Around the world, 74% of surveyed insurers have reported steady or spikes in insurance frauds rates. This growing threat has financial, reputational, and compliance implications for the industry. Chief Data Officers (CDOs) and Chief Information Officers (CIOs) aim to counter it with advanced solutions that detect suspicious activity faster and more efficiently. Among these, artificial intelligence (AI) emerges as a powerful tool.
In the U.S., for example, 81% of surveyed insurers plan to increase their AI spending within the next 12 months. Within 3 to 5 years, it’s projected that 20% of IT budgets will be allocated to AI. Thus, the rapid adoption reflects a broader shift toward modernizing insurance frauds prevention efforts.
In this article, we’ll explore the challenges and opportunities insurers face.
Combating Insurance Frauds: Table of Contents
AI and Machine Learning in Frauds Detection—and Proving ROI
With clean, consolidated data in place, insurers are in a better position to fully leverage AI. In fact, Deloitte has identified several key areas AI can reduce insurance frauds across the value chain:
- Text analytics: Natural language processing (NLP) can analyze claims forms, emails, and social media posts to identify suspicious keywords or entities.
- Audio-image-video analysis: Speech recognition catches deception and image manipulation, whereas video analysis reveals staged damage.
- Geospatial analysis: Drone footage and satellite images provide visual confirmation of claim-related damage and its location.
- Internet of Things (IoT) data: Telematics or smart home sensors offer real-time data for reconstructing accidents and verifying event timelines.
- Simulation models: AI-based simulations can simulate the typical behavior of repair shops, medical providers, or claimants, revealing deviations from norms.
Each of these capabilities builds on solid data foundations, highlighting the need for strong governance and centralized data management before AI can truly deliver.
Major insurers are already seeing results:
- Allianz developed a machine-learning tool called Incognito, which flags fraudulent claims and sends them to fraud experts for review. Since implementation, the flagged claims have saved £1.7 million so far.
- Progressive Insurance AI features a fraud detection algorithm, helping to reduce non-productive spending by 14%.
- AXA Switzerland partnered with Swift to improve fraud detection without sacrificing processing times. In fact, it can detect anomalies in first notice of loss (FNOL) data. The company has reportedly stopped €12m in fraud.
But before insurers can replicate these AI successes at scale, they must overcome the data challenges that limit fraud detection.
Insurance Frauds and How Data Challenges Can Contribute
Various types of insurance frauds exist. Claims fraud, application fraud, and premium fraud, to name a few. As fraudsters grow more sophisticated and data becomes more complex, so do the challenges of uncovering suspicious behavior.
Prevention is always better than a cure—and while insurers can’t control fraudsters, they can take control of their own data and systems.
That starts with tackling critical data hurdles, including:
- High data volume and complexity across multiple insurance lines. Each line has unique data structures and processes, making consolidation and holistic analysis difficult.
- Siloed systems create blind spots. Fragmentation between lines (for example, Property & Casualty vs. Life) can conceal suspicious activity.
- Fragmented policyholder and claims data are stored in legacy systems. This limits data accessibility and accuracy.
- Difficulty integrating reliable third-party data sources. External data (e.g., credit reports, social media signals, or fraud watch lists) is crucial for comprehensive analysis.
- Policy-centric administration systems that limit holistic customer views. Systems based on individual policies rather than customers make it difficult to identify connections across policies, household members, or related entities.
- Rapid growth of digital channels. Online apps and mobile claims improve customer convenience, but they also open new fraud avenues, such as synthetic identities or automated attacks.
All of these issues make it harder to see the full picture needed to detect early signs of fraud. So first things first: insurance companies need to fix their data in order to make AI work for them.
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MDM grants AI models the high-quality, integrated data they need to function accurately and deliver value. Infoverity helps insurers implement scalable MDM, which is a must for modern businesses.
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The Role of Data Governance and Regulatory Compliance
Addressing data fragmentation and complexity improves fraud detection—but it’s also essential for meeting regulatory obligations. Regulations require insurers to maintain control and visibility over how data is collected, used, shared, and stored:
- GDPR and CCPA require consent and data minimization, as well as usage transparency.
- NAIC Model Laws call for secure, traceable records and timely breach reporting.
- Auditability provides clear data trails that are available and accessible.
- Retention and deletion policies must follow strict legal and privacy timelines.
- Cross-border transfers demand lawful frameworks and documented controls.
These requirements make robust data governance a necessity. The good news is that as insurers develop frameworks to meet these standards, they also address the very data challenges that impede fraud detection.
For instance, Infoverity helps insurers implement scalable data governance frameworks that standardize definitions, improve data quality, and ensure enterprise-wide traceability. Thus, the results are accountability and trust, plus priming organizations for more data-driven initiatives like AI.
For example, data governance also plays a central role in managing ethical risks in AI-driven fraud models. Transparency, oversight, and explainability can reduce systemic bias for insurers. This matters a great deal in an industry where systemic bias can lead to serious legal and reputational consequences.
In 2022, State Farm’s fraud detection system was accused of disproportionately making things more difficult for Black homeowners to get an approved claim, despite similar claim patterns of white policyholders.
So, before adopting AI, insurers must first guarantee their underlying data is clean, consistent, and governed. And that starts with Master Data Management (MDM).
Establishing a Single Source of Truth with MDM
Master Data Management (MDM) initiatives eliminate data silos and inconsistencies by creating a single, consistent, and reliable source of truth across the organization. For insurers, this means having one trusted view of each customer, regardless of the product line, channel, or data source.
Therefore, it strengthens fraud detection by:
- Providing a 360-degree view of policyholders across business units.
- Streamlining complex insurance hierarchies
- Analyzing multiple or inconsistent customer records.
- Recognizing family-level relationships
MDM grants AI models the high-quality, integrated data they need to function accurately and deliver value. However, without creating this foundation, fraud detection efforts will remain reactive and fragmented, with blind spots.
Infoverity helps insurers implement scalable MDM, which is a must for modern businesses. In fact, this alone can improve claims accuracy and reduce fraudulent activities. It’s a great start to curbing fraud. Learn how we can help here.
