Senior Manager, Underwriting – AI Transformation

Role Overview

• Define the multi year vision and roadmap for AI enabled underwriting, aligned with the company’s growth, risk appetite, and operational goals.
• Assess the current underwriting lifecycle (intake, analysis, decisioning, documentation, monitoring) and identify specific opportunities for automation, digitization, and AI assisted workflows.
• Translate strategic objectives (speed, loss performance, scalability, customer experience) into concrete underwriting process changes, tools, and success metrics.
• Design and implement AI and rule based decisioning frameworks that augment underwriters and agents, reduce manual effort, and create explainable, repeatable decisions.
• Architect workflows that use LLMs and other AI tools to automate document ingestion and analysis (e.g., financials, bank statements, contracts, KYB docs, site reviews), and convert them into structured inputs and draft risk assessments.
• Partner closely with data science, engineering, and product teams to specify requirements for models, rules engines, and decisioning platforms, including features, thresholds, and human in the loop review logic.
• Define and maintain the configuration of scorecards, rule sets, and policy rules within underwriting systems to reflect evolving risk appetite and business needs.
• Redesign end to end underwriting processes with automation as a first principle, minimizing manual steps, duplicate work, and unstructured decision making.
• Implement standardized templates, checklists, and decision frameworks that embed data, AI insights, and best practices into day to day underwriting workflows.
• Define and track operational KPIs (e.g., decision turnaround time, straight through processing rate, manual touch rate, rework, error rates) and drive initiatives that move these indicators meaningfully.
• Ensure all new processes and tools meet requirements for control, auditability, explainability, and regulatory/compliance expectations (particularly where AI is involved).
• Serve as the primary transformation lead for underwriting, working with Underwriting Management, Risk, Commercial, Legal, and Operations to align on new ways of working.
• Lead structured discovery sessions, pilots, and rollouts to introduce new AI tools, rules, and processes, ensuring they are usable, resilient, and well adopted.
• Act as a key translator between technical teams and business stakeholders, making complex AI/modeling concepts accessible and showing their practical implications for deals, policies, and risk posture.
• Build and maintain a backlog of transformation initiatives, with clear prioritization, business cases, and expected impact.

Requirements

• Bachelor’s degree in quantitative or business field, or equivalent experience
• 5+ years of experience in Merchant Underwriting, Commercial Credit Risk, Counterparty Risk Management, or closely related domains in payments, acquiring, fintech, or lending.
• Demonstrated track record of owning and delivering underwriting or risk transformation programs (e.g., implementation of a decisioning platform, redesign of credit policies, automation of underwriting workflows) from concept to execution.
• Experience operating at a level above individual underwriting management, with responsibility for frameworks, tools, and strategies used by multiple teams or regions.
• Deep comfort working with data: you write your own queries, validate logic, and explore datasets directly rather than relying solely on BI reports.
• Hands on familiarity with risk/decisioning platforms (e.g., rule engines, credit decisioning systems, workflow/orchestration tools) and how they integrate with upstream application flows and downstream servicing.
• Experience working alongside data science/ML teams: defining features, success metrics, and evaluation criteria for models used in underwriting or risk monitoring.
• Experience implementing or significantly enhancing an automated underwriting or decisioning platform in a payments, fintech, acquiring, or lending environment.
• Familiarity with common payment processing stacks (gateways, acquirers, processors), chargeback patterns, and high risk merchant categories.
• Exposure to fraud and transaction risk tools, and understanding of how to integrate fraud insights into credit/underwriting strategies.
• Practical experience designing and deploying AI or ML supported workflows in a production risk or underwriting context.
• Understanding of Generative AI and LLM capabilities and limitations, including prompt design, retrieval augmented generation, summarization, and classification of unstructured documents.
• Ability to design end to end GenAI enabled processes (e.g., document ingestion → extraction → summarization → risk scoring → human review) and measure their impact on efficiency and quality.
• Familiarity with model governance, explainability, and control frameworks to ensure AI driven decisions remain transparent, auditable, and policy aligned.
• Ability to see the underwriting function as a system: policies, data, tools, processes, and people, and to identify leverage points where change will have outsized impact.
• Strong product thinking mindset: user centric approach to designing tools and workflows that underwriters and stakeholders actually adopt and value.
• Comfort building business cases (baseline metrics, projected impact, resource needs) to prioritize transformation initiatives and secure buy in.
• Excellent written and verbal communication skills, with the ability to clearly articulate proposed changes, trade offs, and expected outcomes to technical and non technical audiences.
• Builder and change agent mentality: you identify gaps, propose pragmatic solutions, run pilots, iterate quickly, and push through ambiguity and resistance.
• Ability to read, write, speak and understand the English language in a business environment.

Benefits

• Work from Home
• Employee Belonging Groups
• Healthcare: Dental, Medical, and Vision
• Paid Vacation, Volunteer, and Holiday Time Off
• And so much more!