credit risk with neural networks bankruptcy prediction machine learning
DESCRIPTION
This presentation made on University of Leuven on my research on credit risk analyisTRANSCRIPT
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