📊 Personal Finance Tracker - TrackYourTransactions

A web-based spending tracker that helps users understand, categorise, and visualise their bank transaction history. Simply upload a CSV file exported from your bank, and the app automatically categorises your transactions and generates meaningful insights.

🛠 Tech Stack

• Backend (and fronted) hosted with AWS EC2 and Nginx.

Frontend (Javascript)

• React + TailwindCSS.
• CSV parsing with with react-papaparse
• Data visualisation with Recharts and AG-Grid.
• Data persistence implemented with DexieDB.

Backend (Javascript)

• Node.js + Express.
• MongoDB for login information and metadata storage.

AI Endpoint (Python)

• Classification model(s) trained, evaluated, and implemented using Tensorflow + Pandas + NumPy.
• Text embedding handled using OpenAI's text-embedding-3-small.
• Endpoint containerised using Docker.
• Endpoint hosted using AWS Lambda + AWS API Gateway.

💻 Model/Dataset statistics

Models evaluated:

• MLP
• LogReg
• OpenAI (ChatGPT)
• Random Forest
• SVM

The final model chosen was the Multi-Layer Perceptron (MLP). Trained on a 974 row (with duplicates) training dataset. ~75% accuracy score (actual * 100 / predicted) on a 411 row test dataset.

🔨 Model Architecture:

model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(input,)),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

📊 Datasets - Category Distribution

Test Dataset - Category Distribution (Unique values):

Number of uniques values in each category:
Category
Housing & Bills      6
Finance & Fees       8
Entertainment       11
Income              16
Shopping            21
Other / Misc        39
Health & Fitness    50
Transport           56
Groceries           69
Eating Out          93

Train Dataset - Category Distribution (Unique values):

Number of uniques values in each category:
Category
Income               1
Transfer             1
Housing & Bills      3
Finance & Fees       3
Groceries           13
Other / Misc        14
Entertainment       16
Health & Fitness    23
Shopping            25
Transport           28
Eating Out          41

🚀 Features

🌗 Seamless Light and Dark Mode Support

🧠 AI Integration

Uses OpenAI's text-embedding-3-small model to generate high dimension embeddings from user's transaction data, then classifies these embeddings using a custom, pre-trained MLP. The model weights (global and personalised) are stored in MongoDB, and are accessed by the AI Endpoint. Every transaction is assigned to one of 10 spending categories: Groceries, Eating Out, Transport, Housing & Bills, Entertainment, Shopping, Health & Fitness, Finance & Fees, Income, Other / Misc

⚙️ Multi-model support

• Personalised model: Each user has a personalised model - the personalised model can:
  • Learn more quickly from the user’s historical spending patterns.
  • Better adapt to individual merchant-specific habits (e.g., “Local Market” = Groceries for one user but Eating Out for another).
• Global model: The global model is a highly generalised model, recieving weight updates from all users. This (ideally) makes it a jack-of-all-trades but master of none model.

💡 Continuous Learning

• This allows users to finetune the selected model based on re-categorised transactions. Finetuning the global model has reduced influence compared to finetuning the personalised model.

🔍 Other notable features

• Keyword-based merchant classification using REGEX.
• Fallback rules for ambiguous transactions.
• Clean normalisation of merchant names (“TESCO 1234” → “Tesco”).

📝 Editable Transactions Table

A highly customisable table that stores uploaded transaction data.

Table Features

• Fully custom undo/redo system - supports row editing, row deletion, row addition.
• Model finetuning on user re-categorised rows.
• Filtering by uploaded CSV.
• CSV Export.
• Transaction re-categorisation.
• Filtering by month.
• Row deletion.
• Row editing.
• Sorting by column.

📁 CSV Upload & Parsing

Implements a robust, multi-stage, upload process, allowing the user to map bank CSV columns to values expected by the system. Allows the saving of mappings to streamline future uploads. Implements graceful error-handling and warns the user if it is suspected that a column has been incorrectly mapped.

Upload stages

• Upload CSV: The bank CSV is parsed using react-papaparse.
• Column mapping: Provides the user an interface to map their bank's CSV to the systems expected columns.
  • Transaction Date => Supporting EU or US format.
  • Transaction Description.
  • Amount => Supporting both a single amount column with both negative and positive values and two amount columns (for debit and credit / income and expense).
• Duplicate Detection: Detects and warns user of duplicate transactions between uploaded CSV and stored transactions.
• Confidence scoring: Allows users to recategorise low confidence.

🔐 User-Friendly Data Flows & Secure Design

• Secure and easy account deletion that deletes all associated account data.
• Secure custom login flow adhering to OWASP guidelines, utilising JWT's + Refresh Tokens.
• Auth0 integration to allow login with accredited vendors (Google, Github, etc.).
• CSV upload process streamlined via intuitive progress bar.
• Transaction data only stored on the client-side. (If auto-categorisation is enabled, transaction data is sent to the AI Endpoint upon upload, but it is never stored in our servers).

🧹 Data Cleaning & Preprocessing

• Automatic removal of empty CSV rows.
• Standardising of date formats using date-fns.
• Detecting and correcting negative/positive mismatches in amount column(s).

Data Visualisation

📉 Spending Breakdown Charts

• Pie Chart: Category distribution.
• Line Graph: Monthly totals.
• Bar Graph Income vs. expenses summary.

📅 Timeline View Grid

How your spending evolves month-by-month. Displays:

• Percentage increase/decrease between months for each major category.

💷 Financial Overview Card

Displays:

• Total spend.
• Total income.
• Total net.

⏳ Possible Future Enhancements

• Direct integration with banking apps, bypassing the need to download a CSV.
• Better DB (currently using free DB tier for heavy model storage).
• Improved ML classifier model accuracy and generalisation. (Currently trained on a limited and biased dataset).
• Fully/more customisable dashboard.
• Budgeting system (allowing users to set budgets per category).
• Custom category support.
• Automatic recurring payment detection from analysing transaction data.
• Payment integration (e.g. using Stripe).