efl -- innovative ways to assess client credit-worthiness
DESCRIPTION
Innovative Ways to Assess Client Credit-Worthiness 17th Microcredit Summit 2014 SummitTRANSCRIPT
EFL: An innovative way to assess client credit-worthiness
Introduction to EFLSection 1
3
Introduction
Who we are
Credit scoring company that uses psychometric variables & big data
Create a deep quantitative understanding of individual risk and opportunity in small business (MSME) and consumer financing
20 to 45 minute flexibly-administered software which supplements a bank’s existing loan application
We help over 20 top banks, retailers, and credit bureaus to measure individuals, create scorecards, and introduce technology into their lending process
What we do
Our product
Our work
4
Our global footprint
+$275 million disbursed | 125,000 assessments | 28 languages | 27 countries
Understanding the needSection 2
6
MSMEs – the drivers of economies
7
Traditional loan decision making
Lack of information
8
Traditional sociodemographic evaluation not adequate for most microcredit customers…
Particularly hard on disadvantaged groups: young, elderly, or people with willingness to pay yet bad record due to sickness, etc.
9
In addition, current microfinance operational model with key areas for improvement
1 Subjective
• EOM “better” candidates or “more” candidates• Before lunch, fewer approvals• Give two LO same profile, different decision• No feedback loop
2 Time consuming
• Can take a couple of days to create a file• Several visits• Can’t be administered remotely
3 Costly
• High operating cost (training, turnover)• Hard to scale• Subject to corruption & bribery
10
Not having the appropriate scoring model nor adequate operational model has led MFIs to wrong conclusions
Wrong answer Correct answer
1 Who is your competition?
Other MFIs Non-consumption
2What is your biggest concern?
Over-indebtedness of clients
Get new clients that nobody’s attending
MFIs are fighting other MFIs for the same clients, when there is an abundance of them in the market
11
The emerging market lending opportunity
2.2 Billion People $2.5 Trillion
90% of Unbanked in Emerging
Markets
Development crisis & big business opportunity
EFL product: The solutionSection 3
13
EFL results
Nigeria Indonesia
PakistanPeru
0%
10%
20%
Q1
Def
ault
rate
Q4
~9x
Q2 Q3
0%
20%
40%
Def
ault
rate
Q3
~11x
Q1 Q4Q2
0%
2%
4%
6%
Def
ault
rate
Q2 Q4Q3
~30x
Q1
0%
10%
20%
30%
Q3Q2
Def
ault
rate
~4x
Q4Q1
125,000 applications
27 countries
$275 Million disbursed
Worst 25%
26-50%
51-75%
Best 25%
Q1
Q2
Q3
Q4
EFL quartiles
RiskRisk+ - + -
14
Attitudes & Beliefs
Ethics & Honesty
Fluid Intelligence
Business Skills
WillingnessAbility
Measuring the individual
Systematize & validate LO’s intuition
15
The EFL application
Ethics & Honesty
What percentage of people are likely to steal?
Business skills
Which of the following you should take into account when
calculating your costs?
Attitudes & Beliefs
A big part of success is luck
Remember this number for 5 seconds: 823460
Fluid intelligence
5% DisagreeAgree
IncorrectCorrectRentInventory
Risk
20% 50%
Through metadata points (time, answer sequence, etc.)
Motivation
Developed based on pre-employment screening tools
16
1
~30 minutes PC, Tablet or smartphone
With or without internet
Description of the processPartner institution applies the electronic survey
Remote or in-site
17
Description of the processEFL analyzes the answers and generates a 3-digit credit score
Credit score algorithm developed based on a database of ~150,000
surveys yet customized for region/clients
3-digit credit score
2
Repayment data uploaded monthly, models are constantly customized and improved
18
Applications of the EFL Score
Thin File Clients Existing Customers
Current Approvals No File Clients
EFL results & impactSection 4
20
0.6%
7.8%
0
50
100
150
200
250
300
350
400
450
0%
1%
2%
3%
4%
5%
6%
7%
8%
# cl
ient
s
350-399
0.7%
250-299
EFL score
4.0%
6.6%
<200 >400300-349200-249
1.4%
Default rate
13x
Default rate
Bad
Good
Default rate and volume per EFL score
• Dramatically differentiate risk between high and low scoring borrowers
• Best score bucket defaulted 13x less often than worst score bucket
Portfolio default rate
EFL arranges applicants by risk – willingness to payID more good prospects, reduces exposure on bad ones
21
Typical partner results
India+176%
With EFLWithout EFL
Default rate
Loans
Increase lending
Increase lending by ~180% while maintaining target default rates
With EFL
-46%
Without EFL
Default rate
Loans
Reduce default
Decrease default by ~50% while maintaining acceptance rate
Peru
22
Using innovation to serve the next generation of entrepreneurs
