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New Technologies and Rural Microfinance

New Technologies and Rural Microfinance. Lo ï c Sadoulet ECARES (Free Univ. of Brussels). New Technologies and Rural Microfinance. Clarification: micro finance Businesses < 10 employees Mainly working K requirements (low fixed K) Not necessary agriculture (small retail, prod.)

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New Technologies and Rural Microfinance

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  1. New Technologies and Rural Microfinance Loïc Sadoulet ECARES (Free Univ. of Brussels)

  2. New Technologies and Rural Microfinance • Clarification:microfinance • Businesses < 10 employees • Mainly working K requirements (low fixed K) • Not necessary agriculture (small retail, prod.) • Mainly informal • Question: Can new techs enhance ability to effectively service rural poor? • NB: credit + savings + insurance

  3. What’s special about rural? • Client front: Poor and vulnerable • Low income • Variability (structural, indiv) • Limited buffering capacity • Institutional front: • Limited collateral (assets & property rights) • Low density (transactions costs) • Reliability/cost of getting info • Risk evaluation/management Income variations impact consumption Underserved sector

  4. The “New Tech Promise” • “Effectively servicing” clients requires data (Collecting, processing, and using information) • “Promise”: Tech = lower costs / better pricing

  5. The “New Tech Promise” • 3 issues: (1) Data collection: how much value in data collected? (2) Data processing: do MFIs have the ability or capacity? (3) Use of indicators: are we excluding our core clientele?

  6. ! Collecting information • Generating the client database • New tech: PDAs, IT integration • Microfinance: Short term = fast info accumulation • However, quality of information issue • “Extra legal” businesses → how good is the data? • Currently: collection reveals lots of “implicit” information “Implicit” information holds more value than data for start ups

  7. ! Processing information • “Garbage in, garbage out”→ Robust inference • Complex methodologies • Heavy data requirements (prob: small client base + agg) • Constant updating and performance evaluation → Requires technical capacity (in-house or bought) (Expensive and opaque) Actuarial analysis is hard • (new direction: partnerships)

  8. ! Using information • Credit scoring, product pricing,…→ Forecasting … or extrapolating? • Individual judged on average behavior • Start up financing: no information (informal) • Automatization • Cost cutting requires most decisions automatized • Automatization = loss of staff discretion (power) “Secret” of MF is information revelation, not screening! (i.e.: secret isjudging by behavior)

  9. Important gains in… • “Scoring” for experienced borrowers • Past behavior + concurrent info (business cycle) • New products: insurance, line of credit • Information processing → Speed + no ‘double entry’: product delivery → Standardization: portfolio monitoring / regulatory • New technology to diminish (LT) delivery?

  10. Insurance • Issues • Bad risks signing up • False claims • Ability to cover covariate shocks • Examples • Weather insurance • Repayment insurance (Pricing algorithm) • Re-insurance

  11. Conclusion • New tech not panacea for new clients • Paying for costs in low density / variable environments • However, potential for new products (retention rates)

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