to design and implement AI-driven fraud detection systems for UPI, cards, and digital payments. This role requires deep expertise in real-time fraud detection, anomaly detection, behavioral analytics, and AI/ML-based risk modeling. This role will also help build the business intelligence for the UPI vertical.

What You ll Do Fraud Detection & Prevention

Build and enhance real-time fraud detection models for UPI transactions, QR payments, and card transactions.
Develop AI-driven behavioral risk models to detect abnormal payment activity, device spoofing, and synthetic identity fraud. Design rule-based + ML-driven fraud detection systems to reduce false positives & false negatives. Implement device intelligence, geo-location tracking, and biometric authentication to prevent fraud. Work closely with the engineering team to integrate fraud detection APIs into UPI systems.

Risk Analytics & Model Development

Design machine learning models (supervised & unsupervised) for fraud pattern recognition.
Develop graph analytics models to detect fraud rings, mule accounts, and collusion. Implement real-time anomaly detection for transaction velocity, value, and geolocation mismatches. Optimize UPI risk scoring frameworks based on RBI & NPCI fraud guidelines.

Operational & Regulatory Risk Management

Work with NPCI, RBI, and banking partners to ensure compliance with UPI fraud monitoring regulations.
Establish risk rules & thresholds for suspicious transactions (e.g., high-frequency transactions, cross-border UPI fraud). Monitor fraud trends & suggest real-time interventions for high-risk transactions.

AI-Driven Chargeback & Dispute Management

Design AI-driven chargeback prediction models to minimize fraud losses.
Build an early warning system to flag suspicious refunds and chargeback abuse. Automate dispute resolution workflows using NLP-based document analysis.

Stakeholder & Team Leadership

Collaborate with product, risk, engineering, and compliance teams to enhance fraud mitigation strategies.
Mentor & guide a team of fraud analysts, data scientists, and risk engineers. Stay updated on global fraud trends, AI-driven fraud strategies, and regulatory changes.

What We re Looking For

Technical Proficiency:

Strong foundation in machine learning, statistical analysis, and data modeling techniques.

Programming Skills:

Experience with Python, R, SQL, or similar tools for data analysis. Familiarity with big data platforms like Spark or Hadoop is a bonus.

Analytical Thinking:

Ability to solve complex problems and provide meaningful insights from large datasets.

Collaboration:

Excellent communication and teamwork skills to work effectively with product, engineering, and business teams.

Domain Knowledge:

Have an understanding of BNPL credit risk.

Attention to Detail:

Precision in building models and delivering accurate results.

Experience & Qualifications

Experience:

5+ years in data science, analytics, or a related field. 2+ years in UPI, Cards, or Digital Payments

Educational Background:

Bachelor s or master s degree in data science, Statistics, Mathematics, Computer Science, or a related discipline.

Why Zype

Impactful Work:

Be part of a fast-growing fintech, driving data-driven innovation in BNPL.

Collaborative Culture:

Work alongside talented peers in a supportive and inclusive environment.

Learning Opportunities:

Gain exposure to cutting-edge technologies and methodologies in risk analytics.

Career Growth:

Build your career with opportunities to grow in a high-impact role.

Ready to redefine risk analyticsJoin Zype and shape the future of BNPL in India!

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