Claims Automation & Business Intelligence Analyst

INSURANCE COMPANY OF NORTH AMERICA (A Chubb Company); Mandaluyong; On-site; PHP 520,000 - 900,000; Full time

Job summary

INSURANCE COMPANY OF NORTH AMERICA (A Chubb Company) seeks a Claims Automation & Business Intelligence Analyst to drive data-driven decisions for APAC claims. The role develops SQL queries, Python scripts, dashboards, and reporting solutions to improve efficiency, data quality, and performance monitoring.

This hands-on analyst will build automated workflows, test AI-powered PoCs, and deliver actionable analytics for regional stakeholders, while ensuring robust data governance and documentation

Qualifications

• 1–3+ years in data analytics, BI, or technical operations.
• Strong SQL and Python skills for data analysis and automation.
• Experience building dashboards and automated reports in BI tools.

Responsibilities

• Develop, test, and maintain Python scripts and automated workflows for data preparation and processing.
• Write optimized SQL queries across regional claims databases and ensure data quality.
• Build and refresh automated dashboards tracking claims volumes, turnaround times, and automation KPIs.
• Collaborate with regional teams to validate data definitions and troubleshoot reporting issues.
• Document processes, data mappings, and technical steps for maintainability.

Skills

SQL

Python

Data analytics

Dashboards

Automation

Education

Bachelor's degree in data science, Information Systems, Computer Science, Engineering, Mathematics, Statistics, Business Analytics, or a related quantitative discipline

Tools

Power BI

Tableau

Qlik

Git

Job description

The Claims Automation & Business Intelligence Analyst supports data-driven decision-making and process automation across APAC claims operations. The role involves developing SQL queries, Python scripts, dashboards, and reporting solutions to improve operational efficiency, data quality, and claims performance monitoring.

Role Title:

• Claims Automation & Business Intelligence Analyst - Level 3
• Role Purpose

The Claims Automation & Intelligence Analyst is a hands-on analytical and technical execution role supporting the development of data-driven automation, tactical scripts, and operational reporting across APAC claims markets. This role helps turn operational data and business requirements into reliable dashboards, automated workflows, and analytical outputs.

This role sits at the intersection of claims operations, data analytics, and workflow automation. It is an ideal role for an early-career technical analyst who enjoys writing clean SQL and Python code, experimenting with generative AI tools, and uncovering data insights that help streamline claims processing across the region.

Key Responsibilities:

Claims Automation and Digital Workflow Support

Support the monitoring and analysis of various Claims projects - tracking automation rates, investigating fallout and exception patterns, and compiling data to help refine business rules and eligibility logic.

Develop, test, and maintain tactical Python scripts and automated workflows for routine data preparation, file processing, validation checks, and operational task automation under the guidance of the Automation & Intelligence Lead.

• Perform structured data analysis and exception reporting to support the team in establishing automation quality checks, audit trails, and operational documentation across APAC markets.
• Follow team coding and delivery standards - including version control (Git), unit and regression testing, script documentation, and assisting with UAT validation for core claims system enhancements.

Applied AI & LLM Experimentation & Testing

Assist in building and testing Proofs-of-Concept (PoCs) and tactical workflows using LLMs and generative AI - such as document classification, structured data extraction from claim narratives, and claims summarization.

• Execute structured prompt testing and model benchmarking across sample claims datasets - recording accuracy metrics, latency, and edge-case failures to support tool and model evaluations.
• Prepare evaluation datasets, perform quality assurance checks on AI-generated outputs, and document findings to help define human-in-the-loop review procedures.
• Stay abreast of new generative AI features and developer tools, sharing practical ideas and assisting in prototyping use cases for the claims operations team.

Data Intelligence & Reporting Execution

• Build, refresh, and maintain automated data pipelines and multi-market intelligence dashboards that track claims volumes, turnaround times, and automation KPIs.
• Maintain clean, well-documented reporting scripts and data models that reduce manual spreadsheet work and ensure timely, reliable recurring reports.
• Conduct ad-hoc data extraction and quantitative analysis to support deep-dive investigations and leadership packs - translating raw claims data into clear charts, tables, and summary insights.
• Write, optimize, and troubleshoot SQL queries across regional claims databases; perform routine data quality checks, reconciliations, and discrepancy investigations.

Regional Stakeholder Support & Delivery

• Collaborate with local market claims teams, operations specialists, and regional IT contacts to gather data definitions, validate business logic, and troubleshoot reporting or script issues.
• Prepare clear analytical visuals, PoC demonstration materials, and performance tracking packs for regional review meetings.
• Document business processes, data mappings, and technical workflow steps clearly so both technical peers and operational stakeholders can easily understand and maintain them.

Code Quality & Continuous Learning

• Participate actively in peer code and query reviews - incorporating feedback from the Lead to continuously improve SQL performance, Python code modularity, and analytical accuracy.
• Maintain tidy repositories, runbooks, and data dictionaries in compliance with team standards for version control (Git), reproducibility, and data validation.

Process Analysis & Value Tracking

• Analyze operational data and manual task logs across APAC markets to help quantify processing bottlenecks, error rates, and automation opportunities.
• Track pre- and post-implementation metrics for deployed scripts and process improvements to measure time saved, turnaround improvements, and operational impact.

Experience:

1 to 3+ years of experience in data analytics, business intelligence, process automation, or a technical operations role - ideally within insurance, financial services, or a data-intensive operational environment.

• Strong hands-on proficiency in SQL and Python (e.g., pandas, data manipulation, API calls, and scripting) for data analysis and routine workflow automation.
• Practical familiarity with evaluating analytical or AI outputs - such as measuring accuracy/precision, spotting anomalies in data, and performing structured testing.
• Hands-on exposure to or strong interest in LLM and generative AI applications - such as prompt engineering, working with LLM APIs, or building simple AI PoCs.
• Experience building and maintaining interactive dashboards and automated reports using modern BI tools (e.g., Power BI, Tableau, Qlik, or Python visualisation libraries).
• Clear written and verbal communication skills - able to explain data findings and technical issues clearly to non-technical colleagues.
• Proactive with the ability to learn quickly and deliver reliably in a lean, collaborative team environment.
• Detail-oriented and analytically thorough - takes pride in clean data, reconciled numbers, and well-tested scripts.
• Curious and eager to learn - approaches unfamiliar data structures, legacy workflows, and new AI tools with energy and resourcefulness.

Qualifications:

• Bachelor's degree in data science, Information Systems, Computer Science, Engineering, Mathematics, Statistics, Business Analytics, or a related quantitative discipline
• Must be amenable to work on Morning and Mid-shift

Beneficial Skills & Experience

• Certifications or online credentials in Python, SQL, Power BI/Tableau, or data engineering.
• Coursework or project portfolio demonstrating practical application of machine learning, NLP, or generative AI / LLMs.
• Foundational knowledge of cloud platforms (e.g., AWS, Azure, or GCP) or database administration.