MSc Data Science Research
Senti is a research project exploring machine learning-based credit scoring for informal waste sector workers. Built to prove that transaction data can unlock financial inclusion for those without formal credit histories.
Senti is a research prototype developed as part of an MSc Data Science thesis at the University of Dar es Salaam. The project investigates how operational data from waste collection centres can be transformed into creditworthiness indicators for informal workers.
We started with a simple question: why can't a waste picker get a loan? The answer wasn't lack of incomeâit was lack of records. No transaction history. No way to prove earnings. No financial identity.
Senti addresses this by digitizing collection centre transactionsâevery weighment, every payment, every workerâand using machine learning to derive credit scores from this operational data.
This project combines data engineering, machine learning, and financial inclusion research to develop alternative credit scoring models for underserved populations.
Digital tools for capturing transaction data at collection centresâweighments, payments, worker identificationâreplacing paper ledgers with structured datasets.
Extracting creditworthiness signals from raw transaction dataâconsistency patterns, volume trends, relationship tenure, and behavioural indicators.
Developing and validating machine learning models that predict creditworthiness without traditional credit bureau data or formal employment records.
Demonstrating how alternative data sources can bridge the gap between informal sector workers and formal financial services.
In Swahili, "senti" means centâthe smallest denomination of currency. When people say "senti senti," they mean something insignificant. Too small to matter.
We named this project Senti because we believe the opposite. The waste economy runs on small transactionsâa few shillings per kilogram, a handful of bottles at a time.
This research aims to make every senti visible. To prove that small, consistent work adds up to creditworthiness. To give waste workers the financial recognition they've earned.
Kila senti inahesabika. Every cent counts.
Data collected in partnership with Zaidi Recyclers Limited.
For inquiries about this research or collaboration opportunities, please reach out.
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