An insurer sits on some of the densest data in the whole economy: every policy, every premium, every claim and every reserve is a record you can measure. The catch is that these records live in a dozen places at once, and when the board asks about loss ratio on one product, the answer comes out of a spreadsheet someone stitches together by hand over three days. The real edge is not owning the data but turning it into analytics and reports faster than your competitors and your regulator.
In our experience, the bottleneck is rarely the dashboard idea. What tends to be missing are people who can tie the policy core, the claims system and the actuarial data into one coherent model and keep it alive for years. So on data and BI for insurance we work through competency: we strengthen your team with data engineers, BI specialists and warehouse architects from the RITS Group, instead of making you stand up an analytics department from scratch.
Why is insurer data so scattered?
The fragmentation does not come from bad intentions but from history. An insurer usually runs separate systems for sales, policy administration, claims handling and accounting, and each of them was born in a different decade and a different technology. On top of that come partner channels, multi-agents, market data and actuaries’ spreadsheets that live their own life next to the official databases.
The result is always similar: the same policy looks different across three systems, a customer has four separate identifiers, and reconciling a single number at month-end eats up the very people who should be analysing rather than copy-pasting. So before we build any report, we lay down a shared data model and one customer key, because without that every dashboard is just a prettier version of the chaos.
How do you build a warehouse for policy and claims data?
Analytics rests on a warehouse that collects data from source systems and organises it around business questions. We build it in layers so it can grow without a rewrite:
- pipelines from the core systems (policy, claims, finance) in an ELT approach, with incremental loading and latency checks,
- a raw and a cleansed layer, where we enforce deduplication, change history and dictionary consistency,
- a dimensional model with policy, premium and claim facts plus customer, product, time and channel dimensions,
- a semantic layer that turns tables into concepts an actuary and a controller actually understand.
With this setup, a new product or channel is added as another set of dimensions and facts, not as another silo. We design it to carry both historical reporting and live operational analytics.
How do you compute loss ratio in a metrics layer, not in Excel?
The key claims ratio can have five definitions, because each department counts it slightly differently. So we do not leave it in spreadsheets; we embed it in a metrics layer, where loss ratio has one agreed definition and everyone computes from the same source.
- we define the indicators once, in code (gross and net loss ratio, expense ratio, combined ratio),
- we bind them to dimensions, so the same loss ratio splits by product, channel, region or cohort,
- we keep consistency over time, so a definition change does not break history,
- we expose the metrics to BI tools (e.g. Power BI, Tableau, Looker) through one layer, not through dozens of separate queries.
That way the claims dashboard stops being a monthly project and becomes something that refreshes itself and shows the same figure to everyone who looks at it.
How do you compute customer value (CLV) for an insurer?
Customer value in insurance is more than the sum of premiums. You have to account for retention, renewals, that customer’s claims experience, cross-sell and the cost to serve. We build CLV models on warehouse data, because that is the only place where the full history of policies and claims sits together.
We start with a descriptive version, based on real cohorts and renewal behaviour, and only then, where it makes sense, reach for predictive models. CLV then becomes a dimension rather than a slide: you segment the portfolio by it, set retention priorities and judge the profitability of acquisition channels. We plug that model into the same dashboards where loss ratio lives, so pricing and customer decisions are made on one picture.
How do you secure regulatory and actuarial reporting?
Reports for the supervisor and for the actuarial function play by different rules than a sales dashboard: what counts is not flair but data quality and auditability. There is no room here for a number you cannot derive from the source system.
So in the reporting layer we treat the data path like an accounting entry. Every value can be traced from the report back to the record in the core, changes to the logic are versioned, and data-quality rules run automatically and halt the load when something is off. We prepare data for requirements such as Solvency II and supervisory reporting, making sure the same set of numbers feeds the regulatory report and the internal analytics. As a result, an actuary works on data they trust, and an audit does not end in a hunt for where a given line came from.
Where does the AI layer fit into insurance analytics?
We treat the AI layer as an option on mature data, not as a starting point. Once the warehouse and the metrics work, we add models where they genuinely cut work or catch what rules miss:
- predictive models for churn, claims experience or renewal propensity,
- anomaly detection in claims as support for anti-fraud teams,
- GenAI and RAG over policy and claims documentation, to reach the content of contracts and terms faster.
Our AI and data competencies in the RITS Group are real (including our AI, LLM and data teams; we have delivered GenAI, RAG and LLM projects). We are also honest about it: we do not have an AI deployment at a specific insurer, so we speak about the competency we bring, not about a finished case study.
Which skills do we provide for an insurance data project?
We shape the team around the stage you are at, not “just in case”. Most often we staff the roles that genuinely push an analytics project forward:
- a data / warehouse architect who designs the model and the layers,
- data engineers who build the pipelines from the core systems and keep data quality up,
- BI specialists who assemble the metrics layer and the dashboards,
- analysts and actuarial support on the modelling and reporting side.
We plug such a team into your processes, tools and calendar, working under your management or as a dedicated team. We laid out the differences between these setups in our article on IT engagement models.
How do we do it at RITS?
We work as a nearshore partner from Poland that combines data engineering with an understanding of what insurance actually involves. We draw on the RITS Group’s competencies in Big Data and analytics (including our AI, LLM and data teams) and on experience building software for demanding, regulated industries.
In practice we start from a picture of the data you have, design the warehouse and the metrics layer, and then grow them iteratively together with your team. We verify skills before the start and scale the team in the project’s rhythm, from reinforcing single roles to a dedicated team. Most often we do this as custom software built by our team and, where a specific skill is missing, as team augmentation.
Where do you start with insurance analytics?
Start from one decision that is made too slowly today (say, product profitability or portfolio churn) and from the question of what data it would need to compute itself. That defines the warehouse scope, the first metrics and the roles you need before you build the whole thing. Instead of a two-year programme, we spin up a narrow, working slice and extend it with further products and indicators.
Want to turn policy and claims data into analytics and reports that compute themselves? Let’s talk - we’ll match a team and an architecture to your portfolio.