Staff Fraud and Risk Analyst - Fintech

Category Security, Risk & Fraud Location New York, New York; Mountain View, California Job ID 22025

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Company Overview

Job Overview

Intuit is a global platform company that is on a mission to power prosperity worldwide for consumers, small businesses and the self-employed. Across our four leading brands – TurboTax, Credit Karma, QuickBooks, and Mailchimp – Intuit serves over 100M customers and is one of the few companies in the world to have both a thriving consumer and small business ecosystem. Intuit is known for its innovation track record, customer centricity, and its consistent recognition as a top place to work.

The CK Money team owns fraud policies across Credit Karma's consumer banking products — the debit card, Instant Transfer (push-to-card P2P), and ACH-out (ODFI push + RDFI pull) rails. We're hiring a Staff Analyst, Fraud Policy to own end-to-end policy decisions across these money movement rails: detecting emerging attacks, sizing the exposure, designing controls in our real-time decisioning stack, and defending the tradeoff between loss and member experience to leadership.

This is a high-leverage seat. CK Money moves hundreds of millions of dollars a month across four rails, and a single policy change can shift millions in annual loss. You'll work directly with fraud strategy, data science, engineering, dispute ops, and compliance to keep the ecosystem secure without breaking the experience for legitimate members.

Responsibilities

Fraud Detection & Investigation

• Monitor cross-rail signals to detect emerging attacks: card-not-present fraud, ATO, ATM SHORTAGE abuse, IT push-to-card scams, ACH return abuse (R05/R10/R29), synthetic identity at onboarding, mule rings, and negative balance exploitation.

• Investigate fraud across the full lifecycle: onboarding/KYC, deposit funding (DD, Plaid, ACH pull, mobile check), and outbound money movement (debit auth, IT, ACH-out).

• Use Rule Engine decision logs, and dispute records to reconstruct attack patterns and recommend rule, model, or workflow changes.

Data-Driven Analysis

• Analyze fraud trends in BigQuery (decision engine payload, posted transaction, dispute, FIFO-netted negative balance loss tables) and build dashboards in our internal stack to surface emerging threats and gaps in detection.

• Partnering with fraud strategy and data science to design and tune decision engine real-time rules, and ML score thresholds across all four rails.

• Track and report fraud KPIs: dispute rate (count and dollar), bps loss (gross and FIFO-netted net), false positive rates, OTP/step-up pass rates, ACH return codes (R05/R10/R29), and FY goals.

Operational Efficiency & Documentation

• Respond to fraud escalations from member support, dispute ops, and product on-call.

• Refine cross-functional workflows (e.g., dispute-to-policy feedback loops) to cut response time and prevent loss leakage.

• Document fraud typologies, case investigations, root cause, and the policy changes deployed in response — both for internal knowledge and for SLT/regulatory review.

Cross-Functional Collaboration

• Work closely with compliance (Reg E, BSA/AML), risk strategy, member support, engineering, dispute ops, and external vendors.

• Drive fraud policy for new product launches, rail expansions, limit changes, and regulatory reviews (SAR support, Reg E posture, NACHA compliance).

• Be the policy SME in discussions on account security, identity verification, step-up auth, OTP, and payment flow design.

Qualifications

• 5–7 years of experience in fraud operations, fraud policy, or fraud analytics — preferably in fintech, neobank, or consumer banking.

• Strong grasp of fraud typologies in consumer money movement: ATO, synthetic identity, 1st/3rd party fraud, ACH return abuse, push-to-card P2P scams, card-not-present, and negative balance exploitation.

• Hands-on experience with real-time decisioning platforms (Drools, Taktile, Sift, Kount, or equivalent) and risk hold systems

• Strong SQL (BigQuery preferred) and comfort working directly with raw warehouse tables; Python for analysis is a plus. Familiarity with Looker or other BI tools.

• Familiarity with banking regulations and fraud return codes: Reg E (12 CFR §1005), NACHA rules (R05/R10/R29 etc.), KYC/AML basics, and provisional credit obligations.

• Strong analytical, investigative, and problem-solving skills — comfortable sizing the exposure of a policy change and defending the tradeoff to leadership.

• Excellent written communication: able to translate a fraud incident into a one-pager an SLT audience can act on.

Nice to Have

• Experience in fraud policy for challenger banks or fintech debit/P2P/ACH products.

• Prior work designing or tuning real-time fraud rules in a production decisioning system.

• Experience with ACH dispute handling, chargeback ops, or provisional credit policy design.

• Familiarity with FIFO loss netting, dispute buildout cohorts, or other cohort-based loss attribution

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:

Mountain View: $176,500 - $238,500

New York City: $168,500 - $228,000

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