (RNN). This innovative approach capitalizes on the strengths of the Long-Short Term Memory (LSTM) for long-term predictions and Recurrent Neural Network (RNN) for its recurrent neural network capabilities. A key component of the approach is feature selection, which entails extracting a subset of pertinent features from the credit risk data using RNN in order to help classify loan applications. The researcher chose to use data from Kaggle to study and compare the efficacy of different models. The findings reveal that the RNN-LSTM hybrid model outperforms other RNNs, LSTMs, and traditional models. Specifically, the hybrid model demonstrated distinct advantages, showcasing higher accuracy and a superior Area Under the Curve (AUC) compared to individual RNN and LSTM models. While RNN and LSTM models exhibited slightly lower accuracy individually, their combination in the hybrid model proved to be the optimal choice. In summary, the RNN-LSTM hybrid model developed stands out as the most effective solution for predicting credit risk in digital financial institutions, surpassing the performance of standalone RNN and LSTM models as well as traditional methodologies. This research contributes valuable insights for banks, regulators, and investors seeking robust credit risk assessment tools in the dynamic landscape of digital finance.
Abstract: In response to the rapidly evolving financial market and the escalating concern surrounding credit risk in digital financial institutions, this project addresses the urgency for accurate credit risk prediction models. Traditional methods such as Neural network models, kernel-based virtual machines, Z-score, and Logit (logistic regression model) hav...
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Research Article
Three-parameters Gumbel Distribution: Formulation and Parameter Estimation
Issue:
Volume 10, Issue 2, June 2024
Pages:
25-37
Received:
6 March 2024
Accepted:
3 April 2024
Published:
20 June 2024
DOI:
10.11648/j.ijsd.20241002.12
Downloads:
Views:
Abstract: Modeling extreme value theories is really gaining interest in the world with scientist working to improve the flexibility of the distributions by adding parameter(s). Extreme value distributions are always described to include families of Gumbel, Weibull and Frechet distributions. Of the three distributions, Gumbel distribution is the most commonly used in the extreme value theory analysis. Existing literature has shown that the addition of parameter to a distribution makes it robust and/or more flexible hence the study intends to improve the existing two parameters Gumbel distribution using the Marshall and Olkin proposed method for introducing a new estimator/parameter to an existing distribution. The developed distribution will be important to the applications in some life time studies like high temperature, earthquakes, network designs, horse racing, queues in supermarket, insurance, winds, risk management, ozone concentration, flood, engineering and financial concepts. The parameters for the introduced distribution was estimated using Maximum Likelihood Estimation method. The introduced three parameters Gumbel distribution is a probability distribution function which can be used in modelling statistical data. The maximum likelihood estimates for the three parameters namely shape, location and dispersion are efficient, sufficient and consistent and this makes the function more flexible and better for application. The three parameters Gumbel distribution can be used in modeling and analysis of normal data, skewed data and extreme data since it will provide efficient, sufficient and consistent estimates.
Abstract: Modeling extreme value theories is really gaining interest in the world with scientist working to improve the flexibility of the distributions by adding parameter(s). Extreme value distributions are always described to include families of Gumbel, Weibull and Frechet distributions. Of the three distributions, Gumbel distribution is the most commonly...
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Research Article
Quartic Transmuted Exponential Distribution: Characteristics and Parameter Estimation
Jones Asante Manu*,
Nathaniel Howard,
Bismark Kwao Nkansah
Issue:
Volume 10, Issue 2, June 2024
Pages:
38-47
Received:
30 April 2024
Accepted:
24 May 2024
Published:
26 June 2024
Abstract: The scope for generating high-rank transmuted distributions has expanded beyond the cubic to achieve improved performance in baseline distributions such as those of the Gamma type. This paper develops a Quartic Rank Transmutation Distribution (QRTD), a new family of transmuted distributions with enhanced flexibility for modelling complex data problems, including those with multi-modal distributions. Application is carried out to obtain a transmuted exponential distribution (QTED). Various characteristics of the new exponential distribution are presented, including the cumulative distribution function, the reliability and hazard functions, moments, and relevant order statistics. These features support the legitimacy and robustness of the proposed QTED. Additionally, the paper identifies specific parameter ranges that exhibit notable behaviours in the new distribution and its survival quantities. The maximum likelihood estimates of parameters are described, with simulation studies indicating that their precision improves with larger sample sizes. The performance of the QTED is found to be superior to existing lower-rank cubic and quadratic transmuted exponential distributions based on information criteria using real lifetime data. The applications demonstrate that the high-rank transmutation map could be instrumental in obtaining new transmutations of other relevant distributions with improved performance. This development signifies a major advancement in the field of probability distributions, offering more sophisticated tools for statisticians and researchers to model and analyse complex data patterns more accurately and effectively. Thus, the QRTD and its applications hold significant promise for future research and practical implementations in various statistical and applied fields.
Abstract: The scope for generating high-rank transmuted distributions has expanded beyond the cubic to achieve improved performance in baseline distributions such as those of the Gamma type. This paper develops a Quartic Rank Transmutation Distribution (QRTD), a new family of transmuted distributions with enhanced flexibility for modelling complex data probl...
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