Senior Product Owner · Standard Chartered Bank · Bangalore, India
Nikhil Ranjan
Complex enterprise requirements, turned into usable, measurable product outcomes.
KYC and case-management products, enterprise data platforms and regulated workflows. From discovery to go-live.
Open to Senior Product Owner and Product Manager roles.
13+ years in global financial services
Results at a glance
- 30%
- lower analyst-review effort
- Policy-driven KYC · Product Owner
- 90 → 45
- days per release cycle
- Policy-driven KYC · Product Owner
- 15%
- less analyst time per KYC profile
- Automated CDD Execution · Product Owner
- 8 → 4
- data-provisioning team
- Credit-risk data platform · Product Owner
Experience
From checking the work to owning the product.
Newest first. Open a role to see what was owned and what changed.
Today: Senior Product Owner (Product Manager equivalent)
Nov 2023 to presentProduct Owner, Model Risk and Credit Risk Data PlatformsStandard Chartered BankReplaced hand-built data with governed, automated sourcing. Provisioning team from 8 to 4.
Owning enterprise data platforms
- Took over a data platform loaded with manually extracted data and changed the approach to governed, automated sourcing from the central data lake.
- Set up the governance process and sourced data for the most urgent models first, applying regional data restrictions, compliance rules and masking.
- Rebuilt the manual data logic as tables and aggregated views, and built a reconciliation process that flags problems in the data.
- Extended the platform from corporate to retail banking data, in a separate space.
- Ensured a smooth transfer of a model from another team into this one, with a full handover.
- Owns requirements and business cases for data marts serving IFRS 9, Scorecard and IRB modelling teams across four markets.
- Also owns MVP scope and backlog for a greenfield financial-crime case-management application.
- Uses AI tools daily for stakeholder analysis, requirements drafting and dependency mapping.
- 8 → 4
- data-provisioning team
- 22%
- fewer data-quality issues reported by modellers
- 4
- markets on one reusable design
- Enterprise data platforms
- Data governance
- Business cases
- AI-assisted product management
Jun 2022 to Oct 2023Product Owner, Automated CDD Execution ApplicationStandard Chartered BankAnalysed where analyst time went and proposed the automation. 15% less analyst time per KYC profile.
Turning analysis into automation
- Owned end-to-end product requirements for a Customer Due Diligence platform.
- Analysed where analysts spent their time and proposed automating the search for client information to the KYC process owner.
- Split the work by risk. Low and medium risk profiles are prepared by the system for review. High risk cases get a drafted email for the analyst to send.
- Added machine-learning document capture to cut back-and-forth with relationship managers.
- Translated regulatory and policy obligations into functional specifications for case workflows, review stages and routing rules.
- 15%
- less analyst time per KYC profile
- Data-led analysis
- Risk-based process design
- Regulatory translation
- Requirements management
Jan 2020 to Jun 2022Product Owner, Policy-Driven KYC ApplicationStandard Chartered BankBuilt a greenfield KYC product from an empty backlog. 30% lower analyst-review effort. Release cycle cut from 90 to 45 days.
Owning a policy-driven product and its releases
- Defined MVP scope and built the backlog from the ground up for a greenfield case-management platform.
- Partnered with Compliance, Operations and Technology on governance requirements.
- Redesigned the case review workflow with human-centred design.
- Led the move from waterfall to Agile Scrum. Wrote PRDs, functional specs and outcome-based acceptance criteria.
- Owned release governance across SIT, UAT, Pre-Production and Production, with release-readiness sign-off and defect triage.
- Ran a two-week hypercare after each release.
- 30%
- lower analyst-review effort
- 10%
- lower initial due-diligence checks
- 90 → 45
- days per release cycle
- +8%
- platform processing efficiency
- MVP definition
- Backlog management
- Human-centred design
- Agile Scrum
- Release management
Jul 2018 to Dec 2019Business Analyst, Identity and VerificationStandard Chartered BankOwned Identity and Verification requirements across multiple markets, on Fenergo.
Requirements across markets
- Worked on the Fenergo case-management platform.
- Owned Identity and Verification requirements across multiple markets.
- Wrote BRDs, FRDs and non-functional requirements under a waterfall SDLC.
- Chaired cross-functional working groups and secured process-owner sign-off for policy-impacting changes.
- Kept governance records and audit trails.
- Fenergo
- Requirements management
- Stakeholder alignment
- Governance
Feb 2013 to May 2018KYC Operations Analyst, UAT Analyst, Business AnalystHSBCAnalyst, then UAT, then business analyst on Pega-based KYC and compliance platforms. 95% post-launch defect-free rate.
Quality, testing and operational reality
- Worked on the Pega case-management platform for KYC and compliance.
- Progressed from KYC Operations Analyst to UAT Analyst to Business Analyst.
- 95%
- post-launch defect-free rate
- 20%
- lower UAT testing time
- 15%
- lower KYC processing time, as a KYC Operations Analyst
- Pega
- Testing and validation
- Documentation
- Workflow improvement
Featured case study
KYC & Policy-Driven Applications
- Role
- Product Owner
- Period
- Jan 2020 to Jun 2022
- Focus
- KYC case management
The problem
Complex policy requirements created complex review journeys, and analyst effort grew with every policy change.
The role
Product Owner. Defined MVP scope and built the backlog from zero for a greenfield case-management platform, working with Compliance, Operations and Technology.
What changed
- Redesigned the case review workflow with human-centred design.
- Led the move from waterfall to Agile Scrum.
- Focused policy review on what changed, not the whole policy.
- Moved SIT and UAT into the sprint. Made performance testing and architecture review risk-based.
- Owned release governance across SIT, UAT, Pre-Production and Production, with a two-week hypercare after each release.
Governance stayed in place throughout.
The result
Product outcomes
- 30%
- lower analyst-review effort
- 10%
- lower initial due-diligence checks
Delivery results
- 90 → 45
- days per release cycle
- +8%
- platform processing efficiency
Case study
Automated Client Due Diligence
- Role
- Product Owner
- Period
- Jun 2022 to Oct 2023
- Focus
- Client due diligence
The problem
Analysts spent most of their time on initial due diligence, searching for client information. The KYC process owner asked how to cut the total time to complete a client due diligence.
The role
Product Owner. Analysed where the time went, proposed the automation to the KYC process owner, and owned the end-to-end product requirements.
What changed
- Found that about 70 to 80% of the information for low and medium risk clients was available in approved sources.
- Split the work by risk. For low and medium risk, the system prepares the profile. For high risk and special categories, it drafts an email for the analyst to send.
- Retrieved current details from approved sources, highlighted what changed and saved screenshots as evidence.
- Left the analyst to review the changes and push them into the main KYC application.
- Added machine-learning document capture, so relationship managers could scan a document with a laptop webcam and have it ready for approval in one pass.
The analyst reviews every change. The decision stays with a person.
The result
Analyst effort
- 15%
- less analyst time per KYC profile
Regional key risk indicators were met in the regions where it went live.
Case study
Credit-risk data platform
- Role
- Product Owner
- Period
- Nov 2023 to present
- Focus
- Model data platform
The problem
A regulatory review required a data platform for model development and monitoring. Data for modellers was still collected by hand from different platforms, which was slow and error-prone.
The role
Product Owner, from 2023. Took over a platform loaded with manually extracted data and set a different direction: governed, automated sourcing.
What changed
- Judged that uploading manual extracts did not solve the core problem, and changed the approach.
- Made sourcing from the central data lake the priority, instead of sourcing from many separate applications.
- Set up the governance process and secured the approvals needed, then sourced data for the most urgent models first.
- Applied regional data restrictions and internal compliance rules, including masking.
- Rebuilt the manual data logic as tables and aggregated views, which cut the dependence on the team that built data by hand.
- Built a reconciliation process that flags problems in the data.
- Extended the platform to retail banking data, in a separate space.
Also ensured a smooth transfer of a model from another team into this one, with a full handover.
The result
Team
- 8 → 4
- data-provisioning team
Data quality
- 22%
- fewer data-quality issues reported by modellers
Issues are those reported by modellers, compared over the six months before and after sourcing from the data lake.
Also: moving a model between teamsEnsured a smooth transfer of a model from another team into this one. The handover covered more than the code.
Technology
- Solution architecture and diagrams
- Environments, code and pipelines
- Access and entitlements
- Data lineage and data dictionary
- Upstream and downstream dependencies
- Monitoring, alerts and reconciliation
- Security, masking and data residency
- Technical debt and disaster recovery
Production support
- Runbooks and batch schedules
- Service levels and escalation paths
- Incident history and open tickets
- Known workarounds
- A hypercare period after the transfer
Legacy
- Known legacy issues and defects
- Workarounds that people rely on
Book of work and roadmap
- In-flight items and the backlog
- Current and planned roadmap
- Commitments already made to stakeholders
- Dependencies, budget and resourcing
Governance and regulation
- Open audit or regulatory findings and deadlines
- Control evidence and approvals
- Model validation status, inventory entry and owner
People and process
- Key contacts and knowledge holders
- Training or shadowing
- Request intake and prioritisation
- Decision rights
Acceptance
- Exit criteria
- Sign-off by the receiving team
- A parallel run where needed
- A rollback plan
- A post-transfer review
How I work
A ten-stage loop. AI speeds it up. People stay accountable.
- 01
Understand the problem
Find the real constraint before proposing a solution.
- 02
Align stakeholders
Point business, technology, risk and users at the same outcome.
- 03
Define the outcome
Say what will be different, and how it will be measured.
- 04
Shape the MVP
Cut scope to the smallest product that proves the outcome.
- 05
Prioritise the backlog
Order the work by value and by risk.
- 06
Translate needs into stories
Turn requirements into stories with clear acceptance criteria.
- 07
Align engineering and testing
Keep build and validation working from the same picture.
- 08
Validate with users
Check the workflow with the people who will use it.
- 09
Release safely
Ship through the required gates, with support after go-live.
- 10
Learn and improve
Feed results back into the next cycle.
A reusable way of working. It is not a claim that every stage happened on every project. The last stage feeds the first.
AI accelerates product work. Human judgement stays accountable.
AI tools are part of daily delivery, for stakeholder analysis, requirements drafting and dependency mapping.
- 1
Human input
Stakeholder conversations, requirements and dependencies.
- 2
AI-assisted drafting
Analysis, requirements drafts and dependency maps.
- 3
Human review
Every draft is read, challenged and corrected.
- 4
Product decision
The Product Owner decides and stays accountable.
- Prompt engineering
- AI-assisted product management
Growing the people who build the product.
Manages and mentors a small team of business analysts and testers.
- Mentoring and Coaching
- Cross-Functional and Matrixed Team Leadership
- Stakeholder Management at Executive Level
Skills and credentials
The toolkit, and the proof.
Product strategy and transformation
- Product Vision
- Product Roadmap
- Business Case Development
- Build-Buy-Partner Analysis
- MVP Definition
- Requirements Management
- Backlog Prioritisation
Data and analytics platforms
- Enterprise Data Platforms
- Data Modelling
- Data Quality
- Data Lineage
- Reconciliation
- Regulatory Reporting
- Credit Risk
- Model Risk
- Model Governance
- IFRS 9
- IRB Frameworks
Team leadership and delivery
- Agile Scrum
- SAFe
- PI Planning
- Cross-Functional and Matrixed Team Leadership
- Mentoring and Coaching
- Stakeholder Management at Executive Level
User experience and design
- Human-Centred Design
- User Research
- Journey Mapping
- Epics and User Stories
- Acceptance Criteria
- Success Metrics and KPIs
Domain
- KYC
- AML
- Customer Due Diligence
- Compliance workflows
- Case management
Tools and AI
- Jira
- Confluence
- Azure DevOps
- Fenergo
- Pega
- Python (Basic)
- SQL (Basic)
- Prompt Engineering
- AI-Assisted Product Management
Certifications
Certified Scrum Product Owner (CSPO)
Scrum Alliance
ICAgile Certified Professional, Agile Product Ownership (ICP-APO)
ICAgile
Certified Practitioner of Human-Centred Design
LUMA Institute
McKinsey.org Forward Program Graduate
2025
Education
MBA, Finance
M.S. Ramaiah Institute of Management, India · Jun 2010 to Aug 2012
B.Tech, Dairy Technology
AAIDU, India · 2005 to 2009
Contact
If this is the product you need owned, let's talk.
Senior Product Owner roles in regulated financial services, data platforms and case management.
Bangalore, India