Mathematical Methods in Interdisciplinary Sciences

4Mathematical Methods in Deep Learning

Srinivasa Manikant Upadhyayulaand Kannan Venkataramanan

CRISIL Global Research & Analytics, CRISIL (A S&P Company), CRISIL House, Central Avenue, Hiranandani Business Park, Powai, Mumbai, 400 076, India

4.1 Deep Learning Using Neural Networks

Deep learning allows building quantitative models that constitute a series of processing layers to learn representations of data coupled with multiple levels of abstraction [1]. Neural networks were developed to understand the basic functioning of human brain and the entire central nervous system. Later, the model designed to capture the working of the human nervous system is applied to financial service domains such as link analysis of payments, fraud detection in customer transactions, and anomalies in transactions for potential money laundering.

4.2 Introduction to Neural Networks

A neural network works in the same pattern as that of a neuron in the human nervous system. The fundamental epitome of this learning technique is that it consists of a large number of highly organized and connected neurons working in harmony to solve a specific problem including pattern recognition or data classification. Neural networks are not a recent phenomenon but started before the advent of modern computers. It began with the work of McCulloch and Pitts [2] who created a theoretical representation of neural networks using a combination of human nervous system and mathematics (application of calculus and linear ...