Flex Director of Fraud Risk Management at Flex leads end-to-end fraud risk management for credit card and DDA products. The role involves analyzing fraud patterns, monitoring verification performance, and building detection frameworks.
Responsibilities
Acquisition & onboarding fraud: analyze application fraud patterns by channel and source; assess identity signal quality, document authenticity rates, and synthetic identity indicators at the population level; monitor approval flow for anomalous approval rate shifts that may signal policy exploitation
Identity & entity verification performance: maintain analytical visibility into match rates, challenge rates, and step-up conversion across KYC and KYB verification layers in partnership with compliance; identify where the verification stack may be generating friction for good applicants or gaps for bad ones — and surface those findings as inputs to joint policy discussions
First-party fraud: monitor behavioral signals associated with intentional default — spend acceleration, balance build without payment intent, cash advance abuse, and bust-out patterns; distinguish first-party risk from credit deterioration analytically
Third-party fraud: track unauthorized transaction patterns, account takeover indicators, card-not-present abuse, and compromised credential signals; maintain segment-level views of dispute and chargeback rates by fraud type
Synthetic identity fraud: build and maintain detection frameworks for synthetic identities — thin-file manipulation, credit piggybacking, fabricated entity structures — with particular attention to SMB applicants where bureau data is sparse and entity verification is harder
DDA-specific fraud vectors: monitor ACH manipulation, payee substitution, unauthorized external transfer attempts, and check fraud patterns within the DDA product; maintain visibility into funds flow anomalies that may indicate account misuse or laundering behavior
Authorization & transaction monitoring: analyze real-time and near-real-time authorization patterns for velocity anomalies, geographic inconsistencies, merchant category abuse, and card testing signals
Dispute, chargeback & recovery: own the analytical view of dispute resolution patterns; identify chargeback abuse and friendly fraud at the segment and merchant level; track recovery rates by fraud type and loss emergence timing
