How to Increase Your Straight-Through Processing Rate in Insurance

To increase your straight-through processing (STP) rate in insurance, measure the rate you actually have first, then work four levers in order. Measurement is Step 0. The four levers are the work:

• Structure the submission at intake so the file arrives machine-readable.
• Route each risk by complexity, auto-deciding the clean ones and escalating the rest.
• Calibrate the acceptance thresholds to your underwriting manual, and keep calibrating.
• Write every decision back to the core with its reasons attached.

None of this requires a core migration or a new policy administration system. The highest-leverage work runs as an external AI layer on top of the systems the insurer already operates.

Measure the rate you actually have first

You cannot raise a number you have not defined honestly, so the first move is a clean baseline measured per line of business. A single blended company-wide STP figure hides where the touchless flow actually breaks.

Be strict about the definition. A quote that a human silently rubber-stamps is not straight-through. If an underwriter opens the file, glances at it, and clicks approve, that decision touched a human and should not count. Measure the share of eligible risks that reach a quote, an automatic decline, or a bound policy with no manual intervention at all.

Then look at where the rate is lost. It is rarely lost at the decision itself. It sits upstream, in submissions that arrive as unstructured email and attachments a person has to rekey before any engine can act. Gartner estimates that companies lose 20% to 30% of working time organizing unstructured data, and in underwriting that loss lands squarely between intake and the first automated check. The cost compounds downstream. Deloitte has put the share of underwriter time spent on administrative tasks at roughly 40%, time that is neither risk judgment nor new business.

The rate is a commercial metric before it is an operational one. Capgemini has found that more than 60% of brokers choose an insurer based on response speed, so every submission that stalls in a manual queue is a quote the broker may place elsewhere before the insurer answers. A higher STP rate is a faster answer, and a faster answer wins more of the business the insurer already wants to write. That is the number the board cares about, not the automation percentage on its own.

A realistic target depends on the line. High-volume personal lines with structured data such as auto and home can run largely touchless. Complex commercial and specialty lines sit far lower, because each risk genuinely needs human judgment, and forcing a high STP rate there is how leakage enters the book. Set the target per line, not as one company-wide vanity number.

The STP levers, in the order that moves the rate

With a baseline in hand, four levers move the rate. Work them in order, because each one makes the next safer.

1. Structure the submission at intake. Intelligent document reading extracts the fields from email bodies, PDFs, and spreadsheets so the file is machine-readable before any decision runs. This is the single largest lever, because it attacks the upstream loss most STP programs overlook. It also feeds every step after it, so a clean intake raises the ceiling on everything downstream. See how a touchless underwriting pipeline handles this end to end.

1. Route by complexity, not by queue. Send the low-complexity risks that sit squarely inside appetite straight to an automated decision, and escalate the ambiguous or out-of-appetite ones to a human with the context already assembled. Routing by risk rather than by whoever is free is what lets the clean cases flow and keeps underwriters on the files that deserve their attention.

1. Calibrate the thresholds, and keep calibrating. Set the acceptance thresholds too wide and bad risks slip through as leakage. Set them too narrow and almost everything escalates to a human, which crushes the rate you were trying to raise. The thresholds are not a one-time configuration. Calibration is a loop you re-tune as claims experience and conversion data come back, tightening where losses appear and loosening where the model has earned confidence.

1. Close the loop with a decision that writes itself back. A decision that reaches a quote but then waits in a queue for someone to key it into the policy system is not straight-through. The rate only counts when the outcome, the price, and the reasoning post back to the system of record automatically. This is the difference between an STP rate that looks good in a pilot and one that holds up once real volume runs through it every day.

Keep the AI layer on top of the core, never in its place

The instinct to raise the STP rate by rebuilding the core is what stalls most programs. BCG has found that about 70% of insurers cannot execute the innovation they want because of IT limitations. A multi-year core migration puts the STP roadmap behind the exact constraint that blocked it in the first place.

The faster path is an external intelligence layer. It reads the submission, enriches it, scores the risk against appetite, prices it, and returns a decision, then writes that decision back to the system of record. The AI layer sits on top of the core and never replaces it. Nothing in the policy administration stack has to move, and the insurer's IT team does not have to run a migration to watch the rate climb. This is the practical difference between an AI layer and a core or RPA rebuild.

Governance is what makes a high STP rate defensible

A high STP rate that no one can explain is a liability, not a win. Every automated decision has to carry the reason it was made, the data it used, and the appetite rule it matched. Explainability and a full audit trail are not compliance theater in this practice. They are what lets a regulator, a reinsurer, or an internal auditor sign off on an automated book.

In Brazil this is concrete. SUSEP expects insurers to govern and justify automated underwriting decisions, and the LGPD governs the personal data those decisions touch. An STP flow built to raise the rate has to be built to be audited at the same time, with data encrypted at every step and a decision trail a human can read. Speed and compliance are not opposites here. The same auditable decision trail that satisfies the regulator is what lets the insurer widen the auto-decidable band with confidence.

How WIR raises the STP rate

WIR is the AI layer for insurance, an external platform that automates the quotation and underwriting journey according to the insurer's own risk-acceptance policy. It is 100% external, with no load on the insurer's IT and no core migration.

The platform runs the full flow. Multichannel intake with automatic validation, intelligent document reading, broker enrichment and context, a risk and fraud engine with Machine Learning calibrated to the insurer's appetite and underwriting manual, dynamic pricing, and a final decision that quotes, declines, or escalates to a human, always with an explanation and always written back to the core with its audit trail. Underwriter Intelligence handles routing by appetite and exposure so the clean risks flow and the complex ones reach a person with the context ready.

Each stage maps to one of the four levers. Intelligent document reading structures the submission at intake, routing by appetite and exposure sends the clean risks to an automated decision, the risk engine holds the thresholds calibrated to the underwriting manual, and the write-back closes the loop into the core with the reasoning intact. The insurer keeps its policy system and its underwriting authority. The AI layer adds the intelligence and the speed on top.

WIR is currently in a proof-of-concept with a global insurer in the Transport line. The AI layer for insurance sits on top of the systems the insurer already runs, never in their place. To see how the layer connects without a migration, read how WIR integrates an AI layer with the insurer's core.