AI-powered auto-categorization in accounting software

At Heard, we built an AI-powered auto-categorization system that reduced manual bookkeeping time by 40%. The system uses Python with machine learning libraries to categorize transactions automatically.

The Problem

Mental health professionals spend hours each week categorizing expenses for tax purposes. This is tedious, error-prone work that takes time away from their practice.

The Solution

We built a classification system using:

• Scikit-learn for initial models
• Transformers for better accuracy
• FastAPI for the API layer
• PostgreSQL for transaction storage

The Architecture

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

class TransactionCategorizer:
    def __init__(self):
        self.vectorizer = TfidfVectorizer()
        self.classifier = MultinomialNB()
    
    def train(self, transactions, categories):
        X = self.vectorizer.fit_transform([t.description for t in transactions])
        self.classifier.fit(X, categories)
    
    def predict(self, transaction):
        X = self.vectorizer.transform([transaction.description])
        return self.classifier.predict(X)[0]

Results

• 40% reduction in manual categorization time
• 92% accuracy on common categories
• Continuous learning from user corrections
• Significant user satisfaction improvement

Lessons Learned

1. Start with simple models, iterate based on feedback
2. User corrections are valuable training data
3. Explainability matters-users need to trust the system
4. Handle edge cases gracefully

"AI should augment human judgment, not replace it."