Claims Business Intelligence Lead (Manchester)

Munich Re Specialty - Global Markets, UK
Manchester
1 month ago
Create job alert
Claims Business Intelligence Lead (Manchester)

Location: Manchester, United Kingdom


Posted: 4 days ago


About Us

At Munich Re Specialty – Global Markets (MRS‑GM), it is our ambition to become the leading Primary Specialty Insurance provider, underpinned by an effective and adaptable strategy, superior products and industry leaders working in a supportive environment to achieve this. At the heart of our success is a strong culture where people are encouraged to be present, bold and curious, allowing them to achieve their individual goals. Please see our website for more information.


Job Description

The Claims Business Intelligence Lead is a critical role responsible for designing, delivering, and governing the Claims management information (MI) and reporting framework across the organisation. This role ensures that the Claims function has accurate, timely, and actionable insights to drive operational performance, regulatory compliance, financial accuracy, and strategic decision‑making. This role acts as the key interface between senior Claims leadership, Central Data Office (CDO), IT, Transformation, Underwriting, Compliance, and Finance by translating business needs into clear reporting and insight solutions.


Responsibilities
MI Strategy, Governance & Framework

  • Develop and maintain the Claims MI strategy aligned to Claims and enterprise data strategies.
  • Establish KPI frameworks, data definitions, and reporting standards across Lloyd’s and Company Market business.
  • Implement governance standards for the production, validation, and distribution of Claims MI.
  • Maintain the Claims MI and dashboard roadmap and act as product owner for BI enhancements.

Insight Delivery & Reporting

  • Design, refine and manage Claims KPI’s, dashboards and reporting suites (Power BI/Tableau).
  • Produce monthly and quarterly MI packs for senior leadership, Boards, and regulators.
  • Develop data monitoring framework and deliver insight analyses for performance, including leakage, cycle times, and operational metrics to ensure product health and quality claims service delivery.
  • Develop and deliver reports that are fit for purpose, relevant, accurate, timely, consistent, and actionable.

Stakeholder Engagement

  • Partner with Claims leadership, regional Claims teams, Delegated Claims, CDO, IT, Underwriting, Actuarial, Finance, to understand, prioritise and action critical reporting needs.
  • Design and implement training program for Claims teams for use and interpretation of MI and dashboards to empower self‑service.

Data Quality, Requirements & Integration

  • Define Claims data requirements across portfolio, and partner with Transformation, CDO, IT, Delegated Claims, and Underwriting to improve data standards, models and quality.
  • Monitor data completeness, lineage, and quality issues; conduct impact analysis, escalates critical gaps, and oversees quality improvement to resolution.
  • Work with Enterprise Architecture (Data/Tech) teams to define requirements for claims data architecture, data quality rules and integration pipelines.

Regulatory, Internal and External Reporting

  • Coordinate with Compliance and deliver Claims regulatory reporting and returns in all locations globally required (e.g., CBI, FCA, PMDR, BaFin, etc.).
  • Manage and enable timely, accurate Company Outlier reporting to central global functions.
  • Ensure all regulatory MI meets governance, quality, and audit requirements.

Leadership & Capability Building

  • Lead and develop BI analysts or MI specialists as the function scales.
  • Build BI maturity across Claims through training, documentation, and best practice frameworks.
  • Promote data‑driven decision‑making across global Claims.

Knowledge and Skills

  • Superior ability to quickly understand complex data warehouse environments, data pipelines, and integration of Claims data across multiple systems and tools.
  • Strong understanding of Claims operations, lifecycle, processes, and metrics across Property, Casualty, and Specialty lines.
  • Familiarity with Lloyd’s and Company Market data structures, bordereaux, and regulatory MI.
  • Strong grounding in KPI development, performance frameworks, and operational analytics.
  • Ability to translate complex data into insight narratives for executives.
  • Skilled in trend analysis, forecasting support, variance interpretation, and performance diagnostics.
  • Experience managing cross‑functional stakeholders (Claims, Underwriting, Actuarial, IT, Finance).
  • Ability to work independently, multi‑task, and remain self‑motivated in a fast‑paced environment.
  • ACII qualification / progressing towards ACII qualification or relevant experience.
  • University Degree and/or relevant professional qualification.

If you are excited about this role but your experience does not align perfectly with everything outlined, or you don’t meet every requirement, we encourage you to apply anyway. You might just be the candidate we are looking for!


Diversity, Equity & Inclusion

At Munich Re, Diversity, Equity, and Inclusion foster innovation and resilience and enable us to act braver and better. Embracing the power of DEI is at the core of who we are. We recognise diversity can be multi‑dimensional, intersectional, and complex, so we want to build a diverse workforce that includes a wide range of racial, ethnic, sexual, and gender identities; economic and geographic backgrounds; physical abilities; ages; life, school, and career experiences; and political, religious, and personal beliefs. Additionally, we are committed to building an equitable and inclusive work environment where this diversity is celebrated, valued, and has equitable opportunities to succeed.


All candidates in consideration for any role can request a reasonable adjustment at any point in our recruitment process. You can request an adjustment by speaking to your Talent Acquisition contact.


Learning and Innovating

At Munich Re Specialty – Global Markets our approach to ESG is underpinned by our desire to seize business opportunities and to nurture a stimulating and inclusive work environment. Our ESG strategy aims to deliver holistic impacts across environmental, social and governance topics including supporting a number of local initiatives within our community and offering volunteering opportunities for colleagues.


Learn more about sustainability at Munich Re – choose your impact!

#BePresent #BeBold #BeCurious


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Business Intelligence Developer

Business Intelligence Analyst

Business Analyst - Business Intelligence

Business Intelligence Assistant

Business Intelligence Assistant

Procurement Senior Data Analyst (Supervisor)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.