Driving Scale and Density of Agent Networks in Perú

13 January 2015
Regulatory compliance costs can reduce agent network density.
Different agent networks offer payments and banking services in Perú. Their size and coverage varies, but different design choices have influenced their ability to operate viably in more remote areas of the country. These choices involve the range of services offered, the complexity of agent operations, and the overall network operating model. This Brief describes the relationship among these factors and their impact on the potential for rural outreach. It also extracts high-level insights for providers and regulators in markets beyond Perú.
 
Agent networks allow financial service providers to leverage existing retail infrastructure to expand rapidly into areas where the traditional branch model would not be viable or would be expensive to build. For many low-income customers, agents bring access to a potentially rich portfolio of financial services. Beyond banking, mobile networks also use agents to sell airtime and offer other payments services. In 2005 Perú enacted agent banking rules, and since then a number of players and models have emerged.
 
Based on the concept of agent as an access point for cash-in/cash-out services (be it banking or payments in general), CGAP conducted a study of five agent networks comprising more than 26,000 agents and 24 million monthly transactions to identify key success factors in reaching poor and rural areas. Taking the network as the unit of analysis, the study looks at how design decisions at the network level impact the overall capacity to reach poor and sparsely populated areas.
 
The study identified three network design choices where agent managers in Perú used different approaches. Decisions around one aspect imply tradeoffs across the others. These three nonmutually exclusive choices are
 
1. Aggregation of services, especially those involving transactions that are cash-based (cashy)
2. Simplicity of transactions
3. Lightweight network operating model
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