Senior Fraud Consultant

Straive; Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Straive is seeking a highly analytical Fraud Risk Manager/Lead to design, implement, and optimize fraud strategies for a leading global payments ecosystem. You will leverage SQL and Python to analyze transactions across C2C and C2B flows while balancing risk and customer experience.

The role requires domain expertise in digital payments, strong data skills, and the ability to translate insights into practical risk management actions that reduce losses and improve authorization rates.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field.
• 3–6 years of hands-on experience in fraud strategy in fintech/payments.
• Experience designing and implementing fraud rules using transactional and behavioral data.
• Strong SQL and Python proficiency (Pandas, data manipulation).
• Analytical and problem-solving skills to translate data into actionable risk strategies.
• Experience with C2C/C2B payments and card portfolios in fraud contexts.

Responsibilities

• Design and implement fraud strategies for C2C and C2B payment products.
• Mitigate fraud while optimizing authorization rates and maintaining CX.
• Develop and deploy fraud rules in decision engines and monitoring systems.
• Investigate anomalies, ATOs, and model-driven detections to reduce losses.
• Monitor performance and report on fraud strategy effectiveness.
• Collaborate with product and engineering to iterate on risk rules.

Skills

SQL

Python

Education

Bachelor’s or Master’s degree in Finance, Economics, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field

Job description

Years of Experience: 3 to 6 Years

Job Summary:

We are seeking a highly analytical and strategic Fraud Risk Manager/Lead to join our team. In this pivotal role, you will be responsible for designing, implementing, and optimizing fraud strategies for a leading global payments ecosystem. The ideal candidate will bring strong technical expertise in SQL and Python, coupled with a deep understanding of the payment fraud industry. You will play a critical role in managing fraud losses across Consumer-to-Consumer (C2C) and Consumer-to-Business (C2B) payment flows, while simultaneously improving payment authorization rates and ensuring a frictionless customer experience.

Key Responsibilities:

• Domain Expertise: Proven experience in the Payments Industry, specifically dealing with Transactional Fraud, Digital Wallets, Payment Gateways, and E-commerce fraud
• Fraud Strategy & Mitigation: Design, implement, and manage robust fraud prevention strategies for C2C (peer-to-peer) and C2B (merchant checkout) payment products, as well as Debit Card portfolios (covering both Card Present and Card Not Present transactions).
• Authorization Optimization: Analyze transaction data to improve payment authorization rates across various checkout flows (both branded and unbranded/guest checkouts) while keeping fraud losses within risk appetite.
• Customer Experience (CX): Balance aggressive fraud mitigation with user experience by minimizing false positives (false declines) and reducing unnecessary friction for legitimate customers.
• Data-Driven Investigation: Investigate suspicious transaction patterns, account takeovers (ATO), and payment anomalies to determine root causes and develop proactive statistical models/rules.
• System & Rule Development: Design and deploy fraud rules within decision engines and workflow systems. Build comprehensive monitoring reports to track the performance of fraud strategies.

Required Qualifications:

• Bachelor’s or Master’s degree in Finance, Economics, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
• 3 to 6 years of hands‑on experience in fraud strategy, specifically within Fintech, Digital Payments, E-commerce, or Payment Gateways.
• Demonstrated experience in designing and implementing fraud rules using transactional, behavioral, and third-party data.
• Strong proficiency in:
• SQL: Advanced querying, joins, aggregations, and performance tuning for massive transactional datasets.
• Python: Data manipulation (Pandas, NumBox), exploratory data analysis etc.
• Strong analytical and problem-solving skills with the ability to translate data insights into actionable strategies that directly impact the bottom line