credit risk with neural networks bankruptcy prediction machine learning

Credit Risk with AI tools

The old, the new and the unexpected

ARMANDO VIEIRAArmandosvieira.wordpress.com

C u s t o m e r f a i l st o p a y

L o s i n g m o n e yW r o n g S t r a t e g y

C h a n g e i n m a r k e t

p r i c e s

P r o c e s s i n g f a i l u r e s a n d f r a u d s

R e g u l a t o r y c o m p l i a n c e

C u s t o m e r f a i l st o p a y

L o s i n g m o n e yW r o n g S t r a t e g y

C h a n g e i n m a r k e t

p r i c e s

P r o c e s s i n g f a i l u r e s a n d f r a u d s

R e g u l a t o r y c o m p l i a n c e

RISK

Importance of Credit Risk

A statistical means of providing a quantifiable risk factor for a given applicant.

Credit scoring is a process whereby information provided is converted into numbers to arrive at a score.

The objective is to forecast future performance from past behavior of clients (SME or individuals).

Credit scoring are used in many areas of industries:

Banking

Decision Models Finance

Insurance

Retail

Telecommunications

What is Credit Scoring?

• Predict financial distress of private companies one year ahead based on account balance sheet from previous years.

• Enventualy the probability to become so.

• Obtain reliable data from up to 5 previous years before failure

• Classify and release warning signs

Bankruptcy prediction problem

The curse of dimensionality

Problems• Sparness of the search space• Presence of Irrelevant Features• Poor generalization of Learning Machine• Exceptions difficult to identify

Solutions• Dimensionality reduction: feature selection• Constrain the complexity of the Learning Machine

The Diane Database• Financial statements of French companies, initially of 60,000

industrial French companies, for the years of 2002 to 2006, with at least 10 employees

• 3,000 were declared bankrupted in 2007 or presented a

• restructuring plan 30 financial ratios which allow the description of firms in terms of the financial strength, liquidity, solvability, productivity of labor and capital, margins, net profitability and return on investment

The inputsNumber of employees Net Current Assets/Turnover (days)

Financial Debt / Capital Employed (%) Working Capital Needs / Turnover (%)

Capital Employed / Fixed Assets Export (%)

Depreciation of Tangible Assets (%) Value added per employee

Working capital / current assets Total Assets / Turnover

Current ratio Operating Profit Margin (%)

Liquidity ratio Net Profit Margin (%)

Stock Turnover days Added Value Margin (%)

Collection period Part of Employees (%)

Credit Period Return on Capital Employed (%)

Turnover per Employee Return on Total Assets (%)

Interest / Turnover EBIT Margin (%)

Debt Period (days) EBITDA Margin (%)

Financial Debt / Equity (%) Cashflow / Turnover (%)

Financial Debt / Cashflow Working Capital / Turnover (days)

Hard problem

0

2

4

6

3 4 5 6 7

Class 0Class 1

1

2

First two principal component from PCA

How HLVQ-C works

0

0.5

1.0

1.5

0 0.5 1.0 1.5

Class 0Class 1

After

Before

?d2

d1

X

Y

DIANE 1 (error %)

Model Error I Error II Total

MDA

SVM

MLP

HLVQ-C

26.4

17.6

25.7

11.1

21.0

12.2

13.1

10.6

23.7

14.8

19.4

10.8

DIANE 1 - HLVQC Results

MethodClassification

Weighted Efficiency (%)

Z-score (Altman) 62.7

Best Discriminant 66.1

MLP 71.4

Our Method 84.1

Source: Vieira, A.S., Neves, J.C.: Improving Bankruptcy Prediction with Hidden Layer Learning. Vector Quantization. European Accounting Review,

15 (2), 253-271 (2006).

Personal credit

Results I – 30 days into arrearsClassifier Accuracy (%) Type I Type II

G

Logistic 66.3 27.3 40.1 54.8

MLP 67.5 8.1 57.1 61.1

SVM 64.9 35.6 34.6 52.3

AdaboostM1 69.0 12.6 49.4 55.7

HLVQ-C 72.6 5.3 49.5 52.3

Results I – 60 days into arrears

Classifier Accuracy Type I Type IIG

Logistic 81.2 48.2 11.021.2

MLP 82.3 57.4 9.120.1

SVM 83.3 38.1 12.419.3

AdaboostM1 84.1 45.7 8.014.7

HLVQ-C 86.5 48.3 6.211.9

DIANE II (2002 – 2007)

• More data• Longer history• More features

Year2006

Classifier Accuracy Type I Type II

Logistic 91.25 6.33 11.17

MLP 91.17 6.33 11.33

C-SVM 92.42 5.16 10.00

AdaboostM1 89.75 8.16 12.33

Year2005

Classifier Accuracy Type I Type II

Logistic 79.92 19.50 20.67

MLP 75.83 24.50 23.83

C-SVM 80.00 21.17 18.83

AdaboostM1 78.17 20.50 23.17

Results

How useful?

mexexNV III )1()1(

I

II

e

emmG

x

x

11

The Rating System

French market - 2006

-2-1

01

2

-2

-1

0

1

2-1.5

-1

-0.5

0

0.5

1

cr

eb

Score (EBIT, Current ratio)

MOGAMultiobjective Genetic Algorithms

MOGA – feature selection

S-ISOMAP – manifold learning

The idea behind it

Other approaches

• SVM+ - domain knowledge SVMs• RVM – probabilistic SVMs• NMF – Non-negative Matrix

Factorization• Genetic Programming• …

The Power of Social Network Analysis

Bad Rank Algorithm

Where are the bad guys?

Bad Rank for Fraud Detection

Results with Semi-supervised Learning

Networks Analysis A world of possibilities

• Identify critical nodes / links / clusters• Detailed information of dynamics• Stability / robustness of system• Information / crisis Propagation• Stress tests

Team

João Carvalho das Neves

Professor of Management, ISEG.

Ph.D. in Business Administration,

Manchester Business School

Armando VieiraProfessor of Physics,

& entrepreneur. Ph.D. in Physics and

researcherin Artificial Intelligence

Bernardete Ribeiro

Associate Professor of Computer Science,

University Coimbra,

researcher at CISUC.

Tiago Marques Marketing and

Business Consultant,E-Business Specialist,

Director of Research

Business Director

IT Researcher Marketing

10+ years experience in AI 25 years experience in Credit Risk & Financial Analysis15 years of marketing experience

What do banks need in credit management?

Efficiency Accuracy

Savings of Capital – Basel requirements

This is a highly regulated industry with detailed and focused regulators

What do they get?

Boosting the accuracy of credit risk methodologies will lead to considerable gains for banks

Source: Issue 2 of NPL Europe, a publication overing non-performing loan (NPL) markets in Europe and the United Kingdom (UK)., PriceWaterhouseCoopers

Non-performing loans - Europe

0

50

100

150

200

250

Germany UK Spain Italy Russia Greece

2008

2009

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2005 2006 2007 2008 2009

% Corporate Debt Default - Portugal

Bill

ion

s o

f E

UR

NP

L (

%)

Source: Bank of Portugal

AIRES Solution

AIRES.dei.uc.pt