Fraud Prevention Strategy Manager

About Neo

AI is the most significant shift in how work gets done since the advent of the Internet — yet most organizations fail to capture its value. Neo is changing that.

Founded by Bhavin Turakhia — Co-Founder of Zeta, Radix, and Titan — Neo is building an integrated, AI-first suite of products that capture work, centralize context, and make AI a first-class participant in every workflow:

• Tasket — A radically reimagined, AI-native, work management platform that centralizes context and makes AI delegation seamless for all work.
• Friday — AI Assistant, co-worker and agent platform, pre-integrated into Tasket, Studio, Drive and 1000+ SaaS platforms.
• Studio — AI-native suite for co-creating docs, spreadsheets and diagrams collaboratively with humans and agents.
• Scribe — AI assistant for meetings that goes beyond note-taking and actually does your work while on the call.

About the Role

Ryze is a neobank - fintech offering banking products in USA. You will be the single owner of fraud prevention strategy for Ryze's US checking account, and you will decide which fraud rules are live on any given day. You shall own the fraud risk metrics for the company.

Scope of the Role

Card Rails

• Channels: Debit card on Mastercard and Shazam, ATM, digital wallets
• Fraud typologies in scope: Card-not-present, counterfeit and skimming, lost/stolen, card testing, ATM cash-out, wallet token fraud, stand-in exposure

Money Movement Rails

• Channels: ACH (outbound and inbound), P2P claim-link sends, push and pull via card rails
• Fraud typologies in scope: Unauthorized transfers, authorized push payment scams, ACH return and kiting abuse, beneficiary-add fraud, balance drain

Across Both Rails

• Channels: Account opening, login, profile changes
• Fraud typologies in scope: Account takeover, synthetic identity, first-party fraud, mule accounts used to cash out fraud

Responsibilities

You are responsible for mitigating fraud across all outgoing payment rails, including card payments and money transfers.

Rule lifecycle

• Own the full rule book on our fraud risk management (FRM) platforms
• Respond to an emerging fraud pattern within 24 hours of detection: diagnose, deploy a containment rule, and document the change.
• Run every rule change through a documented process: hypothesis, back-test or shadow mode, expected alert volume, approval, deploy, post-change review.
• Keep a rule inventory with owner, rationale, last-tuned date and performance for each rule
• Own the bank's rule approval workflow and change log

Analytics and models

• Monitor transaction, login and payment data daily to spot new attack patterns before losses build.
• Use SQL/Python to size losses, back-test rules and measure the false-positive cost of every control.
• Apply ML and AI tools (anomaly detection, clustering, LLM-assisted alert triage)

Operations

• Prioritise the fraud roadmap by fraud exposure, friction to good customers and operational cost, and defend the trade-offs with data.
• Partner with the operations team on the disputes related to fraud
• Own the rule playbooks in the fraud analyst SOP and close the feedback loop from analyst outcomes into rule tuning; the Fraud Ops Lead owns the queue, SLAs and card actions.
• Automate repetitive review and reporting work so analyst time goes to judgment calls.
• Lead fraud incident response: contain, quantify, communicate, and write the post-mortem.
• Feed confirmed fraud and dispute outcomes into Reg E dispute handling and chargeback recovery.

Skills & Qualification

• 6+ years in fraud strategy or fraud analytics at a US bank, card issuer, aquirer, neobank or payments company.
• Hands-on rule authoring in a production fraud rules engine, vendor (Featurespace, Feedzai, SAS Fraud Management, FICO Falcon) or in-house.
• Proven track record cutting fraud losses without a matching rise in false positives, with numbers to show it.
• Strong SQL and working Python; comfortable building your own back-tests and dashboards without waiting on a data team.
• Working knowledge of US payment rails and their fraud patterns: card networks, ACH (Nacha rules), and real-time or P2P payments.
• Working knowledge of Reg E error-resolution obligations for card and electronic fund transfers.
• Practical use of ML or AI in fraud: you have shipped, tuned or retired a model-driven control, not just read about one. You also use AI tools daily to automate analysis and manual workflows.