GLI Insurance: How does AI detect bad payers?

GLI insurance is essential to protect homeowners from bad payers. Insurance companies intervene in the choice of tenants and seek to refuse high-risk files. However, between falsified records, document fraud and identity theft, the task is often complex. Let's explore together how artificial intelligence detects these frauds and identifies bad payers.

Why detect GLI insurance fraud?

Impact on GLI insurance and homeowners

Fraud in the GLI insurance industry has repercussions for both insurance companies and homeowners.

For Insurers

Frauds cause significant financial losses. They increase operational costs and decrease profitability. These losses can lead to higher premiums for policyholders. This makes insurance policies less attractive for homeowners.

For owners

Fraud can mean delays or denials in benefits. This complicates their financial situation even further. 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, it can delay payments or even cause coverage to be cancelled.

Challenges in detecting GLI insurance fraud

Detecting fraud in GLI insurance presents several challenges.

Gain in efficiency

Traditionally, insurance companies have relied on manual methods. The agents checked the files periodically to detect anomalies. These methods are not only time consuming but also subject to human error. Document fraud and identity theft were detected too late, or went unnoticed.

Increase the accuracy of controls

Fraudsters are becoming more and more sophisticated. They use advanced techniques to avoid detection. This requires insurance companies to constantly update their verification processes. The use of AI allows for continuous improvement, in the background.

How does AI improve the detection of fraud in GLI insurance?

Identifying Suspicious Behavior and Fraud Patterns

AI is good at identifying suspicious behavior and fraud patterns. It can detect anomalies and deviant behaviors, sometimes indicative of fraud.For example, identify recurring patterns in GLI requests that have previously led to non-payments. Inconsistencies in the information provided by the tenant or similarities with previous fraud will be noted.With this ability to identify subtle patterns, insurance companies can more effectively target high-risk cases. These high-risk files can be re-checked by an expert when low-risk files can benefit from automatic validation.

Real-time and historical data analysis

AI allows the simultaneous analysis of historical and real-time data. This makes it possible to create a more robust and responsive system in detecting GLI insurance fraud.

Historical data

With 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 allows for continuous monitoring of new GLI insurance claims and tenant behaviors. This means that fraud can be detected and dealt with immediately. That is, before they cause significant losses.

Automating verification and validation processes

Traditionally, the processes of verifying the file and its validation require significant human intervention. This can be slow and error-prone. With AI, these verification tasks can be automated:

• Extracting textual information from documents
• Identifying the type of document through visual recognition
• Automated detection of intra- and inter-documentary inconsistencies
• Verification of authenticity and detection of signs of forgery using previously established models

Understand 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 that indicate possible fraud. To know everything about machine learning, read our dedicated article.

How is it used?

With machine learning, systems can learn and improve over time. In this way, they increase their precision.For example, algorithms analyze transaction histories and past tenant behaviors. They can spot suspicious trends and flag potentially fraudulent records before they cause losses.

Natural Language Processing (NLP)

What is NLP?

Natural Language Processing (NLP) allows AI systems to understand and interpret human language. As part of fraud detection, NLP is used to analyze documents submitted by tenants. These documents include rental contracts, payslips, and bank statements.

Use of NLP

This technology can identify inconsistencies and anomalies in textual documents. Whether it's contradictory information or language patterns that don't correspond to 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 allows insurance companies to identify the likelihood of future fraud. These tools aggregate and analyze data from a variety of sources: demographics, payment histories, online behaviors. By cross-checking information, they can assess the risk associated with each GLI request. This ability to quickly process and analyze huge volumes of data allows for faster and more accurate decisions. This therefore reduces the number of undetected fraud.

Neural networks and deep learning

Neural networks and deep learning are sub-fields of machine learning.

Neural networks

Convolutional neural networks are capable of processing unstructured data. Among which, photos of documents, identity documents,..., and videos. This is a particularly useful asset for verifying the authenticity of identity documents.

Deep learning

Deep learning can learn complex and subtle characteristics of fraud. It therefore improves the ability of AI systems to identify suspicious behavior. Behaviors that could have escaped traditional methods.

How to set up detection AI for GLI insurance?

GLI insurance data collection and preparation

The first step in integrating AI into GLI insurance fraud detection is data collection and preparation. This involves bringing together information from a variety of sources. These include tenant payment histories, rental records, credit reports, and other relevant data. Once collected, this data needs to be cleaned up to eliminate errors and inconsistencies. They also need to be formatted to make them compatible with AI algorithms. This preparation phase is crucial, as data quality directly influences the accuracy and effectiveness of AI models.

Training AI models on data sets specific to GLI insurance

Once the data is prepared, the next step is to train the AI models on specific data sets. Machine learning algorithms are trained using historical fraud and rental behavior data. This allows them to learn how to identify the characteristics and patterns associated with fraud. This training process often requires adjustments and iterations to optimize model performance. The exploitation of reliable data specific to the GLI insurance sector is particularly important to develop accurate and adapted models.

Integrating AI systems with existing processes

After the models have been trained, it is essential to integrate AI systems with existing business processes. This may involve integrating AI algorithms into insurance claim management platforms. But also in tenant verification systems and in claims management tools. The aim is to ensure that AI models can analyze new demands 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 routine operations.

Monitoring and continuous improvement of models

Finally, once AI systems are deployed, continuous monitoring and model improvement are required. Fraudulent behavior changes over time. AI models need to be updated regularly with new data and re-trained to maintain their effectiveness. Continuous monitoring makes it possible to quickly detect any decrease in performance and to adjust the models accordingly. To do this, analysts are allowed to report undetected fraud or false positives. The number and proportion of analyses reported make it possible to monitor the effectiveness of the models.