Microsoft Research India

1. Microsoft Research India • Established Jan 2005 - Bangalore • Goals • World-class academic research • Contributions to Microsoft products and businesses • Support growth of research programs in India and elsewhere • Six research areas • Cryptography • Digital Geographics • Hardware, Communications, and Systems • Multilingual Systems • Rigorous Software Engineering • Technology for Emerging Markets • Collaborations with government, academia, industry, and NGOs Computer-skills camp in Nakalabande, Bangalore (MSR India, Stree Jagruti Samiti, St. Joseph’s College) Understand potential technology users in economically poorer communities Adapt, invent, or design technology that contributes to the socio-economic development of poor communities worldwide http://research.microsoft.com/india

2. Towards gains in (contextual) efficiency and user experience Innovations in Information Technology for the Client and MFI Aishwarya Lakshmi Ratan Microsoft Research India Asia-Pacific Regional Microcredit Summit, July 29 2008, Indonesia

3. Overview: IT in microfinance Back-end Front-end • Aggregation of client data • Report generation • Actuarial analysis • Targeting offerings • Account creation (loan, savings & insurance) • Transaction data Info System • Payments from MFI/bank  customer • Payments from customer  MFI/bank • Bank/ investor MFI HQ  MFI branch  MFI retail outlets Cash/ payments

4. (1) IT investments by MFIs Back-end Front-end • Aggregation of client data • Report generation • Actuarial analysis • Targeting offerings • Account creation (loan, savings & insurance) • Transaction data Info System • Payments from MFI/bank  customer • Payments from customer  MFI/bank • Bank/ investor MFI HQ  MFI branch  MFI retail outlets Cash/ payments Work by Aishwarya Ratan, Mahesh Gogineni

5. 1 Reductions in transaction costs? • Examine client-facing information collection and processing transaction tasks in microfinance workflows • Can technology deliver cost savings to the MFI through efficiency gains? • Create an analytical framework in which the cost for a given transaction τ is described by a cost function C (Vl, Vk, O, L, F, N) • Examine the relative cost accrued for task τ under alternate arrangements, LT (low-tech, baseline channel)and HT (high-tech), for a given MFI (For details on the costing model, please see http://research.microsoft.com/~aratan/Cost_Realism_May08_final.ppt )

6. 1 Cases 1 2 3 Installment Processing Rural NBFC Streamline installment data collection Handheld device used by field officer Data uploaded through USB Cut variable cost by 73% Positive NPV over 6 years Customer Acquisition Urban NBFC Improve efficiency of customer acquisition process Smart phone used by field officer Data sent via SMS or GPRS Cut variable cost by 50% Positive RoI Negative NPV over 6 years Installment Processing Rural SHG Fed Streamline book-keeping and installment data collection Smart phone used by field officer Data sent via SMS Little reduction in variable costs High fixed costs for HT channel Negative NPV over 6 years

7. 1 Cost savings comparison

8. 1 Implications: Cost-realistic IT deployments • The higher the labour productivity gains from the HT channel, the greater the transaction cost savings. • The higher the local wage for the task, the higher the productivity-linked transaction cost savings. • The higher the variable capital cost reduction, the greater the transaction cost savings. • A larger number of transactions per unit of labour/per device greatly multiplies the power of productivity gains per transaction from the use of the HT channel. • The larger the operating costs required to run the HT channel (e.g. connectivity costs), the lower the gains from overall cost reduction. • The higher the fixed capital investments called for in the HT channel, the more substantial the requirements for high transactional cost savings and low operating cost differentials to ensure the HT channel’s financial sustainability. Excel-based costing template available at http://research.microsoft.com/~aratan/costing.htm

9. (2) IT in the hands of the client Back-end Front-end • Aggregation of client data • Report generation • Actuarial analysis • Targeting offerings • Account creation (loan, savings & insurance) • Transaction data Info System • Payments from MFI/bank  customer • Payments from customer  MFI/bank • Bank/ investor MFI HQ  MFI branch  MFI retail outlets Cash/ payments Work by Indrani Medhi, Jonathan Donner, Aishwarya Ratan

10. 2 Virtual currency: Cash-in/ cash-out • Rich customers use ATMs & a bank savings account to “store value” • Most of the rich live in dense urban areas and conduct high-denomination transactions • The poor are spread out in rural areas and conduct low-denomination transactions • Current solutions: ATMs in low-income urban neighborhoods; use existing retail networks that serve the urban and rural poor: e.g. pre-paid talktime outlets Mobile ‘stored-value’ outlet

11. 2 Cash-less transactions • Rich customers use PC- or phone-based internet banking; use cheques • The poor do not own or access PCs regularly; often have literacy barriers • The mobile phones they own are not data-enabled • Current solutions: Use SMS or USSD channels, and/or SIM-based applications for mobile payments M-payments user

12. 2 Critical issues in uptake • The intermediaries to the IT channel are critical as informal and flexible mediators (ATM lobby assistants, m-payment agents) • Focus on the density and locations of cash agent networks in low-income neighbourhoods as a core strength • Text entry is very challenging for low-literate customers; SMS applications difficult for direct use • Clarity on charges is critical Cash agent

13. Questions going forward… • What is an optimal User Interface for mobile-banking interactions among low-literate users? • Medhi, I. , A. Sagar and K. Toyama. “Text-free UIs for Low-Literate PC Users,” ICTD 2007 • Is the development impact of mobile-banking services sizeable; is it widespread or specific to particular kinds of poor households? • What happens to microcredit repayment rates when the group’s regular social interaction is interrupted (by IT-enabled channels)? • Related CMF study on meeting frequency and repayment rates by Roy and Davies. • Will efficiency gains in payments channels translate to lower lending rates for microcredit?

14. Acknowledgements: CGAP, PRADAN, CCD/Ekgaon, BASIX, Ujjivan, Eko, EQUITY Bank, Safaricom, Kentaro Toyama, Shabnam Aggarwal, Angelin Baskaran, Rajesh Veeraraghavan, Rahul De Updates on our research projects are available at http://research.microsoft.com/~aratan/FSD.htm ? aratan@microsoft.com