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Data driven microfinance: small bits, Big Data
Philippe BREUL, Partner - Head Office +32 495 32 32 88 pbreul [email protected]
What are this session’s objectives?
1 Understand the Big Data techniques in the context of financial inclusion
2
Identify what the benefits of Big Data can be for customers and providers
Learn how to put Big Data techniques in practice
2
How the different opportunities can drive Financial Inclusion ?
Source: KPMG, Sep. 2016 Source: KPMG, Sep. 2016
Who are this session’s speakers?
Alexis Label, CEO, OpenCBS
Data collection systems and apps. solutions, digitalization of appraisal process
Etienne Mottet, Innovation Analyst at Business and Finance Consulting
Should we mine the big data in microfinance? Introduction with farming case studies
Yasser El Jasouli Sidi, Fonder, MFI Insight Analytics
Data analytics in Microfinance how does it work, practical example of Credit scoring.
Simon Priollaud, Digital Financial Services Consultant at Inbox
Practical experience of projects in Africa on Big Data, results and lessons learned
Data driven microfinance. Small bits, Big Data
Etienne Mottet
Head of Innovation
BFC
Should we mine the Big Data in microfinance?
Case comparison
2 Farming activities
1 Challenge
Get the best yield and profit from their fields
Tylek from Tuyuk Village,
Kyrgyzstan
3 Ha of wheat
2 Ha of parleys
30 livestock head
10,416 Ha
All cultures
High Mechanization
How is Data being used to address this challenge?
III. Decisions
I. Data
Collection
II. Analysis
- Investment in sensors, GPS,
tractor fleet guidance tools
- Big Data agro analysis software
- Live tracking of input and
tractors
- Precision mapping of yield and
other indicators
- Invest in better intelligence
- Tractor fleet management optimization
- Configure input usage automations
- Tractor auto-control
Benefits: 15% input saving, 20% income increase, better cost control, better soil management
Tylek from Tuyuk Village
- Potential for beetroot crop in the region
- Nutrients in soil suitable for growing
beetroot
- Agro expert scoring for the application
- Automated crosscheck with online credit
bureau
- Data analysis & statistical scoring
development
- Consider a new type of crop
- Development of specific
beetroot agro product
- Tylek applies for beetroot loan
- Disbursement decision
Started in 2008. 3000 farmers growing sugar beets.
Factory at max capacity and 2nd factory to be operational by September 2017.
III. Decisions
I. Data
Collection
II. Analysis
- Sugar factory under capacity in Chui Region
- Tylek learns about beetroot opportunity
- Sensor test on field nutrient composition
- Field client information collection
- Product results collection
Situation Comparisons
Live connected equipment
Credit Bureau integration
Expert scoring
XLS based analytical scoring
Digital
information?
Deeper data mining?
Tylek from Tuyuk Village,
Kyrgyzstan
Where could technology improve the process?
III. Decisions
I. Data
Collection
II. Analysis
Management decision
Configure automation (AI)
Agro Big Data solution
Mapping representation
Expertise & score based decision
One-time soil analysis
Client info collection
Word of mouth + farmer gatherings
Knowledge of context
Tablet info
collection?
Follow the digital footprint?
Or nurture strong knowledge of context
Embrace the internet of things?
Or use simple tech smartly
Mine Big Data?
Or smartly leverage existing data
What matters in our context of operation?
13 Big Data or Small Data?
— Should we mine the Big Data in microfinance? Maybe…
— But let’s pick small data first!
Thank you!
OPENCBS
Data driven microfinance: small bits, Big data
European Microfinance Week
Luxembourg, November 18th 2016
VERSATILE OPEN SOURCE CORE BANKING SYSTEM
A free CBS with payable add-ons and services
Additional modules & custom developments
Implementation
Technical Support & Software maintenance
Training of users
100 free users and 20 paying clients
A team of 16 in Bishkek and Hong Kong
More than 10 year experience in Microfinance
OpenCBS introduction
Social business
Affordable for all MFIs
Open architecture
Community oriented
We provide IT services, but we our approach is different
Agora Microfinance Zambia
12,000 clients
60% women
70% in rural areas
Poor network connectivity
opencbs.com
Case study – Tablet application in Zambia
Customisable as
per appraisal
procedures of
MFI
Pictures of clients
Instant receipts
by SMS or mobile
printer
Appraisal process can be paperless where network allows
Synchronisation makes it more efficient to conduct Credit Committee and make decision
www.opencbs.com
info.opencbs
CONTACT DETAILS
Hong Kong Office Unit 1109, 11/F Kowloon Centre 33 Ashley Road Tsimshatsui, KL
Kyrgyzstan office #38, 49/1 Unusalieva street Bishkek
4.0 Credit Scoring Use Case
Situation A small loans provided with wide network was making manual decision in face to face
meeting. Decisions were made manually under wide guidelines. Customers would take
multiple loans each year, often with 2 loans running in parallel.
What we did • We introduced customer management system and behavioural score. On each cycle
• point a score and maximum limit was calculated and 3 possible recommended new loans
• made for those customer which were eligible by the system rules.
The result • Client facing staff appreciated the support and guidelines. Benefits were seen in both;
• Increased sales where sales staff too conservative reduced losses to higher risk customers
• whose relationship with the staff made it difficult to say no
4.1. Risk Mitigation
Credit Assessment can be
done before lending out
loans using Financial Data
and Alternative Data and
such as:
• Demographic Data
• Social Data
• Mobile Data
4.2. Value of Credit Scoring
Risk Assessment Product Offer
Score Product Name
Overall Risk Suggested Loan Amount
Default Probability Suggested Collateral
Odds Annuity
4.3 Impact of Credit Scoring
• Credit Scoring Tools assists in
cleaning the assets by eliminating
borrowers that are not credit worthy
and may effect the portfolio
delinquency and default probability.
• Fewer calculations are needed for
performing data search
4.4 KPIs
• A decrease in the loan
turnaround time from 72 to 6
hours
• An increase in average loan
officer caseload of 134
percent
Building up a commercial segmentation
Simon Priollaud, Lead DFS Consultant
In the three last years
• More than 35 projects in 5 years (3 > 1.8 M. EUR in DFS)
• Commercial segmentation in more than 20 countries in Europe, Africa & Asia
• Largest client has 22 million of clients
Our track record in Africa 2. Inbox’s experience
3. Results - Definition of some segments
Know my clients
Understand my clients
La segmentation peut prendre différentes formes :
Comportementale : basée sur l’activité des clients, leurs habitudes de consommation, elle consiste généralement par le croisement de quelques variables comme la fréquence, la récence ou le montant d’achat mais aussi des indicateurs de multi-détention de produit, de diversification d’achats… Cette segmentation permet d’avoir une vision synthétique et globale du comportement des clients
Valeur / Potentiel : les clients sont représentés sur 2 axes, le premier présente la valeur (généralement le CA) du client sur une période récente, le second le potentiel du client, (par exemple le CA maximum sur une plus longue période). Ce type de segmentation offre une vision du client basée sur le revenu généré et à venir des clients
Mixte : la segmentation mixte intègre les deux types de variables utilisés dans les segmentations précitées
Better serve my clients
Think about the next move…
1. Audit MIS & environment
2. Identify my segments
3. USE the segmentation
objectives Steps
3. Results - Definition of some segments
Youth (<18 ans)
(3 segments)
Inactive (9 segments)
Low income people
(3 segments)
Clients without savings account
(6 segments)
Clients with checking account
(7 segments)
Am
ou
nt
cred
ited
ove
r th
e la
st 1
2 m
on
ths
Overall balance
3. Results - Definition of some segments
Large companies
SME
Microenterprise
VIP Clients
« Working class »
Mass market clients
Low income clients
Commercial
Segmentation
My environment
tomorrow
(hopefully…)
My
environment
today
4. Some advices
1. Do not copy-paste : what you need has to be tailored.
2. Take your time and assess the data you have in your MIS, you most probably already
have all the data you need.
3. Do not underestimate your MIS : segmentation could be integrated in most MIS.
4. Segmentation is a useless tool if you do not use it continually and update it regularly.
