Poverty Stoplight Tech for Microfinance Institutions (MFIs)
Jul 8, 2026
In this session, Larry Reed of Thrive Lights spoke with Luis Fernando Sanabria of Fundación Paraguaya and Smita Bhatnagar and Salma Kagadi of SEWA India about how microfinance institutions can integrate technology, household data, and ongoing support to contribute more directly to poverty elimination.
The conversation emphasized that the success of microfinance should not be measured solely through loan disbursement, productivity, or repayment rates. It should also examine whether families are improving their living conditions, strengthening their agency, and overcoming multidimensional deprivations.
BEYOND LOAN REPAYMENT
Larry recalled that microfinance emerged with the purpose of helping people experiencing poverty. However, many institutions eventually became primarily focused on financial indicators.
The Poverty Stoplight makes it possible to complement portfolio information with data on income, health, housing, food security, education, and other dimensions of well-being. This helps institutions recognize that a family may repay a loan successfully while still facing significant deprivations.
The experiences of SEWA and Fundación Paraguaya demonstrated that it is possible to maintain financial sustainability while incorporating poverty reduction into institutional operations.
SEWA’S EXPERIENCE
SEWA (Self-Employed Women’s Association) is an organization of self-employed women founded in 1972. It has approximately 3.9 million members across 20 states in India and also works in other South Asian countries.
Its objective is to help women achieve full employment and self-reliance, understood as income security, social protection, and the ability to manage their own economic activities.
The organization works with the family as its unit of engagement, but under women’s leadership. Smita recalled an idea expressed by SEWA’s founder, Ela Bhatt:
“Poverty is a form of violence.”
SEWA operates as a family of cooperatives, social enterprises, health services, financial institutions, and value chains. Its work seeks to ensure that women are not only workers, but also owners and managers of their assets.
TECHNOLOGY ADAPTED TO PEOPLE
SEWA explained that it does not adopt technology simply because it is new. A tool must respond to its members’ languages, knowledge, devices, and connectivity conditions.
“If people do not feel that the technology belongs to them, they will not use it.”
To implement the Poverty Stoplight, SEWA incorporated it into its eleven long-standing monitoring questions related to income, food, housing, health, and social security.
The process included participatory indicator definition, translation into the local language, facilitator training, and the development of simple manuals.
One of the main challenges was connectivity. Many women work in rural communities with limited internet access. The offline application made it possible to complete assessments in the field and synchronize them later.
It was also necessary to teach participants, step by step, how to upload the data once they returned to an area with connectivity. The experience demonstrated that technology adoption requires time, support, and continuous adaptation.
FROM DATA TO ACTION
Before using the Poverty Stoplight, SEWA conducted monitoring through meetings and manual records. As the organization grew, this process became increasingly difficult to sustain.
The tool made it possible to systematize information while keeping members at the center of their own assessments.
“The member can understand where she is, where she wants to go, and what she needs to do.”
Women review their green, yellow, and red indicators, select the areas they want to prioritize, and define actions with support from their leaders.
During regular meetings, they discuss what they can resolve independently, what support they need from SEWA, and which responses they can organize collectively. Several members have already succeeded in moving indicators from red to yellow and then to green.
This process strengthens three levels of agency:
• Individual: Each person recognizes her situation and defines her priorities.
• Collective: Groups mobilize resources and solutions.
• Institutional: Data guides programs and public policy proposals.
SEWA uses the information to decide where its interventions need to be strengthened. For example, when a community has numerous red health indicators, the organization can reinforce its programs and advocate for a greater presence of public services.
The data also revealed difficulties faced by young people in accessing employment, leading to the development of employment and entrepreneurship training.
INTEGRATING POVERTY REDUCTION INTO MICROFINANCE
Luis Fernando explained that Fundación Paraguaya initially worked with the hypothesis that increasing income would allow families to address their other needs.
Experience showed that this did not happen automatically. Some families increased their income but continued to lack adequate bathrooms, access to water, education, or other basic conditions.
Fundación Paraguaya therefore incorporated the Poverty Stoplight into its microfinance program. The challenge was to do so without affecting the institution’s financial and operational sustainability.
During the first stage, loan officers applied the tool to a limited number of clients. However, they could only support approximately 5 percent of the portfolio without compromising their other responsibilities.
To expand its reach, Fundación Paraguaya developed Verdeate, an initiative that aims to help all 105,000 clients in the program progressively turn their red and yellow indicators green.
GRADUATION CLASSES AND CONTINUOUS MONITORING
The experience showed that a family usually improves between three and four indicators per year and has an average of between fifteen and twenty red or yellow indicators.
Based on this, Fundación Paraguaya established an approximate five-year goal for each family to make progress on its main deprivations.
Clients are organized into graduation classes. A family that completed its assessment in 2024, for example, belongs to the 2029 graduation class.
Technology enables monitoring:
• By family.
• By indicator.
• By loan officer.
• By branch.
• By graduation class.
Families can update their indicators whenever they achieve a change, without waiting for a new annual assessment.
“We no longer count only how many surveys we conduct; we count how many indicators turn green.”
This makes it possible to integrate multidimensional poverty into daily management. Branches continue to review delinquency and productivity, but they also analyze how many indicators remain red or yellow.
SUPPORT AND PRODUCTS LINKED TO SPECIFIC NEEDS
Loan officers combine individual mentoring, group sessions, and messages sent through WhatsApp.
The information makes it possible to direct specific content to those who need it. For example, families experiencing a health-related deprivation can receive information about health services, while groups that need support with household budgeting can be invited to relevant training sessions.
The data also helps institutions design financial products linked to concrete solutions. Luis Fernando mentioned a bathroom construction kit costing approximately USD 90, with monthly installments of USD 9.
Solutions related to water, cooking facilities, internet access, and education have also been developed.
Fundación Paraguaya also organizes competitions between committees. Through My Bathroom, My Pride, groups identify a member whose bathroom indicator is red or yellow and mobilize labor, materials, and resources to help her turn it green.
During one edition, approximately 700 bathrooms were built, primarily using resources contributed by families and communities.
These initiatives do more than address specific needs. They also strengthen solidarity and people’s ability to help others.
ARTIFICIAL INTELLIGENCE IN SUPPORT OF LOAN OFFICERS
SEWA uses artificial intelligence to draft content, promote products, translate training materials, and analyze information. It is also considering developing introductory AI training for its members.
Smita emphasized that AI-generated results must be reviewed rather than used automatically.
Fundación Paraguaya is incorporating Rosa, an artificial intelligence assistant that helps interpret data from the Poverty Stoplight and the microfinance portfolio.
Luis Fernando compared its role to that of a smartwatch: it can provide information, recommendations, and reminders, but it does not replace decisions or human support.
AI could help a family prepare a budget, but it would be insufficient or potentially risky when addressing issues related to self-esteem or domestic violence.
“Artificial intelligence can help with certain indicators, but we will continue to need social workers.”
DIGNITY AND CONTROL OVER DATA
The participants agreed that technology must serve families rather than turn them into objects of surveillance.
Before each assessment, SEWA explains how the information will be used and assures participants that their data will not be shared without their consent.
Luis Fernando highlighted several principles:
• The family conducts its own assessment.
• Red indicators are starting points, not failures.
• Green indicators help recognize achievements and capabilities.
• The family selects its own priorities.
• Data should not be used to classify families.
• Each person should be able to see their own progress.
“Technology must return control of the family’s own story to the family.”
The process of measuring, acting, and measuring again strengthens dignity and self-determination.
QUESTIONS AND ANSWERS
How can Poverty Stoplight data contribute to broader public policies?
A participant asked how governments, microfinance institutions, social organizations, and communities could collaborate to transform Poverty Stoplight data into public policies capable of reducing poverty at scale.
Luis Fernando noted that implementing organizations can generate highly valuable information, but they do not always have experience in public policy advocacy. He therefore recommended collaborating with organizations specializing in policy dialogue and advocacy that can present the evidence to governments and decision-makers.
Fundación Paraguaya has shared information with local and regional authorities in Paraguay and Ecuador, while other organizations within the network have initiated conversations with public institutions in the United Kingdom.
Smita explained that SEWA combines aggregated data with case studies and testimonies from its members. This evidence helps support proposals related to health, employment, social protection, and other needs identified within communities.
Participants agreed that the information should be organized by territory and used to demonstrate patterns, priorities, and gaps in service provision. At the same time, any data shared with public institutions must respect families’ consent, privacy, and security.How can an institution begin implementing the Poverty Stoplight?
In response to a question about how a microfinance institution should begin implementing the Poverty Stoplight, the participants recommended:
• Adapting the indicators to the organization’s context.
• Involving communities from the beginning.
• Clearly explaining why the data is being collected.
• Avoiding the treatment of the assessment as a quick survey.
• Building trust before asking for sensitive information.
• Starting on a small scale.
• Integrating the tool into existing processes.
• Using the data at every level of the organization.
• Protecting privacy.
• Being patient throughout the process of adoption and ownership.
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You can learn more about the upcoming Cerrito Forum 2026, the annual international global development event that brings together global stakeholders committed to the elimination of poverty from October 12th - 16th by visiting cerrito.com.py.
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