Emw2016 data driven microfinance
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This document summarizes a session on using big data techniques in microfinance. The session objectives are to understand big data in financial inclusion, identify benefits for customers and providers, and learn how to implement big data techniques. The speakers are from companies working in data collection systems, data analytics, and practical applications of big data projects in Africa. One speaker discusses using data to help a farmer in Kyrgyzstan increase profits by identifying suitable new crops. Another discusses implementing a credit scoring system using customer data to improve loan decisions for a microfinance institution. The last speaker discusses segmenting customers to better understand and serve them.
Emw2016 data driven microfinance
- 1. contact@e-mfp.eu Data driven microfinance: small bits, Big Data Philippe BREUL, Partner - Head Office +32 495 32 32 88 pbreul pbreul@phbdevelopment.com
- 2. contact@e-mfp.eu 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
- 3. contact@e-mfp.eu How the different opportunities can drive Financial Inclusion ? Source: KPMG, Sep. 2016Source: KPMG, Sep. 2016
- 4. contact@e-mfp.eu 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
- 5. contact@e-mfp.eu Data driven microfinance. Small bits, Big Data Etienne Mottet Head of Innovation BFC Should we mine the Big Data in microfinance?
- 6. contact@e-mfp.eu 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?
- 7. contact@e-mfp.eu 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
- 8. contact@e-mfp.eu 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
- 9. contact@e-mfp.eu 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?
- 10. contact@e-mfp.eu 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?
- 11. 13 Big Data or Small Data? — Should we mine the Big Data in microfinance? Maybe… — But let’s pick small data first! Thank you!
- 12. contact@e-mfp.eu OPENCBS Data driven microfinance: small bits, Big data European Microfinance Week Luxembourg, November 18th 2016 VERSATILE OPEN SOURCE CORE BANKING SYSTEM
- 13. contact@e-mfp.eu 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
- 14. contact@e-mfp.eu Social business Affordable for all MFIs Open architecture Community oriented We provide IT services, but we our approach is different
- 15. contact@e-mfp.eu Agora Microfinance Zambia 12,000 clients 60% women 70% in rural areas Poor network connectivity opencbs.com Case study – Tablet application in Zambia
- 16. contact@e-mfp.eu On-site collection of information
- 18. contact@e-mfp.eu 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
- 19. contact@e-mfp.eu Synchronisation makes it more efficient to conduct Credit Committee and make decision
- 20. contact@e-mfp.eu www.opencbs.com contact@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
- 21. contact@e-mfp.eu
- 22. contact@e-mfp.eu 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
- 23. contact@e-mfp.eu 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
- 24. contact@e-mfp.eu 4.2. Value of Credit Scoring Risk Assessment Product Offer Score Product Name Overall Risk Suggested Loan Amount Default Probability Suggested Collateral Odds Annuity
- 25. contact@e-mfp.eu 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
- 26. contact@e-mfp.eu 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
- 27. contact@e-mfp.eu Building up a commercial segmentation Simon Priollaud, Lead DFS Consultant spriollaud@inbox.fr
- 28. contact@e-mfp.eu 1. Presentation of Inbox
- 29. contact@e-mfp.eu 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 Africa2. Inbox’s experience
- 30. contact@e-mfp.eu 3. Results - Definition of some segments Know my clients Understand my clients Better serve my clients Think about the next move… 1. Audit MIS & environment 2. Identify my segments 3. USE the segmentation objectivesSteps
- 31. contact@e-mfp.eu 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) Amountcreditedoverthelast12months Overall balance
- 32. contact@e-mfp.eu 3. Results - Definition of some segments
- 33. contact@e-mfp.eu 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
- 34. contact@e-mfp.eu 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.
- 35. contact@e-mfp.eu Any Question ? Simon Priollaud, Lead DFS Consultant spriollaud@inbox.fr
- 36. contact@e-mfp.eu DISCUSSION DATA DRIVEN MICROFINANCE: SMALL BITS, BIG DATA