Job description
The Project Manager will be responsible for the successful planning, execution, and delivery of the Credit Automation and Digitization Project.
He/ She will act as the central point of contact, coordinating between internal stakeholders, covering Business, Credit and Risk Team and a multidisciplinary project team.
The ideal candidate shall have a strong background in business and corporate credit, coupled with a proven track record of managing complex digital transformation projects, preferably within the Banking or Financial services sector.
This role requires a blend of deep domain expertise, technical understanding, and exceptional project management skills to ensure the project is delivered on time, within budget, and to the required quality standards.
Lead the project from initiation to closure, defining scope, goals, and deliverables in collaboration with senior management and stakeholders.
Develop a comprehensive project plan that covers all project phases, including timelines, milestones, resource allocation, and budget management.
Manage the end-to-end implementation of the credit automation platform, ensuring seamless integration with existing systems and workflows.
Oversee the integration of AI/ML models for credit application and risk memorandum automation and preparation, automation and preparation of various other business/ credit memos, risk assessment, and decision automation, ensuring they are developed and deployed responsibly and in line with existing credit policy and DOA for decisioning automation.
Facilitate communication and collaboration between cross-functional teams, including business (relationship managers/ relationship officers), Credit (credit officers/ credit managers), Risk Team, IT specialists, data scientists, and external vendors.
Proactively manage project risks, issues, and dependencies, developing and implementing effective mitigation strategies.
Monitor and report on project progress to all stakeholders, providing regular updates on key milestones, budget, and performance against a defined set of KPIs.
Ensure the project adheres to all relevant regulatory and compliance standards governing credit and data privacy.
Manage the relationship with all third-party vendors, including the workflow engine provider (e.
g., Newgen), ensuring they meet their contractual obligations.
Drive the change management process, ensuring smooth adoption of the new platform and processes by all end-users through effective training and communication.
Handle data integration/migration between T24 Core Banking System, various modules of Moody’s Credit Lens (like ORR, FRR, Covenant, AI Spreading), RAROC and FTP Tool etc.
Educational Background: Bachelor’s degree in Business Administration, Finance, Economics, Information Technology, or a related field.
Project Management Experience: A minimum of 7-10 years of project management experience, with at least 5 years spent leading large-scale digital transformation or automation projects within the banking or financial services industry.
Credit Domain Knowledge: In-depth understanding of business and corporate credit lifecycle, including origination, underwriting, risk assessment, and portfolio management.
Implementation Experience: Project Management and Demonstrable experience in successfully implementing at least one major credit automation, loan origination system (LOS), Business Process Management (BPM) project from start to finish.
AI/ML Project Experience: Experience with projects involving the implementation of AI/ML models for credit decisioning, risk modeling, or fraud detection.
Solution Architecture & Integration Oversight: Proven ability to coordinate and validate solution architecture and system integration across core banking, risk platforms, workflow/BPM systems, and third-party solutions.
Data Migration, Cutover & Operational Readiness: Hands-on experience overseeing data migration, production cutover planning, and operational readiness for large-scale system implementations.
This job post has been translated by AI and may contain minor differences or errors.
