American Express
10+ Lockated Interview Questions and Answers
Q1. What would happen to AMEX's income if petrol and diesel prices decrease?
If petrol and diesel prices decrease, AMEX's income may decrease due to reduced fuel surcharges and lower transaction volumes.
AMEX earns revenue through fuel surcharges on transactions made using their cards.
Lower petrol and diesel prices may lead to reduced fuel surcharges, resulting in a decrease in AMEX's income.
Decreased fuel prices may also impact consumer spending habits, leading to lower transaction volumes and further impacting AMEX's income.
However, if lower fuel pri...read more
Q2. What data would you collect from costumers to improve the sales of your super market?
To improve sales, we would collect data on customer preferences, demographics, and shopping habits.
Collect data on customer preferences for products, brands, and packaging
Gather demographic information such as age, gender, income, and location
Track shopping habits like frequency of visits, time of day, and purchase history
Use surveys, loyalty programs, and social media to gather data
Analyze data to identify trends and make informed decisions on product offerings and marketing...read more
Q3. 1. Why amex 2. what is closed loop model of amex 3. What are the factors that you look into for setting the credit limit of a customer. Give an optimization equation 4. Give an instance of when you have worked ...
read moreAnswers to questions asked in an interview for Management Trainee at Amex
1. Amex is a reputed financial services company with a strong focus on customer service and innovation.
2. Closed loop model of Amex refers to the fact that Amex issues its own cards, processes transactions, and provides customer service, all within its own network.
3. Factors for setting credit limit include credit score, income, debt-to-income ratio, and payment history. Optimization equation: Credit Lim...read more
Q4. A person is described. He comes and asks for credit card. How do you decide to give him a credit card?
The decision to give a person a credit card is based on their creditworthiness and ability to repay the credit.
Evaluate the person's credit history and credit score.
Assess their income and employment stability.
Consider their debt-to-income ratio.
Review their payment history on previous loans or credit cards.
Check if they have any outstanding debts or bankruptcies.
Verify their identity and address.
Assess their financial responsibility and spending habits.
Consider any reference...read more
Q5. Why does a specific algorithm work/Doesn't work for your problem?
A specific algorithm works for a problem if it is designed to handle the problem's characteristics effectively.
The algorithm should be able to process the input data efficiently.
It should consider the problem's constraints and requirements.
The algorithm should produce correct and accurate results for the problem.
The algorithm's complexity should be suitable for the problem's scale.
If applicable, the algorithm should be able to handle edge cases or exceptions.
Example: A sortin...read more
Q6. Explain the classification algorithms you used in your project?
I used multiple classification algorithms in my project.
Decision Tree: Used for creating a tree-like model to make decisions based on features.
Random Forest: Ensemble method using multiple decision trees to improve accuracy.
Logistic Regression: Used to predict binary outcomes based on input variables.
Support Vector Machines: Used for classification by finding the best hyperplane to separate data points.
Naive Bayes: Based on Bayes' theorem, used for probabilistic classificatio...read more
Q7. What does American Express do? What is the AmEx Credit Card Model?
American Express is a financial services company known for its credit card offerings.
American Express is a global financial services company headquartered in New York City.
It is known for its charge cards, credit cards, and traveler's cheques.
AmEx operates a closed-loop network, meaning it both issues cards and processes transactions.
The company offers a range of credit card products, including rewards cards, cashback cards, and premium cards.
AmEx has a strong focus on custom...read more
Q8. how they use data analytics in their field of work.
Data analytics is crucial in my field of work as it helps in making informed decisions and identifying patterns.
We use data analytics to track customer behavior and preferences.
We analyze sales data to identify trends and adjust our marketing strategies accordingly.
We use predictive analytics to forecast demand and optimize inventory levels.
We monitor website traffic and engagement metrics to improve user experience.
We use data visualization tools to present insights in a cle...read more
Q9. A cricket match is going on at Eden Gardens. Estimate the number of 10 rupee notes in entire stadium
Estimating the number of 10 rupee notes in a cricket stadium is challenging due to various factors such as crowd size and ticket prices.
Consider the seating capacity of the stadium
Estimate the percentage of attendees who carry 10 rupee notes
Take into account the average number of notes carried by each person
Factor in the number of vendors and their transactions
Consider the duration of the match and the frequency of note exchanges
Q10. how to use data analytics for credit cards.
Data analytics can be used to identify spending patterns, detect fraud, and personalize offers for credit card users.
Analyze transaction data to identify spending patterns and preferences of customers
Use predictive analytics to detect and prevent fraud
Leverage machine learning algorithms to personalize offers and rewards for customers
Monitor credit scores and credit utilization rates to identify potential risks
Track customer feedback and complaints to improve customer satisfa...read more
Q11. Machine learning models known
Machine learning models are algorithms that can learn from data and make predictions or decisions.
Supervised learning models (e.g. linear regression, decision trees, neural networks)
Unsupervised learning models (e.g. clustering, dimensionality reduction)
Reinforcement learning models (e.g. Q-learning, policy gradients)
Deep learning models (e.g. convolutional neural networks, recurrent neural networks)
Natural language processing models (e.g. sentiment analysis, language transla...read more
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