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.
What you'll work on
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Develop and implement a comprehensive risk management strategy tailored to the evolving digital asset landscape.
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Take action to resolve automatically flagged transactions and individuals
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File suspicious activity reports as required
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Monitor and analyze transaction data to detect potential fraud, suspicious activities, and emerging risk trends.
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Utilize advanced data analysis techniques and fraud detection tools to identify anomalies and potential security threats.
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Create and maintain risk assessment models to evaluate the financial and reputational impact of potential fraud incidents.
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Partner with the engineering team to design and implement fraud detection systems, leveraging machine learning and predictive analytics.
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Ensure alignment with regulatory requirements, including AML, KYC, and digital asset regulations.
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Draft detailed reports and dashboards on risk findings, fraud incidents, and risk mitigation strategies for senior leadership and stakeholders.
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Lead cross-functional risk assessments for new product launches, ensuring security and fraud prevention measures are integrated into product design.
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Stay abreast of emerging risks in the digital asset space, including regulatory changes and new fraud tactics.
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Develop incident response plans for fraud detection and participate in incident response drills to assess and enhance TipLink's risk management framework.
What we're looking for
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Certifications such as Certified Fraud Examiner (CFE), Certified Risk Manager (CRM), or CAMS.
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Experience with machine learning models for fraud detection and predictive analytics.
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Familiarity with incident response protocols and risk mitigation frameworks in financial services.
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Prior experience in a fast-paced startup or scaling fintech environment.