Automating a payout decision is not a single algorithm problem. It requires an engineering team to model liability determination, policy limit validation, fraud signals, third-party recovery logic, and regulatory audit trails as separate, interacting systems before a claim ever reaches a straight-through processing path.
Most insurtech teams underestimate this because the demo case (a clean, single-party, low-dollar claim) hides the branching complexity that shows up the moment a claim involves a third party, a disputed liability split, or a state with its own statute of limitations. Get the data model wrong at the start and no amount of claims adjudication software maturity will fix it later without a rebuild.
TL;DR
Straight-through processing for fully automated claims stays under 10% industry-wide, which means engineering teams must design for the manual-review exception path as a first-class citizen, not an afterthought.
Subrogation logic needs its own data model: liability percentage, third-party identification, policy limits, damage documentation, and state-specific statutes of limitation are separate variables that must be tracked independently, not folded into the primary claim record.
AI claims processing systems need an audit trail by design, not bolted on, to satisfy NAIC claims settlement practices requirements and the 2023 NAIC AI governance bulletin.
Fraud detection should run as a scoring layer that gates (not replaces) adjudication, because false positives have direct regulatory and customer-retention costs.
Integration with third-party data sources (claims history databases, liability verification services) is a core engineering dependency, not an optional enhancement bolted on after launch.
About the Author: 724SOFTWARE is a Vietnam-based engineering partner that has built trading, banking, and compliance-heavy platforms, including real-time risk engines and KYC/AML workflows for capital markets clients, where the same discipline applies: every automated decision needs a traceable, auditable logic path before it touches money. That background in regulated, transaction-heavy systems is directly transferable to claims adjudication and subrogation engineering.
What Is Claims Adjudication Automation, and Why Is It Harder Than It Looks?
Claims adjudication is the process of evaluating a filed claim against policy terms, coverage limits, and liability evidence to determine whether and how much to pay. Automating it means replacing some or all of that human judgment with rules engines, machine learning models, or a hybrid of both.
The difficulty is not the happy path. A single-party auto claim with clear fault, full policy coverage, and complete documentation can be adjudicated by a rules engine in minutes. The hard part is everything that deviates from that: multi-party liability, partial coverage, missing documentation, or a claim that triggers a fraud flag. Industry research backs this up directly.
Datos Insights' 2026 research found that straight-through processing rates for fully automated claims remain under 10 percent across the industry, even with modern AI claims processing tools in production. That number is the single most important constraint an engineering team should design around. If 90% of claims still need some human checkpoint, the system architecture has to treat exception handling as the primary workflow, not a fallback.
This matters because teams that build for the 10% case first (full automation) end up retrofitting the exception logic under pressure, usually after a regulatory complaint or a bad payout decision has already happened.
What Does the Adjudication Engine Actually Need to Model?
An adjudication engine is the rules and decision layer that determines coverage applicability, calculates payout amounts, and routes claims to the correct next step (auto-pay, manual review, or denial). At minimum it needs to model:
Policy terms and coverage limits: what is covered, what is excluded, and the dollar ceiling per claim type.
Documentation completeness: repair estimates, medical bills, police reports, and photos, each with its own validation rules.
Liability determination: percentage fault assignment when more than one party is involved.
Fraud risk score: a separate signal that influences routing, not a binary accept/reject gate.
Regulatory timers: deadlines for written status updates, which under the NAIC's Unfair Property/Casualty Claims Settlement Practices Model Regulation require insurers to send a written explanation every 45 days if an investigation is incomplete.
The engineering mistake here is conflating these into one scoring function. Building on that point, the harder question is how these variables interact, because liability determination and fraud scoring are not independent. A claim with a 50/50 liability split and a mid-range fraud score needs a different review path than a 100%-liable claim with the same fraud score, and that branching logic has to be explicit in the schema, not inferred at runtime.
How Should Subrogation Logic Be Modeled Separately from Claims Adjudication?
Subrogation is the process by which an insurer recovers claim costs from a liable third party or their insurer after paying out a policyholder. It is a distinct workflow from adjudication, not a downstream step of it, and treating it as one is a common architecture mistake.
Subrogation needs its own data model because the variables are different in kind. Insurance industry standard practice requires subrogation logic to track liability percentage determinations, third-party identification (the at-fault party and their carrier), applicable policy limits on both sides, damage documentation for the recovery claim, and state-specific statutes of limitation that govern how long the insurer has to pursue recovery. None of these fields exist in a typical first-party claim record, and the lifecycle is longer: recovery can take months after the original claim has closed.
Engineering teams should model subrogation as a parallel process tied to the original claim by reference, with its own state machine (identified, demand sent, negotiating, recovered, written off) and its own SLA tracking. Recovery and subrogation efforts, including pursuing liable third parties, directly reduce the net financial impact of a claim on the insurer, which is why getting this workflow right has a measurable bottom-line effect. Unlike adjudication, there is no universal regulatory SLA for subrogation recovery; timelines are governed entirely by state-specific statutes of limitation, which vary by jurisdiction and claim type. That variation must be stored in a configurable rules table, not hardcoded logic, because a hardcoded 2-year limit will silently fail the first time a team expands into a state with a 3-year statute.
A related but distinct question is how subrogation connects to external data. Insurtech platforms typically integrate with third-party data providers such as Verisk's ClaimSearch, including its Subrogation Solutions and Liability Navigator modules, and LexisNexis Web Services, to pull industry-wide claims histories, verify third-party coverage, and support fraud detection during recovery. These integrations are not optional middleware; they are the mechanism by which an engineering team avoids pursuing recovery against a party who has no valid coverage, which wastes recovery-team time and damages customer relationships if pursued aggressively against the wrong party.
Where Does Fraud Detection Fit in the Automation Pipeline?
Insurance fraud detection software should function as a scoring and routing layer, not a decision-maker. A fraud score is a probability signal; treating it as a binary pass/fail gate produces false positives that delay legitimate payouts, and false negatives that let fraudulent claims through undetected.
Fraud detection in claims evaluation benefits from AI approaches that combine rules and machine learning for better accuracy and consistency. Some systems in active use show strong performance on well-structured datasets, but an engineering team should treat published performance figures as upper-bound results achieved on clean data, not a guarantee transferable to every claim type on day one. The practical design implication: fraud scores should feed into the same routing engine that handles liability and documentation checks, with a documented threshold for when a score triggers manual review versus automatic hold.
What Compliance and Audit Requirements Shape the Technical Architecture?
Stepping back from the scoring logic, a separate concern is regulatory defensibility. In the US, claims adjudication automation and subrogation are governed by state insurance codes built on the NAIC Unfair Claims Settlement Practices Act and related model regulations, along with the 2023 NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers. These require insurers to maintain written AI governance programs, audit trails, and documented oversight of third-party algorithms to prevent unfair discrimination or inaccurate settlements. In the EU, the GDPR and the AI Act impose parallel obligations around automated decision-making and transparency.
For an engineering team, this translates into concrete build requirements: every automated decision needs a logged rationale (which rules fired, which data points were used, what score triggered which route), not just a final outcome. This is a schema decision made at the start of the project, not a reporting feature added before an audit.
How Should an Insurtech Team Approach Custom Build vs. Off-the-Shelf Tools?
Custom insurance software development makes sense when the claim types, state mix, or subrogation complexity do not fit a generic claims management platform's data model. A general-purpose system built for single-state auto claims will struggle with multi-state liability splits or industry-specific documentation requirements (commercial property, specialty lines) without significant customization.
The decision point is whether the exception-handling logic (the 90%+ of claims that are not straight-through) is generic enough to configure in an off-the-shelf tool, or specific enough to require custom rules engines and integration work. Teams handling standard personal lines can often configure existing platforms. Teams in specialty, commercial, or multi-jurisdiction lines usually need custom-built adjudication and subrogation logic, with integration work against claims history and liability verification APIs as a core deliverable, not an add-on.
Frequently Asked Questions
What is straight-through processing in claims adjudication?
Straight-through processing (STP) is when a claim is fully adjudicated and paid without human intervention. Industry-wide, STP rates for fully automated claims remain under 10%, meaning most systems need exception-routing design with documented logic branches, not just automation for the easy cases.
How is subrogation different from claims adjudication?
Adjudication determines what the insurer pays the policyholder. Subrogation is the separate process of recovering that cost from a liable third party, with its own timeline, data fields, and statute-of-limitation constraints.
What happens if an automated claims system makes an error?
Errors in automated adjudication can lead to manual rework, regulatory fines for unfair claims practices, and customer churn from delayed or incorrect payouts, which is why audit trails and governance documentation are required, not optional.
Do insurers need to disclose how AI is used in claims decisions?
Under the 2023 NAIC Model Bulletin and similar frameworks like the EU AI Act, insurers must maintain governance programs and documentation around AI use in claims, with oversight extending to third-party algorithms.
What third-party data sources support subrogation recovery?
Common integrations include Verisk's ClaimSearch (with its Subrogation Solutions and Liability Navigator tools) and LexisNexis Web Services, used to verify third-party coverage and detect fraud patterns across industry claims histories.
Is there a required timeline for subrogation recovery?
No universal SLA exists. Recovery timelines are set by state-specific statutes of limitation, which vary by jurisdiction and claim type, so this logic needs to be configurable rather than hardcoded.
When should an insurtech company build custom claims adjudication software instead of buying a platform?
When claim types span multiple states, involve complex liability splits, or require subrogation logic that off-the-shelf tools do not model natively, custom development typically produces a more defensible and auditable system.
About 724SOFTWARE
724SOFTWARE is a Vietnam-based engineering partner with 200+ professionals, 58% at senior level, and delivery experience across 10+ countries, working with clients in Fintech, Digital Healthcare, and Enterprise ERP as a long-term technology partner rather than a one-off vendor. The company's capital markets work, including real-time risk engines, KYC/AML workflows, and ISO 8583 transaction processing, reflects the same engineering discipline that claims adjudication and subrogation systems require: auditable logic, regulatory-grade data models, and systems built to handle the exception path, not just the clean case.
724SOFTWARE is aligned with ISO 9001 and ISO 27001:2022 standards and GDPR compliant, and trains its engineering teams to use Claude and other generative AI tools as part of standard delivery practice. Teams can scale from 1 to 50+ pre-vetted engineers in 2 to 4 weeks, embedded directly in a client's workflow as a dedicated team or ODC.
If your team is scoping a claims adjudication or subrogation engineering project and needs delivery capacity that already understands regulated, high-stakes transaction systems, get in touch at https://724software.com.vn.
