Business Project Manager - Credit Automation & Digitization Project

• 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.

Requirements

• 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.