Insurance fraud is an endemic drain on the industry, conservatively costing over $40 billion per year in non-health insurance fraud in the US alone according to FBI estimates. Factoring in exponential health insurance fraud, the Coalition Against Insurance Fraud puts the total annual cost impact at over $100 billion. For context, that‘s more than the GDP of over 150 countries. This rampant fraud taxes insurers and customers alike through higher premiums and diminished benefits.
As a veteran data analyst in the insurtech space, I‘ve seen firsthand how fraudsters have continuously evolved to stay a step ahead of insurance companies. Simple fake claims have given way to complex schemes like staged accidents, misleading repairs, and professional scam rings. Insurers are now turning to advanced technologies to try and tip the scales back in their favor.
The following five technologies demonstrate particular promise in improving fraud detection while also transforming wider insurance operations:
1. Discover Policyholder Behavioral Patterns with Advanced Analytics
Advanced analytics leverages techniques like machine learning and AI to uncover actionable insights. Both supervised and unsupervised machine learning models provide vital capabilities for fraud detection.
Supervised models can be trained on labelled data sets of known legitimate and fraudulent claims. By analyzing thousands of claim features, a supervised model learns to recognize suspicious patterns such as unusual claim sizes, frequencies, locations, repair shops, etc. New claims can be scored against these patterns to identify likely fraud.
Unsupervised models deliver value by detecting novel fraud tactics that lack historical examples to train on. Anomaly detection algorithms can spotlight strange data points among claims that deviate from normal activities. These anomalous outliers often represent emerging fraud schemes.
Another advanced analytics technique gaining traction is behavioral analytics based on policyholder digital interactions. Tracking website navigation, clicks, mobile app usage, locations and other actions provides a baseline for normal customer habits. Significant deviations could flag potential fraud, like drastically different locations or browsing patterns when filing claims.
Integrating these advanced analytics techniques allows insurers to stay on top of both established fraud tactics and new schemes as they arise. USAA reported saving $15 million annually using SAS‘s machine learning models for fraud management. With so much room for improvement, that number could rise dramatically as insurers expand their analytics capabilities.
2. Speed Up Claims Processing with Chatbots
Natural language processing (NLP) chatbots are allowing insurers to completely reinvent the traditionally arduous and labor-intensive claims process. Policyholders can immediately report losses to a chatbot through conversational dialog. The chatbot intelligently guides customers through the first notice of loss (FNOL) process, gathering photos, videos and other critical claim details.
This represents a seismic shift, accelerating FNOL from days or weeks to minutes while eliminating frustrated call center interactions. According to research from Juniper, incorporating chatbots improves customer satisfaction by 33% while lowering claim-processing costs by 30%.
Speeding up claims submission also directly deters fraud by removing the lag time fraudsters exploit to falsify information and coordin ate their stories. Collecting multimedia evidence instantly preserves the truth before it can be distorted.
The UK insurer Direct Line saw a 70% drop in suspicious claims after deploying AI-assisted FNOL chatbots. As chatbots continue improving through advances in NLP and conversations analytics, these fraud prevention benefits will only grow.
3. Assess Cost of Loss with Computer Vision
Computer vision applies deep learning techniques to analyze and derive meaning from visual data. For insurers, this unlocks game-changing potential to automate and enhance critical visual components of claims like assessing damage severity.
Computer vision algorithms can extract actionable insights directly from claim photos and videos that otherwise would require manual human review by claims adjusters and appraisers. Assessing damage virtually through computer vision preserves objective visual evidence and ensures accurate, consistent payouts.
By flagging severe discrepancies between computer vision assessed damage costs and claimed amounts, insurers can target inflated claims for further investigation. Computer vision pioneer Tractable reports reducing automobile claims processing costs by up to 50% using AI visual intelligence. Their technology is already deployed by leading insurers globally.
4. Notify Claims Immediately with IoT
The rise of IoT connected devices is enabling a paradigm shift in insurance towards real-time proactive claims notification. Sensor data from smart homes, vehicles, commercial equipment and more can automatically alert insurers to accidents, breakdowns, natural disasters and other covered losses as they occur.
For example, IoT telematics and crash sensors in policyholders‘ cars can trigger instant claims notification without requiring involvement from the driver. This massive reduction in notification lag similarly reduces fraud opportunities for both established tactics like staged accidents and emerging drone and GPS spoofing threats.
IoT sensor data intake also equips insurers with concrete data to validate claims against. Location, weather, operating conditions and other IoT telemetry can quickly expose discrepancies with a policyholder‘s version of events. This acts as a strong fraud deterrent and allows insurers to approach claims from a position of strength.
Progressive Insurance saw auto claims fraud drop by 10% after implementing their Snapshot IoT telematics program. As IoT continues proliferating, expect those savings to dramatically increase across all insurance lines.
5. Prevent Double Dipping Fraud with Blockchain
Blockchain provides immutable, decentralized tracking of transactions on distributed ledgers. This makes it ideal for preventing "double dipping" fraud schemes where criminals file duplicate claims with multiple insurers.
Attempting to submit the same claim transaction to different blockchain ledgers would be instantly detected and rejected by the network‘s consensus rules. At most, only one instance of the claim transaction can be validated and paid.
Blockchain also ensures claims can‘t be anonymously withdrawn and resubmitted like with traditional centralized databases. Every action is permanenly logged on chain for full transparency and accountability.
The open standard DTL Claim Blockchain already has major backers across the industry after early tests showed it could deter 90% of double dipping attempts. If widely adopted, blockchain could save insurers up to $200 million annually in double dipping fraud.
Insurance fraud will continue evolving as fraudsters dream up new ways to game the system. To stay competitive, insurers must become more agile and innovative in their countermeasures. The technologies highlighted in this article represent the vanguard transforming insurance fraud detection today.
Expanding advanced analytics coverage, accelerating automation, maximizing IoT connectivity, and capitalizing on emerging technologies like computer vision and blockchain should be top priorities for insurers worldwide as we head into 2023. Companies that master these technologies will gain invaluable advantages across fraud prevention, customer experience, claims processing efficiency and more.
While daunting challenges remain, the future of insurance fraud detection is ultimately bright. Insurers at last have momentum and potent new tools to counter this plague on the industry. Though the arms race is far from over, the technological tide has turned in favor of insurers and their customers.