Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Loan Risk Prediction: Comparing Neural Networks and SVMs

Loan Risk Prediction: Comparing Neural Networks and SVMs

Wanjale, Kirti
Wanjale, Aditya

Paperback

Probability & Statistics

ISBN10: 6208421349
ISBN13: 9786208421342
Publisher: LAP Lambert Academic Publishing
Published: Jan 10 2025
Pages: 56
Weight: 0.19
Height: 0.13 Width: 6.00 Depth: 9.00
Language: English
Loan risk assessment plays a pivotal role in the financial industry, and predictive models are essential for making informed lending decisions. This research project delves into the domain of loan risk assessment, a critical aspect of the financial industry, by proposing an innovative approach utilizing the Feed Forward Neural Network (FNN) algorithm. The primary focus is on comparing the efficacy of the FNN algorithm with the widely adopted Support Vector Machines (SVM) for loan risk prediction. The objective is to assess the FNN algorithm's effectiveness in predicting loan defaults, aiming for a comprehensive understanding of its performance in comparison to SVM. The results obtained are promising, indicating the superior accuracy of the FNN model compared to SVM. This highlights the potential of the FNN algorithm in revolutionizing loan risk assessment. Our findings underscore the importance of leveraging AI and ML, specifically neural networks, to enhance the accuracy and reliability of loan risk prediction systems. The FNN model's impressive performance positions it as a game-changer in the field, offering enhanced accuracy and reliability in loan risk prediction systems.

Also from

Wanjale, Kirti

View All

Also in

Probability & Statistics

View All