Introduction: The Challenge of Reaching the People Who Need Help Most
For NGOs working against poverty, the challenge is two-pronged: How do you get aid to the people who need it most? If you have millions of families living below survival level in other countries, how can cash really help? By giving the family enough money to make choices about what they need most. But with limited funds, you can only help a few families; you need to identify them correctly.
Until recently, NGOs have relied on household surveys, community committee nominations, government poverty records, and field surveys to manage their poverty alleviation programs. These approaches are often inefficient, expensive, and difficult to scale up. They are also constrained by out-of-date records, remote communities, and network effects that lock in access for individuals with social and political connections. But a new technology is here to revolutionize this work: artificial intelligence.
NGOs that employ ML models can decipher data trends and gain a more nuanced understanding of their target audiences living in extreme poverty. ML can enable faster identification of recipients, more intelligent deployment of resources, and improved planning of cash transfer programs—including the use of mobile phone data to locate financial aid recipients. Innovators at the forefront of this movement include Make a1.0 founders and BRAC, the largest NGO in the world.
But that doesn’t mean Give Directly and BRAC are using the same ML model for selecting the beneficiaries—each program has to be evaluated separately.
This is the nonprofit message: Technology will transform aid. But equality, transparency, and human dignity are principles that will need to underpin every action. How will you meet the challenge?
What Is Machine Learning, and Why Does It Matter for NGOs?
Visualize a universe where computers have the power of machine learning to mine your data lake, find clues, and provide insights previously seen only through the lens of human intuition. Why exhaust your team trying to look into each individual household? Surely the model can analyze data from an entire population! How do you define poverty households based on the patterns embedded in the existing data?
Imagine an experiment crew that collects info on home income, pay for goods and services, residing conditions, and entry to public providers. They then use this wealth of information to train a model to seek out the income hardship signs. The model then makes use of its skills to explain the area model of members’ data to search out for the similar hardship.
Having these estimates, an NGO can identify where other assessments are more critical, pinpoint regions where the program will need to do more, and deploy its limited resources effectively. Machine learning finds its forte in the places where household records are limited or where surveys are more expensive.
Don’t forget though: these models do not directly ‘know’ about poverty. They find statistical trends that may point toward economic instability. Their effectiveness depends on the quality of the data that feeds them, the depth of their testing, and their ability to resonate with communities. Will they truly understand the people?
GiveDirectly: Exploring AI to Improve Cash Transfer Targeting
Give Directly makes it possible by providing direct cash transfers to people living in poverty. Why should someone on the outside decide what a family needs when they are the ones who live it? With cash, families can take the help they need and use it in whatever way they most require food, education, repairs, you name it.
But it must be accurate when it identifies who can benefit. Enter Give Directly’s data-driven targeting approach. Collaborating with researchers, the approach uses mobile phone data and machine learning to assess vulnerability. The Mobile Aid effort, for example, analyzes mobile phone behavior as a means to determine those in need.
When applied in Togo and Bangladesh, these techniques could lead to faster detection than the more traditional methods. In the vast volumes of mobile activity there are some untold stories that can be revealed through carefully curated patterns, which can be turned into poverty estimates for entire regions.
Here’s the rub: a bunch of scientists find a statistical correlation between mobile-phone use and household expenditures. A machine-learning algorithm exploits that correlation to make predictions about how economically vulnerable people who haven’t been specifically studied may be. But to be sure, mobile phones aren’t giving you your precise salary or exactly how hungry you are. Mobile phones are showing you what your economic situation might be.
This is a massive innovation. NGOs will be able to conduct large-scale screening far easier and cheaper than they have before, without the expense and hassle of face-to-face assessments. But concern remains. How do we address the challenges faced by the non-phone owner and the marginalized? The algorithm needs to be looked at to address such issues.
Give Directly’s work is not just about the transaction; it’s about dignity and the ability to decide. When technology meets humanity, we start to wonder, might this be the ultimate solution for smarter, better aid?
Understanding BRAC’s Role in Poverty Reduction
BRAC’s history of transforming the lives of poor communities through work in livelihoods, education, health, financial inclusion, and social development is there for all to see. But what does it really take to lift a family out of poverty? Poverty is not only a matter of empty pockets. It is a lack of access to health and education, exclusion, insecure employment, and a lack of opportunities to improve your lives. How do we identify those poor households who need us the most?
Although a family is earning, it may be unable to spend on health services for or education of its children. Or, it may suddenly find itself in destitution, for example, due to a health crisis, a natural disaster, or a temporary loss of income. These often-missed shifts can be buried in old government data and statistics.
Yes, data can highlight trends, but only people engagement can explain the ‘why.’ Interviews from the community reveal obstacles that the algorithm didn’t see, and chats with households uncover changing needs. This understanding helps NGOs that plan to use AI for beneficiary selection: machine learning can support but not replace community knowledge.
The mixing of the quantitative analysis with the qualitative research, community consultations, and the program’s screening criteria does not mean that a decision can be made more flexibly or more specifically. Perhaps more importantly, we do not mean to draw a general lesson from the program but a specific, technological lesson.
How AI-Powered Beneficiary Targeting Works
To understand the potential of machine learning in humanitarian aid, it helps to look at the process from the beginning.
1. Identifying the Problem and Defining Eligibility
First and foremost, the NGO should get clear about what they want to achieve before they start collecting data or designing algorithms. Wondering why that’s important? Well, let’s go through them: Are you trying to reach out to poor households living in extreme conditions, flood-affected households, female-headed households experiencing poverty, or those who lost their employment?
Take a program that provides cash transfers to families affected by drought: Loss of crops, food insecurity, and water access would be key considerations. But a program designed for city-dwellers might focus on rent, job security, and cost of living.
Here’s the deal: Transparent eligibility criteria provide the model the information it needs to address the program’s real requirements, not surface-level correlations.
2. Collecting Relevant Information
The next step involves gathering information that can help estimate household vulnerability.
Depending on the setting, relevant data may include:
- Household surveys and income or expenditure information.
- Living conditions and access to essential services.
- Existing social protection or poverty records.
- Mobile phone usage patterns, where their use is lawful and appropriate.
- Satellite imagery and geographical indicators.
- Information gathered through community consultations and field visits.
Not every program needs all these data sources. An NGO should collect only the information required for its stated purpose and avoid gathering sensitive personal information simply because it is available.
Data collection must also consider whether certain groups are underrepresented. If rural families, people with disabilities, or households without digital access are missing from the dataset, the model may not work equally well for them.
3. Training the Machine Learning Model
Are you curious about how researchers determine economic vulnerability? Here’s the trick: it starts with data collection. After researchers gather the necessary data, they use machine learning techniques to teach a model to recognize the relationships between different indicators and established measures of economic conditions.
Suppose a model learns that there’s a relationship between the features of a house and how a household spends its money that predicts a greater probability of poverty. It then applies these learned relationships to predict how economically vulnerable a different household might be. That’s only a start.
The researchers must also test the model with fresh, unedited data. Why? To check if the model can accurately classify households whose data it hasn’t been trained on. If the model has a high success rate with training data but fails with the new data, it’s likely the model is overfitting to the training dataset. Ever wonder why models change over time?
4. Ranking or Identifying Potential Beneficiaries
Interested in how organizations determine which households could use more support? Once they’ve been validated, models can give you predictions that you can use to select which households should be validated next.
Or say an NGO were to use these results to identify areas where there are lots of likely poor families, or to create a list of households that might need further verification. But wait, what we’re showing isn’t a cause of poverty or ineligibility. It’s just an educated guess – the right decision still has to be made by program rules.
This difference can make a significant impact in your family’s ability to purchase necessary items and services.
5. Verifying Information and Making Decisions
The organisation can then verify eligibility using appropriate methods.
These may include household interviews, checks against reliable records, discussions with trained field staff or a review of information supplied by the applicant.
Verification should be proportionate. If the process requires too many documents or repeated visits, it can create new barriers for the poorest households.
NGOs should also provide an accessible way for people to correct inaccurate information, explain changes in their circumstances or request a review.
6. Delivering Cash Assistance
Did you ever think about how the household eligible for the payment really receives it? If selected, payments are sent via mobile money, bank transfers, or other accessible means in the location, but think about this: not every eligible household has a bank account, phone, data signal or internet. What about that? Assistance has to be delivered through alternative channels, that’s it: NO digital hurdles for you, and no tired transactions from our side. Would you rather receive assistance that way?
7. Monitoring Results and Improving the System
But the game is not over once funds are transferred. For non-governmental organizations NGOs , there’s still monitoring and evaluation to do. Did the intended recipients really receive the intervention? Were some deserving households ignored? Was the targeting method equitable? It also helps to be aware of local circumstances. Sometimes the model that works in 2023 won’t in 2024 because of local socioeconomic shifts, technology adoption, or household circumstances, making continuous review essential.
Can Machine Learning Remove Human Bias from Beneficiary Selection?
Can AI be the answer for a better selection of beneficiaries in humanitarian work? It’s the question everyone talks about today. You may be aware that conventional techniques can be biased by the influence of community ties, local politics, and the absence of information on some households. A procedure that is based on the community may unintentionally select houses with better standing in the community.
Now, picture an ML model working its measured way through the crowd. It’s powerful, because it’s reliably identifying prospective beneficiaries that may not appear on traditional beneficiary lists. But one thing’s for sure: consistency does not necessarily equal justice.
When a model is trained on biased data, it can reinforce biases. Also, if the training data isn’t representative enough of certain groups, your algorithm is likely to be biased. Imagine a system that relies primarily on mobile data. A family that has phone access all the time will generate a wealth of data for a model to make predictions. But a family with no access to a phone at all? They’ll generate very little.
The second household could have been in just as bad, if not worse, shape, but the model may not realize the first household was so far behind. And, the records likely don’t reflect the shock of a sudden job loss, homelessness, disease, and other reversals.
So, charities need to ensure that their models perform fairly across all sections of the population. Maintaining human review processes and the ability to appeal decisions is still essential. The point is not to remove human engagement entirely but to reduce unnecessary decisions. This would preserve local knowledge, accountability and compassion, as opposed to assuming AI can always deliver on these. What’s your opinion – can AI make a difference here?
The Biggest Benefits of AI in Cash Transfer Programmes
Faster Identification of Households in Need
Traditional household surveys can take considerable time, particularly when programmes cover remote areas or large populations.
Machine learning may help NGOs screen available data more quickly and prioritise where additional assessments are required. This can be particularly valuable during emergencies, when delays can worsen the impact of food shortages, displacement or income loss.
However, speed depends on whether suitable data and technical systems already exist. Building a reliable model, obtaining permissions and establishing payment infrastructure can still take time.
Lower Administrative Costs
Conducting detailed assessments for every household can consume a substantial part of a programme’s budget.
AI-assisted screening may reduce some of the costs associated with identifying potential recipients, especially when large populations need to be assessed.
Lower targeting costs could allow organisations to allocate more resources to assistance itself. Nevertheless, NGOs must account for the costs of technology, data protection, staff training, independent evaluation and appeals procedures.
The cheapest targeting method is not necessarily the best if it leaves many eligible families without support.
Better Use of Limited Funding
Many NGOs operate under strict funding constraints. They may have enough resources to support only a portion of the households experiencing hardship.
A carefully evaluated targeting model can help organisations prioritise assistance according to their programme’s eligibility criteria.
This may improve resource allocation, but organisations must decide what their programme is designed to achieve. A model that identifies the poorest households may not necessarily be the right model for a programme focused on disaster recovery, disability support or temporary unemployment.
Improved Coverage in Hard-to-Reach Areas
Remote communities and informal settlements may be missing from official records or difficult to reach through conventional assessment methods.
Geographical analysis and other data sources can help NGOs identify underserved areas and plan field visits more effectively.
Still, data coverage should never be confused with actual need. Organisations must investigate whether the communities that generate less data are also the communities most likely to be overlooked.
More Consistent and Measurable Decisions
A documented model can help NGOs explain how they identify potential beneficiaries, evaluate the accuracy of their decisions and compare results over time.
This can strengthen programme monitoring and make it easier to identify weaknesses in existing targeting methods.
Consistency also supports internal accountability. Staff can review whether established criteria were followed instead of relying entirely on undocumented individual judgements.
The Risks: What Happens When an Algorithm Gets It Wrong?
AI-powered targeting can offer significant benefits, but mistakes can have serious consequences.
1. Excluding Families Who Need Assistance
One of the biggest risks is a false negative: a household that qualifies for assistance but is incorrectly classified as less vulnerable.
Imagine a family whose main earner recently lost a job. Its existing records may still reflect the period when the household had a stable income. If the model relies on those records, it may underestimate the family’s current hardship.
For this reason, organisations should use updated information where possible and create routes for households to request reassessment.
2. Protecting Personal Information
Using mobile phone data or household records can raise serious privacy concerns.
People may not fully understand how their information will be used, who can access it or whether it could be shared with other organisations. Sensitive information could also create risks if it is exposed or used for purposes unrelated to aid delivery.
NGOs should establish clear rules about data collection, retention, sharing and deletion. They should also explain the purpose of data processing in language that people can understand.
Where personal information is involved, organisations must follow applicable privacy laws and ensure that any required permissions or consent procedures are in place.
3. Digital Exclusion
Technology-based systems may disadvantage people who lack mobile phones, internet access, digital literacy or formal identification documents.
These barriers can affect older people, people with disabilities, displaced families and households living in remote areas.
A responsible programme should provide alternative ways to apply for assistance and verify eligibility. Access to emergency support should not depend entirely on a person’s ability to use digital services.
4. Limited Transparency
When a family is rejected by an automated process, it may be difficult to understand why.
A lack of explanation can damage trust and leave people feeling that decisions are arbitrary or impossible to challenge.
NGOs should communicate eligibility requirements clearly and explain the role of AI in the selection process. They should also provide a straightforward way to raise complaints, correct errors and request a human review.
5. Models That Become Outdated
Economic conditions change. A model trained on information collected several years ago may not accurately reflect current poverty levels.
Inflation, conflict, natural disasters, migration and changes in employment can quickly alter household circumstances.
Regular testing and updates are therefore essential. Organisations should monitor whether model predictions remain accurate and suspend or modify a system if it creates unacceptable risks.
Why Human Oversight Still Matters
Technology can process large amounts of information, but it cannot fully understand every aspect of a family’s circumstances.
A household may have some income but face extraordinary medical expenses. A family may appear financially stable in older records but have recently been displaced. Another may live in a household with assets that cannot easily be converted into money for food or medicine.
These details can be difficult to capture through automated analysis alone.
Human oversight allows NGOs to consider additional information, identify unusual cases and respond to circumstances that fall outside the model’s assumptions.
This does not mean every decision must be made manually. Instead, organisations can combine automated screening with trained staff, clear eligibility criteria and a review process for difficult cases.
The most effective approach is one in which technology helps people make better-informed decisions while people remain accountable for the consequences.
How NGOs Can Introduce AI Responsibly
Smaller NGOs do not necessarily need to develop their own machine learning models. Many can begin by improving their data management, evaluating existing targeting methods and identifying tasks where technology could provide practical value.
The following steps can help organisations get started.
Start with a real programme need. Identify the problem that needs to be solved, such as outdated beneficiary records, high assessment costs or difficulty reaching remote communities. Do not introduce AI simply because it is a popular technology.
Use appropriate and reliable data. Assess whether the information is accurate, current, representative and legally available for the intended purpose. Avoid using sensitive data without a clear justification and suitable safeguards.
Compare AI with existing methods. Test whether a model improves accuracy, reduces costs or expands coverage compared with surveys, community assessments and other approaches. The results should guide the decision to adopt the technology.
Evaluate fairness across groups. Check whether the model makes more errors for particular communities or demographic groups. Review who is being included and who is being missed.
Maintain human review and appeals. Ensure that people can correct mistakes and request reassessment. Do not make an algorithm the only route through which vulnerable households can access essential assistance.
Protect privacy and explain decisions. Establish clear data policies, restrict access to sensitive information and explain how the system influences beneficiary selection.
Monitor outcomes continuously. Measure not only administrative savings but also whether the programme reaches the right households and improves their access to support.
For NGOs, responsible adoption is not about having the most advanced technology. It is about choosing tools that solve genuine problems without creating new barriers for the communities they serve.
Measuring Success: Is AI Actually Improving Aid Delivery?
An AI-powered programme should be evaluated on more than the number of households screened or the speed at which a list of beneficiaries is produced.
NGOs should consider several measures.
Targeting accuracy: How reliably does the system identify households that meet the programme’s eligibility criteria?
Exclusion rate: How many eligible households are missed by the model?
Inclusion error: How many households are selected even though they do not meet the established criteria? This should be assessed carefully, particularly where programme eligibility is strict.
Cost per household assessed: Does the system reduce the cost of identifying potential recipients without sacrificing quality?
Time to assistance: Does the new process reduce the time between identifying a need and delivering cash?
Fairness across communities: Does the model perform similarly for different population groups, including people with limited digital access?
Recipient experience: Do beneficiaries understand the process, trust it and have a practical way to seek help?
These measures allow NGOs to evaluate the actual impact of technology rather than assuming that automation automatically produces better results.
In some programmes, AI may offer a clear advantage. In others, community-based assessments or a combination of methods may be more effective.
The best approach is the one that delivers reliable results while protecting the people the programme is designed to support.
What the Future Holds for AI and Humanitarian Aid
The use of machine learning in poverty targeting is part of a broader transformation in the humanitarian and development sectors.
As data systems improve, NGOs may be able to identify emerging needs more quickly, map underserved communities and improve how they plan assistance. AI may also help organisations analyse programme feedback, identify gaps in service delivery and evaluate whether their interventions are reaching their intended audiences.
For example, an NGO responding to a natural disaster could combine geographical information with field reports to identify communities that may require urgent assessment. A poverty reduction programme could use regularly updated data to determine where additional household surveys are needed.
However, predictive tools should not be treated as substitutes for direct assessments, community participation or emergency response. The absence of a strong data signal does not mean that a community has no need for assistance.
There is also an important difference between predicting poverty and understanding its causes. A model may help identify households that appear economically vulnerable, but it cannot independently determine whether the most effective response is cash assistance, healthcare, education, employment support or another intervention.
That decision requires programme expertise, community knowledge and an understanding of the local context.
The future of AI in humanitarian work will therefore depend on collaboration between researchers, technology providers, NGOs, governments and the communities affected by these systems.
How NGOs.ai Supports More Effective NGO Work
Small or grassroots NGO is not an easy affair. Did you think identifying beneficiaries is the toughest issue? That is merely the start. Then you have to go on working hard for fundraising, write excellent proposals, prepare budgets and report to funders on progress. How do small organizations manage to meet these requirements and remain loyal to working for the community?
With the Sample Proposal Generator, you can create your proposal drafts in a flash. The AI Budget Builder makes it easy to structure your budget line items, and the Concept Note Generator takes raw project ideas and converts them to professional concept notes. Looking for help with proposal writing, budgeting, fundraising, or project planning? The NGOs.ai Assistant has got your back.
Are these tools meant to replace people or ensure funding? Never! These tools are here to help you make things easier, to free yourself from some of the boring work and to help you focus on more important, value added ones. But be mindful and take time to doublecheck the content, reflect it locally and make sure it meets the preferences of the donors before you submit.
By leveraging the combination of AI and the human brain, NGOs can focus their limited time and resources to increase the benefit they can deliver to the populations they assist.
Conclusion: Technology Should Help Aid Reach the People Who Need It Most
Could machine learning open the door to the next generation of targeting poor households? For aid organizations, the prospect glows with optimism. Thanks to the pioneering work of Give Directly and its partners, data-powered approaches to cash transfer programs are set to become faster, cheaper, and more scalable than ever. But the complex weave of poverty can still elude data.
What if AI wrote patterns that could analyse and inform uniform decision-making for economic vulnerability? On the other hand, it can get it wrong, amplify inequalities and unless carefully designed and monitored, even endanger privacy. So where does the real opportunity for NGOs lie?
The real power comes from leveraging the power of the technology with the knowledge, skills, accountability and experience of those involved in community work. It’s not the amount of technology used. It’s making sure the right people are given assistance with dignity and if that assistance truly makes a difference.
AI can help NGOs identify the needs of individuals. But it is responsible, citizen-centric action that will help prevent leaving anyone behind.
NGOs.ai ‘s aim is to bring the utility of AI into the non-profit sector so that organizations can spend less time on time-consuming paperwork and more time making a lasting, impactful difference.
