Financial Risk Manager Part 1

Explanation

Option C is correct because:

• Neural networks consist of interconnected nodes (neurons) organized in layers (input, hidden, output)
• The connections between nodes have weights that are adjusted during training
• They are particularly effective at modeling complex non-linear relationships due to their ability to learn hierarchical representations of data
• The activation functions in neurons (like sigmoid, ReLU, tanh) introduce non-linearity, enabling the network to approximate complex functions

Why other options are incorrect:

• Option A: Neural networks are inspired by biological neural networks but are not precise replicas of the human nervous system
• Option B: While neural networks can perform regression, they are not based on linear regression models - they can model non-linear relationships
• Option D: This describes a decision tree structure, not a neural network. Neural networks have layered architectures, not tree-like structures

Neural networks excel at pattern recognition, classification, and regression tasks where the relationships between variables are complex and non-linear.