1. New Technologies and Rural Microfinance Lo ï c 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 is judging 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)