The Use of Client
Assessment Scorecard
in Buusaa Gonofaa MFI,
Ethiopia
European Microfinance Week 2009
November 24 – 26, 2009, Luxembourg
Presentation by Teshome Y. Dayesso,
General Manager, [email protected]
Outline of Presentation


Expectations when developing BG’s ‘social ledger’ or
poverty assessment tool
How it works and experience so far






Collection of data from clients
Training and role of Loan Officers
Data capture, data analysis and reporting
Use of the information – how the scorecard guide SP
management, better segmentation of clients and
better adaptation of products, loan size determination
Interests and challenges – operational and cost
implications; other issues triggered with the Award
Perspectives and the way forward
Why ‘Social’ Ledger or Scorecard?

To answer a 5 Million Dollar question asked
by Board Members: “what is happening with
achieving our social mission, not only financial
sustainability?”.




Whom do we reach? How poor are they?
Is there a change (+ve, -ve) in our clients’ livelihood?
Where do we succeed in changing client’s livelihood?
Where do we fail? Why?
Who benefits from BG most? Does our loan assist
either survivalists or entrepreneurial poor? Or both?
Poverty
Indicator
Measurable Indicators
1
Year of Scoring
2
3
4
5
m1/yy m2/yy m3/yy m4/yy m5/yy
Date of scoring as Month/Year:
Household wealth
o
# Oxen
18
0
1
3
3
2
o
# Cows
16
1
1
1
3
2
# Sheep/goats
2
0
1
4
1
1
2/4
2
2
2
2
2
2
0
1
1
1
1
1
100
1
127
1
183
1
213
1
188
30
60
100
150
160
Household o
Wealth o
# Bed type – Metal/Wood
o
# Tape recorder
o
# TV
24
Total Score of HH wealth:
WC/Business Assets
Growth in Deduct: Score for debt/credit
Business Score: Net Business Assets
Total Score: HH & Bus. Wealth
Progress
% Change of total wealth:
30
0
30
130
-24 -36 -48 -48
36
64 102 422
163 247 315 610
25% 52% 27% 94%
4
Poverty category & cut-off points
Poverty
category
Score
range
Approximate
Income range
Very poor
0 – 29
≤ $1/day
Poor
30 – 54
$1 – $2/day
≥54
≥ $2/day
Not so poor

A person with total score of 15 is poorer than a
person with score of 20, and vice versa
5
Experience so far – how it works





Collection of data from clients – the scorecard is part of routine
loan application process;
Intake – a baseline data is gathered from all new clients upon
entry to the program, at home with spouses (20 minutes)
Poverty scoring – LOs conduct assessment interview (scoring)
on every new loan cycle (5 minutes), at group meeting place at
end of current loan before taking the next loan;
 home visit of 5 clients per group (ave. group size = 15);
 random checking by branch manager, internal auditors,
Intensive training loan officer (LO) on the tool; but gaps in
interviewing skills, mapping of house location
Data capture and analysis – data is entered on Access data
base, report generated by Crystal Reports (C# Application)
Moving from SP Assessment to
SP Management

It provided key information for decision to resolve
tension between social and financial goals.



BG’s average loan size is the lowest in Ethiopia, a
source of constant pressure from staff to increase
loan size;
Board insists on small loan size to maintain focus
on the poor & very poor as primary target group
The scorecard helps to guide SP management

better segmentation of clients by poverty status,
gender, location (rural/urban), clients’ loan use
pattern (IGA/MEs, agri/farming, consumption,
housing improvement, etc.)
Should we increase loan size? For whom?
43%
43%
39%
32%
25%
18%
16%
17%
9%
Very Poor
Poor
Not So Poor
Total Active Borrowers (n=9,318)
Clients Dissatisfied with Very Small Loan Size
Client Suggesting Increase in Loan Size
Rural-urban segmentation was
made possible by the scorecard
Loan Cycle
Rural Loan
Urban Loan
1st
2nd
3rd
4th
5th
6th
7th
8th
9th
$ 63
$ 125
$ 146
$ 167
$ 188
$ 190
$ 250
$ 271
$ 292
$ 83
$ 125
$ 167
$ 208
$ 250
$ 292
$ 333
$ 375
$ 417
9
The loan size was fine-tuned to fit
business size of clients’ segment
$228
$105
$73
$77
Very Poor
$88
$86
Poor
Client's Business Asset (US$)
Not so poor
Loan Size ($US)
Benefits and Interests of the
Scorecard System



It encourages accountability – it boosts MFI’s awareness
of poverty mission, not just ad; social responsibility to
clients as one important end akin to financial performance
It can be used as a benchmark to set SP goals, track
progress over time – no of poor progressing to next level?
It helps to segment clients into poverty levels, business
nature or type, etc… and offer tailored products
Incentives - eventually to set performance targets and
compare poverty outreach among branches, staff, etc
 BG’s winning of European Microfinance Award 2008
has helped BG to spearhead the agenda of client
1/11/2017
protection internally and in the Ethiopian MF sector.11

Challenges and the Way
Forward




Locating rural clients’ home address for data auditing
is a great challenge. Dispersion of rural HHs & poor
infrastructure makes home visits very expensive.
Limited local capacity to develop the data base; the
data base is rigid to generate various reports, thus
limiting the advantages of existing data mining.
The ‘social ledger’ data processing is not integrated
with the loan tracking system.
At least 3 rounds of scoring (~3 yrs) is needed to
detect some pattern of change in clients’ livelihood.
1/11/2017
12
Poverty
Indicator
Measurable Indicators
1
Year of Scoring
2
3
4
5
m1/yy m2/yy m3/yy m4/yy m5/yy
Date of scoring as Month/Year:
Household wealth
o
# Oxen
18
0
1
3
3
2
o
# Cows
16
1
1
1
3
2
# Sheep/goats
2
0
1
4
1
1
2/4
2
2
2
2
2
2
0
1
1
1
1
1
100
1
127
1
183
1
213
1
188
30
60
100
150
160
Household o
Wealth o
# Bed type – Metal/Wood
o
# Tape recorder
o
# TV
24
Total Score of HH wealth:
WC/Business Assets
Growth in Deduct: Score for debt/credit
Business Score: Net Business Assets
Total Score: HH & Bus. Wealth
Progress
% Change of total wealth:
30
0
30
130
-24 -36 -48 -48
36
64 102 422
163 247 315 610
25% 52% 27% 94%
13
Thank You!