SME-Ledger — ADTC 2026 Laptop LLM Submission

Africa Deep Tech Challenge 2026

SME-Ledger is a lightweight, offline-first financial transaction intelligence system designed to transform unstructured transaction messages into structured financial records and useful financial insights.

The project began with a focus on M-Pesa SMS messages, addressing the problem of valuable financial information being trapped inside unstructured mobile-money notifications. During the Africa Deep Tech Challenge semifinal evaluation and subsequent model analysis, however, an important limitation became clear: a model trained on a narrow set of transaction examples could struggle to generalize beyond the formats it had seen during training.

SME-Ledger was therefore expanded beyond its original M-Pesa-only training scope.

The current model is trained on approximately 20,000 diverse examples, including synthetic financial transaction messages, banking notifications, mobile-money messages, payment messages, Pochi transactions, Paybill transactions, Till payments, transfers, Fuliza-related transactions, reversals, failures, and capability-oriented instruction examples.

The result is a more general financial transaction understanding model, with M-Pesa as an important use case and starting point rather than the only environment in which the model is relevant.

Your Data. Your Device. Your Financial Intelligence.

Competition Submission

Field	Details
Challenge	Africa Deep Tech Challenge 2026
Track	Laptop LLM
Team ID	`sme-ledger-from-messy-massages-logs`
Primary Domain	Financial Transaction Intelligence
Model	Gemma 3 270M Financial Intelligence
Quantization	GGUF Q4_K_M
Runtime	llama.cpp
Target Hardware	8 GB RAM budget laptop
Inference	Fully offline
Training Dataset	SME-Ledger V2 Synthetic Financial Intelligence Dataset
Training Scale	~20,000 examples
Initial Focus	M-Pesa and mobile-money transaction messages
Current Scope	Diverse financial and transaction messaging

The Problem

A large amount of financial information exists as unstructured messages.

Depending on the service and country, users may receive notifications for:

Mobile-money transactions
Bank deposits
Bank withdrawals
Account transfers
Merchant payments
Bill payments
Wallet transactions
Loan disbursements
Loan repayments
Transaction reversals
Failed transactions
Account balance updates

These messages often contain valuable financial information, but they are difficult to analyze automatically because they exist as free-form or semi-structured text.

For example, a transaction notification may contain:

A transaction reference
Sender or recipient information
Transaction amount
Account or wallet balance
Date and time
Transaction cost
Merchant information
Transaction status

Without structured processing, this information remains a wall of text.

Users may have hundreds or thousands of transaction notifications but still struggle to answer questions such as:

How much money came in this month?

How much did I spend?

What types of transactions are happening most frequently?

What is my net cash flow?

Are there duplicate or inconsistent transactions?

Can these messages be converted into a usable financial ledger?

SME-Ledger addresses this problem using a small, specialized language model that can understand diverse transaction-message patterns while operating locally on constrained hardware.

🔄 From Semifinal Feedback to a More General Model

A major part of the SME-Ledger development journey was learning from the limitations observed during the semifinal stage.

The earlier version of the model was trained on approximately 200 examples, primarily focused on M-Pesa transaction messages.

This approach demonstrated that a small model could learn the desired extraction task, but it also revealed an important limitation:

A model trained on a narrow distribution of examples may perform well on familiar message formats while generalizing poorly to different transaction domains, formats, or capability-oriented questions.

During evaluation and subsequent testing, it became clear that the model needed more diversity than simply adding more examples of the same M-Pesa message structure.

The solution was therefore not just to increase the dataset size.

The training strategy itself was redesigned.

The updated dataset was expanded to approximately 20,000 examples covering multiple transaction domains, message structures, linguistic variations, extraction tasks, financial reasoning contexts, and capability-oriented instructions.

This helped move the model from a narrowly specialized M-Pesa parser toward a broader financial transaction intelligence model.

Training Dataset

The current SME-Ledger V2 training dataset contains approximately 20,000 examples.

A significant portion of the additional data is synthetically generated to create controlled diversity across transaction types and message formats while avoiding the use of real personal financial records.

The dataset covers multiple categories of financial communication.

Transaction and Financial Message Coverage

Examples include:

Mobile Money

Money received
Money sent
Wallet payments
Merchant transactions
Cash withdrawals
Airtime purchases
Balance notifications

M-Pesa Ecosystem

Send Money
Receive Money
Paybill payments
Till payments
Pochi transactions
Fuliza drawdowns
Fuliza repayments
Fuliza fees
Reversals
Failed transactions
Balance updates

Banking and Transfers

Bank deposits
Bank withdrawals
Incoming transfers
Outgoing transfers
Bank-to-wallet transfers
Wallet-to-bank transfers
Transaction confirmations
Account balance notifications

Payment and Merchant Messages

Merchant payments
Bill payments
Service payments
Business transactions
Payment confirmations

Transaction States

Successful transactions
Pending transactions
Failed transactions
Reversed transactions
Refunded transactions

The objective is to expose the model to the underlying concepts of financial transactions, rather than allowing it to memorize a small number of M-Pesa templates.

Capability Instruction Training

One weakness identified during earlier testing was that the model could perform a transaction-related task but was not always able to clearly explain what it could do when asked directly.

For example, questions such as:

What can you do?

What types of messages can you understand?

Can you analyze this transaction?

What information can you extract?

could produce responses that did not accurately represent the model's intended functionality.

The updated dataset therefore includes capability-oriented instruction examples.

These examples help the model understand and communicate its role, including its ability to:

Understand transaction messages
Extract structured financial information
Identify transaction participants
Extract transaction amounts
Extract balances
Identify dates and times
Classify transaction types
Recognize transaction status
Handle multiple transaction-message formats
Return structured outputs
Support downstream financial analysis

This improves the model's ability to communicate its capabilities while remaining grounded in its actual specialization.

Why Synthetic Data?

The expansion from approximately 200 examples to approximately 20,000 examples required a scalable way to create diverse training data.

Synthetic generation allows the project to systematically vary:

Transaction types
Message structures
Entity names
Merchant names
Amounts
Dates
Times
Account balances
Transaction references
Transaction status
Banking terminology
Mobile-money terminology
Payment formats

The goal is not to reproduce the financial history of real people.

Instead, the goal is to create realistic transaction-language patterns that teach the model how to understand the structure and meaning of financial messages.

The synthetic approach also allows the dataset to avoid including real customer financial records or personally identifiable information.

Dataset Generation & Provenance

The SME-Ledger V2 dataset was generated using a reproducible synthetic data-generation pipeline.

The dataset-generation process is documented in the Kaggle notebook:

Kaggle — SME-Ledger Data Generator

The generator is responsible for creating diverse instruction-tuning examples across financial transaction categories and model capability questions.

The repository includes a provenance/ directory containing information required to understand the origin and structure of the training data.

provenance/
├── README.md
└── dataset_sample.jsonl

The provenance documentation should include:

Dataset version
Dataset generation methodology
Generation seed
Dataset size
Dataset splits
Transaction categories
Capability-instruction categories
Data source classification
PII policy
Kaggle generator reference
Dataset license

A representative sample is included in dataset_sample.jsonl so the training format can be inspected without requiring the full dataset to be stored in the submission repository.

Beyond a Single Country or Payment Platform

SME-Ledger originated from a Kenyan problem: financial information embedded in M-Pesa transaction messages.

M-Pesa remains an important and practical starting point for the project.

However, the underlying problem is broader.

Financial institutions, mobile-money providers, banks, wallets, merchants, and payment platforms around the world generate transaction notifications that contain similar concepts:

Who?
↓
Did what?
↓
With how much money?
↓
When?
↓
What was the result?
↓
What is the resulting balance or status?

The specific wording changes from one provider to another, but the underlying financial concepts remain similar.

The expanded training strategy therefore focuses on learning these concepts across diverse message structures.

This makes SME-Ledger potentially adaptable beyond:

M-Pesa
Kenya
A single mobile-money provider
A single transaction-message template

The current model should therefore be understood as a financial transaction intelligence model with a strong mobile-money foundation, rather than a model designed exclusively for one Kenyan messaging format.

Solution Architecture

SME-Ledger separates language understanding from deterministic financial computation.

                Transaction Messages
                         │
                         ▼
              ┌─────────────────────┐
              │  SME-Ledger LLM     │
              │  Gemma 3 270M       │
              └──────────┬──────────┘
                         │
                         ▼
              Structured Transaction
                  Understanding
                         │
                         ▼
              ┌─────────────────────┐
              │ Validation Layer    │
              │ + Normalization     │
              └──────────┬──────────┘
                         │
                         ▼
              ┌─────────────────────┐
              │ Local Ledger        │
              │ / Analytics Engine  │
              └──────────┬──────────┘
                         │
                         ▼
              Deterministic Financial
                    Computation
                         │
                         ▼
              Financial Insights

The LLM handles

Transaction-language understanding
Entity extraction
Amount extraction
Balance extraction
Date identification
Transaction classification
Transaction status recognition
Structured output generation
Capability-oriented responses

Deterministic software handles

Arithmetic
Income totals
Expense totals
Net cash flow
Financial summaries
Transaction aggregation
Balance consistency checks
Duplicate detection
Ledger generation

This hybrid design keeps the model focused on what language models are good at while allowing deterministic code to handle operations where consistency is critical.

Model

The final model is based on:

Base Model:
Gemma 3 270M

Fine-Tuning:
LoRA / PEFT Instruction Tuning

Training Data:
~20,000 diverse financial transaction and capability examples

Final Format:
GGUF Q4_K_M

Runtime:
llama.cpp

The model was selected because the project requires a balance between:

Small memory footprint
Fast CPU inference
Offline operation
Financial language understanding
Structured output generation
Deployment on constrained hardware

The objective is not to build the largest possible financial model.

The objective is to demonstrate that a relatively small model can acquire useful, specialized financial transaction understanding when trained with sufficiently diverse and carefully designed data.

Privacy-First Architecture

Financial messages contain sensitive information.

SME-Ledger is designed around local processing:

                 INTERNET
                    X
                    │
          ┌─────────▼─────────┐
          │    User Device    │
          │                   │
          │ Transaction SMS   │
          │       ↓           │
          │ Local LLM         │
          │       ↓           │
          │ Local Ledger      │
          │       ↓           │
          │ Local Analytics   │
          │       ↓           │
          │ Financial Insight │
          └───────────────────┘

During inference, the model is designed to run without requiring an external AI API or cloud-based language model.

Designed for the 8 GB Laptop

The ADTC environment emphasizes constrained hardware.

SME-Ledger therefore uses a small model that can run through llama.cpp with GGUF quantization.

The target environment includes:

4 CPU cores
8 GB RAM
Integrated graphics
No dedicated GPU requirement
Offline inference

The project demonstrates that improving model capability does not necessarily require increasing parameter count.

In this case, a major improvement came from improving training-data diversity and coverage rather than simply using a larger model.

📁 Repository Structure

adtc-2026-submission-template/
│
├── metadata.json
├── README.md
├── REPORT.md
├── download_model.sh
├── .gitignore
│
├── model/
│   └── sme-ledger-financial-intelligence-Q4_K_M.gguf
│
├── provenance/
│   ├── README.md
│   └── dataset_sample.jsonl
│
└── submission.json

The model weights are not committed to Git.

The download_model.sh script retrieves the public GGUF model before evaluation.

Project Evolution

Stage 1
│
├── ~200 M-Pesa-focused examples
│
├── Demonstrated structured SMS extraction
│
└── Limitation:
    Narrow generalization outside familiar formats

                ↓

Semifinal Evaluation & Analysis

                ↓

Stage 2
│
├── ~20,000 training examples
│
├── Diverse financial transaction domains
│
├── Banking messages
│
├── Mobile-money messages
│
├── Pochi / Paybill / Till coverage
│
├── Transaction failures and reversals
│
├── Multiple message structures
│
└── Capability-oriented instruction tuning

                ↓

Current Direction

Lightweight General Financial
Transaction Intelligence Model

                ↓

Offline Deployment

8 GB Laptop → Future Android / Edge Deployment

Long-Term Vision

SME-Ledger begins with transaction-message intelligence.

The longer-term opportunity is broader:

Transaction Messages
        ↓
Structured Financial Data
        ↓
Personal / SME Ledger
        ↓
Cash-Flow Intelligence
        ↓
Financial Health Signals
        ↓
Credit Readiness
        ↓
Privacy-Preserving Financial Intelligence

Potential future applications include:

Personal finance
SME bookkeeping
Mobile-money analytics
Banking message analysis
Wallet transaction intelligence
Cash-flow monitoring
Financial anomaly detection
Balance consistency validation
Loan affordability analysis
SACCO decision support
Financial-institution integrations
On-device Android deployment

Project Resources

📄 License

This submission repository is licensed under the GNU GPL v3 License.

Dataset provenance and licensing information are documented in the provenance/ directory.

Acknowledgement

SME-Ledger was developed as an iterative research and engineering project for the Africa Deep Tech Challenge 2026.

A key part of its development was learning from model limitations observed during evaluation and responding by improving data diversity, task coverage, and generalization.

The project demonstrates an important principle for edge AI:

Better specialization is not always achieved by making the model bigger. Sometimes the biggest improvement comes from teaching a small model a more complete view of the problem.

SME-Ledger

Your Data. Your Device. Your Financial Intelligence.

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GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

4-bit

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