assignment

[reviandi01]

According to available data Risk Analytics In Banking & Financial Services 1 | Kaggle

To answer questions 1 to 3, please use EDA (Exploratory Data Analysis):

a. You are asked to identify real patterns from a group of data, if a client has difficulty paying installments, what response from the bank appears (eg; reducing the loan, or borrowing at a relatively higher interest rate).
b. Identify what factors encourage someone to take out a loan.
c. How can banks use the above information to construct portfolios and risk assessments?

any one have ide how to solve it? thanks

[hmfa]

Hi, @reviandi01

Welcome to the KNIME Community Forum.

I hope I don’t offend the experts, but I will humbly try to give you a push.
I’m not an expert, so I asked GPT.

Here’s his step-by-step approach to identify those patterns for point 1).
There can be others.

1. Data collection.

Clean and prepare the data to ensure accuracy and consistency.

Interactive Data Cleaning – knime

2. Exploratory Data Analysis:

Perform exploratory data analysis to gain insights into the distribution of variables, identify outliers, and uncover initial relationships between variables. Use data visualization techniques like histograms, scatter plots, and correlation matrices.

Automated Visualization – knime

3. Feature Engineering:

Create new features or transform existing ones to enhance the predictive power of the data. For example, calculate debt-to-income ratio.

4. Model Selection and Training:

Choose appropriate machine learning or statistical models to identify patterns associated with payment difficulties.

Common models include logistic regression

Logistic Regression – knime

decision trees,

Credit Scoring – maitri

and random forests.

Training a Random Forest – knime

Credit Scoring – knime

Train these models using the prepared data to predict which clients are more likely to experience payment issues.

5. Model Evaluation and Interpretation:

Evaluate the performance of the trained models using metrics like accuracy, precision, and recall.

01_Evaluating_Classification_Model_Performance – knime

Analyze the model’s decision-making process to understand the most influential features and identify patterns in the data.

6. Pattern Identification and Interpretation:

Based on the model’s results, identify common characteristics or patterns among clients who have difficulty paying installments. This may include factors such as age related to number of keeds, income level, debt-to-income ratio, credit history, and past payment behavior.

7. Application and Refinement:

Utilize the identified patterns to execute risk assessment and proactive intervention strategies.
Implement these strategies to identify clients at risk of payment difficulties.

Loan Analysis – yashg

Reduce the loan amount for clients who are considered to be a good credit risk and who have a reasonable chance of being able to repay the reduced loan.
Weight the potential benefits and drawbacks of reducing the loan against the specific circumstances of the client.

Higher interest rate loan might be acceptable as the loan period is shorter, limiting the overall interest paid.

I hope this can give you some highlights and I recomend you to take a look at the knime Community hub

KNIME Community Hub

I wish you good luck.
Br