GLI insurance is essential to protect landlords against bad payers. Insurance companies are involved in the selection of tenants and seek to reject high-risk files. However, between falsified files, document fraud and identity theft, the task is often complex. Let's explore how artificial intelligence can detect these frauds and identify bad payers.
Why detect GLI insurance fraud?
Impact on GLI insurance and homeowners
Fraud in the GLI sector has repercussions for both insurance companies and homeowners.
For insurers
Fraud leads to substantial financial losses. They increase operating costs and reduce profitability. These losses can translate into higher premiums for policyholders. This makes insurance policies less attractive to owners.
For homeowners
Fraud can mean delayed or denied benefits. This further complicates their financial situation. In the event of non-payment of rent, landlords rely on GLI insurance to cover losses and honor the lease. When fraud is discovered after the fact, this can delay payments or even lead to cancellation of coverage.
Challenges in detecting GLI insurance fraud
Detecting fraud in GLI insurance presents several challenges.
Gaining in efficiency
Traditionally, insurance companies relied on manual methods. Agents would spot-check files to detect anomalies. These methods were not only time-consuming, but also prone to human error. Document fraud and identity theft were detected too late, or went undetected.
Increase control accuracy
Fraudsters are becoming increasingly sophisticated. They use advanced techniques to avoid detection. This forces insurance companies to constantly update their verification processes. The use of AI enables continuous improvement in the background.
How AI improves GLI insurance fraud detection?
Identifying suspicious behavior and fraud patterns
AI performs well in identifying suspicious behavior and fraud patterns. It can detect anomalies and deviant behavior, sometimes indicative of fraud.
For example, identify recurring patterns in GLI applications that have previously led to non-payments. Look for inconsistencies in the information provided by the tenant, or similarities with previous frauds.
Thanks to this ability to identify subtle patterns, insurance companies can target high-risk files more effectively. These high-risk files can be re-checked by an expert, while low-risk files can benefit from automatic validation.
Historical and real-time data analysis
AI enables simultaneous analysis of historical and real-time data. This creates a more robust and responsive system for detecting GLI insurance fraud.
Historical data
Thanks to them, AI systems can learn from past fraud patterns. They adjust their models to improve the accuracy of their predictions.
Real time
Real-time analysis enables continuous monitoring of new GLI applications and tenant behavior. This means that fraud can be detected and dealt with immediately. That is, before they cause significant losses.
Automation of verification and validation processes
Traditionally, file verification and validation processes require significant human intervention. This can be slow and error-prone.
With AI, these verification tasks can be automated:
- Extraction of textual information from documents
- Visual recognition to identify document type
- Automated detection of intra- and inter-documentary inconsistencies
- Authenticity checks and detection of signs of falsification using previously established models
Understanding the technologies used to detect fraud
Machine learning
Understanding machine learning
Machine learning is at the heart of GLI insurance fraud detection. This technology uses algorithms to analyze huge volumes of data and identify patterns or anomalies indicating possible fraud. Find out more about machine learning in our dedicated article.
How is it used?
Thanks to machine learning, systems can learn and improve over time. This increases their accuracy.
For example, algorithms analyze transaction histories and past tenant behavior. They can spot suspicious trends and flag potentially fraudulent files before they cause losses.
Natural language processing (NLP)
What is NLP?
Natural language processing (NLP) enables AI systems to understand and interpret human language. In fraud detection, NLP is used to analyze documents submitted by tenants. These documents include rental contracts, pay slips and bank statements.
Using NLP
This technology can identify inconsistencies and anomalies in text documents. Whether it's contradictory information or language patterns that don't match authentic documents. NLP can also be used to monitor written communications, detecting signs of fraud in email or chat exchanges.
Predictive analytics and Big Data
Predictive analytics enable insurance companies to identify the likelihood of future fraud. These tools aggregate and analyze data from a variety of sources: demographics, payment history, online behavior. By cross-referencing information, they can assess the risk associated with each GLI application.
This ability to rapidly process and analyze huge volumes of data enables faster, more accurate decision-making. This in turn reduces the number of undetected frauds.
Neural networks and Deep Learning
Neural networks and Deep Learning are sub-domains of machine learning.
Neural networks
Convolutional neural networks can process unstructured data. These include photos of documents, ID cards, etc., and videos. This is particularly useful for verifying the authenticity of identity documents.
Deep learning
Deep Learning can learn complex and subtle fraud characteristics. It therefore improves the ability of AI systems to identify suspicious behavior. Behaviors that may have escaped traditional methods.
How do I set up AI detection for GLI insurance?
Collecting and preparing GLI insurance data
The first step in integrating AI into GLI insurance fraud detection is data collection and preparation. This involves gathering information from a variety of sources. These include tenant payment histories, rental records, credit reports and other relevant data.
Once collected, this data must be cleaned to eliminate errors and inconsistencies. It must also be formatted to make it compatible with AI algorithms. This preparation phase is crucial, as data quality directly influences the accuracy and efficiency of AI models.
Training AI models on GLI insurance-specific datasets
Once the data has been prepared, the next step is to train the AI models on specific datasets. Machine learning algorithms are trained on historical fraud and rental behavior data. This enables them to learn to identify the characteristics and patterns associated with fraud.
This training process often requires adjustments and iterations to optimize model performance. The use of reliable data specific to the GLI insurance sector is particularly important for developing accurate and appropriate models.
Integration of AI systems with existing processes
Once the models have been trained, it is essential to integrate the AI systems with the company's existing processes. This may involve integrating AI algorithms into insurance claims management platforms. But also in tenant verification systems and claims management tools.
The aim is for AI models to be able to analyze new applications and tenant data in real time. They will then provide alerts and recommendations to risk managers and fraud analysts. Seamless integration ensures that the benefits of AI are fully realized without disrupting current operations.
Monitoring and continuous improvement of models
Finally, once AI systems have been deployed, ongoing monitoring and model improvement are required. Fraudulent behavior evolves over time. AI models need to be regularly updated with new data and retrained to maintain their effectiveness.
Continuous monitoring makes it possible to quickly detect any drop in performance and adjust models accordingly. This is achieved by allowing analysts to report undetected fraud or false positives. The number and proportion of analyses reported enable us to monitor the effectiveness of our models.