Data and AI Solutions for Insurance

We align each model with your AI program and the requirements of the states or countries where you operate. In practice that means maintaining a model inventory, documenting intended use and data sources, testing for unfair discrimination and proxy variables, recording human oversight points and monitoring performance after deployment. We apply the same controls to vendor models you integrate. The result is an evidence package your compliance team can present during market conduct exams, rather than documentation reconstructed after the fact.

Yes. We connect to core insurance platforms through their APIs, event streams, database replicas or batch exports, depending on what each system supports. Where you run multiple policy or claims systems, we map them to a common insurance data model so analytics and AI work consistently across lines of business. We can also write results, such as triage scores or extracted submission data, back into the systems adjusters and underwriters already use.

A first production-ready model typically takes three to five months, including data preparation, model development, back-testing against historical claims and integration with your workflow. The biggest variable is access to clean historical claims and outcomes data. We usually start in shadow mode so you can compare model recommendations with actual handling decisions before allowing the model to influence routing or referrals.

It can be, with the right design. We deploy models in environments where your data is not used for training, restrict access according to existing permissions, log every interaction and require human review for decisions that affect coverage or payment. We also build evaluation sets from real documents to measure extraction accuracy before go-live. Sensitive data such as health information is handled according to the privacy rules that apply to you.

We focus on the data layer that feeds actuarial engines and reporting tools: sourcing policy, premium, claims and expense data, grouping contracts into the required cohorts, reconciling with the general ledger and providing lineage for auditors. We do not replace your actuarial software, but we make its inputs reliable and its outputs traceable, which typically shortens closing cycles and reduces audit queries.

Cost depends on scope, data complexity and the engagement model. A discovery and proof-of-value phase is usually a modest fixed-price project. Larger programs are delivered by dedicated teams billed monthly, or as managed services for ongoing operations. Because our delivery hub is in Hanoi, Vietnam, clients typically access senior data and AI engineers at a lower cost than equivalent onshore teams, while keeping working-hour overlap and weekly written progress updates.