What makes data "actionable" rather than just available?

Three properties, and all three are required. It has to be transparent about where a number came from, or nobody will defend a decision made on it. It has to be dependable enough to act on. And it has to be fast enough to matter — an answer that arrives after the decision is a report, not intelligence.

Why must a detection model keep learning?

Because technology cuts both ways. It gives insurers better tools to detect fraud, and it gives fraudsters better tools to commit it. A model that does not keep learning is already describing last year, and keeping pace with how fraud evolves is the whole job rather than a maintenance task.

Do we need to replace our data platform first?

Not usually. The point of an analysable view is that it reads the systems already holding the data. A programme that starts by replacing the platform spends its first year producing no intelligence at all, which is how these initiatives lose their sponsor.

What does success look like here?

Fewer analyst hours spent assembling a picture, and more spent acting on one. If the team is still exporting and reconciling before it can answer a question, the supply chain is not yet trustworthy — regardless of how good the models are.

Actionable Data Intelligence

Reimagining Your Information

Data only repays the investment in technology and AI once the supply chain behind it is trustworthy — transparent about where a number came from, dependable enough to act on, and fast enough to matter. We build that with the expertise and technology your objectives actually call for, so analytics produce decisions rather than dashboards.

Easing workloads

Fraud signals are scattered across systems that were never designed to be read together — application data, claims records, statements, medical records, and external sources such as industry fraud alerts and watch lists. Bringing them into one analysable view is why insurers turn to analytics and AI to fight fraud.

Technology cuts both ways. It gives insurers better tools to detect fraud, and it gives fraudsters better tools to commit it — which is why a detection model that does not keep learning is already behind.

Keeping pace with how fraud evolves is the whole job, and it is what NANO's data analytics is built to do.

Powering healthcare with data science and technology

Comprehensive view of every patient

NANO Health develops the patient treatment plans by a comprehensive view of every patient, which comprises patient experience insights produced by considering the provider’s feedback, prognostic care, and case facts and figures. Our solutions help the patient access secure mobile tools and system portals for better satisfaction and improved patient care transparency.

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How this works with the rest of the suite

NANO BRAIN
The models that read across those sources are BRAIN, which is what makes a single analysable view worth building.
NANO FWA
Payment integrity is this capability in product form — the same signals, assembled into a case an investigator can work.
NANO DDI
The deep-data layer is where the combined view is queried rather than only assembled.
NANO Reports
An insight nobody outside the analytics team can see is an insight that changes nothing.

Frequently asked questions

What makes data "actionable" rather than just available?: Three properties, and all three are required. It has to be transparent about where a number came from, or nobody will defend a decision made on it. It has to be dependable enough to act on. And it has to be fast enough to matter — an answer that arrives after the decision is a report, not intelligence.
Why is fraud detection a data-integration problem?: Because the signals are scattered across systems that were never designed to be read together: application data, claims records, statements, medical records, and external sources such as industry fraud alerts and watch lists. No single one of them is conclusive. Bringing them into one analysable view is the reason insurers turn to analytics at all.
Why must a detection model keep learning?: Because technology cuts both ways. It gives insurers better tools to detect fraud, and it gives fraudsters better tools to commit it. A model that does not keep learning is already describing last year, and keeping pace with how fraud evolves is the whole job rather than a maintenance task.
Do we need to replace our data platform first?: Not usually. The point of an analysable view is that it reads the systems already holding the data. A programme that starts by replacing the platform spends its first year producing no intelligence at all, which is how these initiatives lose their sponsor.
What does success look like here?: Fewer analyst hours spent assembling a picture, and more spent acting on one. If the team is still exporting and reconciling before it can answer a question, the supply chain is not yet trustworthy — regardless of how good the models are.