General liability claims in commercial insurance can arise from accidents or incidents during business operations. These claims are typically filed against businesses for injuries or damages sustained by third parties, such as customers, clients or other individuals.

In this article, we will focus on liability claims resulting from injuries suffered by these third parties and how insurers can utilize artificial intelligence to help minimize the amount paid per claim while treating the injured party fairly.

Injury claim types

Many incidents can lead to bodily injury claims. Some common types of incidents that generate general liability claims include:

• Slip-and-fall accidents: These accidents occur when someone slips, trips or falls on the business premises due to wet or slippery surfaces, uneven flooring, poor lighting, or other hazardous conditions.
• Other bodily injuries: If a third party sustains injuries on the business premises or due to the business operations, they may file a general liability claim. These accidents can range from construction site accidents, product-related injuries, fire and explosions to accidents involving machinery, equipment and others.
• Completed operations: Claims can arise from injuries or damages after a business has completed a service or project, such as faulty workmanship or installation.

Many of these hazards and the injuries they cause seem preventable, but eliminating them can be cost-prohibitive. General liability insurers and companies that self-insure need means at their disposal to minimize the impact of claims when they occur. In today's world, increasingly, the answer to this problem is artificial intelligence (AI).

AI Is the edge insurance carriers, captives need

Artificial intelligence can help insurance companies minimize the cost of general liability injury claims by streamlining various processes and providing valuable insights to support decision-making. Some ways AI can be utilized in this context include:

1. Predictive claim analytics: AI can analyze historical claims data to identify trends and patterns suggesting an increased likelihood of slip-and-fall accidents in certain locations or under specific conditions. This information can help insurers proactively address potential hazards, reducing the frequency and severity of claims.
2. Claims processing automation: AI can streamline the claims process by automating tasks such as document review, data extraction, and initial claim assessment. This can help insurers reduce administrative costs, shorten the claims resolution time, and provide a better experience for policyholders.
3. Fraud detection: AI algorithms can analyze large data sets and identify patterns or anomalies that may indicate fraudulent claims. Insurance companies can reduce costs associated with paying out undeserved settlements by detecting and preventing fraudulent slip-and-fall injury claims.
4. Litigation strategy and support: AI can assist insurers in identifying claims with a higher likelihood of litigation and help them develop effective defense strategies by analyzing historical case data, legal outcomes, and trends.
5. Loss prevention recommendations: AI can help insurers develop targeted loss prevention strategies by identifying risk factors associated with slip-and-fall accidents, such as poor lighting, slippery surfaces, or cluttered walkways. Insurers can then provide policyholders with actionable recommendations to mitigate these risks and reduce the likelihood of future claims.

Reducing claim frequency and severity

Several artificial intelligence techniques can assist carriers, self-insureds and TPAs in reducing the frequency of claims. AI can identify patterns of incidents that lead to injury claims, enabling insureds to take proactive preventative actions to reduce injuries. It can monitor data in near real time to detect if these patterns change because of corrective action. And AI can highlight emerging patterns of new sources of injuries that may prove troublesome going forward. Among the wide range of AI techniques available, these are well-suited to these tasks:

1. Machine learning (ML): ML algorithms learn from data, enabling them to identify patterns, make predictions, and improve their performance over time. There are several types of machine learning techniques, such as supervised learning, unsupervised learning, and reinforcement learning, which can be used to identify patterns in data depending on the problem at hand.
2. Natural language processing (NLP): NLP focuses on enabling computers to understand, interpret and generate human language. NLP techniques can identify patterns in textual data, such as sentiment analysis, topic modeling, or named entity recognition.
3. Anomaly detection: This technique is used to identify unusual or unexpected patterns in data that deviate from the norm. Anomaly detection algorithms can be statistical, machine learning-based or deep learning-based, depending on the specific application and data set.
4. Clustering: Clustering is an unsupervised learning technique that groups data points based on similarity, allowing for pattern identification within the data. Standard clustering algorithms include K-means, DBSCAN, and hierarchical clustering.
5. Time series analysis: Time series analysis involves the study of data points collected over time to identify trends, seasonality and other patterns. Techniques like autoregressive integrated moving averages (ARIMA), exponential smoothing, and deep learning methods like LSTMs can be employed to model and forecast time series data.
6. Generative AI: An emerging technology under the umbrella of AI, GAI applies what it learns from creating a language model of the subject at hand, in this case casualty claims, and guides adjusters to take specific actions, supported by explanatory text statements. One way to think of GAI is that it integrates insights from the other AI techniques mentioned and communicates them to adjusters in an easy-to-understand manner.

Applying AI to the problem

When it comes to reducing the severity of claims, many of these same techniques can be used to build models that triage claims at the first notice of loss (FNOL), predict which claims will be litigated, and discover fresh insights from relationships in unstructured data contained in medical bills, doctors' reports, and the plethora of other documents associated with injury claims.