Financial Fraudulent Detection using Vortex Search Algorithm based Efficient 1DCNN Classification

Repeated loan fraud threatens financial stability and drives away clients. Financial and banking institutions must immediately recognize network fraud. The banking sector in India and other countries is using AI-driven technology and machine learning algorithms to tackle fraud and unauthorized access. Innovative thinking, increased globalization, and technical developments are raising fraud detection costs. The suggested study would help banks identify credit application fraudsters. The study automated data preprocessing using Kaggle's K-Nearest Neighbor (KNN) algorithm. A one-dimensional convolutional neural network classified. Using the Vortex Search Algorithm (VSA) to fine-tune the classifier hyperparameters improved results. VSA determined the model's hyperparameter sweet spot. The suggested model outperforms other categorization methods with 98.62% accuracy. Better lending banking fraud detection may result from the proposed approach. The VSA-based 1DCNN model detects fraud faster and more precisely.