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Software Engineer Fraud Detection Jobs (NOW HIRING)

BNY

Fraud Hub Lead Engineer

Manhattan, NY · On-site

$113K - $148K/yr

This role serves as the firmwide engineering authority for Fraud detection, prevention, and ... Qualifications & Experience Required * 10+ years of software engineering experience, with at least ...

BNY

Fraud Hub Lead Engineer

New York, NY · On-site

$112K - $147K/yr

This role serves as the firmwide engineering authority for Fraud detection, prevention, and ... Qualifications & Experience Required * 10+ years of software engineering experience, with at least ...

Roblox

Senior Software Engineer - Fraud

San Mateo, CA · On-site

$139K - $183K/yr

As an Economy Fraud engineer, you will be in a data-driven environment developing both classical and novel approaches to detect and prevent this bad behavior. You Have: * 4+ years of professional ...

Flagstar Bank

Check Fraud Detection Analyst

Troy, NY · On-site

$19.34 - $31.22/hr

Understanding of common fraud types (e.g., phishing, account takeover, social engineering, etc ... Familiarity with fraud detection systems and banking platforms (e.g., DNA, Nautilus, Salesforce ...

BNY Mellon

Fraud Hub Lead Engineer

Manhattan, NY · On-site

$113K - $148K/yr

This role serves as the firmwide engineering authority for Fraud detection, prevention, and ... Qualifications & Experience Required * 10+ years of software engineering experience, with at least ...

Roblox

Senior Software Engineer - Fraud

San Mateo, CA · On-site

$196K - $243K/yr

As an Economy Fraud engineer, you will be in a data-driven environment developing both classical and novel approaches to detect and prevent this bad behavior. You Have: * 4+ years of professional ...

BNY

Fraud Hub Lead Engineer

Pittsburgh, PA · On-site

$99K - $131K/yr

Required : • 10+ years of software engineering experience, with at least 5 years delivering Fraud ... detection, or real-time risk decisioning. • Demonstrated success leading and scaling global ...

BNY

Fraud Hub Lead Engineer

Manhattan, NY · On-site

$112K - $148K/yr

... fraud detection, prevention, and response platforms while driving AI-first solutions ... Required : • 10+ years of software engineering experience, with at least 5 years delivering Fraud ...

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All JobsSoftware Engineer Fraud Detection Jobs

Software Engineer Fraud Detection information

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$24K

$104.9K

$189K

How much do software engineer fraud detection jobs pay per year?

As of Aug 27, 2026, the average yearly pay for software engineer fraud detection in the United States is $104,863.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,000.00 and $120,000.00 per year, depending on experience, location, and employer.

What does a software engineer fraud detection do?

A Software Engineer in Fraud Detection designs and develops systems to identify and prevent fraudulent activities within digital platforms, such as banking or e-commerce environments. They build algorithms to analyze user behavior, detect anomalies, and flag suspicious transactions in real time. Their work often involves machine learning, big data analysis, and close collaboration with data scientists and security teams to continuously improve fraud detection accuracy. These engineers play a key role in protecting businesses and customers from financial loss and cybercrime.

How does a software engineer fraud detection typically collaborate with data scientists and analysts to identify fraudulent activity?

Software Engineers in Fraud Detection work closely with data scientists and analysts to build, refine, and deploy systems that detect and prevent fraud. While data scientists may develop models and identify patterns from large datasets, engineers are responsible for integrating these models into scalable, real-time systems within the company's technology stack. Regular communication and joint problem-solving are essential, as engineers must understand the logic behind models and analysts' findings to ensure accurate implementation and continuous improvement. This collaborative environment helps create robust fraud detection mechanisms that adapt to evolving threats.

What are the key skills and qualifications needed to thrive as a software engineer fraud detection, and why are they important?

To thrive as a Software Engineer in Fraud Detection, strong programming skills (such as Python, Java, or Scala), a solid understanding of algorithms, data structures, and experience with machine learning or statistical analysis are generally required, often supported by a degree in computer science or a related field. Familiarity with big data platforms (like Hadoop or Spark), real-time analytics systems, and fraud detection tools or frameworks is typically expected. Analytical thinking, problem-solving abilities, and effective communication are key soft skills that differentiate top performers in this field. These skills are crucial for developing robust systems that can quickly identify and prevent fraudulent activities, protecting both users and organizations.

What is the difference between Software Engineer Fraud Detection vs Data Scientist Fraud Detection?

AspectSoftware Engineer Fraud DetectionData Scientist Fraud Detection
Required CredentialsBachelor's in CS or related field, programming skillsBachelor's or higher in CS, Statistics, or Data Science
Work EnvironmentDevelops fraud detection systems, writes code, implements algorithmsAnalyzes data, builds models, interprets results
Employer & Industry UsageFinancial institutions, fintech, e-commerceFinancial services, tech companies, insurance
Common Search & ComparisonFocuses on software development for fraud detectionFocuses on data analysis and modeling for fraud detection

While both roles work in fraud detection, Software Engineer Fraud Detection primarily develops and maintains detection systems through coding, whereas Data Scientist Fraud Detection analyzes data and builds models to identify fraudulent activity. Both roles often collaborate but differ in their core focus and skill sets.

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Software Engineer Fraud Detection jobs near you
Infographic showing various Software Engineer Fraud Detection job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $104,863 per year, or $50.4 per hour.

Staff+ Software Engineer, Financial Fraud

Anthropic

San Francisco, CA

Full-time

Re-posted 17 days ago

Job description

About the role

The Fraud Prevention team protects Anthropic's payment and monetization surfaces from financial abuse - keeping fraud losses, dispute rates, and network monitoring exposure in check while preserving a smooth experience for legitimate customers. As a software engineer on this team, you will build the systems that make risk decisions in real time, manage the dispute and chargeback lifecycle, and detect monetization abuse across subscriptions, in-app purchases, and promotions. The ideal candidate can see things from attackers' perspectives, anticipate their responses to countermeasures, and never loses sight of the fact that a false positive here is a paying customer.

Payments fraud is more externally coupled than most trust and safety work - you'll collaborate closely with finance, support, and legal teams internally, and with payment processors and platform partners externally.

Responsibilities:
  • Design and build real-time risk decisioning that scores transactions at authorization time, balancing fraud loss, approval rates, and latency constraints
  • Build tooling and automation for the dispute and chargeback lifecycle, from review queues to evidence collection and loss reporting
  • Engineer fraud signals at scale - device fingerprinting, BIN and issuer signals, velocity features, and cross-account linkage - and detect monetization abuse across subscriptions, trials, promotions, and in-app purchases
  • Own a portfolio of metrics - loss rate, dispute rate, authorization approval impact, and false-positive rate - rather than optimizing any single number
  • Lead investigations into emerging fraud patterns, building multi-layered defenses designed for attacker adaptation rather than point-in-time rules
  • Work cross-functionally with finance, support, legal, and data science, and with external payment processors and platform partners
Minimum Qualifications: 
  • Proficiency in Python, SQL, and data analysis tools
  • Experience building or operating fraud, risk, or abuse detection systems in production
  • Strong communication skills and ability to explain complex technical tradeoffs to non-technical stakeholders
Preferred Qualifications: 
  • 8+ years of industry software engineering experience, with a focus on payments fraud or risk
  • Fluency with payments rails: card networks, payment service providers (e.g., Stripe, Adyen), in-app purchase platforms (Apple, Google), refund flows, and the chargeback and dispute lifecycle
  • Direct experience combating fraud typologies such as card testing, stolen-card monetization, refund and chargeback abuse, subscription and trial abuse, promotional abuse, and friendly fraud
  • Understanding of fraud loss accounting - fraud loss vs. dispute fees vs. card network monitoring programs (e.g., VDMP, i VFMP, Mastercard ECP) - and why chargeback rate thresholds carry existential stakes
  • Experience building hybrid rules-and-ML risk systems: real-time scoring at authorization plus post-authorization review workflows
  • Experience at a marketplace or subscription business, or on a processor-side or issuer-side risk team

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