Product Manager Banking Domain

Role: AI Product Manager – Banking (Fraud, AML, KYC & Client Lifecycle)
Location: Remote

Job Type: w2 only

NO OF ROLES: 5

Role Summary
We are seeking an AI Product Manager with deep Banking and Financial Services domain expertise to drive AI-powered transformation across Fraud, AML, KYC, Customer Onboarding, and Client Lifecycle Management processes.
This role will work closely with business stakeholders, risk and compliance teams, operations, data scientists, and engineering teams to identify high-value opportunities, design AI-enabled solutions, and rapidly experiment with new capabilities that improve customer experience, operational efficiency, risk management, and regulatory compliance.
The ideal candidate combines strong banking domain knowledge, product management experience, and a proven ability to reimagine business processes using AI and automation.
 
Key Responsibilities
1. Identify & Prioritize High-Impact AI Use Cases
Work with Banking, Risk, Compliance, Operations, and Technology stakeholders to identify opportunities across:
•    Fraud Detection & Prevention
•    AML Monitoring & Investigations
•    KYC & Customer Due Diligence
•    Customer Onboarding & Account Opening
•    Regulatory Compliance & Reporting
•    Client Lifecycle Management
Translate business challenges into clear AI-driven problem statements and product opportunities.
 
2. Drive AI-Led Process Transformation
•    Analyze existing workflows and identify opportunities for AI augmentation.
•    Redesign manual and rule-based processes using AI, automation, and intelligent decisioning.
•    Develop business cases and success metrics for AI initiatives.
•    Lead workshops with business stakeholders to define future-state operating models.
3. Drive Rapid Experimentation & Innovation
•    Convert ideas into testable hypotheses and pilot programs.
•    Design MVPs and Proof of Concepts leveraging:
o    Generative AI
o    Machine Learning
o    Intelligent Automation
o    Predictive Analytics 
•    Execute rapid experimentation cycles:
o    Prototype → Pilot → Measure → Refine → Scale
4. Own AI Product Lifecycle
•    Lead product development from concept through deployment.
•    Define product vision, roadmap, user stories, and success metrics.
•    Partner with Engineering, Data Science, and Architecture teams to deliver scalable solutions.
•    Ensure products align with regulatory, security, and governance requirements.
5. Bridge Business & Technology Teams
Act as the primary interface between:
•    Banking Business Teams 
•    Risk & Compliance Functions
•    Operations Teams
•    AI/ML & Data Science Teams
•    Engineering Teams
Translate business objectives into actionable product requirements and AI use cases.
 
6. Measure Business Impact & Scale
Track and improve:
•    Fraud loss reduction
•    AML investigation efficiency
•    KYC onboarding cycle times
•    Operational productivity
•    Customer experience metrics
•    Regulatory compliance outcomes
Scale successful pilots into enterprise-wide solutions.
 
Required Qualifications
•    8–15+ years of Banking or Financial Services experience.
•    5+ years in Product Management, Product Ownership, Consulting, or Business Transformation roles.
•    Strong expertise in one or more areas:
o    Fraud Management
o    AML
o    KYC
o    Customer Onboarding
o    Regulatory Compliance
o    Client Lifecycle Management
•    Experience participating in or leading AI, Analytics, Automation, or Digital Transformation initiatives.
•    Proven ability to work across business and technology stakeholders.
•    Strong communication, presentation, and executive stakeholder management skills.
Key Traits
•    Strong problem solver and business transformer.
•    AI-first mindset.
•    Ability to challenge existing processes and redesign them.
•    Consulting-style communication and stakeholder engagement skills.
•    Comfortable operating in ambiguity and rapidly evolving AI environments.
•    Outcome-focused and data-driven.
Success Profile (First 6 Months)
•    Identified and prioritized multiple AI opportunities across Fraud, AML, KYC, or Onboarding.
•    Delivered 2–3 AI pilots or proof-of-concepts with measurable business outcomes.
•    Established a repeatable experimentation framework for AI use cases.
•    Demonstrated improvements in:
o    Operational efficiency
o    Customer onboarding experience
o    Fraud prevention effectiveness
o    Compliance and risk management
•    Built strong credibility with business and technology stakeholders.