Artificial Neural Network And Bayesian Network Models For ✓ Solved

Artificial neural network and Bayesian network models for

Artificial neural network and Bayesian network models for credit risk prediction.

Prediction of credit risks in lending bank loans using machine learning.

Predicting credit card delinquencies: An application of deep neural networks.

An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market.

Enterprise credit risk evaluation based on neural network algorithm.

Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches.

Machine Learning Models and Bankruptcy Prediction.

Should Credit Card Issuers Reissue Cards in Response to a Data Breach? Uncertainty and Transparency in Metrics for Data Security Policymaking.

Credit card fraud detection: realistic modeling and a novel learning strategy.

An experimental study with imbalanced classification approaches for credit card fraud detection.

A Multiple Classifiers System for Anomaly Detection in Credit Card Data with Unbalanced and Overlapped Classes.

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine.

A Closer Look into the Characteristics of Fraudulent Card Transactions.

Blast-ssaha hybridization for credit card fraud detection.

Comprehensive Review of Cybercrime Detection Techniques.

Credit card fraud detection using AdaBoost and majority voting.

Paper For Above Instructions

In recent years, the integration of artificial intelligence (AI) in the financial industry has transformed various aspects of banking, particularly in credit risk prediction and fraud detection. This paper aims to discuss the evolution and effectiveness of models such as artificial neural networks (ANNs) and Bayesian networks, which are extensively used for predicting credit risks and detecting fraud in credit card transactions.

Artificial Neural Networks in Credit Risk Prediction

Artificial Neural Networks have gained popularity due to their unique ability to model complex relationships within data. As outlined by Teles et al. (2020), ANNs can capture non-linear patterns that traditional statistical models might overlook. These models have been utilized effectively in predicting credit risks by analyzing historical data such as borrowers' credit histories, transaction records, and economic indicators (Lakhani et al., 2019).

For example, deep neural networks have been applied by Sun and Vasarhelyi (2018) to anticipate credit card delinquencies, providing financial institutions with critical insights needed for making informed lending decisions. The ability of ANNs to learn from and adapt to evolving patterns makes them particularly useful in environments that experience frequent fluctuations in consumer behavior.

Bayesian Networks in Credit Risk Assessment

Bayesian networks represent another pivotal technology in the realm of credit risk assessment. By utilizing probabilistic graphical models, these networks can infer the likelihood of various outcomes based on prior knowledge as highlighted by Huang et al. (2018) in their research on enterprise credit risk evaluation. Bayesian networks uniquely incorporate uncertainty into their models, which is essential for banking institutions that must navigate the unpredictable nature of credit markets.

This adaptability is crucial when faced with incomplete or changing data, helping to make better predictions regarding both risk levels and potential defaults. Integrating Bayesian approaches with machine-learning algorithms enhances the robustness of credit risk methodologies by applying a statistical foundation to machine-learning insights (Chi et al., 2019).

Hybrid Models for Credit Risk Prediction

The complexity of credit risk necessitates innovative approaches, leading to the rise of hybrid models that leverage both ANNs and Bayesian networks. According to Barboza et al. (2017), combining different modeling techniques can yield improved prediction accuracy and reliability. Hybrid models are designed to harness the strengths of various algorithms, creating a more comprehensive tool for risk evaluation and management.

For instance, a combination of neural networks and traditional statistical methods can counteract overfitting, enhance generalization, and improve the quality of predictions in distressed mortgage markets (Fitzpatrick & Mues, 2016). These hybrid models have shown significant potential in creating superior risk assessment frameworks for banking and finance.

Machine Learning in Credit Card Fraud Detection

On the issue of credit card fraud, the application of machine learning technologies merits attention for their effectiveness in identifying fraudulent transactions in real-time. Research by Dal Pozzolo et al. (2017) details the importance of realistic modeling in fraud detection, emphasizing the need to outperform simple heuristic approaches. Machine learning adds predictive capabilities that enable sharp, data-driven distinctions between legitimate and illegitimate transactions.

Moreover, strategies such as the use of AdaBoost for credit card fraud detection, as highlighted by Randhawa et al. (2018), showcase how ensemble methods can enhance detection rates by amalgamating various classifier outputs. The incorporation of these advanced methodologies aids in keeping pace with increasingly sophisticated fraud tactics used by cybercriminals.

Challenges and Future Directions

Despite the promising advancements in credit risk prediction and fraud detection, various challenges remain. Issues such as data quality, interpretability, and ethical implications of algorithmic biases require ongoing research and development. Additionally, as financial systems become more interconnected and digitized, the need for adaptive and resilient models that can respond to emerging threats continues to grow.

Future directions may involve advancements in explainable AI, which aims to make machine-learning models more transparent and understandable to stakeholders (Kundu et al., 2009). By improving the interpretability of complex models, institutions can gain greater trust in technology-driven decisions and enhance regulatory compliance concerning decision-making processes in credit assessments.

Conclusion

The adoption of artificial neural networks and Bayesian networks signifies a transformative shift in the way credit risk is predicted and fraud is detected within the financial sector. As technology evolves, embracing hybrid methodologies and tackling associated challenges will play a crucial role in enhancing the reliability and effectiveness of financial risk management systems. Maintaining a proactive stance and investment in research will ensure these technologies can adapt and evolve to meet future challenges.

References

• Barboza, F., Kimura, H., & Altman, E. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Systems with Applications, 83.
• Chi, G., Uddin, M. S., Abedin, M. Z., & Yuan, K. (2019). Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches. International Journal on Artificial Intelligence Tools, 28(05).
• Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit Card Fraud Detection: Realistic Modeling and a Novel Learning Strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8).
• Fitzpatrick, T., & Mues, C. (2016). An Empirical Comparison of Classification Algorithms for Mortgage Default Prediction: Evidence from a Distressed Mortgage Market. European Journal of Operational Research, 249(2).
• Huang, X., Liu, X., & Ren, Y. (2018). Enterprise Credit Risk Evaluation Based on Neural Network Algorithm. Cognitive Systems Research, 52.
• Kundu, A., Panigrahi, S., Sural, S., & Majumdar, A. K. (2009). Blast-SSAH Hybridization for Credit Card Fraud Detection. IEEE Transactions on Dependable and Secure Computing, 6(4).
• Lakhani, M., Dhotre, B., & Giri, S. (2019). Prediction of Credit Risks in Lending Bank Loans Using Machine Learning. SAARJ Journal on Banking & Insurance Research, 8(1), 55-61.
• Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit Card Fraud Detection Using AdaBoost and Majority Voting. IEEE Access, 6.
• Sun, T., & Vasarhelyi, M. A. (2018). Predicting Credit Card Delinquencies: An Application of Deep Neural Networks. Intelligent Systems in Accounting, Finance and Management, 25(4).
• Teles, G., Rodrigues, J. J., Rabê, R. A., & Kozlov, S. A. (2020). Artificial Neural Network and Bayesian Network Models for Credit Risk Prediction. Journal of Artificial Intelligence and Systems, 2.