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Fraud Detection Agent Jobs (NOW HIRING)

ProRata

Principal Program Manager

Bellevue, WA · On-site +1

$220K - $260K/yr

... agent technology . Our ProRata Ads platform enables real-time, contextual, prompt-driven ... fraud detection capabilities launch on time, with high quality, and aligned to business impact.

ProRata.ai

Principal Program Manager

Bellevue, WA · On-site

$220K - $260K/yr

... agent technology . Our ProRata Ads platform enables real-time, contextual, prompt-driven ... fraud detection capabilities launch on time, with high quality, and aligned to business impact.

Replit

Senior Software Engineer, Risk

Foster City, CA · On-site

$210K - $265K/yr

Design and implement LLM guardrails that detect abuse scenarios in AI-generated code and agent ... Background in fraud detection, payment abuse, or financial crime * Familiarity with device ...

Replit

Staff Software Engineer, Risk

Foster City, CA · On-site

$250K - $315K/yr

Design and implement LLM guardrails that detect abuse scenarios in AI-generated code and agent ... Background in fraud detection, payment abuse, or financial crime * Familiarity with device ...

Cerebra Consulting, Inc.

Enterprise Architect

$70.75 - $91/hr

... the Managing General Agent (MGA) market, insurance core platforms, and AI-driven digital ... fraud detection, predictive analytics, and customer engagement. * Identify emerging technologies ...

New

FIS

Commercial Product Manager, Senior

New York, NY

$138K - $182K/yr

Define how AI agents and autonomous decisioning systems transform fraud detection and identity ... Design and deliver agent-based workflows that continuously evaluate identity risk and fraud signals

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Fraud Detection Agent information

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How much do fraud detection agent jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for fraud detection agent in the United States is $17.22, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $18.99 per hour, depending on experience, location, and employer.

What does a Fraud Detection Agent do?

A Fraud Detection Agent is responsible for monitoring financial transactions and activities to identify and prevent fraudulent behavior. They analyze data, review suspicious activities, and use specialized tools and software to detect anomalies. When potential fraud is identified, they investigate further, gather evidence, and may collaborate with law enforcement or other departments. Their goal is to protect customers and organizations from financial losses and maintain trust in the company's services.

What is the difference between Fraud Detection Agent vs Customer Service Representative?

AspectFraud Detection AgentCustomer Service Representative
Required CredentialsHigh school diploma; certifications in fraud prevention or security are a plusHigh school diploma or equivalent; customer service training often preferred
Work EnvironmentCall centers, financial institutions, online platformsRetail stores, call centers, service centers
Employer & Industry UsageFinancial services, e-commerce, bankingRetail, telecommunications, hospitality
Common Search & Comparison IntentUnderstanding roles in fraud prevention, job requirements, career pathCustomer interaction, problem-solving, service skills

Fraud Detection Agents focus on identifying and preventing fraudulent activities within financial or online platforms, requiring knowledge of security protocols. Customer Service Representatives handle client inquiries and support, emphasizing communication skills. While both roles may work in call centers and require strong interpersonal skills, Fraud Detection Agents specialize in security and fraud prevention, making their roles more technical and investigative.

What are some common challenges faced by Fraud Detection Agents and how are they addressed on the job?

Fraud Detection Agents often face challenges such as quickly identifying new fraud patterns, managing high volumes of suspicious transactions, and maintaining accuracy under pressure. To address these, agents typically receive ongoing training on emerging fraud tactics and use advanced analytical tools to help flag anomalies. Collaboration with other departments, such as IT security and customer service, is crucial in investigating cases and implementing preventive measures. Regular team meetings and knowledge sharing also help ensure that agents stay informed and effective in their roles.

What are the key skills and qualifications needed to thrive as a Fraud Detection Agent, and why are they important?

To thrive as a Fraud Detection Agent, you need strong analytical skills, attention to detail, and a background in finance, business, or a related field. Familiarity with fraud detection software, data analysis tools, and knowledge of regulatory compliance standards are typically required. Excellent communication, critical thinking, and problem-solving abilities help agents identify and resolve suspicious activities effectively. These skills ensure the accurate detection and prevention of fraudulent transactions, protecting organizations and customers from financial losses.
What states have the most Fraud Detection Agent jobs? States with the most job openings for Fraud Detection Agent jobs include:
What job categories do people searching Fraud Detection Agent jobs look for? The top searched job categories for Fraud Detection Agent jobs are:
Fraud Detection Agent jobs near you
Infographic showing various Fraud Detection Agent job openings in the United States as of July 2026, with employment types broken down into 94% Full Time, 4% Part Time, and 2% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution, with an average salary of $35,810 per year, or $17.2 per hour.

Senior ML Data Scientist (Credit Risk)

Zed Financial PH, Inc

San Francisco, CA • On-site

Full-time

Posted 28 days ago

Job description

About Zed
Zed is building the first AI-native, licensed neobank in the Philippines designed to democratize access to premium financial services for young professionals in global markets. The current banking system is broken, often shutting out the world's youngest and fastest-growing consumer classes-we're here to fix it.
Our team is uniquely positioned to solve this. We are Stanford engineers and former YC founders who have spent our careers at the intersection of banking and hyper-growth startups like Square, Facebook, and Box. We've been here before, having previously built and exited Symple (YC W'17), a fast-growing B2B payments company.
We are backed by world-class investors, including Accel, Valar, Immad Akhund (Mercury), Dalton Caldwell (Y Combinator), and Kunal Shah (Cred).
The Role
Zed underwrites credit using foundation models that profile risk from transaction data, financial documents, and other structured and unstructured sources - not credit scores. That means our ML stack looks less like a traditional bank's and more like a modern AI system: embedding models, transformer architectures, and LLM-assisted data pipelines sitting alongside classical credit and fraud models. We're hiring a Senior ML Data Scientist to work directly with our data lead across all of it - core credit models, account management, and fraud detection - with a particular focus on pushing the frontier of how we represent and reason about financial data. This is a senior role, which means we expect you to have opinions about the stack, shape how we build, and set the technical bar for ML at Zed as the team grows. If you're the kind of person who's deploying neural networks and transformer-based models in production rather than just reading about them, this role was written for you.
What You'll Do
  • Work closely with our data lead on the full risk model suite: core credit decisioning, account management, and fraud detection
  • Own data preparation pipelines for model inputs - including using NNs and LLMs to represent transaction data as vector embeddings for quantitative analysis
  • Experiment with and deploy neural network and transformer-based architectures in the underwriting process
  • Build agent scaffolding and harnesses within underwriting workflows - this is active, in-production experimentation, not research
  • Design and deploy fraud detection models combining rule-based systems and ML to identify suspicious activity in real time
  • Engineer features from structured and unstructured data sources
  • Develop monitoring systems to keep models accurate and reliable in production
  • Partner with engineering and risk operations to integrate model outputs into decisioning systems
  • Influence technical direction - you'll have a seat at the table when we make decisions about how ML is built and deployed at Zed
What You Bring
  • 7+ years of experience in applied ML or data science with a focus on credit risk, fraud, or financial services
  • Hands-on experience with LLMs, embeddings models, or transformer-based architectures - not just familiarity, but production or near-production deployment
  • Proficiency in Python and SQL; experience with frameworks such as XGBoost, LightGBM, PyTorch, or similar
  • Strong feature engineering skills - you know how to extract signal from messy, sparse, or heterogeneous financial data
  • Solid statistical foundation: anomaly detection, supervised classification, model calibration, experimentation design
  • Experience building and monitoring production models including alerting on performance degradation and concept drift
  • Familiarity with both rule-based and model-driven approaches - and when to use each
  • A point of view on how ML systems should be built - you can articulate tradeoffs, push back on bad decisions, and bring junior team members along
  • Comfort operating as a senior ML voice at an early-stage company - you own problems end-to-end and set the standard for others
  • Experience with emerging markets or data-sparse environments is a plus

We hire exceptional people from diverse backgrounds because different perspectives build better products.
If you're excited about this role but don't check every box, apply anyway. We value potential, ownership, and alignment with our values more than perfect résumés.
We are an equal opportunity employer and do not discriminate based on legally protected characteristics. We provide reasonable accommodations throughout the hiring process.
Compensation includes salary, equity, and benefits. Final offers are based on role scope, location, and experience.

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