When context insights stay hidden
On the same day, two women walk into branches of the same microfinance institution in different regions of Cambodia. They are the same age, have the same type of business, and are requesting the same loan amount. A year later, one has expanded her small business and enrolled her daughter in secondary school, while the other struggles with irregular repayments and mounting stress. The difference between these two women is not luck—it’s context.
FSPs instinctively know they should tailor their products and approaches to local factors, market conditions, and customer characteristics. Yet people like the women in this example are often offered identical financial products, not because providers don’t recognize that context matters, but because the right data is not available or is difficult to analyze. Furthermore, tailoring products is costly – an FSP’s organizational incentives often prioritize operational efficiency over customer fit.
We still struggle to predict when interventions will work, for whom, and under what circumstances—leaving us to make critical decisions based on average effects that miss many people’s actual experience.
This represents one of financial inclusion’s most persistent challenges: the disconnect between recognizing that context matters and being able to systematically incorporate contextual insights into how we design, deliver, and evaluate financial services. Despite decades of expanding access and conducting evaluations, we still struggle to predict when interventions will work, for whom, and under what circumstances—leaving us to make critical decisions based on average effects that miss many people’s actual experience. Understanding why this gap persists requires examining the limitations of our current evaluation methods.
The heterogeneity question
The financial inclusion sector has invested heavily in impact evaluation, particularly experimental methods like randomized controlled trials (RCTs), to establish whether interventions work. These studies have been valuable. We now have credible evidence, curated through CGAP’s Impact Pathfinder, that financial services can help people manage risk and capture opportunities. But not in all cases – in particular with microcredit. While experimental methods have served us well in establishing generalized causal relationships, they limit our ability to understand when and why those relationships hold and when and why they don’t.
There are four particular challenges that constrain our ability to understand these relationships.
1. The average limitation. When an evaluation reports a 10% average improvement, this might mean 30% of clients benefit substantially while 70% see minimal change. Without understanding these patterns of variation, we risk scaling solutions that work on average but miss the majority of intended beneficiaries. As CGAP has noted, the same set of financial services can have different impacts depending on context, yet we still struggle to predict when positive outcomes will occur.
2. Context as noise, not signal. Most studies treat context as something to control rather than understand. Yet practitioners know intuitively that a credit product that thrives in urban Kenya might fail in rural areas of the country. Traditional methods rarely capture how variations in factors like market infrastructure, social norms, or digital connectivity fundamentally alter effectiveness.
3. Timing mismatches. RCTs take a long time to deliver insights. By the time results emerge, market conditions may have shifted, technologies may have evolved, and opportunities may have been missed. Organizations need evidence that matches the demands of operational decision-making and the speed at which contexts themselves change.
4. Predetermined outcomes. RCTs require researchers to specify in advance which outcomes to measure, risking the possibility that real impacts occur in unexpected areas that go unobserved. For example, micro, small, and medium enterprise (MSME) credit evaluations have typically focused on business growth metrics, potentially missing significant impacts on resilience—effects that weren’t captured simply because researchers weren’t looking for them. This pre-specification constraint means we may sometimes conclude “no impact” when meaningful changes occurred along pathways we hadn’t anticipated.
These limitations create a fundamental mismatch between the evidence we generate and the decisions practitioners face: Is now the right time to launch a new savings product in a rural market? And how do I maximize its take-up? Which customers would benefit most from combining credit with business training? Why do loan performance patterns vary between seemingly similar branches? These decisions are not helped by impact evaluations conducted in a different location, in a different macro-economic context, and without the necessary granularity of insights. Producing evidence that would help make these decisions demands that we utilize both new tools and new ways of thinking about evidence.
Fortunately, methodological and technological advances are creating new opportunities to address these limitations. We should embrace those opportunities and rethink our approach to generating evidence for decision-making within the inclusive finance sector.
From intuition to precision
Technological advances are creating the capability to pinpoint and systematize knowledge and evidence. FSPs and mobile money operators generate continuous data in the form of digitized transactions, such as savings and loan behavior and mobile money usage. Combined with demographic information and contextual data (e.g., weather patterns, market prices, local infrastructure), this creates unprecedented opportunities for insight.
The breakthrough isn’t just identifying and understanding these differences—it’s discovering that customers in even the most constrained environments can achieve success rates comparable to those in more supportive contexts when services are precisely calibrated to their realities.
But data alone doesn’t lead to enhanced understanding – it’s the combination of these relatively new and large data sets with advanced analytical methods that creates new possibilities. Machine learning can now detect complex patterns that conventional statistical analysis would miss, identifying how multiple factors interact to produce outcomes. Meanwhile, cloud computing, API integrations, and visualization platforms make the results of the analysis more accessible to decision-makers.
The breakthrough isn’t just identifying and understanding these differences—it’s discovering that customers in even the most constrained environments can achieve success rates comparable to those in more supportive contexts when services are precisely calibrated to their realities. This shifts the conversation from “can our customers succeed?” to “what does each segment need to succeed?”.
CGAP’s approach: Two complementary strategies
Recognizing both the potential and limitations of different approaches, CGAP is pursuing and piloting two complementary strategies to understand how context shapes outcomes. Building on our call in Financial Inclusion 2.0: generating new evidence to maximize impact, these initiatives represent concrete steps toward evidence that can guide real-world decisions.
1. Mining the collective wisdom embedded in existing research
Despite the existence of thousands of impact studies, most generate valuable findings for individual contexts rather than revealing how effects vary across different populations and circumstances. However, the data underpinning these studies may hold more insights for us and offer a means to gain more precise insights into who is likely to benefit from financial services like microcredit. To better understand what further lessons historical data may hold, CGAP has partnered with Innovations for Poverty Action (IPA) and the Global Poverty Research Lab at Northwestern University to harmonize data from 29 microcredit RCTs into a unified dataset that will enable further exploration into what works, for whom, and under which circumstances. We are partnering with researchers to test the dataset’s potential.
This pooled dataset, containing information on thousands of borrowers across diverse contexts, will enable analysis that would be impossible with individual studies. We will now be able to examine questions like: Do older women respond differently to microcredit than younger women? How do urban versus rural settings change impact patterns? What household characteristics predict success or failure?
This retrospective mining of existing data provides insights beyond the original studies, but of course, they are limited by the variables captured by the research. For example, we found few studies that looked in detail at credit product characteristics. The exercise does confirm that RCTs have a valuable role to play in contributing to useful knowledge in the sector, while pointing to the value of designing future studies with cross-study comparison and granular learning in mind.
2. Piloting precision-guided learning
CGAP has partnered with five financial institutions—AMK Cambodia, BBVA Microfinance Foundation, FINCA International, Grameen Costa Rica, and the Central Bank of Brazil—to pilot Precision Causal Modeling (PCM), an emerging methodology for understanding heterogeneous impacts.
PCM takes previous customer segmentation approaches to another level by creating “block matched groups”: customers segmented across key contextual dimensions(e.g., previous usages of financial services, community context, family support, and economic environment).
Within these closely matched groups, PCM then analyzes naturally occurring variation in other factors, in particular the nuanced features of financial products, and how they are delivered and utilized in practice. While FSPs may offer very similar loan products on paper, the devil can be in the details: customers experience different combinations of loan amounts, repayment terms, officer interactions, and service delivery. This reality means that FSPs have unintentionally created natural experiments—different treatment combinations delivered to similar customers—which innovative methodologies can now leverage to retrospectively identify and analyze a range of cases. PCM uses algorithms to systematically identify customers who experienced different outcomes and determine what differentiated them. This approach maintains rigor by reducing selection bias – comparing customers in similar circumstances who experienced different intervention patterns and outcomes.
CGAP’s work so far with financial institutions shows PCM’s potential to identify specific intervention combinations that can dramatically improve outcomes for different customer segments. Early analysis of partners’ data illustrates this power through an approach that systematically identifies what works within the matched groups. In one example, analysis of over 1,600 customers using seven years of historical data revealed seven distinct matched groups—customers sharing similar business types, family structures, and community resources. Within groups in economically challenged locations, some achieved 31% poverty reduction while others experienced only 6%. Machine learning algorithms revealed the key differentiator: the more successful group of customers had received a specific sequence of financial products—starting with structured loans, then resilience-focused bridge credit, then growth financing—whereas the group that experienced 6% poverty reduction had only been offered standard products. Analysis of the same data set revealed that for customers in more favorable and less economically challenged environments, flexible repayment schedules and progressive pricing proved optimal, achieving 77% poverty reduction versus 28% for standard approaches.
With another MFI partner, analysis of over 5,000 customers identified nine segments with success rates of moving out of poverty that ranged from 13% to 60%. Here, the findings challenged conventional wisdom by showing that rural customers with strong asset bases often outperformed urban customers despite lacking digital connectivity. In this example, the different poverty outcomes seem to be driven more by geographic and social contexts than by different product features.
The key point is that pathways to outcomes are context-specific, and we need evidence streams that account for this reality. While these two approaches that CGAP and its partners are piloting show promise, we believe they represent just the beginning of a broader transformation in how we will generate and use evidence as a sector, taking advantage of new methodologies and technologies.
Humans in the loop
It is also important to note that these new approaches, like PCM, cannot replace human judgment. Algorithms can identify patterns, but practitioners must interpret their meaning. Machine learning suggests optimal product and service combinations, but FSP staff best understand whether those combinations are feasible from an implementation and business standpoint. The most effective analytical approaches combine computational power with human expertise: algorithms surface insights, experts validate their plausibility, practitioners test them in practice, and continuous feedback refines both models and understanding.
Expanding the evidence toolkit
While precision analytics offer powerful capabilities, they represent just one addition to our expanding “evidence toolkit”, which is the suite of methods we use to understand what works, for whom, and when. This does not mean that we’re now abandoning RCTs or other types of traditional evaluation – we are simply expanding the toolkit. Different questions require different methods, and each approach serves distinct learning objectives. RCTs remain essential when testing entirely new interventions or when high causal certainty is required. Quasi-experimental methods offer practical alternatives when randomization isn’t feasible. Machine learning approaches excel at optimization and understanding variation. Routine monitoring provides day-to-day performance feedback. Rather than seeing these as competing approaches, we can view them as complementary tools in an expanded evidence toolkit that serves different decision-making needs.
In looking at the totality of our expanded toolkit, it is vital to acknowledge the limitations of these new methodologies alongside their strengths, to better understand how they can complement existing techniques. For instance, as a quasi-experimental methodology that leverages machine learning, PCM offers powerful capabilities for understanding heterogeneous impacts and identifying causal mechanisms in complex real-world settings. While RCTs remain the gold standard for causal certainty, PCM complements them by approximating experimental logic through quasi-experimental design and machine learning–based discovery of contextual factors that drive outcomes. Like any analytic approach, PCM’s performance depends on data quality and coverage. Its multi-layered validation process — including cross-validation, input shuffling, and within-group inferential testing — helps mitigate bias and strengthen reliability even when data are imperfect. Important questions around how easily findings can be translated into new contexts remain: we cannot simply assume that because an algorithm identifies a pattern, that pattern represents a universal truth, nor that it will persist indefinitely in a dynamic world. As with any adaptive model, periodic retraining and recalibration are essential to maintain accuracy as data and operating conditions evolve. In this way, PCM should be seen not as a substitute for traditional methods but as an evolution in how we approach causal inference when randomized control isn’t possible — expanding our ability to detect and act on the underlying drivers of change.
While RCTs remain the gold standard for causal certainty, PCM complements them by approximating experimental logic through quasi-experimental design and machine learning–based discovery of contextual factors that drive outcomes.
Such limitations do not invalidate machine-learning-based approaches or the place they can hold in our evidence toolkit; rather, they define when such methodologies are most useful. The inclusive finance sector should embrace these methods where they add value: optimizing product delivery within existing programs, identifying heterogeneous effects in established interventions, and personalizing services based on demonstrated patterns – while maintaining the experimental rigor of RCTs for fundamental questions about causation and for testing genuine innovations.
Ultimately, this expanded toolkit opens up new opportunities to create better outcomes for customers and FSPs. When financial services truly meet customer needs and help people build better livelihoods, this creates a virtuous cycle—satisfied customers are more likely to repay loans, maintain accounts, and recommend services to others. Meanwhile, FSPs that focus on genuine impact often find this approach supports rather than undermines their financial sustainability, as serving customers well builds the trust and loyalty essential for long-term business success.
Building the future together
The inclusive finance sector is on the cusp of change. FSPs can now collect, store, and analyze customer behavioral data well beyond loan performance. At the same time, new methodologies like PCM enable us to move from theory and intuition to systematic insights about what has worked for different customer segments under observed conditions—recognizing that these insights illuminate historical patterns and support optimization rather than guaranteeing outcomes in fundamentally changed circumstances or for genuinely novel interventions.
We recognize that this transformation faces institutional constraints. Capturing meaningful outcome data is resource-intensive for FSPs. It often requires significant changes to data systems that were designed purely for risk management and operational efficiency. FSPs need to acquire or develop staff with the right capabilities, including not only data science skills but also critical judgment about model limitations, bias detection, and appropriate validation protocols.
Equally important, these technical advances will require organizational cultures that value continuous improvement over established practices, creating incentives for sharing both successes and failures while accepting that “it depends” is sometimes the most honest answer to impact questions, and that predictive analysis must be carefully distinguished from experimental evidence.
The payoff is that FSPs mastering this transition will be better able to serve both social and financial objectives. They are also likely to be more attractive to investors, increasingly looking for evidence of impact. Investors often default to binary decisions based on average impacts. However, as the field matures, we believe that they will see the benefits of heterogeneous impact analysis and reward FSPs that have embraced it.
Let’s challenge ourselves to collectively cultivate a sector where FSPs move beyond viewing customers as risk categories and instead use data-driven impact segmentation to recognize them as people with diverse needs, and design products accordingly.
Let’s challenge ourselves to collectively cultivate a sector where FSPs move beyond viewing customers as risk categories and instead use data-driven impact segmentation to recognize them as people with diverse needs, and design products accordingly. Where investors allocate resources not only on expected returns but also on predicted impact for specific segments – and grow comfortable with the complexity this entails. Where researchers contribute to shared databases that accelerate collective learning, balancing methodological rigor with practical applicability. And where policymakers demand and use reliable, granular data to shape regulation and programs that respond to real-world needs. For all of us, this vision requires accepting that precision costs more upfront, calls for new skills, and challenges long-standing assumptions – but also promises deeper insights and more meaningful impact.
The alternative is that, despite knowing the importance of context, we continue to make decisions based on averages or just intuition; continue to waste resources on interventions that work for some while failing others; and continue to fall short of financial inclusion’s promise for those who need it most.
Early adopters are already demonstrating the value of precision-driven financial services. The challenge now is scaling these approaches while maintaining rigor, ethics, and a focus on human wellbeing – not because technology makes it possible, but because the people we serve deserve nothing less.
