Top 5 Fraud Analyst Interview Questions for 2024

Centered around rogue financial practices, fraud analysis entails collecting and utilizing big data techniques to prevent financial fraud. As the number of people engaging in online banking and relying on online transactions continues to rise, fraud analytics has become essential for financial institutions to ensure compliance with the latest guidelines and protect their customers.

Apart from financial institutions, fraud analysis is also utilized in healthcare, federal services, and retail.

Fraud analysts use data analysis and investigative techniques to identify and prevent rogue activities. Their job includes analyzing large datasets to identify patterns, developing ML models to prevent and report fraudulent activities, and assessing an organization’s vulnerability to fraud.

As someone applying for a fraud analyst role, you’ve come to the right place to catch a glimpse of questions that can be expected in a fraud analyst interview in 2024.

1. Let’s say you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

How to Answer

When building a binary classifier on an imbalanced dataset, it’s important to consider the accuracy and labeling of the data, particularly regarding undetected fraud. The choice of model, such as one of the tree-based models, is also crucial. We must weigh the trade-off between model interpretability and accuracy, especially if unknown fraud patterns could impact future feature engineering. Techniques like reweighting data, custom loss functions, and generating synthetic examples can also play a role in improving model performance.

Real-World Scenarios

A large bank may use a binary classifier to detect fraudulent credit card transactions within an imbalanced dataset, where fraud is rare. They prioritize recall to catch more fraudulent activities by applying tree-based models, reweighting, and SMOTE techniques to enhance detection while minimizing financial losses from undetected fraud.

2. Imagine you work at a major credit card company and are given a dataset of 600,000 credit card transactions to build a fraud detection model.

How would you approach this task?

How to Answer

Begin by analyzing the dataset to understand the frequency and sources of fraudulent transactions. Feature engineering is essential to identify key indicators of fraud. Given the data imbalance, rebalancing techniques and boosting algorithms can help enhance model performance. Finally, assess the cost-benefit of misclassifications to determine the optimal probability threshold for flagging fraud.

Real-World Scenarios

A major credit card company may use a boosting algorithm to improve fraud detection by analyzing transaction patterns and adjusting for the imbalance between fraudulent and legitimate transactions while also considering the financial impact of false positives and negatives.

3. If given a univariate dataset, how would you design a function to detect anomalies?

What if the data is bivariate?

How to Answer

For univariate anomaly detection, you can use methods such as percentile-based filtering to identify outliers by eliminating values at extreme percentiles. For bivariate datasets, anomalies can be detected by examining deviations in the relationship between two variables or using machine learning algorithms like isolation forest, DBSCAN, or Bayesian Gaussian mixture models that account for anomalies in the joint distribution of the variables.

Real-World Scenarios

A company monitors server performance using univariate anomaly detection to identify unusual spikes in CPU usage. It also employs bivariate anomaly detection to spot unusual combinations of CPU usage and memory usage that deviate from expected patterns.

4. There was a robbery from the ATM at the bank where you work. Some unauthorized withdrawals were made, and you need to help your bank find out more about those withdrawals.

However, the only information you have is that there was more than 1 withdrawal, they were all performed in 10-second gaps, and no legitimate transactions were performed between two fraudulent withdrawals.

We’re given a table of bank transactions with three columns: user_id, a deposit or withdrawal value transaction_value, and created_at time for each transaction.

Write a query to retrieve all user IDs in ascending order whose transactions have exactly a 10-second gap from one another.

Note: Assume that there are only withdrawals from the ATM, which are denoted with a positive transaction_value.

Example:

Input:

bank_transactions table

Column	Type
user_id	INTEGER
created_at	DATETIME
transaction_value	FLOAT

For given table bank_transactions:

user_id	created_at	transaction_value
1	2017-01-01 10:10:15	10
2	2017-01-01 11:11:11	20
3	2017-01-01 12:12:12	10
4	2017-01-01 10:10:20	30
5	2017-01-01 10:10:34	40
6	2017-01-01 11:11:21	50

Output:

user_id
2
6

How to Answer

To identify user IDs with transactions occurring exactly 10 seconds apart, use SQL window functions like LAG() and LEAD() to compare each transaction’s timestamp with the previous and next transactions. Compute the time difference in seconds and filter users based on whether the differences are exactly 10 seconds. Finally, retrieve the distinct user IDs in ascending order.

Real-World Scenarios

A bank might need to detect patterns of ATM fraud by finding users who made multiple withdrawals exactly 10 seconds apart. Using SQL, the bank compares transaction timestamps for each user to identify such patterns and flag potential suspicious activity.

5. We are given three tables about a digital community, users, comments, and comment_votes, representing a forum of users and their comments on posts.

We want to figure out if bad actor users are creating multiple accounts in order to upvote their own comments.

1. What kind of metrics could we use to figure this out?
2. Write a query that could display the percentage of users on our forum that would be acting fraudulently in this manner.

Example:

Input:

users table

Column	Type
id	INTEGER
created_at	DATETIME
username	VARCHAR

comments table

Column	Type
id	INTEGER
created_at	DATETIME
post_id	INTEGER
user_id	INTEGER

comment_votes table

Column	Type
id	INTEGER
created_at	DATETIME
user_id	INTEGER
comment_id	INTEGER
is_upvote	BOOLEAN

Output:

Column	Type
voter_id	INTEGER
voter	VARCHAR
commenter_id	INTEGER
commenter	VARCHAR
vote_perc	FLOAT

How to Answer

The SQL query should calculate the number of upvotes each user gives relative to their comments, identifying users with an unusually high proportion of upvotes on their own comments. For example, forum administrators might use these metrics to detect suspicious patterns where a user consistently upvotes their own comments from multiple accounts, thereby flagging potential fraudulent activity.

Real-World Scenarios

In a forum, administrators want to detect if users are creating multiple accounts to upvote their own comments. They use SQL queries to calculate metrics such as the upvote ratio and self-upvote rate, flagging users whose behaviors exceed typical thresholds, indicating possible fraudulent activity.

To excel in a fraud analyst interview, demonstrate your technical expertise, problem-solving skills, and understanding of fraud trends. Here are some key preparation tips:

Research the Company

Begin by thoroughly researching the company you’re interviewing with. Understand the industry they operate in and keep up with any recent news or developments that might affect them. For instance, if the company is in the financial sector, look into the latest regulatory changes or prevalent types of fraud in that industry.

Brush Up on Technical Concepts

Technical knowledge is a cornerstone of the fraud analyst role. Review fundamental concepts such as data mining, statistical analysis, and machine learning. These are often at the heart of fraud detection techniques. You should be comfortable explaining how you would use data mining to identify unusual patterns or apply statistical analysis to assess the likelihood of fraud in a given dataset.

Practice Case Studies

Fraud analyst interviews often include case study questions that assess your analytical and problem-solving abilities. To prepare, practice solving hypothetical fraud scenarios.

For example, consider a situation where there is a sudden spike in chargeback rates—how would you investigate and address this issue? Walk through the steps you would take to analyze the data, identify potential fraud patterns, and implement preventive measures.

Understand Fraud Trends

Fraud is a constantly evolving field, with new threats emerging as technology and criminal tactics advance. To stand out, stay updated on the latest fraud trends and techniques, such as the rise of synthetic identity fraud, advancements in AI-driven fraud detection, or the increasing prevalence of cryptocurrency-related fraud.

Demonstrating knowledge of these trends during your interview not only shows that you’re informed but also that you’re proactive about staying ahead of potential threats.

Practice Behavioral Questions

In addition to technical knowledge, employers will assess your soft skills, particularly how you handle situations, work within a team, and solve problems. Prepare for behavioral questions by reflecting on your past experiences. For instance, think about a time when you identified a potential fraud and had to convince a reluctant team to take action.

Be Prepared for Technical Questions

Finally, be prepared to discuss specific fraud detection tools, techniques, and methodologies. Employers may ask about your experience with SAS, Python, SQL, or specific fraud detection systems. They might also quiz you on methodologies like anomaly detection, risk scoring, or pattern recognition.

Prepare to explain not just what these tools are but also how you’ve used them in past roles to detect and prevent fraud. For instance, you might describe how you used SQL queries to sift through transaction data and identify suspicious activities.

Cracking a fraud analyst interview requires more than just preparing for the interview and memorizing a set of standard questions. With fraudsters constantly evolving their methods, a fraud analyst must stay ahead of the curve.

This means continually updating your knowledge of emerging risks and mastering the latest fraud prevention techniques to safeguard against new threats effectively.

Here are a few fraud analyst risks and trends to help you stay updated:

Synthetic Identity Fraud

A subset of typical identity theft, in synthetic identity fraud, fraudsters create a fictitious account with fabricated personal information to, for example, obtain loans using someone’s Social Security Number, causing significant financial losses, damage to credit scores, and legal issues.

AI-Based Attack Vectors

In its basic form, AI-based attack vectors use artificial intelligence to launch cyberattacks, such as deepfake attacks, adversarial ML attacks, and automated phishing, to compromise sensitive data, disrupt operations, and extract money. A simple example of this could involve a deepfake video of a CEO endorsing a fraudulent investment scheme to deceive investors.

For-Hire FaaS

Similar to SaaS, it’s a growing trend among cybercriminals to offer Fraud-as-a-Service (FaaS) to the desiring individuals who lack the technical expertise to facilitate fraudulent operations. These kinds of services can be anything from fraud-related tools to anonymously using stolen credit card details.

For example, a recent article highlighted that distributed denial-of-service (DDoS) attacks aimed at disrupting competitors’ websites have become a service offered by FaaS providers.

Contactless Payment Fraud

While convenient, contactless payment technology also introduces new avenues for fraudsters. Proximal card cloning, relay attacks, and NFC malware exploits are the most common contactless payment frauds in 2024. These attacks can result in financial losses for both individuals and businesses.

A2P Fraud

Application-to-person (A2P) messaging is a critical digital channel for sending marketing, sales, and operational notifications to customers. It’s also a trusted mode for 2FA protocols via SMS. However, it can be used for malicious purposes, leading to financial losses, spam, and privacy violations. For example, a fraudster might send spam messages to a large number of mobile phone users, promoting fraudulent schemes through a spoofed, trusted A2P channel name.

Friction vs Fraud

Finding the right balance between security and convenience is critical for organizations to protect themselves from fraud while maintaining customer satisfaction. This is especially true for e-commerce websites, where introducing more friction in the form of 2FA or biometrics may cause bounce rate and cart abundance to skyrocket.

In another example, a bank may implement stricter authentication measures to prevent fraud, but this can also lead to increased customer frustration.

Traditional Fraud Detection Techniques

While fraud analysts are predominantly hired to develop advanced fraud detection techniques, traditional methods continue to play a critical role. Rule-based systems, such as flagging transactions over certain amounts and predefined limitations, are quite effective in identifying large transactions.

On the other hand, a customer’s deviation from normal behavior patterns, like unusual spikes in transaction volumes or changes in purchase habits, can be tracked and reported through statistical analysis.

In addition to these measures, techniques such as 2FA, 3FA, biometrics, and tokenization are employed to prevent generic attacks.

AI-ML Driven Fraud Detection

Artificial intelligence (AI) and machine learning (ML) techniques are now considered the gold standard in fraud analysis, offering significant advantages over traditional methods. Fraud analysts develop ML models to perform real-time transaction analysis and immediately detect fraudulent activities. These systems are trained on vast amounts of data and can learn from new data to adapt to evolving fraud patterns.

In most of these models, supervised learning, unsupervised learning, reinforcement learning, and NLP techniques are used to detect and prevent payment card fraud, insurance fraud, identity theft, and financial crimes.

Preparing for a fraud analyst interview requires technical knowledge, problem-solving skills, and awareness of current fraud trends. By focusing on the top interview questions, understanding the latest fraud techniques, and following effective interview preparation tips, you can increase your chances of success in securing a fraud analyst position. All the best!