Fraud Analytics
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AI Summary
The role involves investigating flagged transactions and refining fraud rules to reduce losses across various transaction types. The candidate will also optimize risk thresholds and strengthen system integrations to balance fraud prevention with cardholder experience.
About US: We are a company that provides innovative, transformative IT services and solutions. We are passionate about helping our clients achieve their goals and exceed their expectations. We strive to provide the best possible experience for our clients and employees. We are committed to continuous improvement and innovation, and we are always looking for ways to improve our services and solutions. We believe in working collaboratively with our clients and employees to achieve success.
DS Technologies Inc is looking for Fraud Analytics role for one of our premier clients.
Job Title: Fraud Analytics
Location: Remote
Position Type: Full-time
Note: All Visa's are Accepted
Job Description:
Location: Remote
Position Type: Full-time
Note: All Visa's are Accepted
Job Description:
The Embedded Personnel will investigate flagged transactions and recommend risk threshold adjustments based on Client behaviors. They will also work with the Client’s team to provide recommendations across the Client’s various fraud rules decisioning tools and systems.
- Minimum 4 years of experience in Fraud Strategy.
- Overall experience of 4-8 years
- Experience in credit / debit card fraud.
- IIM, IIT, NIT or BITS background
The following key activities are the focus of this workstream:
(a) Review Current Fraud Strategy
(a) Review Current Fraud Strategy
- Conduct a review of the Client’s current fraud rules by transaction type and customer segment.
- Identify gaps, inefficiencies, and potential areas for improvement in collaboration with the Client’s fraud management leadership team.
(b) Reduce Fraud Losses
- Evaluate and refine existing fraud rules across transaction types (e.g., card-present, card-not-present).
- Simplify and streamline complex rules in VRM
- Recommend a rule strategy for high-risk merchants, unusual transaction behavior, and cross-border activity that employs velocity rules.
(c) Minimize Cardholder Impact
- Back-test rules using historical data while measuring impact to key KPIs.
- Recommend risk appetite for different categories of transactions (e.g., CP, CNP, XB).
- Develop a rule framework for treatment across various cardholder segments, with a focus on minimizing fraud losses while balancing cardholder experience (e.g., knockout rules, rules for manual review, escalations, exclusions, etc.).
(d) Strengthen System Integration
- Map and analyze the interaction between existing tools and platforms.
This is a remote position.
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