New opportunity

FCC Domain SME

Capgemini · Bangalore, India

About this role

The FCC Leader role focuses on transformation through data, predictive AI, generative AI and AI agents for banking and financial services clients. The ideal candidate brings first- and second-line FCC business expertise and practical AI delivery experience, supported by strong data capabilities, compliance operations experience and third-line assurance knowledge.

Responsibilities

Domain Leadership and Practice Growth: Proven expertise in FCC strategy, AI-first solutions, GTM and senior client engagement, shaping pursuits and growing banking and financial services accounts.
Client Transformation and Solution Productization: Demonstrated experience modernizing FCC through predictive models, GenAI and governed agents, productizing reusable solutions and data assets.
Thought Leadership: Established capability to develop FCC and AI perspectives, present at industry forums and demonstrate practical solutions to financial services clients.
Talent Development: Proven ability to build and mentor FCC consultants, data specialists, AI engineers and data scientists across business design, data mapping, model evaluation and agent delivery.
Lead FCC discovery and solution workshops and communicate recommendations and business cases to senior stakeholders.
Manage scope, plans, estimates, dependencies, risks and quality across multidisciplinary FCC, data and AI teams.

Required qualifications

15+ years in banking or financial services FCC, advisory or transformation; 5+ years leading workstreams or teams, with deep expertise in at least two FCC domains.
Proven delivery of at least 2-3 FCC AI use cases through a controlled pilot or production.
Strong data analysis and source-to-target mapping skills, with working SQL knowledge.
Practical knowledge of FCC operations, independent assurance, AI/model risk governance, NIST AI RMF and ISO/IEC 42001.
Bachelor’s degree in finance, business, law, computer science, data science or a related field, or equivalent professional experience.
Strong consulting, stakeholder management, executive communication and team leadership skills.

Preferred qualifications

Production FCC AI delivery, Python analysis or prototyping, and cloud data/AI experience, including model monitoring, registries or governance platforms.
Familiarity with NICE Actimize, Oracle FCCM, SAS, Quantexa or comparable platforms and their integration with customer, payments, data and case management systems.
Consulting, proposal development and reusable solution experience across banking and financial services sectors or jurisdictions.
CAMS, CGSS, CFE, CISA or AI governance certification; relevant advanced degree.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AIAI AgentsPredictive AIMachine LearningNLPRAGSQLPythonNIST AI RMFISO/IEC 42001

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

FCC Domain Leadership and Practice Growth
Drive Financial Crimes Compliance strategy, shape pursuits, engage senior banking clients, and lead practice expansion within Capgemini's Insights & Data unit.
AI-Driven FCC Solution Productization
Modernize compliance operations by designing, delivering, and productizing predictive models, generative AI, and governed agent workflows for use cases like KYC, AML, and sanctions screening.
AI Governance and Risk Management
Ensure robust control effectiveness, manage model and agent risks, handle hallucination controls, and align implementations with frameworks like NIST AI RMF and ISO/IEC 42001.
Cross-Functional Talent Development
Build and mentor multidisciplinary teams comprising FCC consultants, data specialists, AI engineers, and data scientists across business design and model evaluation.

Likely challenges to think through

Balancing Innovation with Strict Regulatory Compliance
The challenge lies in deploying cutting-edge generative AI and agentic workflows while maintaining strict adherence to banking regulations and risk frameworks like NIST AI RMF. Prepare by demonstrating concrete experience in establishing model governance, audit trails, and human-in-the-loop safeguards.
Bridging Complex FCC Data Mappings with Advanced AI
Connecting legacy financial data, source-to-target mapping, and entity resolution with modern AI features can create integration bottlenecks. Prepare by reviewing your expertise in data lineage, semantic models, and structuring clean data inputs for AI pipelines.

Practice your answers

Technical practice

Translating Regulatory Obligations to AI Features

What to coverDiscuss how you translate complex AML or KYC typologies into measurable data attributes and AI model features. Your answer should explain how you maintain traceability between regulatory requirements, model outputs, and retained audit evidence.

Designing Governed Agent Workflows for FCC

What to coverExplain how you would design an autonomous agent workflow for SAR/STR support or adverse media screening. Address how you implement controlled tool/API access, human approval gates, action logging, and hard stop conditions.

Managing AI Governance under NIST AI RMF and ISO 42001

What to coverDescribe your approach to implementing AI risk tiering, model cards, and impact assessments in financial compliance. Highlight how you balance false-positive reduction with control effectiveness and regulatory compliance.

Source-to-Target Data Mapping and Entity Resolution

What to coverExplain your methodology for handling field-level mapping, customer hierarchies, and entity resolution across disparate banking and payment data sources. Discuss how you validate data quality, labeling, and leakage.

Evaluating GenAI and RAG Models in Compliance

What to coverOutline the evaluation metrics and datasets you use to test RAG pipelines and generative models for hallucinations, groundedness, bias, and drift. Explain how you establish clear acceptance criteria for production readiness.

Prioritizing FCC AI Use Cases

What to coverWalk through your framework for prioritizing AI use cases in financial crimes compliance based on control effectiveness, business value, data readiness, and implementation risk.

Integrating AI with Compliance Operations Platforms

What to coverDiscuss how AI solutions integrate with traditional case management and AML platforms like Oracle FCCM or NICE Actimize. Address data synchronization, user acceptance testing, and workflow redesign.

Mitigating Prompt Injection and Data Exposure Risks

What to coverDetail the specific technical and governance controls you implement to prevent prompt injection, unauthorized data exposure, and excessive agent permissions in financial services applications.

SQL and Data Analysis for Compliance Audits

What to coverDescribe how you utilize SQL and data analysis to investigate anomalies, reconcile exceptions, and audit transaction monitoring data during independent assurance reviews.

Scaling Practice Growth and Reusable Assets

What to coverExplain how you approach productizing successful FCC AI implementations into reusable solutions and data assets to accelerate go-to-market strategies across banking accounts.

Behavioural practice

Leading Cross-Functional Compliance Transformation

What to coverDescribe a situation where you led a multidisciplinary team of consultants, data scientists, and compliance officers through a major regulatory transformation. Focus on how you managed scope, aligned stakeholder expectations, and delivered results.

Managing Disagreements on AI Risk and Governance

What to coverProvide an example of a time when business stakeholders pushed for rapid AI deployment while risk or compliance teams raised objections. Explain how you facilitated effective challenge, balanced speed with control, and reached a consensus.

Handling Unexpected Data Quality Issues in a Pilot

What to coverShare an instance during an AI pilot or production rollout where severe data quality gaps or lineage issues threatened project delivery. Describe your action steps to remediate the data and the resulting impact on model accuracy.

Advising Senior Stakeholders and Board Members

What to coverRecount a scenario where you presented complex AI compliance strategies or risk assessments to senior banking executives. How did you tailor your communication to secure buy-in and investment for the initiative?

Mentoring Talent Across Domain and Technical Lines

What to coverDescribe how you built and mentored a mixed team of FCC domain experts and technical AI engineers. Discuss the challenges of bridging domain knowledge with technical execution and how you fostered collaboration.

Sharpen your knowledge

Topics to prepare

Financial Crimes Compliance (FCC) Strategy and Risk Frameworks
Review first- and second-line FCC domains including BSA/AML, KYC/EDD, sanctions screening, transaction monitoring, and suspicious activity reporting. Exercise: Map a complex regulatory obligation or typology (e.g., beneficial ownership evasion) directly to control scenarios and data attributes. Relevance: Essential for leading FCC strategy, risk assessments, and policy remediation across banking clients.
AI Governance and Risk Management Frameworks
Study NIST AI RMF and ISO/IEC 42001 standards as applied to financial compliance. Exercise: Draft an AI use case risk-tiering and impact assessment template including model cards, oversight boundaries, and hallucination controls. Relevance: Directly required for ensuring control effectiveness, managing model risk, and coordinating independent reviews with legal and compliance teams.
Agentic AI Workflows and Model Evaluation in FCC
Examine how RAG, predictive ML, and governed agent architectures apply to SAR/STR support and adverse media screening. Exercise: Design an agent workflow blueprint incorporating controlled tool/API permissions, human-in-the-loop approval gates, and explicit stop conditions. Relevance: Directly tied to the requirement of delivering 2-3 FCC AI use cases through controlled pilots or production.

Skills to revise

Source-to-Target Data Mapping and SQL Analysis
Practice source-to-target and field-level data mapping across customer, transaction, and payment datasets, alongside working SQL queries. Exercise: Write SQL queries to extract and reconcile customer account hierarchies and transaction exceptions from fragmented data sources. Relevance: Directly required for handling complex data analysis, entity resolution, and tracing AI feature inputs to outputs.

Your preparation checklist

Prepare an FCC AI Use Case Walkthrough
Compile a detailed portfolio or structured narrative describing 2-3 financial crimes compliance AI use cases (such as KYC/EDD, sanctions screening, or SAR support) you have taken through pilot or production. Completion looks like having clear documentation on business design, model evaluation metrics, governance controls, and measured operational results.
Map Regulatory Obligations to AI Features
Draft a sample source-to-target mapping artifact connecting complex AML or KYC regulations to specific data attributes and AI features. Completion looks like a clear matrix showing traceability from regulatory requirements through model outputs to retained audit evidence and human decisions.
Design a Governed Agent Workflow Blueprint
Create a architectural diagram and workflow specification for an autonomous agent handling triage or investigations. Completion looks like a documented design featuring controlled tool/API access, human-in-the-loop approval gates, action logging, and explicit stop conditions.
Align Practice with NIST AI RMF and ISO 42001
Prepare a summary of your experience applying AI risk governance standards within financial services. Completion looks like a structured risk-tiering and impact assessment template including model cards, oversight boundaries, and hallucination controls.
Review Data Mapping and SQL Competency
Refresh your technical knowledge of source-to-target data flows, entity resolution, and database queries. Completion looks like having ready examples of SQL queries used to reconcile customer hierarchies, transaction exceptions, and data lineage in compliance systems.
Formulate Strategic Questions for the Employer
Develop thoughtful questions regarding Capgemini's Insights & Data practice growth, client acquisition strategies, and reusable asset development. Completion looks like a concise list of 4-5 targeted questions about team structure, stakeholder engagement, and scaling compliance solutions.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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