In Nepal, as in many developing nations, a significant portion of the population remains underserved by traditional financial institutions. This lack of access to fundamental services—from savings accounts to micro-loans—creates a substantial barrier to economic participation and personal well-being. For developers and data scientists working in the finance sector here, this isn't just a problem; it's a profound opportunity. I've spent considerable time exploring how we can harness the power of AI to bridge this gap, and what I've consistently found is that even with limited data, a thoughtful application of machine learning can democratize financial services, offering a practical, impactful path to greater financial inclusion.
Key Takeaways
- Proxy data, when thoughtfully mapped, can effectively simulate real-world financial scenarios for initial AI model development and concept validation.
- Combining diverse machine learning algorithms (Logistic Regression, Decision Trees, Random Forests) and evaluating them with appropriate metrics provides a robust approach to classifying financial transactions.
- Robust error handling for network requests and data parsing is critical for any production-grade system interacting with external APIs.
- Model persistence using libraries like `joblib` enables the seamless integration of trained AI models into downstream applications for inference.
- The journey from a proof-of-concept to a production-ready financial inclusion tool demands meticulous attention to data privacy, ethical AI, and regulatory compliance.
The Problem: Bridging the Financial Access Gap with AI
Imagine a scenario where a significant part of the population operates outside formal financial systems. They might rely on informal lenders, lack credit histories, or simply be too geographically remote for traditional bank branches. This isn't theoretical; it's the reality for many Nepalis. Traditional financial models often struggle with these segments due to perceived risk, high operational costs for small transactions, or simply a lack of relevant data. This creates a vicious cycle, hindering economic growth and perpetuating inequality. My goal with this post is to show you how a developer can take the first concrete steps, using Python and machine learning, to build tools that can predict financial behaviors, assess risk, or even identify opportunities for micro-lending, thereby extending financial services to those who need them most.
Data and Sources
For this practical demonstration, we’ll use the JSONPlaceholder Todos API. While not real financial data, its structured nature allows us to effectively simulate financial transaction records. We'll interpret:
userIdas a unique customer or account identifier.idas a unique transaction ID.titleas a textual description of a financial transaction (e.g., "bill payment," "loan application," "deposit").completedas the status of a transaction – whether it was successfully completed (True) or failed/rejected (False). This will be our target variable for classification.
This proxy approach allows us to focus on the technical aspects of building and evaluating AI models without grappling with sensitive real financial data, which has its own stringent privacy and regulatory requirements. Data accessed on 2024-07-30.
Step 1 — Data Collection and Preprocessing
The first hurdle in any data science project is getting the raw data into a usable format. For our simulated financial transactions, this means fetching data from the API and transforming it into a structured DataFrame suitable for machine learning. We need to handle potential network issues and ensure our "transaction descriptions" are ready for analysis.
import requests
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report
import joblib
import sys
# Constants
API_URL = "https://jsonplaceholder.typicode.com/todos"
MODEL_FILENAME = "financial_inclusion_model.joblib"
def fetch_and_preprocess_data(url: str) -> pd.DataFrame:
"""
Fetches todo data from a URL, simulates financial transaction data,
and preprocesses it for machine learning.
"""
try:
response = requests.get(url, timeout=10)
response.raise_for_status() # Raise an exception for HTTP errors
raw_data = response.json()
except requests.exceptions.RequestException as e:
print(f"Error fetching data from {url}: {e}", file=sys.stderr)
return pd.DataFrame() # Return empty DataFrame on error
if not raw_data:
print("Fetched empty data. Returning empty DataFrame.", file=sys.stderr)
return pd.DataFrame()
try:
df = pd.DataFrame(raw_data)
# Feature Engineering:
# We'll use the length of the 'title' as a numerical feature.
# This could represent the complexity or detail of a transaction description.
df['title_length'] = df['title'].apply(len)
# We'll use 'userId' directly as a categorical feature,
# but for simplicity and to avoid one-hot encoding for many users,
# let's just use it as a numerical feature for now (or a simple hash/bin).
# For a truly production system, userId would need proper one-hot encoding or embedding.
# Here, we'll keep it simple for demonstration.
df['user_id_feature'] = df['userId']
# Target variable: 'completed' (True/False) mapped to 1/0
df['is_completed'] = df['completed'].astype(int)
# Select relevant features and target
features = df[['title_length', 'user_id_feature']]
target = df['is_completed']
return features.join(target)
except KeyError as e:
print(f"Data parsing error: Missing expected key '{e}' in API response.", file=sys.stderr)
return pd.DataFrame()
except Exception as e:
print(f"An unexpected error occurred during data preprocessing: {e}", file=sys.stderr)
return pd.DataFrame()
Here, the `fetch_and_preprocess_data` function first attempts to retrieve the data. It includes essential error handling for network issues, a common pit
