TL;DR

• Fewer than 10% of AI proof-of-concepts meet their own success criteria, per a 2025 industry survey. Most data science investment in insurance never reaches production at all.
• A handful of use cases consistently pay off: underwriting and risk scoring, claims triage, fraud screening, and retention or lapse prediction. Each one changes a specific decision. A dashboard nobody opens does not count as one of them.
• What separates a production model from a stalled pilot is data readiness and workflow integration. Model sophistication matters far less.
• Explainability decides whether a model survives review. A model that cannot show its reasoning at the point of a claim or underwriting decision does not clear a regulated audit, however accurate it tests.
• The honest caveat: a sophisticated model built on fragmented data, sitting outside the decision workflow, with no explanation attached, earns nothing. The use case matters less than whether the model reaches the point where a decision gets made.

Ask an insurer’s data science team what they are working on and the list is usually long: fraud models, pricing models, churn models, a dozen experiments in various states of progress. Ask how many of those models are changing a decision today, and the list gets short fast. Data science in insurance earns its keep in a specific, narrow set of places. Everywhere else, it is an expensive way to generate a slide deck.

This piece covers why most data science investment in financial services stalls before it reaches production, which insurance use cases consistently pay off, what separates a model that ships from one that does not, and why explainability decides whether a model survives regulatory review.

Most AI Pilots in Financial Services Never Prove Their Worth

A 2025 IDC survey of 900 organizations, commissioned by Lenovo, found that fewer than 10% of AI proof-of-concepts met the business goals they were built against. The organizations surveyed ran roughly two dozen proofs of concept apiece over 18 to 24 months. Most of that effort produced a result nobody acted on.

Insurance carries the same pattern. A pricing model, a churn model, or a fraud model can perform well in a notebook and still never touch a live policy or a live claim. The gap between a model that scores well in testing and a model that changes a real decision is where most data science budget quietly disappears.

That gap is the actual question this piece answers. Not which algorithm performs best on a benchmark, but which use cases and which conditions let a model cross from experiment into production, and stay there.

The Insurance Use Cases That Consistently Reach Production

Four use cases show up again and again among the insurance data science projects that ship.

Underwriting and risk scoring changes the approval decision itself. It does more than add risk commentary on the side of someone else’s decision. A model that scores an application and routes it toward automated approval, manual review, or decline changes what happens next for that applicant, which is why underwriting models have one of the higher production rates among insurance data science projects.

Claims triage and fraud screening work the same way at first notice of loss, reading a claim and routing it before a person ever opens the file. The earlier guide on insurance claims automation covers this in depth: a model that screens every claim at intake catches patterns a human reviewer working a caseload would take weeks to notice.

Retention and lapse prediction changes which policyholder gets a retention call before the policy lapses. A model that flags lapse risk two months out gives an insurer time to act while the policy is still active. The same model built to explain lapse risk after the policy has already lapsed only describes what already happened.

iTuring runs 140+ of these use cases live in production today, across banking, insurance, and fintech, each with full lineage and approval workflows attached. That count matters here specifically because it describes models that crossed into production. A pilot that performed well and then stalled would never make this list.

What Separates a Production Model From a Stalled Pilot

The model itself is rarely what kills a data science project in insurance. Two things usually do.

The first is data readiness. A model trained on clean, well-labeled historical data often meets a production environment where the same fields are missing, inconsistently coded, or scattered across systems that were never built to talk to each other. A model that performs well against a curated training set and poorly against the data it sees in production never gets trusted enough to run unsupervised.

The second is workflow integration. A model that outputs a risk score into a dashboard, for someone to check if they remember to, changes nothing. A model wired into the underwriting queue, the claims intake system, or the retention outreach tool changes a decision automatically, at the moment the decision gets made. Where the model’s output lands decides which of those two deployments happens, far more than the model itself does.

A team that solves both of these before it starts tuning the model spends less time stuck in pilot and more time running something that pays for itself.

Explainability Decides Whether a Model Survives Review

A model that reaches production in insurance still has to survive a review, from a regulator, an internal auditor, or a policyholder who disputes a decision. An underwriting model that declines an application needs a reason attached to that decision, one a human can read and defend. A claims model that flags a file for fraud investigation needs the same thing: a traceable basis for the flag that a human can check and defend.

This is where most off-the-shelf data science tooling runs into trouble in a regulated industry. A model that performs well but cannot produce a prediction-level explanation, an approval trail showing who signed off on it, and a record of what data it used, gets pulled from production the first time someone asks it to justify a decision. Accuracy earns a model a chance. Explainability is what keeps it running.

A claims or underwriting leader evaluating a data science platform should ask one question before any accuracy benchmark: when this model makes a decision, can it show its work to someone who was not in the room when it was built.

A data science leader weighing where to invest next should ask which of these use cases would change a decision tomorrow if the model were trusted enough to run unsupervised. Book a discovery call to map that against your own data.

Sources

• iTuring.ai about-us page, use-case and production-scale claim (“140+ Use cases live across banking, insurance, and fintech with full lineage and approval workflows in production”). https://ituring.ai/about-us/
• IDC CIO Playbook 2025 survey, commissioned by Lenovo. https://pages.lenovo.com/rs/183-WCT-620/images/CIO%20Playbook%202025%20-%20Its%20Time%20for%20AI-nomics_February%202025_AP242508IB.pdf