Financial Crime World

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Neural Networks Outshine Others in Financial Fraud Detection

A recent study has revealed that a trio of artificial neural network (ANN) techniques has emerged as the most efficient and feasible methods for detecting financial fraud. The three techniques - ANN with automatic ontology learning, convolutional ANN based on functional sequencing, and ANN with Gradient Boosting Decision Tree (XGBoost) - have been found to excel in all four established criteria, including experimental study, algorithm development, theoretical background, and accuracy of the algorithm.

Study Highlights

  • Average accuracy range of 79% to 98.74%
  • Top three techniques achieved average accuracies above 95%

According to a comprehensive analysis of 32 documents, these three techniques achieved an average accuracy range of 79% to 98.74%. Notably, D[27] and D[18] stood out with average accuracies of 98.63% and 98.65%, respectively, utilizing Bayesian regularization Gradient Descent Adaptive GDA (GDA) for optimization and ontology neural network.

Other Notable Techniques

  • ANN with denoiser autoencoder
  • ANN with SMOTE
  • Deep neural network (DNN)
  • ANN with cortical learning algorithm (CLA) and hierarchical temporal memory (HTM)

While these studies achieved good ratings in experimental study and accuracy of the algorithm criteria, they require a stronger theoretical background and broader explanation of their algorithm development.

Implications for Financial Institutions

The findings have significant implications for financial institutions and organizations seeking to improve their fraud detection capabilities. The study’s conclusions highlight the need for continued research and development in neural network algorithms to stay ahead of increasingly sophisticated fraudulent methods.

Accuracy of ANN Complementary Techniques

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A graphical representation of the average accuracy of compiled techniques in the 32 documents is provided below.

[Insert Figure 5: Accuracy Of Ann Complementary Techniques]

The graph shows an average accuracy range between 79% and 98.74%. Notably, several studies achieved average accuracies above 95%, including D[27], which utilized Bayesian regularization GDA for optimization, and D[18], which employed ontology neural network.

Conclusions

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In conclusion, the study has demonstrated that ANN with automatic ontology learning, convolutional ANN based on functional sequencing, and ANN with XGBoost are the most efficient and feasible methods for detecting financial fraud. The findings highlight the importance of continued research and development in neural network algorithms to stay ahead of increasingly sophisticated fraudulent methods.

Neural networks have become a crucial tool in financial fraud detection, offering high accuracy rates and robust performance. As the study’s results demonstrate, these techniques can help organizations improve their fraud detection capabilities and mitigate the risks associated with financial crime.

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