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Wells Fargo Credit Risk Analyst Interview Questions and Answers

Updated 18 Nov 2024

Wells Fargo Credit Risk Analyst Interview Experiences

1 interview found

Interview experience
1
Bad
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
No response

I applied via Indeed and was interviewed in Oct 2024. There was 1 interview round.

Interview Preparation Tips

Interview preparation tips for other job seekers - I had interview for credit risk role, Interviewer was some pilani and Ramesh from Bangalore, they had literally no idea of process, poor English, they had come up with few questions and expected candidates to answer as per there written Google answers which is worst.
Wells Fargo has been so worst in recent days, they offer manager and lead roles just based on experience internally with Zero subject knowledge and no proper English.

And I felt, I have wasted my valuable time for an interview

Interview questions from similar companies

Interview Preparation Tips

Round: Group Discussion
Experience: There was a GD based on a case study.

Round: technical interview
Experience: The interview was based mainly on resume. They asked one question on Econometrics and one question on Microeconomics apart from the resume description. From resume they asked mainly about the projects and internships. Overall it was a great experience for a first job interview.

General Tips: Do’s and Don’ts :
1. Take 4-5 printouts of your resume before going for interview. Have a notepad and a pen with you in a folder along with the resume prints.
2. Don’t be frustrated. Always remember that there is a company meant for you. Now, it is possible that it may come on day 1 or day 7 or even later. Always keep faith and believe in yourself.
3. If you are placed and some of your friends are still in the process, support them and do help them in preparation. Moral support is very important in tough times. Don’t go home immediately once you are placed but stay here for a few extra days. This would mean a lot to them.
4. Sleep well during the placement season. You should sleep for at least 5-6 hours.
College Name: IIT Kanpur
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed before May 2022. There were 3 interview rounds.

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare according to job description
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-

I applied via Referral and was interviewed in Feb 2021. There was 1 interview round.

Interview Questionnaire 

15 Questions

  • Q1. Tell me about yourself
  • Q2. How would you perform outlier analysis- detection and treatment?
  • Ans. 

    Outlier analysis involves identifying and treating data points that are significantly different from the rest.

    • Identify outliers using statistical methods such as box plots, scatter plots, and z-scores.

    • Determine the cause of the outlier and decide whether to remove it or keep it in the dataset.

    • Consider the impact of outliers on the analysis and adjust the model accordingly.

    • Use techniques such as winsorization or data tr...

  • Answered by AI
  • Q3. How would you impute missing value when we don't ant to use single value for imputation?
  • Ans. 

    Multiple imputation can be used to impute missing values by creating multiple datasets with imputed values.

    • Use multiple imputation to create multiple datasets with imputed values

    • Combine the results from the multiple datasets to obtain a final imputed dataset

    • Consider using predictive models to impute missing values

    • Evaluate the quality of imputation using metrics such as mean squared error or R-squared

  • Answered by AI
  • Q4. How would you perform variable selection before modelling/ multicollinearity?
  • Ans. 

    Variable selection can be done using techniques like correlation matrix, stepwise regression, and principal component analysis.

    • Check for correlation between variables using correlation matrix

    • Use stepwise regression to select variables based on their significance

    • Perform principal component analysis to identify important variables

    • Check for multicollinearity using variance inflation factor (VIF)

    • Consider domain knowledge a...

  • Answered by AI
  • Q5. How would you test variable importance
  • Ans. 

    Variable importance can be tested using various methods such as permutation importance, drop column importance, and SHAP values.

    • Permutation importance involves randomly shuffling the values of a variable and measuring the decrease in model performance.

    • Drop column importance involves removing a variable from the model and measuring the decrease in model performance.

    • SHAP values provide a measure of the contribution of ea...

  • Answered by AI
  • Q6. What is Logistic Regression and when do we use it?
  • Ans. 

    Logistic Regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.

    • It is used when the dependent variable is binary (0 or 1).

    • It estimates the probability of an event occurring based on the values of the independent variables.

    • It is commonly used in credit risk analysis to predict the likelihood of default.

    • It can also be used in...

  • Answered by AI
  • Q7. How would you test model performance of classification models?
  • Ans. 

    Model performance of classification models can be tested using various metrics.

    • Use confusion matrix to calculate accuracy, precision, recall, and F1 score.

    • ROC curve and AUC can be used to evaluate model's ability to distinguish between positive and negative classes.

    • Cross-validation can be used to test model's performance on different subsets of data.

    • Use lift charts to compare model's performance with random selection.

    • U...

  • Answered by AI
  • Q8. What is Loss function in Logistic Regression?
  • Ans. 

    Loss function in Logistic Regression measures the difference between predicted and actual values.

    • It is used to optimize the model parameters during training.

    • The most common loss function used in logistic regression is the binary cross-entropy loss.

    • The goal is to minimize the loss function to improve the accuracy of the model.

    • The loss function is calculated using the predicted probabilities and the actual labels.

    • Other l...

  • Answered by AI
  • Q9. What is Xgboost? How it is different from Random Forest?
  • Ans. 

    Xgboost is a gradient boosting algorithm used for classification and regression tasks. It is faster and more accurate than Random Forest.

    • Xgboost stands for Extreme Gradient Boosting

    • It is a type of gradient boosting algorithm that uses decision trees

    • It is faster and more accurate than Random Forest

    • Xgboost uses a more regularized model formalization to control overfitting

    • Random Forest builds multiple decision trees and c...

  • Answered by AI
  • Q10. What loss function is used in Xgboost?
  • Ans. 

    The loss function used in Xgboost is customizable and can be specified by the user.

    • Xgboost supports various loss functions such as binary logistic regression, multi-class classification, and regression.

    • The default loss function for binary classification is logistic regression while for regression it is mean squared error.

    • Users can specify their own loss function by defining a custom objective and evaluation function.

    • Th...

  • Answered by AI
  • Q11. What are the parameters in Xgboost?
  • Ans. 

    Xgboost parameters include learning rate, max depth, subsample, colsample by tree, and more.

    • Learning rate controls the step size during training.

    • Max depth limits the depth of each tree.

    • Subsample controls the fraction of observations to be randomly sampled for each tree.

    • Colsample by tree controls the fraction of features to be randomly sampled for each tree.

    • Other parameters include min child weight, gamma, and lambda fo

  • Answered by AI
  • Q12. What is the use of Learning rate in Xgboost?
  • Ans. 

    Learning rate controls the step size at each boosting iteration in Xgboost.

    • Learning rate is a hyperparameter that determines the contribution of each tree in the final output.

    • A smaller learning rate requires more trees to be added to the model, but can lead to better performance.

    • A larger learning rate can speed up the training process, but may result in overfitting.

    • Typical values for learning rate range from 0.01 to 0....

  • Answered by AI
  • Q13. How would you measure relationship between two features?
  • Ans. 

    The relationship between two features can be measured using correlation coefficient.

    • Calculate the correlation coefficient using statistical methods.

    • Correlation coefficient ranges from -1 to 1.

    • A positive correlation indicates a direct relationship between the features.

    • A negative correlation indicates an inverse relationship between the features.

    • A correlation coefficient of 0 indicates no relationship between the feature

  • Answered by AI
  • Q14. What is p value and what it's interpretation?
  • Ans. 

    P value is the probability of obtaining a result as extreme or more extreme than the observed result, assuming the null hypothesis is true.

    • P value is used in hypothesis testing to determine the significance of a result.

    • A small p value (less than 0.05) indicates strong evidence against the null hypothesis.

    • A large p value (greater than 0.05) indicates weak evidence against the null hypothesis.

    • P value should not be used a...

  • Answered by AI
  • Q15. Given a 4 coordinates, write a memory efficient program to check if it's forming a square
  • Ans. 

    Program to check if 4 coordinates form a square

    • Calculate distance between all pairs of points

    • Check if all distances are equal

    • Check if diagonals are equal

    • Use Pythagorean theorem to calculate distance

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - The questions were mostly around Classification and decision trees model as the role requires knowledge of classification model and also I worked on these topics. they asked questions to check understanding of basic concepts of these analytics techniques and models. There were some case study also related to programming.

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

I applied via Recruitment Consulltant and was interviewed in Jun 2023. There were 3 interview rounds.

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I appeared for an interview before Apr 2023.

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-

I applied via Campus Placement and was interviewed in Sep 2023. There were 4 interview rounds.

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed before Apr 2023. There were 2 interview rounds.

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Recruitment Consulltant and was interviewed before Feb 2022. There were 3 interview rounds.

Interview Preparation Tips

Interview preparation tips for other job seekers - Read the JD thoroughly.
It is important to highlight the matching aspects of your current role with the JD. This shows your experience as well as preparedness for the interview.

Be ready for counter questions.
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Wells Fargo Interview FAQs

How many rounds are there in Wells Fargo Credit Risk Analyst interview?
Wells Fargo interview process usually has 1 rounds. The most common rounds in the Wells Fargo interview process are One-on-one Round.

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Wells Fargo Credit Risk Analyst Interview Process

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Wells Fargo Credit Risk Analyst Salary
based on 20 salaries
₹12 L/yr - ₹23 L/yr
52% more than the average Credit Risk Analyst Salary in India
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based on 5 reviews

3.7/5

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3.4

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4.3

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3.6

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4.3

Job security

3.9

Company culture

3.3

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