Transcript The Benefits of Reporting Positive Payment Data in Latin America

POLITICAL & ECONOMIC
RESEARCH COUNCIL
The Benefits of Reporting Positive
Payment Data in Latin America
By Michael Turner, Ph.D.
Intercontinental Hotel
Tegucigalpa--11 May 2006
Agenda
Introduction
Findings
Conclusion
2
Why are We Here?
Objectives:
 Broaden
access to affordable mainstream credit
 Reduce delinquencies/defaults in financial services and nonfinancial services sectors.
 Increase growth in private sector lending and overall economy.
Methods:
 Increase
full-file reporting from financial and non-financial
firms for


increased predictive power of scoring models.
capturing more consumers, especially lower income.
 Increase


access to public record data for
greater accuracy.
better matching.
3
Why are We Here? (con’t)
What is being asked of you?:


Provide comprehensive financial and non-financial payment
information
 Delinquencies and defaults, but also
 Regular on-times payments (and 30-day and 60-day)
Not
 Income/salary
 Asset values
 Dependents, spouse, parents, etc.
4
Benefits of Reporting
Positive Payment Data
Consumers



Reduced probability of over-extension
Greater and fairer access
Credit offers reflect credit risk and credit capacity
Lenders



Improved loan portfolio performance
Reduced provisioning and capital adequacy
requirements (Basel 2)
Sustainable & affordable growth into new markets
The Economy
Consumers
Lenders
The Economy


Better financial services efficiencies
Affordable growth in domestic consumption
5
Credit Reporting & Its Impact
This presentation demonstrates these benefits
by answering four critical questions:
 What is the impact of reporting positive payment information
on credit access & growth in credit markets?
 What is the impact of reporting positive payment information
on loan performance?
 What is the impact of reporting positive payment information
on economic growth?
 What is the impact of reporting positive payment information
on the distribution of credit?
6
Types of Reporting Systems
NEGATIVE ONLY
Applications (not approvals)
 Delinquencies (90+)
 Defaults
 Bankruptcies
POSITIVE
PAYMENT
 All negative data,
or delinquencies
(30+ days past
dues)
 All Positive (ontime) payment data
 Public record data
 Account balance
 Account type
 Lender
 Date opened
 Purged 5 years
 Inquiries
FULL FILE
(also includes)
 Debt ratios (revolving to total
debt)
 Portion of accounts repossessed/
written off
 Estimated income range
 Assets
 Obsolete 7-10 years
7
Latin American Context
Large differences among Latin American credit reporting systems.
CREDIT REPORTING COVERAGE AND COMPREHENSIVENESS IN LATIN AMERICA
Country
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
Dominican Republic
Ecuador
El Salvador
Guatemala
Honduras
Mexico
Nicaragua
Panama
Paraguay
Peru
Uruguay
Venezuela
Mean (excl. absent bureaus)
Max
Min (excl. absent bureaus)
Public registry
coverage1
(% adults with files)
22.10%
10.30%
9.60%
45.70%
0.00%
34.80%
19.20%
13.60%
17.30%
0.00%
11.20%
0.00%
8.10%
0.00%
8.70%
30.20%
5.50%
16.80%
18.1%
45.7%
5.5%
Private bureau
coverage (% adults
with files)
95.00%
24.60%
53.60%
22.10%
31.70%
73.40%3
34.60%
0.00%
78.70%
9.90%
18.70%
49.40%
0.00%
40.20%
52.20%
27.80%
80.00%
0.00%
46.13%
95.0%
46.13%
Positive Information on
Consumer in Files (% of
total)2
25% to 49%
< 5%
n/a
25% to 49%
75% to 100%
< 5%
75% to 100%
25% to 49%
10% to 24%
75% to 100%
75% to 100%
75% to 100%
n/a
n/a
n/a
50% to 74%
75% to 100%
n/a
QUESTION FOR RESEARCH:
/. For 2005.
Source: World Bank,Doing Business Database. w ww.doingbusiness.org/Ex ploreTopics/GettingCredit
nduras, w hich is from 2005. From Arturo Galindo and Margaret Miller, “Can Credit
The data is for 2001, sav e for Costa Rica, Colombia and Ho
Registries Reduce Credit Constraints.” March 2001. Research Department. I nter-American Dev elopment Bank, Washington, D.C. Additiona l
information from interv iews w ith TransUnionLatin America.
-143
fm?fuseaction=Publications.View &pub_id=S
w ww.iadb.org/res/index .c
1
2
How do differences affect profitability and availability of credit?
3
8
Latin American Context:
Financial Services Sector
Relatively small and modest private sector borrowing
(Private sector borrowing as a share of GDP, 1995-2004)
120%
100%
80%
60%
40%
20%
0%
North
America
NA/E/ANZ
Europe
(N=16)
Aust/NZ (N=16)
East
Asia
EA
(N=5)
(N=5)
Middle
East
MENA
(N=5)
N. Africa
(N=5)
E.
EEEurope
(N=8)
(N=8)
LA (N =18)
Latin
America
S. (N=3)
Asia
SA
(N=3)
Sub-Saharan
Afr (N=12)
Africa
(N=12)
(N=18)
1995
Source: International Financial Statistics, IMF
1996
1997
1998
1999
2000
2001
2002
2003
2004
9
Methodology:
Two Ways to Show Benefits
1.
Statistically compare the private sector lending in
economies with different reporting systems and
different participation rates
2.
Simulate different reporting systems using 5.1
million complete files from “close” or similar
economy (Colombia):
a.
b.
c.
Generated 4 scenarios of varying participation*
Tested distributional impact of changes in participation
(sociodemographic analysis).
Used commercial grade scoring model.
* Scenarios

75% provide positive and negative information, 25% only negative

50% provide positive and negative information, 50% only negative

25% provide positive and negative information, 75% only negative

100% provide only negative information
10
Agenda
Introduction
Findings
Conclusion
11
Finance is Crucial to
Economic Growth
Established: Financial sector mobilizes savings and allocates
capital for investment and consumption  growth.
Some estimates of impact.* If private sector lending, increased by
33% of GDP, results for economy:

+1.0% annual per capita GDP growth

+0.8% annual per capita capital stock growth

+0.8% annual productivity growth
*Derived from findings of Ross Levine, “Financial Development and Economic Growth: Views and Agenda” Journal of Economic Literature,
Vol. 25(June 1997), pp. 688–726. Their findings are consistent with those of other studies, see Jose De Gregorio and Pablo Guidotti,
“Financial Development and Economic Growth.” World Development, Vol. 23, No. 3, (March 1995) pp. 433-448. Their reported impacts were
larger.
12
Economic Growth-Australia
Evidence suggests the use of comprehensive credit data allows:
One-off increase in capital productivity of 0.1%, which would
translate into economic benefits to the Australian economy of up
to $5.3 billion, in net present value terms, over the next decade.
ACIL Tasman (2004)
13
Estimations: Private Full-File Coverage
and Private Sector Borrowing
VARIABLE
Consta nt
Log of GDP per capita
(adjusted for PPP)
Avg. Change in GDP
(1995-2004)
Legal Rights of Creditors
(from 0 to 10)
Credit Information 1
(from 0 to 6)
Private Full-file Coverage
(0 to 100, as percentage of adults)
Private Negative-only Coverage
(0 to 100, as percentage of adults)
Public Full-fi le Coverage
(0 to 100, as percentage of adults)
Public Negativ e-only Coverage
(0 to 100, as percentage of adults)
R squared
F-stat
(p value)
Residual Standard Error
N
Model I
-142.40***
(35.31)
20.31***
(4.65)
-1.20*
(0.70)
4.55**
(2.07)
-3.87
(2.88)
0.72***
(0.20)
-0.02
(0.86)
-0.11
(0.41)
0.16
(0.46)
0.7075
16.93
(1.88e-012)
Model IV
(reduced)
-130.80***
(32.20)
16.85***
(3.87)
4.80**
(1.97)
0.67***
(0.16)
0.6883
44.9
(1.887e-015)
29.45
65
29.12
65
Lesson: what matters?
• Wealth
• Creditor Rights
• Reporting
o private
o full-file
o with widespread participation
For a country, going from no adults to
having all (100% of) adults with positives
and negatives in a private bureau increases
private sector lending by more than 60% of
GDP.
(Without the US and UK, which have high
private sector lending, the estimated
increase is still more than 45% of GDP.)
* p < 0.1
** p < 0.05
***p < 0.01
Source: IMF International Financial Statistics; World Bank, Doing Business database
14
Estimations Consistent With
Previous Studies
Study by Harvard and World Bank economists of
129 countries (for years 1999-2003)*
Private bureaus increase lending as a share of
GDP by an estimated 20 percentage points
But didn’t take into account effects of participation rate or
reporting system (negative only vs. full-file)
15
*Simeon Djankov Caralee McLiesh Andrei Shleifer, “Private Credit In 129 Countries.” National Bureau Of Economic Research,
Working Paper 11078, http://papers.nber.org/papers/w11078.pdf
Private Sector Lending
in Honduras
2003
2004
2005
Growth in private sector lending
12.3%
15.5%
18.25%
Private sector lending
(as share of GDP)
40.92%
41.5%
42.7%
3.5%
5.0%
4.2%
GDP growth (in 1978 prices)
16
Source: Hong Kong Monetary Authority
Rationale Behind Simulations
Simulations based on the files of one country allows



Measure of access
Performance metrics
Distribution of credit across groups
In this instance, we use Colombian files:




Institutionally, economically close to the rest of Latin
America (cluster analysis)
Robust--participation from financials and non-financials
Standardized files with reliable, accurate information
Consistent reporting of positives for 25 years
17
Background: Existing Research
World Bank study uses Latin American credit files to make a case for full-file
reporting (Miller and Galindo, 2001)
Research uses Public Credit Registry data, restricted to larger, most
likely collateralized loans. Focus on reported data. The open question:
What is the impact of participation in private full-file system?
Source: World Bank
18
Change in Acceptance Rates
(Market Size) for a Performance Target
Full sample (5.1 million files)
ACCEPTANCE RATES BY TARGET DEFAULTS,
UNDER DIFFERING LEVELS OF PARTICIPATION
Share of furnishers providing full-file information (remainder provides negatives only)
Target
Default rate
3%
5%
7%
10%
12%
100%
10.00%
41.35%
58.82%
73.06%
77.80%
75%
6.64%
28.96%
45.59%
68.09%
77.21%
50%
4.73%
19.28%
36.42%
68.08%
76.49%
25%
4.80%
9.69%
25.71%
68.09%
75.06%
0%
2.56%
5.15%
13.60%
54.97%
72.26%
For a target loss rate, consumers shrink with
a loss of positive information.
19
Change in Non-Financial Acceptance
Rates for a Performance Target
Full sample (3.1 million files)
Non-Financial Acceptance Rates, by Scenario (Colombia)
Share of tradelines consisting of both positive and negative information
Target De fault rate
5%
7%
10%
12%
100%
5.50%
37.30%
61.03%
69.75%
75%
4.00%
29.95%
49.36%
63.27%
50%
2.95%
17.96%
43.14%
57.70%
25%
1.96%
10.07%
36.01%
50.43%
20
Source: Hong Kong Monetary Authority
Change in Default Rates
for a Target Market Size
DEFAULT RATES BY TARGET ACCEPTANCE,
UNDER DIFFERING LEVELS OF PARTICIPATION
Share of furnishers providing full-file information (remainder provides negatives only)
Target
Acceptance Rate
20%
30%
40%
50%
60%
100%
3.52%
4.12%
4.89%
5.86%
7.20%
75%
3.72%
4.62%
5.66%
6.70%
7.73%
50%
4.66%
5.74%
6.67%
7.49%
8.49%
25%
5.91%
6.78%
7.52%
8.22%
9.25%
0%
8.46%
9.06%
13.85%
14.40%
15.30%
Furnishers can reduce losses.
Consistent with World Bank results.
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Reducing Overextensions:
The Case of Hong Kong
 1998-2002, Hong Kong experienced growth in personal bankruptcy of 1,900%.
 Around 12% of all personal bankruptcy was caused by credit card debt.
 Credit card write-offs stood at 13.6% by the end of 2002.
 Higher than comparable Asian nations, Singapore and Korea, 5.5% and 6.1%
respectively.
 Defaulting customers in Hong Kong had acquired debts up to 55 times monthly
income in 2000 and 42 times monthly income in 2002.
 Following the shift to more comprehensive reporting, between December 2002 and
December 2004:*
 Credit card write-off ratios declined from 13.6% to 3.76%; and
 Credit card delinquency ratios declined from 1.25% to 0.44%.
22
Source: Hong Kong Monetary Authority
Reducing Delinquencies:
The Case of US Utilities
 Verizon (US)

Reported 4 million landline trades in March 2005 (Virginia) to 1 bureau

Raised number of trades reported to 10 million within 2 quarters

By Q1 2006 reporting over 20 million landline trades nationally

Delinquencies reduced substantially (poke factor)
 Not uncommon response

Nicor Gas (Illinois)

Reported full-file customer data to TransUnion (US) despite objections of state regulator

Engaged in active customer communications campaign

1 year later, defaults (90+ days past due) reduced by 20%

Reductions in delinquencies continue to grow
 WE Energies (Wisconsin)

Reported full-file data to TransUnion

Engage in active customer communications campaign

Delinquencies and defaults reduced substantially
 WHY: Moral hazard--carrots and sticks
23
Acceptance-Default Trade-Offs
15%
Default Rates
12%
9%
6%
3%
0%
0%
15%
30%
45%
60%
75%
90%
Acceptance Rates
100% Reporting Full File
75% Reporting Full File
25% Reporting Full File
0% Reporting Full File
50% Reporting Full File
Furnishers can reduce losses.
24
Change in Share Accepted by Gender
100%
75%
25%
50%
0%
Women are hit disproportionately;
thinner files.
25
Change in Share Accepted by Age
100%
75%
25%
50%
0%
Young also hit disproportionately;
thinner files.
26
Loss of Information Means More
Mistakes are Made
CHANGES IN ERROR RATES
(MEASURED AS A PERCENT OF ALL CREDIT-ELIGIBLE ADULTS)
Share of tradelines consisting of both positive and negative information
75%
50%
Type I (false positives, or mistaking a high risk
borrower for a low risk one)
+1.00%
+2.22%
Type II (false negatives, or mistakin g a low risk
borrower for a high risk one)
+3.81%
+5.32%
25%
+3.31%
+7.53%
Issues of giving credit where credit is due.
27
Implications of Shifts in Error Rates
If the 25% scenario had obtained:
o nearly 181,000 people who are bad risks
would be extended credit
o nearly 411,000 who are good risks would be
denied access.
The latter is another point of fairness, in addition
to distribution of loss of access across sociodemographic categories.
28
Evaluating Payment History vs.
Socio-Demographic Information
Results of comparison are meant to be suggestive. Starting points are rather
different.
o Costa Rica’s per capita GDP is twice that of Colombia’s
o Yet, private sector lending as a share of GDP is largely equivalent
averaging for the period 1999-2003
• 26.6% in Colombia and
• 26.7% in Costa Rica
Some differences:
o Overall default rates in Costa Rica are small, observed 90+ day
delinquency rate of 3.78%.
o Colombia’s observed delinquency rate of 27.49% in files (but 3.6% for
loans--Bankscope).
However, this difference may very well be an artifact of the system of reporting
rather than of consumer behavior.
o Approximately two-thirds of data furnishers in Costa Rica do not report
negatives less than 120 days past due.
o Many delinquencies, defined as 90+ days past due, therefore do no make it
on the credit reports.
o Non-financials
29
Evaluating Payment History vs.
Socio-Demographic Information
Question of how to measure the relative merit of approaches. K-S. The ability to
discern goods from bads (or true positives from false positives) increases
considerably in moving from the Colombian negative only to the Colombian full-file
scenario. By contrast, socio-demographic information improves the ability to
distinguish goods from bads in Costa Rica files by much less of a degree.
K-S SCORES OF ADDING SOCIO-DEMOGRAPHICS,
COMPARING COSTA RICA AND COLOMBIA
Costa Rica Restricted
Costa Rica Complete
40.5
49.3
Colombia Negative Only
Colombia Full-File (ACIERTA)
54.2
67.3
Issue 2: why the difference is starting points (40.5 vs. 54.2)?
 Accuracy
 For better predictions
 For matching (also reduces mistakes)
30
Agenda
Introduction
Findings
Conclusion
31
Lessons
 Reporting positive payment data enables growth in lending to private sector
o
o
PERC 2006--up to 45% of GDP (moving from 0% to 100% participation in private bureaus)
Harvard/World Bank 2005--up to 21% of GDP (private v. public)
 Reporting positive payment data improves economic growth
o
(e.g. 25% of Colombia’s 3.9% GDP growth in 2005 result of increased private sector lending--40%
as ratio of GDP--enabled by private full-file credit reporting system).
 Reporting positive payment data results in smarter lending – lower default rates with better
access (developed & emerging economies).
o
PERC 2006, World Bank 2001, Hong Kong Monetary Authority 2005
 Comprehensive data results in fairer credit. (financial & non-financials)
o
o
Improves mainstream access for the under-served (developed & emerging economies).
Increases access to affordable mainstream credit for women and young.
 Positive payment data has no impact on personal security.
 Benefits financial and non-financial sectors
32
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