Design of Financial Risk Control Model Based on Deep Learning Neural Network

Area of Science:

• Financial Technology
• Data Science
• Machine Learning

Background:

• Increasing financial business volume leads to rising risks and complex fraud cases.
• Concealed criminal methods necessitate advanced risk management solutions.
• Current risk control models struggle with data imbalance and time-series complexities.

Purpose of the Study:

• To design an effective financial risk control model using deep learning.
• To reduce financial risks through enhanced fraud detection.
• To improve the identification of fraudulent customers in financial transactions.

Main Methods:

• Preprocessing financial data using the Borderline-SMOTE algorithm for data imbalance.
• Employing an oversampling method to address data imbalances.
• Utilizing the long short-term memory (LSTM) deep neural network (NN) for time-series data analysis.

Main Results:

• The LSTM model achieved a high accuracy of 0.9715, outperforming traditional methods.
• The proposed sample preprocessing and risk control model demonstrated superior fraud identification capabilities.
• The deep learning model exhibited faster iteration efficiency compared to existing approaches.

Conclusions:

• The developed deep learning model, incorporating LSTM and Borderline-SMOTE, is highly effective for financial risk control.
• This approach significantly enhances the ability to detect fraudulent customers.
• The model offers improved accuracy and efficiency for modern financial risk management.