1. Neural Networks & a case with bankruptcy predictionBy
Jinhwa Kim

2. Neural Computing: The Basics
Neural Computing is a problem solving methodology that attempts to mimic how human brain functions
Artificial Neural Networks (ANN)
A Field in Machine Learning

3. Neural Computing
Computing technology that mimic certain processing capabilities of the human brain
Neural Computing = Artificial Neural Networks (ANNs)
Purpose of ANN is to simulate the thought process of human brain
Inspired by the studies of human brain and the nervous system

4. The Biology Analogy Neurons: brain cells
Nucleus (at the center)
Dendrites provide inputs
Axons send outputs
Synapses increase or decrease connection strength and cause excitation or inhibition of subsequent neurons

5. Artificial Neural Networks (ANN)
A model that emulates a biological neural network
Software simulations of the massively parallel processes that involve processing elements interconnected in a network architecture
Originally proposed as a model of the human brain’s activities
The human brain is much more complex

6. Artificial Neural Networks (ANN)
Three Interconnected Artificial Neurons
Biological Artificial
Soma Node
Dendrites Input
Axon Output
Synapse Weight
Slow speed Fast speed
Many neurons Few neurons
(Billions) (Dozens)

7. ANN Fundamentals Components and Structure
“A network is composed of a number of processing elements organized in different ways to form the network structure”
Processing Elements (PEs) – Neurons
Network
Collection of neurons (PEs) grouped in layers
Structure of the Network
Topologies / architectures – different ways to interconnect PEs
Figure 15.3

8. ANN Fundamentals
Figure 15.4

9. Learning in ANN Compute outputs Compare outputs with desired targets
Adjust the weights and repeat the process
An Example
: Y = C*X
Find C iteratively
When X = 5, making Y = 10
Set random value to C
Increment/decrement C until it reaches right value

10. Neural Network Architecture
Feed forward Neural Network
Multi Layer Perceptron, - Two, Three, sometimes Four or Five Layers

11. Testing Test the network after training
Examine network performance: measure the network’s classification ability
Black box testing
Do the inputs produce the appropriate outputs?
Not necessarily 100% accurate
But may be better than human decision makers
Test plan should include
Routine cases
Potentially problematic situations
May have to retrain

12. ANN Development Tools NeuroSolutions Statistica Neural Network Toolkit
Braincel (Excel Add-in)
NeuralWorks
Brainmaker
PathFinder
Trajan Neural Network Simulator
NeuroShell Easy
SPSS Neural Connector
NeuroWare

13. Benefits of ANN
Pattern recognition, learning, classification, generalization and abstraction, and interpretation of incomplete and noisy inputs
Character, speech and visual recognition
Can provide some human problem-solving characteristics
Can tackle new kinds of problems
Fast prediction
Powerful hybrid systems

14. Limitations of ANN Lack explanation capabilities
Limitations and expense of hardware technology restrict most applications to software simulations
Training time can be excessive and tedious

15. ANN Demonstration www.roselladb.com NeuroSolutions
by NeuroDimentions, Inc.
DMWizard
By Knowledge Based Systems, Inc.
Funded by US Army

16. Business ANN Applications
Accounting
Identify tax fraud
Enhance auditing by finding irregularities
Finance
Signatures and bank note verifications
Foreign exchange rate forecasting
Bankruptcy prediction
Customer credit scoring
Credit card approval and fraud detection*
Stock and commodity selection and trading
Forecasting economic turning points
Pricing initial public offerings*
Loan approvals …

17. Business ANN Applications
Human Resources
Predicting employees’ performance and behavior
Determining personnel resource requirements
Management
Corporate merger prediction
Country risk rating
Marketing
Consumer spending pattern classification
Sales forecasts
Targeted marketing, …
Operations
Vehicle routing
Production/job scheduling, …

18. A case study with NN(Neural Networks) :Bankruptcy Prediction with NN
Based on a paper
Published in Decision Support Systems, 1994
By Rick Wilson and Ramesh Sharda
NN Architecture
Three-layer (input-hidden-output) MLP
Backpropagation (supervised) learning network
Training data
Small set of well-known financial ratios
Data available on bankruptcy outcomes
Moody’s industrial manual (between 1975 and 1982)

19. Bankruptcy Prediction with ANN
Application Design Specifics
Five Input Nodes
X1: Working capital/total assets
X2: Retained earnings/total assets
X3: Earnings before interest and taxes/total assets
X4: Market value of equity/total debt
X5: Sales/total assets
Single Output Node: Final classification for each firm
Bankruptcy or
Nonbankruptcy
Development Tool: NeuroShell

20. Bankruptcy Prediction with ANN

21. Bankruptcy Prediction with ANN
Training
Data Set: 129 firms
Training Set: 74 firms; 38 bankrupt, 36 not
Ratios computed and stored in input files for:
The neural network
A conventional discriminant analysis program
Parameters
Number of PEs
Learning rate and Momentum
Testing
Two Ways
Test data set: 27 bankrupt firms, 28 nonbankrupt firms
Comparison with discriminant analysis

22. Bankruptcy Prediction with ANN
Results
The neural network correctly predicted:
81.5 percent bankrupt cases
82.1 percent nonbankrupt cases
Accuracy of about 80 percent is usually acceptable for this problem domain