Compliance monitoring · Production

Shift Technology detects underwriting fraud for top 5 US P&C insurer, projecting $30M+ in annual mitigation

The problem

A top 5 U.S. P&C insurer needed to detect fraud and misrepresentation in new auto policies during the underwriting 'free look' period without expanding staff, while ghost broker fraud networks were generating claims with a 500% average loss ratio threatening customer satisfaction and brand reputation.

Workflow diagram · grounded in source
1
Free-look period analysis trigger
trigger
“daily analysis during the new business "free look" period was critical”
2
Misrepresentation detection
ai_action
“Risk detection algorithms specifically designed to analyze new policies in order to catch fraud and misrepresentation during the "free look" period”
3
Ghost broker network detection
ai_action
“Shift implemented powerful network analysis AI to uncover patterns of seemingly "normal" policies connected to ghost brokers”
4
Entity resolution across policy and claims
ai_action
“Shift's entity resolution AI uncovered policyholders using fraudulent information to hide prior claims history”
5
Underwriting team review
human_review
“100% explainability and complete audit trail, so that the insurer could stop fraud with their existing Underwriting team”
6
Risk disposition decision
routing
“the insurer could adjust the risk tier, cancel fraudulent policies, or intensify account monitoring”
Reported outcome

Shift's Underwriting Risk Detection generated more than $15 per new policy in incremental prevented losses, projecting over $30M USD annually in underwriting mitigation, with a 40% impact rate on policy alerts and 500% average fraud network loss ratios avoided — all while maintaining existing Underwriting staff levels.

Reported metrics
Incremental prevented losses per new policymore than $15 for every new policy
Annual projected underwriting risk mitigationmore than $30M USD
Fraud network loss ratio avoided500%
Policy alert impact rate40%
Show all 5 reported metrics
incremental prevented losses per new policymore than $15 for every new policy
annual projected underwriting risk mitigationmore than $30M USD
fraud network loss ratio avoided500%
policy alert impact rate40%
proof of concept policy volume2.8 million auto policies
Reported stack
Shift Claims Fraud Detectionnetwork analysis AIentity resolution AI
Source
https://www.shift-technology.com/resources/case-studies/top-5-us-auto-underwriting-policy-fraud-detection
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Shift's Underwriting Risk Detection generated more than $15 per new policy in incremental prevented losses, projecting over $30M USD annually in underwriting mitigation, with a 40% impact rate on policy alerts and 500…

What tools did this team use?

Shift Claims Fraud Detection, network analysis AI, entity resolution AI.

What results were reported?

Incremental prevented losses per new policy: more than $15 for every new policy; Annual projected underwriting risk mitigation: more than $30M USD; Fraud network loss ratio avoided: 500%; Policy alert impact rate: 40% (source-reported, not independently verified).

How is this compliance monitoring AI workflow structured?

Free-look period analysis trigger → Misrepresentation detection → Ghost broker network detection → Entity resolution across policy and claims → Underwriting team review → Risk disposition decision.