Neural Networks & a case with bankruptcy prediction By Jinhwa Kim

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

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

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

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

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)

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

ANN Fundamentals Figure 15.4

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

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

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

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

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

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

ANN Demonstration www.roselladb.com NeuroSolutions http://www.nd.com/neurosolutions/products/ns/nnandnsvideo.html by NeuroDimentions, Inc. www.nd.com DMWizard By Knowledge Based Systems, Inc. Funded by US Army

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 …

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, …

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)

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

Bankruptcy Prediction with ANN

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

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