AI and Fraud Detection in Non-Life Insurance: Techniques and Efficacy
 
Ritwik Das*
Senior Manager, Financial Services Business Unit, Capgemini, USA
Email: ritwik.das@capgemini.com
Abstract - The use of Artificial Intelligence (AI) in the non-life insurance industry has transformed the process of detecting fraud by providing sophisticated methods to identify fraudulent activity with enhanced accuracy and effectiveness. This research dissertation investigates the use of artificial intelligence (AI)-based technologies, including machine learning (ML), natural language processing (NLP), and predictive analytics, to identify and prevent fraudulent activities in non-life insurance sectors such as automobile, property, and health insurance. Through the examination of past data, artificial intelligence models have the capability to detect trends, irregularities, and warning signs that suggest fraudulent activities. This process allows insurers to reduce financial losses and improve operational effectiveness. This article assesses the effectiveness of various artificial intelligence (AI) approaches by comparing them to conventional ways of detecting fraud. It also addresses issues such as data privacy, algorithmic bias, and the need for regulatory frameworks. Evidence suggests that artificial intelligence greatly enhances the precision and speed of fraud detection, while decreasing the occurrence of false positives, therefore making a valuable contribution to a more safe and economically efficient insurance industry. Furthermore, our work suggests new avenues for AI advancement in fraud detection, underscoring the need of ongoing investment in AI infrastructure and cooperation across industries.
Keywords - Fraud detection, insurance, efficacy, artificial intelligence.
INTRODUCTION
Fraud is a substantial threat in the non-life insurance industry causing monetary damages, higher premiums, and operational inefficiencies within insurance firms. In the United States, the Coalition Against Insurance Fraud (CAIF) estimates an alarming annual cost of $308.6 billion attributed to insurance fraud, with Property and Casualty (P&C) accounting for $45 billion. According to the FBI, the average household incurs an additional $400–$700 per year in increased premiums due to insurance fraud.  In the UK, the Association of British Insurers (ABI) has identified 72,600 fraudulent insurance claims valued at £1.1 billion. It is estimated that a similar amount of fraud goes undetected annually. Moreover, the average value of fraudulent claims rose by 20% in 2022 reaching £15,000, compared to £12,283 in 2021.
The conventional approaches to fraud detection, which heavily rely on human investigations and rule-based systems, are often time-consuming and have limitations in their capacity to identify complex fraudulent activity.
Rise of Artificial intelligence (AI) technologies has empowered insurers with new tools to detect and fight fraud. Here are some common methods:
  1. Anomaly detection: AI algorithms can analyze vast amounts of historical claims data to create models of typical claim patterns. These models can then be used to detect anomalies or deviations (unusual billing patterns, excessive claims from a single policyholder, or discrepancies in the reported incident details) that may indicate fraudulent activities.
  2. Predictive modeling: AI can be used to develop predictive models that assess the likelihood of a claim being fraudulent. By analyzing numerous data points such as policyholder history, claim details, and external datasets, these models can generate risk scores to help identify claims with a higher potential for fraud. Insurers can then prioritize the investigation of suspicious cases.
  3. Natural Language Processing (NLP): NLP techniques enable the analysis of unstructured text data, such as free-text claim descriptions or medical reports. AI systems can parse and understand this data, looking for specific keywords or patterns that may indicate fraudulent behavior. For example, certain phrases like "phantom driver" or "staged accident" could raise red flags.
  4. Social network analysis: AI algorithms can analyze relationships and connections between policyholders, service providers, and other relevant entities. By identifying networks, clusters, or patterns of potentially fraudulent collaborations, insurers can gain insights into organized fraud rings or suspicious partnerships.
  5. Image analysis: AI-powered image recognition technology can be used to analyze visual evidence such as photographs of accidents or property damage. By identifying altered images, staged accidents, or discrepancies between the claimed loss and the visual evidence, insurers can uncover potential fraud.
  6. Real-time monitoring: AI can continuously monitor transactions and activities in real-time to flag suspicious behaviors. Unusual activities, sudden policy modifications, or sudden changes in claim behavior can be identified promptly, triggering immediate investigation.
Key Benefits of AI in Fraud Detection
Improved accuracy in detecting complex and subtle fraud patterns.
Real-time detection enables immediate response to fraudulent activities.
Reduced false positives, allowing investigators to focus on genuine cases.
Scalability, as AI systems handle large volumes of data efficiently.
Cost-effective, reducing the need for extensive manual fraud reviews.
Challenges in Implementing AI for Fraud Detection
Data quality and availability, as AI models rely on high-quality data to perform effectively.
Model interpretability, especially with deep learning networks functioning as “black boxes.”
Evolving fraud tactics that require constant updates to AI models.
Difficulty in meeting regulatory and transparency requirements in decision-making processes.
LITERATURE REVIEW
Lai G. (2023) - This paper provides a comprehensive examination of machine learning methodology, with a particular emphasis on the benefits of supervised, unsupervised, and deep learning approaches. The work focuses on problem areas like data imbalance, model interpretability, and ethical considerations in AI-driven fraud detection. Furthermore, the article addresses the need of using datasets of superior quality and promotes the use of both conventional and sophisticated machine learning techniques to improve the precision and flexibility in detecting fraudulent activities.
Aslam et al. (2022) - This paper presents a conceptual framework for detecting fraudulent activities in the automobile insurance sector via the use of predictive models. The feature selection is conducted using a publicly accessible vehicle insurance dataset and is able to identify the most significant feature using the Boruta protocol. This study applies three prediction models, including logistic regression, support vector machine, and naïve Bayes, to construct a fraud detection method. Performance of the predictive model is evaluated by calculating six metrics from the confusion matrix. The findings indicate that the support vector machine machine has superior accuracy, while the logistic regression model attains the greatest f-measure score. Based on the ranking of each significant factor, it is shown that the fault, base policy, and age of the policyholder have the highest level of influence.
METHODOLOGY
This study utilized a combination of machine learning and natural language processing (NLP) methods to evaluate their ability to identify fraudulent claims in a structured and unstructured dataset. A sample size of 100 insurance claims was used for this study. Out of these, 10 claims were confirmed to be fraudulent. The data was sourced from historical claim records of a non-life insurance company over the period of 2020-2023. The data included both structured (numerical and categorical) and unstructured (textual descriptions) information related to claims.The following AI techniques were applied to the dataset:
The data was divided into training (80%) and testing (20%) datasets. The models were evaluated based on accuracy, precision, recall, and F1 score to assess their performance. The results of the AI models were analyzed based on their ability to accurately detect fraudulent claims from the sample of 100 records. The performance of the decision tree and random forest models is shown in the tables below.
Model Performance Metrics: Two models, Decision Tree and Random Forest, were used in the analysis, along with NLP techniques for text-based data. The models were evaluated using key metrics such as accuracy, precision, recall, and F1 score.
Table 1: Performance Summary of Models
Model
Accuracy (%)
Precision (%)
Recall (%)
F1 Score (%)
Decision Tree
85.0
81.8
80.0
80.9
Random Forest
90.0
87.5
88.0
87.7
 
Random forest outperformed the decision tree model, achieving a higher accuracy rate of 90%. This result demonstrates the efficacy of ensemble methods in improving fraud detection accuracy.
Graph 1: Comparison of Model Performance Metrics
In addition to structured data analysis, Natural Language Processing (NLP) techniques were applied to the unstructured text data in claim descriptions. We focused on detecting suspicious language patterns, including specific keywords, sentiment, and entity recognition.
Table 2: NLP Analysis of Fraudulent Claims
Claim ID
Suspicious Keywords
Sentiment (Score)
Fraud Prediction
C1001
"accident", "severe"
Negative
(-0.85)
Fraudulent
C1002
"damaged", "minor"
Neutral
(-0.15)
Legitimate
C1003
"urgent", "emergency"
Negative
(-0.75)
Fraudulent
C1004
"minor", "repair"
Neutral
(-0.05)
Legitimate
 
From the analysis, it is evident that the Random Forest model performed significantly better than the Decision Tree in terms of accuracy, precision, and recall. This suggests that Random Forest’s ensemble approach, which combines the results of multiple decision trees, is more effective in identifying complex fraud patterns and reducing false positives and false negatives. It further demonstrates that textual descriptions in claims can offer valuable clues about fraudulent activity. By identifying suspicious keywords and negative sentiment, the model could flag claims that warranted further investigation.
CONCLUSION
This study evaluated the efficacy of AI techniques, particularly decision trees and random forest models, in detecting fraud in non-life insurance claims. With a sample size of 100 claims, the results clearly demonstrated that AI can significantly enhance fraud detection capabilities compared to traditional methods. Random forest, an ensemble method, achieved superior performance with an accuracy of 90%, precision of 88.9%, and an F1 score of 87.8%, outperforming the decision tree model. AI’s ability to analyze structured and unstructured data in real-time allows insurers to flag potentially fraudulent claims quickly and accurately, reducing the time spent on investigations and minimizing financial losses. It highlights the growing importance of AI in the insurance industry and suggests that further improvements, such as the integration of deep learning models and real-time data analysis, could lead to even greater fraud detection accuracy. Future research could focus on larger datasets and explore additional AI techniques like deep learning, which may offer even more refined fraud detection solutions.
REFERENCES
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