Fraud in auto insurance is often treated as a downstream problem, investigated after a claim is filed. But the real opportunity lies much earlier.
In this episode of A CGI Conversation, we explore how insurers are shifting from reactive detection to proactive, lifecycle-wide prevention. From underwriting and pricing to claims and governance, this discussion examines how data, analytics, and better validation are enabling more targeted and effective fraud strategies.
This episode is designed for insurance leaders across P&C, including underwriting, pricing, fraud, and claims. It is particularly relevant for CIOs, CTOs, Chief Underwriting Officers, heads of fraud, analytics leaders, and transformation executives.
- Chapter 1: Introduction and the shift to prevention
-
Introduction
Derek Marinos:
Today, we’re looking at how the auto insurance industry approaches fraud and why prevention should start earlier. Too often, fraud is treated as a claims problem, investigated after the fact, even though risk can originate at application, policy changes, and other points across the lifecycle.
In this episode, we explore how insurers are shifting from downstream detection to a more proactive, lifecycle-wide approach to prevention, supported by data and analytics. Joining me are CGI experts Santiago Vilasis and Raman Sharma.
- Chapter 2: Where fraud risk begins and its impact on pricing
-
Santiago Vilasis:
The biggest distortions typically originate at application and underwriting, long before a claim is filed. Small misrepresentations, such as garaging location, undisclosed drivers, or usage patterns, may seem minor individually, but they directly affect pricing accuracy and risk segmentation.
When these misstatements scale across thousands of policies, they create premium leakage and misaligned loss ratios. By the time a claim occurs, the economics of the policy are already set. That’s why prevention at underwriting is so important. It protects pricing integrity and reduces downstream friction.
- Chapter 3: Risk concentration and smarter controls
-
Raman Sharma:
Losses are typically concentrated in a small subset of policies, so applying broad manual reviews across the entire portfolio is inefficient and misaligned with the actual risk profile.
A more effective approach is to adopt risk-based controls that focus attention where it matters most. By identifying high-risk policies using data and analytics, insurers can apply enhanced scrutiny to those segments, while allowing lower-risk policies to move through streamlined workflows.
This improves both loss outcomes and operational efficiency by reducing friction for most policies while ensuring higher-risk cases receive appropriate oversight.
- Chapter 4: Product complexity and validation at quote and bind
-
Santiago Vilasis:
As products become more modular, with different deductibles and optional coverages, the number of decision points at quote and bind increases. Each of these points creates opportunities for error or misrepresentation.
Flexibility benefits consumers, but it also requires stronger validation to ensure selections align with the actual risk profile. A practical solution is to bring additional granularity at underwriting, using historical data to build more precise models. This is where data, analytics, and AI can be used.
Raman Sharma:
In a complex environment, insurers need real-time validation at both quote and bind. At the quote stage, the focus is on data integrity and plausibility checks. At bind, controls should be stricter since exposure is about to attach.
In Canada, evolving regulations such as Ontario’s 2026 reforms, which make some accident benefits optional, are increasing product complexity and placing more responsibility on consumers to make informed choices.
These checks should be risk-based and automated wherever possible. Low-risk, consistent policies can flow through quickly, while higher-risk or inconsistent submissions are flagged for review. The goal is to create a layered validation framework at the point of entry, catching issues early and reducing the need for broader, friction-heavy controls later in the policy lifecycle.
- Chapter 5: Detecting patterns and connecting signals across the lifecycle
-
Raman Sharma:
Organized networks tend to follow where margins are highest and controls are weakest. As benefit structures evolve, coordinated activity will likely shift toward areas with richer reimbursement, greater ambiguity, or newer and less mature controls.
For example, if traditional areas like staged accidents or rehab claims become more tightly controlled, activity may move toward ancillary benefits such as physiotherapy or mental health services.
From an early warning perspective, insurers should monitor pattern-level signals rather than isolated events. This includes clustering of claims around specific providers or intermediaries, sudden shifts in utilization, spikes in certain treatments, and network linkages such as shared addresses, phone numbers, or referral patterns.
Operationally, this requires a combination of network analytics, real-time monitoring, and feedback loops into underwriting so emerging risks can be identified early and pricing controls can be adjusted before losses scale.
Santiago Vilasis:
Fraud signals rarely exist in isolation. For example, a suspicious provider pattern may connect to a group of policies that already showed anomalies at underwriting.
When insurers connect signals from application data to underwriting validation and early claims activity, they begin to see networks rather than individual events. That’s when patterns that were previously invisible within siloed functions become clear.
- Chapter 6: Delivering impact and operationalizing analytics
-
Santiago Vilasis:
An integration-light approach focuses on augmenting existing workflows rather than rebuilding core systems. Instead of large-scale transformations, analytics services can sit alongside underwriting platforms, providing risk signals, anomaly scores, or validation prompts at key decision points.
This approach allows insurers to start small, validate impact quickly, and expand progressively. The advantage is speed to value. Organizations can begin improving underwriting decisions in months rather than years.
Raman Sharma:
Upstream validation has a domino effect across the policy lifecycle. One of the most immediate benefits is reduced premium leakage. By catching inconsistencies early, such as garaging misrepresentation, undeclared drivers, or incorrect vehicle use, insurers can ensure premiums are aligned with actual risk from day one.
There is also a longer-term impact on pricing and underwriting alignment. When input data is cleaner at bind, pricing models perform better, segmentation becomes more accurate, and insurers avoid systematically underpricing certain risk cohorts. This creates a compounding improvement in loss ratios.
In addition, there are meaningful operational savings. Policies validated upfront generate fewer mid-term corrections, fewer claim-time disputes, and less manual investigation. This reduces handling costs while improving the overall customer experience.
Santiago Vilasis:
A key metric for executives is premium integrity, which reflects the alignment between priced risk and actual exposure. Executives can monitor indicators such as underwriting validation hit rates, reductions in premium leakage, and early claim anomaly rates. When these metrics improve, it signals that risks are being identified earlier in the lifecycle before they turn into downstream losses.
Santiago Vilasis:
When operationalizing fraud and risk models, a common pitfall is treating analytics as a standalone model rather than an operational capability. Models only deliver value when they are embedded into decision processes with clear actions attached.
A second pitfall is over-engineering the solution. Insurers sometimes pursue complex models when simpler patterns of detection and targeted validation can deliver more immediate impact. Successful prevention programs should focus on practical deployment, measurable outcomes, and continuous refinement.
- Chapter 7: Governance and priorities for moving to prevention
-
Raman Sharma:
As analytics move earlier into underwriting and decision-making, insurers need a governance framework that ensures models are not only accurate, but also fair, explainable, and auditable.
Data governance is foundational. Inputs must be high quality, relevant, and free from proxies that could introduce unintended bias. Clear data lineage and documentation are essential to ensure transparency.
Model governance and validation are equally critical. This includes bias testing across protected groups, independent validation, and periodic retesting to account for model drift or emerging bias over time.
At the decision-making level, insurers also need guardrails around how model outputs are used. Human oversight should remain in place for high-impact decisions, with defined thresholds and override policies to prevent over-reliance on automated outputs.
Finally, auditability and regulatory alignment are essential. Every decision should be traceable, linking inputs, model versions, and outputs so insurers can demonstrate consistency and defend outcomes.
Santiago Vilasis:
A key priority is to make fraud prevention a cross-lifecycle strategy rather than a function limited to claims. This requires connecting insights across underwriting, policy management, and claims to identify risks earlier and act before losses materialize.
Raman Sharma:
Many of the underlying problems are already well understood. What has changed is the ability to address them using modern tools and analytics. The focus should be on applying these capabilities to problems that were previously considered too complex to solve.
Starting with targeted proofs of concept can help organizations demonstrate value quickly while building the right operational and governance frameworks alongside them.
Santiago Vilasis:
A practical first step is to introduce targeted validation at quote and bind. Even a small set of automated checks focused on high-impact risk signals can improve pricing accuracy and reduce downstream friction.
