Microsoft Research India

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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.
Josephs 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
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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
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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
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(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
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Reductions in transaction costs?
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• 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 t is described by a cost
  function C (Vl, Vk, O, L, F, N)
• Examine the relative cost accrued for task t
  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 )

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Cases
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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
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Cost savings comparison
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Implications Cost-realistic IT deployments
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• 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 channels financial sustainability.

Excel-based costing template available at
http//research.microsoft.com/aratan/costing.htm
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(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
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Virtual currency Cash-in/ cash-out
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• 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
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Cash-less transactions
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• 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
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Critical issues in uptake
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• 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
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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 groups 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?

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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_at_microsoft.com