Fin

Non-custodial Solana wallet with link-based transfers

Risk Analyst

Financial Crime & AMLConfirmed live in the last 24 hours

Full-timeNew York, NY, USA$100k - $175k /yrIn-person

About the Job

Requirements

A bachelor's degree in Finance, Economics, Computer Science, Data Science, or a related field is required.
At least 5 years of experience in fraud analysis, risk management, or financial crime prevention is required; experience in fintech, digital assets, or blockchain environments is ideal.
Demonstrated experience with fraud detection systems, transaction monitoring tools, and data analysis platforms including SQL, Python, or R is required.
Strong knowledge of digital asset platforms, blockchain technology, and stablecoin ecosystems is required.
Experience with regulatory compliance, particularly anti-money laundering (AML), know your customer (KYC), and financial crime prevention, is required.
Strong analytical and problem-solving skills and a data-driven approach to decision-making are required.
Strong written and verbal communication skills, including the ability to explain complex risk findings to non-technical stakeholders, are required.

Responsibilities

Develop and implement a risk management strategy tailored to the digital asset landscape.
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.
Use 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 using machine learning and predictive analytics.
Ensure alignment with regulatory requirements, including AML, KYC, and digital asset regulations.
Draft 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.
Monitor 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 improve the risk management framework.

Desired Qualifications

Certification 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.

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.

Fin