Role: AI Product Manager – Banking (Fraud, AML, KYC & Client Lifecycle)
Location: Remote
Rate: Keep it competitive
Constraints: Candidate must be on your W2. No multilayering
NO OF ROLES: 5
Role Summar
yWe 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 Responsibilitie
s1. Identify & Prioritize High-Impact AI Use Case
sWork with Banking, Risk, Compliance, Operations, and Technology stakeholders to identify opportunities across
- :Fraud Detection & Preventio
- nAML Monitoring & Investigation
- sKYC & Customer Due Diligenc
- eCustomer Onboarding & Account Openin
- gRegulatory Compliance & Reportin
- gClient Lifecycle Managemen
tTranslate business challenges into clear AI-driven problem statements and product opportunities
.2. Drive AI-Led Process Transformatio
- nAnalyze 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 & Innovatio
- nConvert ideas into testable hypotheses and pilot programs
- .Design MVPs and Proof of Concepts leveraging
- :Generative A
- IMachine Learnin
- gIntelligent Automatio
- nPredictive Analytic
- sExecute rapid experimentation cycles
- :Prototype → Pilot → Measure → Refine → Scal
e4. Own AI Product Lifecycl
- eLead 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 Team
sAct as the primary interface between
- :Banking Business Team
- sRisk & Compliance Function
- sOperations Team
- sAI/ML & Data Science Team
- sEngineering Team
sTranslate business objectives into actionable product requirements and AI use cases
.6. Measure Business Impact & Scal
eTrack and improve
- :Fraud loss reductio
- nAML investigation efficienc
- yKYC onboarding cycle time
- sOperational productivit
- yCustomer experience metric
- sRegulatory compliance outcome
sScale successful pilots into enterprise-wide solutions
.Required Qualification
- s8–15+ 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
- :Fraud Managemen
- tAM
- LKY
- CCustomer Onboardin
- gRegulatory Complianc
- eClient Lifecycle Managemen
- tExperience 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 Trait
- sStrong 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
- :Operational efficienc
- yCustomer onboarding experienc
- eFraud prevention effectivenes
- sCompliance and risk managemen
- tBuilt strong credibility with business and technology stakeholders
.