Fraud Risk Analyst

Money. It's always on our mind and often comes with a rollercoaster of emotions and complex jargon. That’s why at Jupiter, our mission is to improve your financial well-being by giving you full control over your money, helping you track, save, and invest with confidence.

We’re a financial services platform that uses technology to simplify money management. Whether it’s a savings account, payments, loans, credit cards, investments, or smart money tools it’s all on Jupiter. We break down banking jargon, offer spending insights, and give users modern features to make better financial decisions.

Jupiter was founded in 2019 by Jitendra Gupta (founder of Citrus Pay), who saw how broken personal finance felt compared to customer-first experiences like food or entertainment. We launched in 2021 with a 100,000+ waitlist. Today, 30 Lakh+ users trust us with their money.

We've built a team of creative thinkers and domain experts, driven by a shared vision of a transparent and inclusive financial ecosystem.

We’ve embraced cutting- edge technology with high ownership and deep customer obsession. Our team, spanning Mobile, Platform, Data, AI & ML — is building to scale products across the board. From AI to behavioural science, we’re creating world class banking experiences, and we’re looking for more builders to join us.

We are looking for a data-first Fraud Risk Analyst with 2–4 years of hands-on experience to strengthen our fraud detection, prevention, and monitoring capabilities across multiple products. This role sits at the intersection of risk, analytics, operations, and product, and will directly influence fraud loss, customer experience, and platform trust.

This is not an operations role, you will spend a most part of your time working with data to identify patterns, test hypotheses, and influence product and policy decisions.

Act as the first line of defense for fraud risk decisions within defined thresholds and playbooks.

Monitor fraud risks across Lending, Credit Cards, UPI, Savings, Insurance, and Investment products

Identify emerging fraud patterns, abuse vectors, and policy loopholes leveraging deep hands-on analysis of large datasets

Design and maintain transaction monitoring rules, alerts, and thresholds

Conduct detailed investigations into suspicious activities and high-risk accounts

Translate insights into clear recommendations that influence product flows and policies

Own end-to-end fraud analysis from data extraction → insight → decision recommendation → post-impact measurement.

Analyze large datasets to identify fraud trends, root causes, and early warning signals

Build and track fraud KPIs such as fraud rate, false positives, customer impact, and recovery

Support development and tuning of rule-based and ML-driven fraud models

Recommend improvements to fraud controls across onboarding, transactions, and lifecycle events

Conduct root-cause analysis on fraud spikes and incidents

Document fraud scenarios, SOPs, and escalation frameworks

Partner with operations teams to improve case handling efficiency and quality

Support audits, regulatory reviews, and internal risk assessments

Work closely with Product, Engineering, Data Science, Operations, and Compliance teams

Provide fraud inputs during new product launches, feature changes, and experiments

Translate fraud insights into clear business recommendations

              2–4 years of experience in fraud risk, transaction monitoring, or financial crime analytics

              Strong proficiency with Sql and Python

              Experience with real-time payments (UPI), card networks, or digital lending flows

              Exposure to ML-based fraud models or feature engineering

  Strong analytical rigour and problem-solving ability (IIT/NIT background or equivalent hands-on data depth preferred)

  Excellent communication, problem-solving, and cross-functional collaboration skills.

Prior experience in scaling fraud systems in high-growth environments.

           Understanding of RBI guidelines, AML/KYC basics, and financial crime frameworks          

                      Our Journey