The Monarch Benchmark

Written By Ian Wambai

A new standard for AI in underrepresented markets.

Introduction

The AI industry has made incredible progress in recent years, but most AI models are optimized for Western use cases, leaving African markets underserved. While large-scale language models demonstrate remarkable performance on English-centric tasks, their effectiveness diminishes when applied to low-resource African languages, financial AI for mobile banking, and region-specific regulatory compliance. The lack of African-specific datasets and benchmarks has led to poor AI model generalization across these domains.

To address this gap, we are launching The Monarch Benchmark, a comprehensive AI evaluation framework designed to measure and improve AI models for African NLP, fintech, legal AI, compute efficiency, healthcare, agriculture, and education. By establishing a structured standard, we aim to ensure AI models are optimized for real-world African applications rather than just adapted from Western datasets.

Why The Monarch Benchmark Matters

Existing AI benchmarks largely focus on Western finance, standardized English NLP, and cloud-optimized AI models. While these benchmarks are effective for many global applications, they fail to account for multilingual diversity, regional fintech models, and the infrastructure constraints that define AI use in Africa.

  • NLP models struggle with African languages. Many widely used AI models perform poorly on languages like Swahili, Hausa, Amharic, Yoruba, and Zulu due to a lack of high-quality training data.

  • AI models trained on Western financial systems do not generalize well to African banking structures. The dominance of mobile money transactions (e.g., M-Pesa, Flutterwave, Paystack) in Africa creates a unique financial ecosystem that requires specialized AI optimizations.

  • Cloud-dependent AI models are expensive and impractical for many African businesses. With limited access to high-performance computing (HPC) clusters, there is a pressing need for AI models that can run efficiently on low-power or locally hosted hardware.

  • Healthcare, agriculture, and education require AI solutions tailored to local conditions. Western medical AI models fail to account for regional disease patterns, agricultural AI is often trained on non-African climate data, and education AI lacks support for multilingual classroom environments.

The Monarch Benchmark provides a structured evaluation standard that directly addresses these gaps, ensuring AI models can adapt to the needs of African businesses, researchers, and developers.

Core Evaluation Areas

1. NLP & Language Understanding

AI models have historically been evaluated on their performance in English-dominant benchmarks like GLUE and SuperGLUE. However, these benchmarks fail to measure AI competency in African languages, dialects, and linguistic challenges.

Key NLP Evaluation Tasks:

  • Machine Translation – Evaluates AI translation performance across Swahili, Hausa, Amharic, Yoruba, and Zulu.

  • Named Entity Recognition (NER) – Measures accuracy in identifying African-specific entities, such as names, locations, and government institutions.

  • Code-Switching Detection – Tests model fluency in mixed-language texts (e.g., Sheng, Pidgin, Franglais), a common linguistic pattern in African communication.

Accessible Datasets:

  • Masakhane NLP project – A collaborative dataset supporting African language translation research.

  • Inkuba-Mono dataset – A collection of African monolingual corpora for improving text generation and comprehension.

  • Lacuna Fund resources – Curated datasets aimed at expanding AI training data for low-resource languages.

2. Financial & Legal AI

Africa’s financial ecosystem is defined by mobile-first banking, informal lending models, and government-driven financial policies. Western-trained AI models fail to process these structures correctly, making custom benchmarks essential.

Key Financial & Legal Evaluation Tasks:

  • Banking Document Summarization – AI’s ability to process and summarize financial statements, central bank reports, and credit risk assessments.

  • Fintech API Interactions – AI model effectiveness in understanding and generating code for African fintech APIs like M-Pesa, Flutterwave, Paystack.

  • Contract and Regulatory Analysis – Measures AI’s ability to extract key information from legal contracts, government policies, and regulatory filings.

Accessible Datasets:

  • Central bank reports and financial regulatory filings.

  • Judicial case law databases from African courts.

  • Public fintech API documentation from major mobile payment platforms.

3. Code Generation for African Developers

While existing models perform well on standard coding tasks, they are not optimized for African fintech, mobile development, and automation frameworks.

Key Code Evaluation Tasks:

  • Fintech API Code Generation – AI’s ability to generate accurate, secure, and functional M-Pesa and Paystack API integration scripts.

  • Low-Code/No-Code Assistance – AI-generated code recommendations for Flutter, React Native, and Python/Django web applications.

  • Automation Frameworks – Performance on scripting tasks related to financial transaction automation, mobile app USSD development, and regulatory compliance tools.

Accessible Datasets:

  • Open-source fintech repositories (Django, Node.js, Python financial tools).

  • Publicly available government software digitization archives.

  • Community contributions to AI-driven automation for African businesses.

4. Compute Efficiency

African businesses and institutions often lack access to high-performance cloud compute, making it crucial to evaluate AI models on their ability to run efficiently on lower-end hardware.

Key Compute Efficiency Evaluation Tasks:

  • Inference Speed on Local GPUs – Benchmarks model performance on RTX 3090, A100, and edge devices.

  • Memory-Efficient Quantization – Measures the impact of QLoRA (4-bit LoRA) and other lightweight AI deployment methods.

  • Energy Consumption – Evaluates power efficiency when running AI inference on low-resource environments.

Accessible Datasets:

  • Pre-existing AI model runtime benchmarks.

  • On-premise testing datasets for real-world energy efficiency measurements.

5. Agriculture, Healthcare and Education AI

  • Agriculture AI: Tests models on crop disease detection, yield prediction, and climate forecasting using satellite imagery and field sensor data.

  • Healthcare AI: Evaluates medical NLP, diagnostic AI, and disease prediction models based on clinical datasets from African hospitals and public health agencies.

  • Education AI: Benchmarks AI models for automated tutoring, multilingual content generation, and adaptive learning systems.

What Comes Next?

The Monarch Benchmark is an open-source initiative designed to improve AI research for Africa and beyond. As we continue refining the benchmark, we will release:

  • Updated datasets that enhance AI training in African NLP, fintech AI, healthcare, agriculture, and education.

  • New evaluation metrics to address additional challenges in AI accessibility and deployment.

  • AI model testing tools for researchers and developers to validate performance in real-world applications.

This is just the beginning. There’s a lot to build, and we’re taking the first steps: learning, refining, and pushing forward to create AI that truly works for Africa.

Ian Wambai
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