Risk Analyst
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- Work arrangement
- Not stated
- Employment
- FullTime
- Stated compensation
- USD 100,000–175,000 1 YEAR
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Requirements
1 with evidence8 unclear or unstated- Minimum experience (years)
- 5
“5+ years of experience in fraud analysis, risk management, or financial crime prevention, ideally within fintech, digital assets, or blockchain environments.”
See matched passage in original description ↓ - Employment type
- FullTime
- Compensation
- USD 100,000–175,000 1 YEAR
8 fields not stated in this posting
- Remote policy
- Unclear
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
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Original job description
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About Fin
Fin is a next-generation payments platform built for high-value, global, and instant transactions. We are a Series A-stage company backed by Sequoia, Circle, and other notable investors. Powered by stablecoins, Fin enables users and businesses to move millions of dollars in seconds - whether to other Fin users, directly into bank accounts, or across crypto rails. By combining the speed of crypto with the reliability and trust of traditional finance, Fin reimagines how money moves worldwide. If banks and payment products were reinvented today, they would look like Fin.
Role Overview
We are hiring our first Fraud/Risk Analyst to join our Risk & Compliance team . This role will focus on identifying, analyzing, and mitigating risks associated with digital asset transactions – including ACH fraud and compliance with applicable regulations like the Patriot Act and Bank Secrecy Act. This is a critical position, reporting directly to the CEO, and will require a combination of technical, analytical, and regulatory expertise to build a robust fraud detection and risk assessment framework from the ground up.
Key Responsibilities
Develop and implement a comprehensive risk management strategy tailored to the evolving digital asset landscape.
Take action to resolve automatically flagged transactions and individuals
File suspicious activity reports as required
Monitor and analyze transaction data to detect potential fraud, suspicious activities, and emerging risk trends.
Utilize advanced data analysis techniques and fraud detection tools to identify anomalies and potential security threats.
Create and maintain risk assessment models to evaluate the financial and reputational impact of potential fraud incidents.
Partner with the engineering team to design and implement fraud detection systems, leveraging machine learning and predictive analytics.
Ensure alignment with regulatory requirements, including AML, KYC, and digital asset regulations.
Draft detailed reports and dashboards on risk findings, fraud incidents, and risk mitigation strategies for senior leadership and stakeholders.
Lead cross-functional risk assessments for new product launches, ensuring security and fraud prevention measures are integrated into product design.
Stay abreast of emerging risks in the digital asset space, including regulatory changes and new fraud tactics.
Develop incident response plans for fraud detection and participate in incident response drills to assess and enhance our risk management framework.
Qualifications
Bachelor's degree in Finance, Economics, Computer Science, Data Science, or related field.
5+ years of experience in fraud analysis, risk management, or financial crime prevention, ideally within fintech, digital assets, or blockchain environments.
Demonstrated experience with fraud detection systems, transaction monitoring tools, and data analysis platforms (SQL, Python, R).
Strong knowledge of digital asset platforms, blockchain technology, and stablecoin ecosystems.
Experience with regulatory compliance, particularly regarding AML, KYC, and financial crime prevention.
Exceptional analytical and problem-solving skills with a data-driven approach to decision-making.
Strong written and verbal communication skills, with the ability to clearly articulate complex risk findings to non-technical stakeholders.
Preferred Qualifications
Certifications such as Certified Fraud Examiner (CFE), Certified Risk Manager (CRM), or CAMS.
Experience with machine learning models for fraud detection and predictive analytics.
Familiarity with incident response protocols and risk mitigation frameworks in financial services.
Prior experience in a fast-paced startup or scaling fintech environment.
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