Evolving FICO® Scores
from GFS to Mature Market Scores
John Hadlow
Senior Director
FICO
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Agenda
► Evolving
Scores—Global Landscape
► FICO® Scores Global Deployment Map
► GFS Approach and Key Benefits
► Empirical Scores and Key Benefits
► Russia and South Africa—Examples
► Traditional Scores-Takeaways
► Improvements from Non-Traditional Tools
► Value from Non-Traditional Data
► Key Takeaways
► FICO® Score for International Markets in Romania
► Implementing the FICO® Score in México
2
© 2014 Fair Isaac Corporation. Confidential.
Evolving Scores—Global Landscape
► Positive
Credit Data is key to production of good quality credit bureau scores
► For
now, Positive Credit Data is now mostly accepted as the most predictive type of
information that is acceptable to regulators, lenders and consumers for supporting credit
decisions around the world
► Scores
► There
& FICO® Scores are now available from a majority of credit bureaus
is other ‘big’ data that is predictive, powerful but there are challenges:
►
Hard to operationalize
► Can be unpalatable to regulators and/or consumers
► Some data adds insufficient value in current methodologies
► Clearly,
models are very important but good, complete data is paramount
supplies Global FICO® Scores (GFS), empirical FICO® Scores and has
new ‘Big Data’ based offerings
► FICO
3
© 2014 Fair Isaac Corporation. Confidential.
FICO® Scores Deployed Globally—2014
GREENLAND
ALASKA (USA)
SWEDEN
ICELAND
RUSSIAN FEDERATION
FINLAND
NORWAY
CANADA
ESTONIA
LATVIA
DENMARK
LITHUANIA
REPULIC OF
IRELAND
UNITED
KINGDOM
BELARUS
NETHERLANDS
GERMANY
POLAND
BELGIUM
CZECH
REPUBLIC
UKRAINE
SLOVAKIA
KAZAKHSTAN
AUSTRIA
MONGOLIA
HUNGARY
FRANCE
SWITZ.
ROMANIA
ITALY
UZBEKISTAN
BULGARIA
PORTUGAL
GEORGIA
KYRGYZSTAN
SPAIN
NORTH
KOREA
GREECE
TURKEY
UNITED STATES of AMERICA
TURKMENISTAN
SYRIA
IRAN
IRAQ
TUNISIA
MOROCCO
SOUTH
KOREA
CHINA
JAPAN
PAKISTAN
ALGERIA
FICO Score Deployment Legend
TAHKISTAN
AFGHANISTAN
NEPAL
LIBYA
WESTERN SAHARA
EGYPT
MEXICO
SAUDI
ARABIA
TAIWAN
UAE
OMAN
INDIA
VIETNAM
CUBA
MYANMAR
MAURITANIA
LAOS
MALI
FICO® Score Deployed
NIGER
GUATEMALAHONDURAS
CHAD
SUDAN
SENEGAL
THAILAND
YEMEN
NICARAGUA
PHILIPPINES
CAMBODIA
BURKINA
GUINEA
NIGERIA
COSTA RICA
PANAMA
FICO® Score Available at
Credit Bureau
VENEZUELA
LIBERIA
GUYANA
FRENCH
SURINAME GUIANA
COLOMBIA
ETHIOPIA
GHANA
COTE
D’IVOIRE
SRI
LANKA
CENTRAL
AFRICAN REPUBLIC
CAMEROON
MALAYSIA
SOMALIA
UGANDA
KENYA
GABONCONGO
ECUADOR
DEMOCRATIC
REPUBLIC OF
CONGO
TANZANIA
Credit Information Legend *
PAPUA
NEW GUINEA
INDONESIA
BRAZIL
PERU
ANGOLA
ZAMBIA
BOLIVIA
MADAGASCAR
ZIMBABWE
NAMIBIA
PARAGUAY
BOTSWANA
AUSTRALIA
Full/Partial +ve Data Sharing
URUGUAY
REPUBLIC
OF SOUTH
AFRICA
CHILE
Negative-only Data Sharing
ARGENTINA
NEW
ZEALAND
No Data Sharing
*Source: World Bank. 2013. Doing Business 2014: Understanding Regulations for Small and Medium-Size Enterprises. Washington,
DC: World Bank Group. DOI: 10.1596/978-0-8213-9984-2. License: Creative Commons Attribution CC BY 3.0
© 2014 Fair Isaac Corporation. Confidential.
Why Global FICO® Score?
►
Business Requirements: Demand
►
►
►
►
►
►
►
Competitive Stresses and challenges
►
►
►
►
►
‘Everyone’ can build a score these days!
FICO is a partner to bureaus and partners like a share of the revenue
Short term performance vs long term tolerance and stability
Thin files and unique populations and data
Unique FICO Advantages in meeting the Requirements and Challenges
►
►
►
5
(Not really for mature markets except for new entrants)
Highly predictive credit bureau score with the same features and benefits as FICO ® Score
Able to take whatever data is available/usable and score it like a FICO® Score
Make it sufficiently transparent and well documented to meet all likely compliance/regs
Build in enough unique IP and features to make it valuable and different
….and ideally make it part of an international standard for the long term
FICO IP and diligence is still giving extra performance
The FICO International Data Consortium and R&D data pools
FICO tools (Model Builder) processes (Best Practice, Compliance and delivery mechanisms (Software)
© 2014 Fair Isaac Corporation. Confidential.
What Does GFS Bring in a New Market or Entrant?
pre-built predictive performance ‘out of the box’*
► Data to validate performance of the score is needed but not to build it
► 300–850 plus reason codes
► Data tolerance over time and stability
► Best practice documentation and compliance for most markets
► A platform for further localization and to gain even more performance, segments
► Ease of implementation due to delivery in software
► Excellent
Most importantly—Extremely strong ROI through low investment cost which utilizes and
leverages high investment and data consortium from FICO over several years
*Requires mapping of bureau interface to GFS interface
6
© 2014 Fair Isaac Corporation. Confidential.
So What Does a Full Empirical Score Bring?
► An
obvious question then follows—so why bother with a full empirical FICO® Score?
► Whilst
the GFS framework brings clear benefits, it ultimately restricts the data input utilized
and characteristics used in the models—and hence ultimate performance
► A full
ground up development will give the best possible performance using all the data
available but obviously the investment costs are much higher
► Potentially,
complete transparency and ultimate separation of goods from bads with the
potential trade off of shorter term durability and stability
► Therefore
it is more a matter of ‘horses for courses’—Both options are best at what they are
designed to do;
►
GFS for new, developing markets, or data constrained markets
► FICO® Score empirical with more stability for larger more mature environments
7
© 2014 Fair Isaac Corporation. Confidential.
Russia Score Enhancement—More Data, Better Scores
NBKI Russia FICO® Score: Account Management Performance
NBKI v1
NBKI v2
NBKI v3
100
90
Cumulative % Bads
80
70
60
50
40
30
20
10
0
0
8
© 2014 Fair Isaac Corporation. Confidential.
20
40
60
Cumulative % Total
80
100
South Africa Score Enhancement—Changing Data, Scores
TU South Africa—Empirica Score: Account Management Performance
Empirica v1
Empirica v2
Empirica v3
Empirica v4
100%
Cumulative % Bads
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0%
9
© 2014 Fair Isaac Corporation. Confidential.
20%
40%
60%
Cumulative % Total
80%
100%
Traditional Scores—Takeaways
► There
is much to be gained by evolution of Scores through improved tools, data
and models
► GFS
is a great starting point for some markets and offers great performance and ROI
► FICO® Scores built empirically add even more value in developed markets with good ROI
► It is a matter of matching the approach to the market or client requirements
► Data changes often mean trade offs between ultimate performance and stability
► …but
10
what of Other Data and scores?
© 2014 Fair Isaac Corporation. Confidential.
Example Improvement from Non-Traditional Tool
11
© 2014 Fair Isaac Corporation. Confidential.
Value from Non-Traditional text based Data
12
© 2014 Fair Isaac Corporation. Confidential.
Key Takeaways
► There’s
still a way to go and great value in using ‘traditional’ credit bureau data in
FICO® Scores and other scores in many markets ahead of going for the ‘Big Data’
► But...there is significant additional value in Big Data if you can operationalize
► Embrace machine learning, but challenge is to generate fully comprehensible models
► Data-driven models typically need to be refined by domain experts before they can be deployed
► New approaches allow you to combine the power of machine learning with the benefits of domain
knowledge
► There is power in new data sources, such as unstructured text
► Powerful analytic approaches such as topic modeling and semantic scorecards allow you to
comprehend the value and meaning of text data for predicting consumer behavior
► Either
13
way FICO is able to lead and support partners through these changes
© 2014 Fair Isaac Corporation. Confidential.
FICO® Score for International
Markets In Romania
(formerly Global FICO® Score)
The “Win-Win” Solution During the Crisis
in Romania
Cristian Racu
IT Manager
Romanian Credit Bureau
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
FICO® Score for International Markets is a
key element in supporting Romanian
lenders during the economic crisis
and an important source of revenue for the Romanian Credit Bureau
15
© 2014 Fair Isaac Corporation. Confidential.
Agenda
►Market
►The
Elements
Romanian Credit Bureau
►FICO®
►Initial
Score from Biroul de Credit
Observations
►Actions
►Current
16
Situation
© 2014 Fair Isaac Corporation. Confidential.
Market Elements
Romania
► Population:
► GDP:
21.6 million
$188 billion
► Number
of banks: 40
► Total
household savings: $38,376 million
► Total
bank placements to retail: $29,415 million
17
© 2014 Fair Isaac Corporation. Confidential.
The Romanian Credit Bureau
► Established
in 2004
► Shareholders:
► Info
23 banks
type: negative and positive, on individuals and sole traders
► Market
Coverage: 99.8% retail banking sector + major non-banking
financial lenders
► Data
► Hit
up-dating: daily
ratio (2014): 86.48%
► Response
18
time: less than 5 seconds
© 2014 Fair Isaac Corporation. Confidential.
The Romanian Credit Bureau
Total Credit Reports
9
8
7
Millions
6
5
4
3
2
1
0
2004
19
2005
© 2014 Fair Isaac Corporation. Confidential.
2006
2007
2008
2009
2010
2011
2012
2013
2014
(8M)
FICO® Score from Biroul de Credit
► Scoring
bid  main request  world class provider  FICO
► Launched
► Sales
in 2009 (at the start of the crisis)
Evolution
► Usage
Evolution
► Stability
20
© 2014 Fair Isaac Corporation. Confidential.
FICO® Score from Biroul de Credit
Sales Evolution
FICO® Score from
Biroul de Credit Inquiries
Number of Participants
25
Portfolio monitoring
Other inquiries
4,000,000
20
3,500,000
3,000,000
15
2,500,000
2,000,000
10
1,500,000
1,000,000
5
500,000
0
0
2009
21
2010
2011
© 2014 Fair Isaac Corporation. Confidential.
2012
2013
2014 (8M)
2009
2010
2011
2012
2013
2014 (8M)
FICO® Score from Biroul de Credit
Usage Evolution
► 2009-2012:
Collection
► Fast
tool to help collection
► Key element for selling the bad accounts to the external collectors
► Stability element for the de-calibrated socio-demographic scores
► 2013: Account
Management
► Portfolio
scoring for major lenders
► Early warning system using the FICO® Score
► 2014:
Re-start lending
► Big
lenders build their marketing strategies on the portfolio analysis
► Market target groups using the FICO® Score
22
© 2014 Fair Isaac Corporation. Confidential.
FICO® Score from Biroul de Credit
An Effective Score in a Changing Environment
► Despite
► The
bad rate of the lowest
scoring deciles is consistently
100x that of the lower scoring
deciles
Overall Population Bad Rate
Aug-11
9.5%
23
Sep-12
13.7%
Aug-13
18.2%
Feb-14
16.4%
© 2014 Fair Isaac Corporation. Confidential.
Bad Rate by Decile Over Time
Aug-11
100%
Sep-12
Aug-13
Feb-14
90%
80%
Interval Bad Rate
the changing
economic landscape the
FICO® Score from Biroul de
Credit nicely rank orders the
consumer risk
70%
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
5
FICO®
6
7
Score Decile
8
9
10
Initial Observations
► No
scoring alternative—big confidence in socio-demographic scores (before the
crisis) and their fast de-calibration (within the crisis)
► Non-homogenous
► The
market know-how on bureau scoring
increasing “cost issue” due to the crisis
► Analytic
services provided by external consultants with no knowledge about the
local market
24
© 2014 Fair Isaac Corporation. Confidential.
Actions
► Initial
marketing campaign (FICO training seminars, face-to-face meetings with
the lenders)
► Retro-fit
analysis discount offer (first year)
► Volume
based discount offer for account management (last 4 years)
► Dedicated
software for retro-fit analysis
► Dedicated
sales team
► Lenders
25
“education” using analytic team
© 2014 Fair Isaac Corporation. Confidential.
Current Situation
► A (more)
homogenous market know-how
► Constant
increase of FICO® Score from Biroul de Credit’ usage
► Usage
extension for new purposes (provisioning, Basel III)
► Risk-based
26
pricing marketing campaigns using FICO® Score from Biroul de Credit
© 2014 Fair Isaac Corporation. Confidential.
Implementing the
FICO® Score in México
Juan Manuel Ruiz Palmieri
Commercial VP
Círculo de Crédito
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Agenda
►México
►About
►Our
Credit Market Overview
Círculo de Crédito
Challenges
►New
data (Microlending)
►FICO®
Score Implementation
►Metrics
and Results
►Benefits
28
to Market
© 2014 Fair Isaac Corporation. Confidential.
México Credit Market Overview
► Consumer
► 50%
credit culture still a challenge
of population on informal jobs
► Traditional
banks and retail companies
► Targeting
medium to high incomes
► Focus on credit cards
► Savvy in the use of sophisticated risk tools
► Microfinancial
► Service
29
institutions evolved from ONG’s to Regulated Institutions
and Utilities companies starting to use predictive models
© 2014 Fair Isaac Corporation. Confidential.
México Credit Market Overview
Monthly income by family in USD
Banks
A
0.79%
B
1.87%
$ 12,000 – $ 100,000
C + 3.39%
Cm 5.92%
$ 1,450 – $ 11,999
C - 8.13%
D + 11.97%
Microlending
Entities
Dm 14.84%
$ 400 – $ 1,449
D - 22.06%
E
31.03%
$ 60 – $ 399
Source: Sigmarket 2010
30
© 2014 Fair Isaac Corporation. Confidential.
About Círculo de Crédito
► We
attend Banks, Retail, Microlending, Services and Utilities industries
► We
bring new data to the market (Microlending)
► 10
years operating in México
► More
than 3,000 customers
► Positive
► We
and negative information
are regulated and supervised by
► Secretaria
de Hacienda y Credito Publico (IRS)
► National Banking and Security Commission (SEC)
► Central Bank (FED)
31
© 2014 Fair Isaac Corporation. Confidential.
Numbers
► 40%
of market share and growing
► Consumer
► 57
database:
Millions, reporting 300 millions of credit accounts
► Corporate
► 800K,
database:
reporting 2.5 million of credit accounts
► Inquiries
► 80
million transactions per year
*Cifras en miles
32
© 2014 Fair Isaac Corporation. Confidential.
Products
*Cifras en miles
33
© 2014 Fair Isaac Corporation. Confidential.
Our Challenges
► 2008,
first origination score developed by one of the top 3 American Credit
Bureaus, did not perform as expected
► FICO´s
performance in the traditional banking sector is a sure bet, the challenge
was to perform in all sectors
► The
goal was:
► Prove
the effectiveness in microlending and provide the first Score for this market
► Outperform other scores in all segments
34
© 2014 Fair Isaac Corporation. Confidential.
New Kid in Town
Microlending
► Growing
for the last 10 years
► Different legal frame
► Banks,
Regulated Entities (Saving, IPO’s) and Non regulated
► High
employee turnover
► High
cost of operation
► Increased
► More
► New
► Big
35
competition
than 400 entities
in risk management
market to serve
© 2014 Fair Isaac Corporation. Confidential.
Microlending Methodology
► Individual
Credit
► Mostly
for self-employment
► Weekly payments (16 weeks)
► Credit amount (Minimum $115 USD, maximum $12,000 USD and average $1,300 USD)
► Group
Credit
► Between
15 and 20 people
► Each of them ask for a specific amount
► All in group are joint guarantees
► They meet weekly to make the payment
36
© 2014 Fair Isaac Corporation. Confidential.
FICO® Score Implementation
► Sample
► Model
► First
► IT
size 2M files
adjusting and customization
validation: 6 months
Statistic
Value
K-S
58.24
K-S Score
556
ROC Area
0.817
implementation 2 months
► Results:
► Strong
rank ordering of risk demonstrated on the development population
► Existing data elements showed strong predictive capability
► Better results in each and every subsequent yearly validations (3 years now)
37
© 2014 Fair Isaac Corporation. Confidential.
Metrics and Results
16.0%
40.0%
14.0%
12.0%
% Population
30.0%
10.0%
8.0%
20.0%
6.0%
4.0%
10.0%
2.0%
0.0%
0.0%
300-479
500-519
540-559
580-599
% Distribution
Bad = 90+dpd 12 months after first purchase.
38
© 2014 Fair Isaac Corporation. Confidential.
620-639
660-679
% Bad
700-719
740-759
780-850
Benefits to Market
► Reduce
time in granting credit
► Automate
origination process
► Have
a standard risk metric
► More
homogeneous analysis of credit report
► No
training required
► One
39
score for all the market
© 2014 Fair Isaac Corporation. Confidential.
Thank You!
Juan Manuel Ruiz Palmieri
Commercial VP
Círculo de Crédito
Cristian Racu
IT Manager
Romanian Credit Bureau
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
John Hadlow
Senior Director
FICO
Please rate this session online!
Juan Manuel Ruiz Palmieri
Commercial VP
Círculo de Crédito
41
© 2014 Fair Isaac Corporation. Confidential.
Cristian Racu
IT Manager
Romanian Credit Bureau
John Hadlow
Senior Director
FICO