For years, underwriting rested on experience, rating tables and spreadsheets that only their author truly understood. Today a decision to accept a risk and price it is made faster, on far more data, and under growing regulatory pressure that asks why a policy got the price it did. For a director of underwriting, risk or actuarial, that means one thing: risk assessment is becoming a digital product, and its quality depends on software as much as on the expertise of the people.
In our experience, the hard part is not inventing a scoring model but wiring it into the decision process, the data and the people who make the calls. We do not have a case study in this area that we could put on display here, so let us be plain: what we deliver first is skills and people who build this, as team augmentation or a dedicated team, backed by real data and ML capability within the RITS Group.
Why does an insurer need digital underwriting?
Because the number of decisions keeps rising while the time for each one shrinks. An underwriter no longer has the luxury of mulling over a case for days, yet still has to be able to defend every decision to an audit and a regulator. Digital underwriting moves the routine into the system and leaves the expert with what genuinely calls for judgement.
In practice we aim for three outcomes: a faster, repeatable decision, a consistent risk price that does not depend on who happens to be handling the case, and a complete audit trail that shows which data and rules drove the result. This is not about replacing the underwriter, but about handing them a tool that calculates faster and forgets nothing.
How do you build risk scoring and pricing models?
A scoring model is not one algorithm but a chain: from input data, through features, to a result that feeds the process. We build it so it can be maintained and explained, not just demoed once.
- training on historical data from the portfolio, loss experience and external sources, with quality control and data versioning,
- exposing the model through an API, so scoring is called from the policy system, the agent portal or the buying journey in real time,
- explainability of the result (feature importance, reason codes), because the decision has to make sense to the underwriter, the client and the auditor,
- drift monitoring and a clear retirement rule, before the model starts getting new data wrong.
We ground our data and ML work in the capability of the RITS Group, including our AI, LLM and data teams, where we have delivered LLM, RAG and machine-learning projects. That is genuine data experience, though we will not pretend it comes from a rollout at a specific insurer.
How do you design the underwriter’s workbench?
The workbench is where the underwriter spends the whole day, so every click counts. A good workstation pulls data into one view instead of forcing people to hop between systems, and guides the decision rather than merely recording it.
- data aggregation on the risk from the policy system, external sources and the model output on a single screen,
- acceptance and exception rules that let simple cases through automatically and route hard ones to a human,
- a decision queue with priorities, SLAs and a clear status for every case,
- a decision trail captured automatically, with the underwriter’s comment and the values the result rested on.
We design the workbench around the team’s real working rhythm, not a diagram from a slide. The most value here usually comes from automating the simple decisions, which frees expert time for the cases that truly need judgement.
How do you detect fraud in the insurance process?
Fraud detection works best when it combines two approaches. Rules catch what we already know; anomaly models catch what we have not yet captured in a rule.
- a rules layer built on known fraud patterns and red flags, readable for the anti-fraud team,
- anomaly detection models that surface unusual patterns in applications, claims and links between parties,
- a fraud risk score attached to the case, so the underwriter sees the signal up front, not after a payout,
- a feedback loop from confirmed cases, so model retraining rests on the team’s real decisions.
The goal is not to block customers but to direct attention to where the probability of fraud is real, with as few false alarms as possible.
How do you report portfolio exposure?
A single decision is one thing, but the risk director looks at the whole. Exposure reporting ties underwriting decisions into a portfolio picture: the distribution of risks, accumulations, limits and how much of them is used. We build the reporting layer so it answers the questions of the board and the regulator without manually stitching data from several systems. The key is that the numbers come from the same source as the decisions, so reporting and operations rest on one consistent data model rather than two competing truths.
How do you exchange data with reinsurance?
Reinsurance lives on the data it gets from the cedant, so the exchange has to be structured and repeatable. We automate the preparation of bordereaux, exposure reports and claims data in the formats agreed with the reinsurer, tie it to the reporting calendar and watch for completeness before anything leaves the building. A well-designed exchange shortens period close and cuts the disputes that come from mismatched numbers.
How do you keep a human in the decision loop?
The model advises, but a person carries the responsibility for the risk, and that is how it should stay. We design the process so the underwriter can accept, reject or override the model’s recommendation, always with a reason that stays in the system. Those overrides later become valuable material: they show where the model is wrong and what is worth fixing. A human in the loop is not a compliance formality; it is the mechanism that guards against blind trust in automation and teaches the system at the same time.
Which skills do we provide for underwriting projects?
We staff roles for the specific phase of the project, not “just in case”. Depending on the need, a team can cover:
- data and ML engineers who build and maintain scoring and anomaly-detection models,
- backend and integration developers who connect models, policy systems and APIs,
- frontend and UX specialists who design the underwriter workbench,
- architects and DevOps responsible for scaling, data security and deployments.
Our data and ML skills are real (the RITS Group, including our AI, LLM and data teams, with GenAI, RAG and machine-learning projects), and our experience in demanding industrial projects carries over as the engineering discipline a regulated environment demands.
How do we do it at RITS?
We work as a nearshore partner from Poland that strengthens the insurer’s team with skills, rather than selling a finished box. We start from your process and data, shape the team around the specifics of the project, and verify skills before the start. We work in a close time zone, so our people step into code reviews, meetings and the working rhythm without a long ramp-up. Most often we do this as custom software built by our team, or as team augmentation when you need to top up missing data, ML or engineering skills fast.
Where do you start supporting underwriting?
Start from the one decision that hurts most: the one that takes longest, goes wrong most often, or is hardest to justify. Map the data, rules and roles around it, then decide whether you need a model, a better workbench, or both at once. That first slice delivers a measurable result and sets the architecture for the rest. We covered the differences between engagement models in our article on IT engagement models.
Want to support underwriting and risk assessment? Let’s talk - we’ll match a team and the skills to your process.