Data driven microfinance: small bits, Big Data

Transcription

1 Data driven microfinance: small bits, Big Data Philippe BREUL, Partner - Head Office pbreul pbreul@phbdevelopment.com

2 What are this session s objectives? 1 Understand the Big Data techniques in the context of financial inclusion 2 Learn how to put Big Data techniques in practice 2 Identify what the benefits of Big Data can be for customers and providers

3 How the different opportunities can drive Financial Inclusion? Source: KPMG, Sep Source: KPMG, Sep. 2016

4 Who are this session s speakers? 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. Alexis Label, CEO, OpenCBS Data collection systems and apps. solutions, digitalization of appraisal process Simon Priollaud, Digital Financial Services Consultant at Inbox Practical experience of projects in Africa on Big Data, results and lessons learned

5 Data driven microfinance. Small bits, Big Data Should we mine the Big Data in microfinance? Etienne Mottet Head of Innovation BFC

6 Case comparison 2 Farming activities Tylek from Tuyuk Village, Kyrgyzstan 1 Challenge Get the best yield and profit from their fields 10,416 Ha All cultures High Mechanization 3 Ha of wheat 2 Ha of parleys 30 livestock head How is Data being used to address this challenge?

7 - Invest in better intelligence - Tractor fleet management optimization - Configure input usage automations - Tractor auto-control III. Decisions - Big Data agro analysis software - Live tracking of input and tractors - Precision mapping of yield and other indicators II. Analysis I. Data Collection - Investment in sensors, GPS, tractor fleet guidance tools Benefits: 15% input saving, 20% income increase, better cost control, better soil management

8 Tylek from Tuyuk Village - Consider a new type of crop - Development of specific beetroot agro product - Tylek applies for beetroot loan - Disbursement decision III. Decisions - 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 II. Analysis I. Data Collection - 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 Started in farmers growing sugar beets. Factory at max capacity and 2 nd factory to be operational by September 2017.

9 Situation Comparisons Live connected equipment Agro Big Data solution Mapping representation I. Data Collection II. Analysis Tylek from Tuyuk Village, Kyrgyzstan One-time soil analysis Client info collection Knowledge of context Digital Credit Bureau integration information? Expert scoring XLS based analytical scoring Tablet info collection? Word of mouth + farmer gatherings Management decision Configure automation (AI) III. Decisions Where could technology improve the process? Deeper data mining? Expertise & score based decision

10 What matters in our context of operation? 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

11 Big Data or Small Data? 13 Should we mine the Big Data in microfinance? Maybe But let s pick small data first! Thank you!

12 OPENCBS VERSATILE OPEN SOURCE CORE BANKING SYSTEM Data driven microfinance: small bits, Big data European Microfinance Week Luxembourg, November 18 th 2016

13 OpenCBS introduction 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

14 We provide IT services, but we our approach is different Social business Affordable for all MFIs Open architecture Community oriented

15 Case study Tablet application in Zambia Agora Microfinance Zambia 12,000 clients 60% women 70% in rural areas Poor network connectivity opencbs.com

16 On-site collection of information

17 Cash-flow modelling

18 Appraisal process can be paperless where network allows Customisable as per appraisal procedures of MFI Pictures of clients Instant receipts by SMS or mobile printer

19 Synchronisation makes it more efficient to conduct Credit Committee and make decision

20 CONTACT DETAILS info.opencbs Hong Kong Office Unit 1109, 11/F Kowloon Centre 33 Ashley Road Tsimshatsui, KL Kyrgyzstan office #38, 49/1 Unusalieva street Bishkek

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22 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 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 4.2. Value of Credit Scoring Risk Assessment Product Offer Score Overall Risk Default Probability Odds Product Name Suggested Loan Amount Suggested Collateral Annuity

25 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 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 Building up a commercial segmentation Simon Priollaud, Lead DFS Consultant spriollaud@inbox.fr

28 1. Presentation of Inbox

29 2. Inbox s experience Our track record in Africa 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

30 1. Audit MIS & environment 3. Results - Definition of some segments 2. Identify my segments Know my clients Understand my clients 3. USE the segmentation Better serve my clients Think about the next move Steps objectives

31 Amount credited over the last 12 months 3. Results - Definition of some segments Youth (<18 ans) (3 segments) Inactive (9 segments) Clients without savings account (6 segments) Low income people (3 segments) Clients with checking account (7 segments) Overall balance

32 3. Results - Definition of some segments

33 3. Results - Definition of some segments Commercial Segmentation Large companies SME Microenterprise VIP Clients My environment tomorrow (hopefully ) «Working class» Mass market clients My environment today Low income clients

34 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 Any Question? Simon Priollaud, Lead DFS Consultant

36 DATA DRIVEN MICROFINANCE: SMALL BITS, BIG DATA DISCUSSION