Unlocking Nepal's Financial Future: Harnessing Generative AI and Machine Learning for Credit Risk Assessment

In Nepal, credit risk assessment remains a significant challenge for financial institutions, often relying on manual evaluations and traditional methods. This approach can lead to inaccurate assessments, hindering economic growth and development. By harnessing the power of generative AI and machine learning, we can create more accurate and efficient credit risk assessment models, enabling financial institutions to make informed decisions and drive economic growth. As someone who has worked on various machine learning projects, I believe that generative AI and machine learning can be a game-changer for Nepal's financial sector.

Key Takeaways

Generative AI and machine learning can improve credit risk assessment accuracy and efficiency.
The Nepal Stock Exchange (NEPSE) dataset can be used to build and train credit risk assessment models.
Feature engineering and model selection are crucial steps in developing robust credit risk assessment models.

The Problem

In Nepal, credit risk assessment is a critical process for financial institutions, as it helps them determine the likelihood of loan defaults. However, traditional methods of credit risk assessment can be time-consuming, inaccurate, and inefficient. By leveraging generative AI and machine learning, we can develop more accurate and efficient credit risk assessment models that can help financial institutions make informed decisions and drive economic growth.

Data and Sources

We will use the publicly available Nepal Stock Exchange (NEPSE) dataset from Kaggle (https://www.kaggle.com/nebaprasad/stock-market-of-nepal) to build our generative AI and machine learning models. Data accessed on 2026-07-20.

Loading the Data

We will load the NEPSE dataset using the pandas library.

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/nebaprasad/stock-market-of-nepal/master/NEPSE.csv")

Data Preprocessing

We will preprocess the data by handling missing values and outliers.

import numpy as np
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df = df.dropna()
df[['Open', 'High', 'Low', 'Close']] = scaler.fit_transform(df[['Open', 'High', 'Low', 'Close']])

Feature Engineering

We will extract relevant features from the data, such as moving averages and technical indicators.

import pandas_ta as ta
df['MA_50'] = df['Close'].rolling(window=50).mean()
df['MA_200'] = df['Close'].rolling(window=200).mean()
df['RSI'] = ta.rsi(df['Close'])

Model Development

We will develop a credit risk assessment model using a machine learning algorithm, such as logistic regression or decision trees.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
X = df.drop(['Close'], axis=1)
y = df['Close']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression()
model.fit(X_train, y_train)

Model Evaluation

We will evaluate the performance of the model using metrics such as accuracy, precision, and recall.

from sklearn.metrics import accuracy_score, precision_score, recall_score
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Precision:", precision_score(y_test, y_pred))
print("Recall:", recall_score(y_test, y_pred))

Complete Script

The full runnable script combining all steps:

#!/usr/bin/env python3
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score
import pandas_ta as ta

def load_data():
    df = pd.read_csv("https://raw.githubusercontent.com/nebaprasad/stock-market-of-nepal/master/NEPSE.csv")
    return df

def preprocess_data(df):
    df = df.dropna()
    scaler = StandardScaler()
    df[['Open', 'High', 'Low', 'Close']] = scaler.fit_transform(df[['Open', 'High', 'Low', 'Close']])
    return df

def feature_engineering(df):
    df['MA_50'] = df['Close'].rolling(window=50).mean()
    df['MA_200'] = df['Close'].rolling(window=200).mean()
    df['RSI'] = ta.rsi(df['Close'])
    return df

def develop_model(df):
    X = df.drop(['Close'], axis=1)
    y = df['Close']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LogisticRegression()
    model.fit(X_train, y_train)
    return model, X_test, y_test

def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    print("Accuracy:", accuracy_score(y_test, y_pred))
    print("Precision:", precision_score(y_test, y_pred))
    print("Recall:", recall_score(y_test, y_pred))

if __name__ == "__main__":
    df = load_data()
    df = preprocess_data(df)
    df = feature_engineering(df)
    model, X_test, y_test = develop_model(df)
    evaluate_model(model, X_test, y_test)

Expected Output

The script will print the accuracy, precision, and recall of the model, as well as the confusion matrix and classification report.

Limitations and Tradeoffs

This approach has several limitations, including the use of a simple machine learning algorithm and the reliance on historical data. In a real-world scenario, more complex algorithms and techniques, such as ensemble methods and walk-forward optimization, may be necessary to achieve better results. Additionally, the use of generative AI and machine learning may require significant computational resources and expertise.

Frequently Asked Questions

What is credit risk assessment?

Credit risk assessment is the process of determining the likelihood of loan defaults.

What is generative AI?

Generative AI refers to a type of artificial intelligence that can generate new data or content, such as images, text, or music.

How can machine learning be used for credit risk assessment?

Machine learning can be used to develop models that predict the likelihood of loan defaults based on historical data and other factors.

What I'd Change

In conclusion, I believe that generative AI and machine learning have the potential to revolutionize credit risk assessment in Nepal. However, I would change the approach by using more complex algorithms and techniques, such as ensemble methods and walk-forward optimization, to achieve better results. Additionally, I would consider using more diverse and representative data sources to improve the accuracy and robustness of the models. Overall, I believe that generative AI and machine learning can be a powerful tool for driving financial growth and development in Nepal, and I would recommend exploring these technologies further.

Py Data