An Ai- Powered and Blockchain – Integrated Model for Automated Claims Processing and Fraud Detection in the Insurance Industry

The insurance industry is going through a major shift as a result of the integration of Blockchain and Artificial Intelligence (AI). Artificial intelligence (AI) refers to the process of teaching computers to mimic human intellect so that they can make decisions and act similarly to humans, while blockchain refers to a distributed digital ledger that guarantees honesty, integrity, and immutability across several systems.

Blockchain systems may benefit from AI integration for safer, smarter, and more efficient processes [1]. Innovative solutions to trust, efficiency, security, and transparency are altering the business environment using blockchain and AI [2]. AI helps firms automate, forecast, and make data-driven decisions. Blockchain provides decentralised, impermeable data management, storage, and confirmation. The combination of these two technologies enables innovative use cases including AI-enhanced smart contracts, decentralised identity management, predictive supply chain optimisation, and advanced fraud detection [3]. These technologies may boost, financial transactions, supply chain logistics and operations. Smart contracts may improve insurance claims processing transparency and efficiency, according to this research [4]. This study presents an AI-Blockchain hybrid system to optimise insurance operations by detecting fraud and storing immutable data.

The insurance industry is smartly moving away from labour-intensive manual procedures and towards intelligent, automated technologies, according to the trend of AI adoption. There used to be delays in claims processing and holes in fraud detection systems since insurance operations were so dependent on human judgement and established standards. The insurance sector has gone through three separate waves of digital transformation. The first wave was all about automating simple processes; the second wave was all about sophisticated analytics; and the current wave is all about cognitive technologies and generative AI [5]. Early digital solutions addressed just 15% of insurance value chain operations before 2020, mainly because they concentrated on digitising current processes rather than radically rethinking them.

Natural language processing, machine learning and predictive analytics have all made significant strides in the previous decade, which has led to a rapid acceleration in the adoption of AI. The percentage of insurance businesses that had implemented AI solutions in at least one business function had increased to 67% by the year 2023, which is a threefold rise from the 22% that had done so in 2018. Significant performance gains have been the driving force behind this quick adoption. For example, AI-enhanced procedures have shown 45% better efficiency compared to conventional techniques in areas like as underwriting and claims assessment.

This trend is especially noticeable in insurer fraud detection and claims processing. Modern machine learning models can identify complex patterns across massive datasets, replacing rule-based systems that used predetermined parameters to flag suspicious activity. Advanced AI fraud detection tools have decreased fraudulent claims by 60% and investigative expenses by 40% for insurance firms [6]. Traditional methods analyse 20-30 factors every claim, whereas machine learning models analyse over 500 variables per claim. Additionally, claims processing has moved from manual review to hybrid systems where AI handles regular claims and humans handle difficult instances needing subtlety judgement.

Industry statistics confirm this change story. 75% of insurers consider AI crucial for competitiveness, and 82% aim to boost AI investments by 35% in 2025 compared to 2024 [6]. Leading insurance carriers have reduced claims processing time by 75% and increased typical claim straight-through processing rates from 15% to 85% with mature implementations. Early insurance adopters saw a 250% return on investment from AI-driven operational improvements over three years. This historical framework helps explain how Salesforce's Einstein AI capabilities have accelerated and improved insurance technology.

Figure 1: AI adoption in insurance: coverage of value chain activities by digital tools and efficiency gains over traditional methods.

1. To integrate AI and blockchain for automating insurance claims and improving efficiency.
2. To enhance fraud detection accuracy using AI analytics and blockchain transparency.

Data Collection

The study's overarching goal was to find ways to use AI to streamline the insurance claims system and identify fraud. Thousands of claims, some legitimate and some not, were supplied by insurance companies for the dataset. To ensure accuracy and resilience across situations, this extensive dataset was crucial for training and assessing the artificial intelligence model. The information was obtained from prominent insurance firms in India, including both private and public sector entities. This location was chosen because of India's rapidly expanding InsurTech business and the rising use of AI and blockchain technologies in financial services.

Preprocessing of Data

Converting raw data into an analysable format requires careful preparation. This critical step included data cleansing, standardisation, and transformation into functional formats. Data cleansing focused on removing extraneous items and rectifying any deficiencies or missing values. The goal of normalisation was to provide consistency in the dataset by standardising formats and scales. Textual data was transformed into numerical representations necessary for NLP applications using transformation techniques like word embeddings. The dataset was ready for accurate and efficient analysis at every step.

Selection of Model

The AI-powered system integrated multiple machine learning algorithms, each chosen for its unique advantages. NLP was essential in examining claim texts to identify linguistic patterns suggestive of fraud. Alongside this, anomaly detection techniques such as Isolation Forest and One-Class SVM were applied to detect irregular data patterns, enabling the system to effectively identify potentially fraudulent activities.

Model Testing

A validation dataset was used to evaluate the trained model's performance. Recall, precision, accuracy, and F1 score were the key performance indicators. Using these standards, we were able to evaluate how well the model distinguished between different types of claims and detected instances of fraud.

Recall and Precision: evaluated how well the model captured all real frauds (recall) and how well it could detect fraudulent claims (precision).

Accuracy: calculated the percentage of claims—both fraudulent and legitimate—that were accurately detected.

F1 Score: Presented a unified performance evaluation score that was both accurate and recall-worthy.

Here, TP stands for correctly identified positive cases, TN for correctly identified negative cases, FP for cases that were incorrectly classified as positive, and FN for positive cases that were incorrectly classified as negative.

Validation and Training

To create and assess the model, the dataset was divided into validation and training sets. In order to prevent overfitting and guarantee generalizability, cross-validation methods were used. During the training phase, there were:

Model Training: used labelled data (fraudulent and genuine claims) to train the model using supervised learning methods.

Feature Engineering: determined and produced pertinent features from the data to support the model's efficient learning.

Implementation of the AI System

A modular architecture was used in the design of the AI system to guarantee easy maintenance and effective scalability. One of its main parts was a DIM that collected and prepared incoming claims information. To examine the allegations, the Analysis Engine used anomaly detection methods and natural language processing. The Decision Support System flagged dubious statements and made suggestions for human experts to consider. This thorough approach shows how blockchain and artificial intelligence (AI) may be used to enhance insurance fraud detection and claims processing.

Integration with Blockchain

Blockchain technology was utilised by the system to enhance data security and transparency. This technology renders all transactions and data inputs irreversible and auditable. Maintaining credibility necessitated this amalgamation. By prohibiting any undetected change of recorded data, the blockchain component guaranteed data integrity. Furthermore, it improved transparency by providing an unambiguous, traceable record of every action related to processing claims, enabling comprehensive auditing and verification.

Algorithm

Through blockchain integration and machine learning, the AI-powered fraud detection and claims processing system streamlined and safeguarded the insurance claim lifecycle. NLP approaches were used to preprocess, normalise, and convert historical claim data. Key variables such as claim type, amount, and policy length were then extracted using feature engineering. The system achieved great accuracy and reliability by successfully detecting abnormalities and fraudulent patterns using Isolation Forest and One-Class SVM. By storing all validated claims in a decentralised ledger, blockchain integration guaranteed transparency and data immutability. The implemented system, which included modules for data ingestion, analysis, and decision support, reduced operating time and increased confidence in the insurance process while enabling automated settlements, real-time monitoring, and enhanced processing accuracy.

Exceptional precision and efficiency were demonstrated by the system that was powered by artificial intelligence and was created for the goal of processing claims and identifying fraud. In order to carry out the study, a massive dataset that included both valid and fraudulent insurance claims was used. In order to evaluate performance, a number of important criteria were utilised. F1 score, recall, precision, and accuracy were some of the criteria. You can examine the model's classification results in their entirety in Figure 2, which displays the confusion matrix. The system's ability to distinguish between legitimate and fraudulent claims is shown by the display of the numbers of TP, TN, FP, and FN.

Table 1: Fraud Detection Confusion Matrix

Figure 2: Fraud Detection Confusion Matrix

A thorough evaluation of the model's classification performance may be found in the confusion matrix. The results demonstrate that there were 320 instances of fraudulent activity that were appropriately detected as true positives. The number of false negatives was sixteen, which indicates that there were occasions in which fraudulent actions were overlooked. On the other side, 22 genuine operations were falsely identified as fraudulent, which is an example of a false positive. Last but not least, there were 636 instances of real negatives, which means that authorised behaviours were correctly detected. By providing such a comprehensive breakdown, the model demonstrates both its high level of accuracy and its capacity to effectively reduce the number of incorrect classifications.

Acceleration of the Processing Time: One of the most significant benefits that emerged as a consequence of the adoption of the AI-powered system was a considerable reduction in the amount of time that was necessary to process claims. Not only did the technology make the process of detecting fraudulent activity more accurate, but it also made the process of processing claims in general substantially faster.

Table 2: Processing Time Reduction Before and After AI Implementation

Figure 3: Processing Time Reduction Before and After AI Implementation

As seen in Figure 3, the amount of time necessary for processing has reduced both before to and subsequent to the implementation of the system that is driven by artificial intelligence. There was a thirty percent gain in production as a result of the reduction in the usual length of time needed for processing, which was reduced from ten days to seven days. Claims are handled more rapidly as a direct consequence of this reduction in processing time, which adds directly to greater customer satisfaction. This reduction in processing time brings about this enhanced customer happiness.

Performance Metrics: In order to conduct a full evaluation of the efficacy of the AI model, the performance metrics from the model were assessed. In accordance with the information presented in table 3 and figure 4, the most important metrics are accuracy, recall, precision and F1 score.

Table 3: Performance Metrics of AI Model

Figure 4: Performance Metrics of AI Model

In Figure 4, we can see the model's performance metrics graphically represented in a line chart. This collection of measures includes parameters such as, 95.1% recall, 93.2% precision, 94.1%, F1 score and 94.6% accuracy. As shown by the metrics, the model has a performance that is well-balanced across a variety of evaluation criteria, which indicates that it performs very well in recognizing both fraudulent and true claims. Consequently, the dependability and trustworthiness of the model's assessments are ensured by the remarkable performance of the model, which indicates its effectiveness in properly categorising claims.

An exhaustive evaluation was conducted to have a comprehensive grasp of the matter. This assessment set out to contrast the AI model's efficiency with that of more conventional methods like Logistic Regression, Random Forest, and Decision Tree.

Table 4: AI Model vs. Traditional Methods Comparison

Figure 5: AI Model vs. Traditional Methods Comparison

When compared to more conventional approaches, the AI model demonstrates higher performance across all major criteria, as will be seen in Table 4. Not only was the AI model able to attain greater levels of accuracy, recall, precision and F1 score, but it also considerably decreased the amount of time required for processing.

This research found that the AI–Blockchain paradigm improves claims processing and fraud detection. The technology outperformed existing approaches with 94.6% accuracy, 93.2% precision, and 30% processing time savings. These results show that blockchain plus machine learning techniques like Isolation Forest and One-Class SVM may improve insurance transparency, security, and dependability. AI and blockchain in claims processing are transforming the insurance industry, as previously found. They support [7], which showed that AI-based automation improves insurance process efficiency and accuracy. AI shifts the insurance paradigm from loss payment to proactive loss prevention, improving this model's predictive fraud detection [8]. AI-driven models decrease human contact, enabling faster and more trustworthy claim assessments [9], while explainable AI promotes transparency and trust, which blockchain integration improves [10]. Safe AI–blockchain architectures boost fraud detection and risk management [11]. This hybrid solution speeds claim settlements, prevents fraud, and builds insurance ecosystem client confidence by eliminating human inefficiencies.

The research also shows that blockchain integration improves data quality and auditability, supporting blockchain's function in safeguarding financial transactions and ensuring transparency across decentralised networks. AI-driven automation was predicted to transform insurance operations by enhancing client happiness and operational effectiveness [12]. The hybrid method follows these concepts. Advanced anomaly detection algorithms and blockchain's decentralised ledger make the proposed insurance claims administration platform scalable, safe, and efficient. [13] This study adds to the evidence that AI–Blockchain convergence increases fraud detection accuracy and promotes digital transformation and ethical governance in the global insurance business.

This research demonstrates that the integration of AI and blockchain offers a robust, technology-based solution to persistent issues in claims administration and fraud detection in the insurance industry. The model's enhanced performance in accuracy, transparency, and processing speed illustrates its capacity to replace conventional manual processes with automated, intelligent alternatives. By integrating predictive analytics with immutable data management, insurers can bolster confidence, mitigate financial losses, and expedite equitable claim settlements. This study enhances the existing evidence for digital transformation in financial services and underscores the crucial role of new technologies in developing ethical, efficient, and customer-focused insurance ecosystems.