How Carriers Can Reduce Fraud With ProActive Risk Engagement
In today’s tech-driven world, fraud has become more complex and sophisticated. Fraudsters are using advanced digital tools to develop schemes and fraudulent claims.
Luckily, modern insurance carriers have tools of their own. However, it is no longer enough to respond to threats. AI detection and comprehensive analytics are helping to identify and reduce fraud across the entire claims experience, not only after fraud occurs.
At HOMEE, we work with carriers to integrate ProActive risk engagement. By moving from reactive to preventive models, carriers can better position themselves to reduce fraudulent activity.
From Reactive to Proactive: The New Fraud Protection Strategy
While fraud detection is used to identify and investigate suspicious claims, fraud prevention is a proactive approach that helps carriers protect themselves before fraud occurs.
AI-powered models analyze data at first notice of loss, identifying risk indicators. Machine learning continuously adapts to fraud patterns, enabling carriers to stay ahead of fraud schemes.
By embedding AI and advanced analytics directly into claims workflows, carriers move from reactive investigations to proactive protection. The result is faster decision-making, reduced fraud leakage, lower operational costs, and a more seamless experience for legitimate customers.
HOMEE’s Approach: ProActive Risk Engagement Before the Claim
HOMEE's ProActive risk engagement enables pre-claim inspections, reducing fraud, facilitating accurate underwriting, and allowing for faster triage.
We use geo-intelligence to alert carriers when events like wind, hail, and flood impact high PIF areas. Real-time intelligence, such as automated scope recommendations from field data, aerial imagery, and inspection insights, provides information on conditions such as previous roof damage, excessive debris, or any other pre-existing issues before teams are deployed on site.
This allows carriers to validate claims and detect potential fraud by comparing post-event imagery with previous data to determine which damages occurred after a policy was in effect.
Myth vs. Reality: Fraud Drivers Claims Teams Must Track in 2026
Several outdated beliefs still shape fraud strategies. By separating myth from reality, claims teams can set themselves up for success in 2026.
Myth 1: Fraud Only Happens After a Claim Is Filed
Reality: Fraud can occur at any point in the claims journey, including the binding period well before a claim is filed.
This includes:
- Application fraud: Falsifying information and padding, staging assets, and using fake identities to purchase policies
- Policy manipulation: Coverage stacking and suspicious cancellations or reinstatements
- Pre-loss staging: Intentional risk exposure and organizing accidents, vehicle damage, or property destruction ahead of a planned claim
- Identity & social engineering fraud: Phishing and acquiring confidential information to exploit policy coverage later
This emphasizes the importance of safeguarding against fraud throughout the entire claims process.
Myth 2: Fraud Is Primarily Opportunistic and Low-Tech
Reality: With advancements in technology, fraud has become more organized and sophisticated.
Today, carriers are seeing coordinated activity, such as fraud rings and professional claimants who:
- Exploit digital intake
- Use stolen or fake identities to create fraudulent insurance policies
- Submit multiple claims across different carriers for the same loss
Fraud is no longer limited to exaggerating damages or single-event attacks. It has evolved into complex fraud ecosystems. Carriers should not underestimate the advanced tactics of fraudsters. By taking these risks into account, insurers can strengthen their defenses and implement more effective prevention strategies.
Myth 3: More Data Automatically Means Better Fraud Detection
Reality: Having more data usually enhances fraud detection, but it doesn’t automatically ensure better outcomes.
The effectiveness of the data heavily depends on its quality and how it is used. In this, data informs processes, but people transform that data into actionable insights.
For example, simply collecting thousands of claims without verifying their accuracy or relevance won’t improve detection.
However, when high-quality data, such as verified claim histories, customer behavior patterns, and past fraud indicators, is analyzed using advanced analytics or machine learning, it can reveal suspicious patterns that help investigators make informed decisions.
Myth 4: Deepfakes Are a Future Threat
Reality: Fraud sophistication has evolved through the use of deepfakes and synthetic identities. Although this tech may seem futuristic, it’s already being used today.
Deepfakes are realistic, fabricated personas, documents, and video footage used to impersonate individuals and deceive insurers. By acknowledging the current threat posed by these technologies, carriers can protect against them using AI detection software and identity verification.
Tool Spotlight: AI-Powered Fraud Detection in Action
AI-powered fraud detection tools bring intelligence directly into the claims workflow. At intake and throughout the claims lifecycle, AI models analyze data across policies, claims history, and behaviors to detect suspicious patterns and activity.
Key AI-powered fraud detection tools include:
- Real-time risk scoring to prioritize claims from the moment they’re reported, with coverage determination recommendations
- Pattern, network, and behavior analysis to expose organized fraud rings and repeat actors
- Natural language processing (NLP) that analyzes notes, emails, and documents for inconsistencies or unusual language
- AI technology, such as computer vision, that interprets submitted video footage and photographs to verify authenticity
- Automated decisioning that routes high-risk claims for investigation while fast-tracking low-risk ones
When put into action, these tools help investigators prevent and detect fraud with greater accuracy and efficiency.
Bolster Your Defenses with HOMEE
Although fraud has become more sophisticated and complex, insurers can protect themselves by developing preventive fraud models.
HOMEE’s ProActive risk engagement uses AI automation, real-time intelligence, and predictive analytics to identify suspicious activity and uncover hidden patterns.
Our goal is to help you develop a smarter, faster, and more efficient fraud strategy that minimizes losses and enhances the customer experience by leveraging AI-driven insights alongside human judgment for complex decisions.
To learn more about reducing fraud cycle times with advanced analytics, read this white paper by WNS Decision Point.
Contact HOMEE today for a ProActive fraud defense and detection model.