Fraud Analyst: What Is It and What Do They Do? (Updated in 2024)

In the third quarter of 2024, there were an estimated 49.3 and 75.3 billion transactions done via major general-purpose card companies like Mastercard and Visa, respectively. With the global trend toward cashless transactions, there’s been a growing demand for skilled professionals trained in observing and flagging potentially fraudulent activity. These professionals are called fraud analysts. But what is it, and what do they do?

Today, we’ll explore what a fraud analyst is, the required skills and education they must have, and other essential information you’ll need to dive into the field.

Fraud analysts wear many hats in the industry, but most work towards a common goal: monitoring, flagging, and reducing fraudulent transactions. Detecting fraud involves a wide range of tasks, including:

• Data Analysis and Monitoring: Fraud analysts leverage advanced data analysis tools and monitoring systems to carefully review a vast array of data points. They’re trained to spot abnormal patterns, behaviors, or transactions that might signal suspicious activity.
• Investigation: Another part of the role involves investigating and uncovering potential fraud cases. When fraud does occur, fraud analysts are responsible for investigating the incident to determine how it happened, who was involved, and the financial impact on the organization. They also work to gather evidence and prepare reports for law enforcement and other stakeholders.
• Risk Assessment and Action: Analysts assess the level of risk associated with specific transactions or accounts. They may look at purchase history, habits, and even location to determine whether a transaction has a high risk of fraud.
• Fraud detection and prevention strategies: Fraud analysts work closely with other departments to implement policies and procedures to prevent fraud from occurring. This may include developing risk assessments, creating fraud awareness training programs, and implementing fraud detection software.

Becoming a fraud analyst involves a solid educational foundation and relevant work experience. As a field, there’s no set path that you absolutely must follow to secure a role, but most fraud analysts have worked on or studied in areas like finance, criminal justice, and even accounting.

In addition to formal education, many fraud analysts acquire different training and certifications. For example, the Certified Fraud Examiner (CFE) demonstrates a high level of expertise in fraud detection and deterrence. Another common certification is the Certified Information Systems Auditor (CISA), which is especially helpful for those looking to specialize in analyzing and assessing information systems.

Fraud analytics, like any other data career, has plenty of opportunities, but the path will vary depending on your background. While entry-level positions are available, most roles in fraud analysis require several years of experience in a relevant domain like finance, accounting, or banking.

• Start with a relevant bachelor’s degree: Consider majors like accounting, finance, or computer science. These will give you a solid foundation for understanding and dissecting financial data.
• Get hands-on experience in your chosen field: Working in related industries like banking, insurance, or healthcare can provide valuable insights into how different sectors operate. This will equip you with the skills and knowledge to detect and prevent fraud effectively.
• Consider taking an online course or boot camp for fraud analytics: These programs can introduce you to the latest fraud detection techniques and teach you how to use data analysis tools to uncover suspicious activity.

Once you have the necessary education and skills, start working on personal projects connected to fraud analysis. This will help you gain experience and build your portfolio. When you’re ready to apply for jobs, be sure to tailor your resume and cover letter to each position/industry. You can check our job board here for available positions.

Fraud analysts need strong technical and interpersonal skills to be successful in their careers. As a fraud analyst, you should be able to use data science techniques and have a keen eye for details to identify irregularities.

Here are some specific skills that are required for the role:

• Data science and analysis skills: Fraud analysts need a robust understanding of data science principles and analysis techniques to effectively identify and monitor fraudulent activities. This includes a strong foundation in statistics, data mining, predictive modeling, and machine learning. Proficiency in data visualization and experience with SQL, Python, and specialized fraud detection software is crucial. In addition, it’s important to be able to handle large datasets and extract actionable insights from complex data.
• Critical thinking and problem-solving skills: These skills are essential when devising strategies to prevent fraud, resolving issues related to false positives/negatives in fraud detection systems, and recommending improvements to anti-fraud processes.
• Communication and interpersonal skills: Effective communication is key in conveying findings, recommendations, and complex information clearly to various stakeholders, including law enforcement agencies and non-technical team members.

In addition to these skills, having experience with financial regulations and compliance and a strong attention to detail and accuracy can help you stand out.

Fraud analysis is a rapidly growing field with a wide range of career opportunities. The career trajectory for fraud analysts generally consists of three levels: entry-level, intermediate, and senior.

1) Entry-level or junior fraud analysts typically have a bachelor’s degree in accounting, finance, business administration, or other related areas. Some may also possess internship/work experience in the field. Junior fraud analysts work under the guidance of more senior analysts and are responsible for tasks like collecting data, conducting analysis, and preparing reports.

2) Intermediate fraud analysts usually have a few years of experience in fraud analytics. They may also have a master’s degree in a related field. At this level, their responsibilities include developing and implementing strategies for detecting fraudulent activities and investigating suspected cases of fraud.

3) Senior fraud analysts are at the highest level in the fraud analytics career ladder and usually possess at least five years of experience in the field. They may also have a professional certification, such as the CFE.

Senior fraud analysts are responsible for managing and overseeing fraud detection and prevention programs. They may also be involved in developing and implementing new fraud detection techniques.

Here are some steps you can take to kick-start your career in fraud analytics:

• Start with a relevant bachelor’s degree: Consider majors like accounting, finance, or computer science. These will give you a solid foundation for understanding and dissecting financial data.
• Get hands-on experience in your chosen field: Working in related industries like banking, insurance, or healthcare can provide valuable insights into how different sectors operate. This will equip you with the skills and knowledge to detect and prevent fraud effectively.
• Consider taking an online course or boot camp for fraud analytics: These programs can introduce you to the latest fraud detection techniques and teach you how to use data analysis tools to uncover suspicious activity.

Once you have the necessary education and skills, start working on data science projects connected to fraud analysis. This will help you gain experience and build your portfolio. When you’re ready to apply for jobs, be sure to tailor your resume and cover letter to each position/industry. You can check our job board here for available positions.

There are various types of companies that hire fraud analysts, including banks, credit card companies, insurance and investment firms, retailers, government agencies, and non-profit organizations.

Some specific examples of companies currently hiring fraud analysts include Amazon, Bank of America, Meta, Google, eBay, Microsoft, Walmart, UnitedHealthcare, the Federal Bureau of Investigation (FBI), Aetna, Wells Fargo, JPMorgan Chase, Citigroup, Airbnb, Pinterest, and Target, among others.

For more companies to consider, check out our company interview guide section.

The average salary for a fraud analyst is $62,616 per year in the United States area. However, salaries vary depending on experience, location, and industry. For example, fraud analysts in finance tend to earn more.

Interview Query offers a wide range of questions to help you improve your skills as you prepare for your fraud analyst Interview. Here are some of the topics that you might encounter:

1. How would you build a model to detect fraud on a banking platform?

Let’s say that you work at a bank that wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction. The customer can then approve or deny the transaction with a text response.

How would we build this model?

2. Given transaction data, how would you build a model to detect fraud?

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

3. Develop a strategy to detect data anomalies based on the structure of the dataset.

Given a univariate dataset, how would you design a function to detect anomalies?

What if the data is bivariate?

Note: univariate means one variable, while bivariate means two variables.

4. Write a SQL query to identify potentially fraudulent ATM transactions.

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 one withdrawal, they were all performed in 10-second gaps, and no legitimate transactions were performed in between two fraudulent withdrawals.

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

5. Given a dataset, how would you determine if users are creating multiple accounts to upvote their own comments?

We’re given three tables representing a forum of users and their comments on posts. We want to figure out if users are creating multiple accounts 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 acting fraudulently in this manner.

6. Using ten years of transaction data, build a model to predict credit card fraud. Discuss your approach, including model choice, bias-variance tradeoff, and handling class imbalance.

You’re asked to build a machine-learning model to predict credit card fraud using ten years of transaction data in this task. The inputs include transaction amount, merchant category, and location data, with a binary output for fraud. You need to consider model choices like logistic regression and how to manage the bias-variance tradeoff, which affects model accuracy and generalization.

The challenge of class imbalance, where fraudulent transactions are rare, makes accuracy a misleading metric. Instead, you’re expected to use metrics like the F1 score, which balances precision and recall, to ensure the model effectively detects fraud without overwhelming false positives.

7. How would you verify if Facebook is losing young users, and what metrics would you examine?

In this task, you’re asked to verify the claim that Facebook is losing young users and identify relevant metrics to investigate this. The key metric to focus on is churn rate—how many users leave the platform over time. You’ll define “young users” by bucketing them into specific age groups, such as teenagers (12-18) and young adults (18-30), and analyze churn rates across different time frames (daily, weekly, monthly). Comparing these trends with overall user activity will help determine if the decline is significant.

Additionally, you’ll want to consider other factors, such as geographic trends and seasonal patterns, to contextualize the data. If increased churn among young users is confirmed, expanding the analysis to include engagement metrics like posts per user and time spent on the platform will provide a deeper understanding of the issue. This comprehensive approach will help you assess whether Facebook is truly losing young users and the extent of their disengagement.

8. Write a query to get the total three-day rolling average for deposits by day.

The solution requires transforming a table of raw transaction data into daily aggregates first, where deposits are filtered by ensuring the transaction value is greater than zero.

Once the daily aggregates are created, the challenge is to compute the rolling average. Since SQL doesn’t natively iterate over rows like Python, you can use a self-join. By joining the daily aggregated table to itself, filtering for rows within a three-day range of each date, and computing the average of these values, you effectively calculate the rolling average. This approach ensures that each day reflects the average of its deposits and the deposits from the two prior days, making it suitable for analyzing short-term deposit trends.

9. You notice that the credit card payment amount per transaction has decreased. How would you investigate what happened?

To investigate the decrease in credit card payment amounts per transaction, segment the data by customer demographics, regions, and merchant categories to identify trends. Analyze weekly or monthly aggregates to spot patterns, and compare metrics like transaction volume and average spend to uncover potential causes like more low-value transactions or shifts in customer behavior.

Review external factors, such as economic conditions or merchant practices, alongside internal policies like credit limits or fees that might impact payments. These steps help pinpoint the issue and guide effective solutions.