Over the last decade, India’s insurance market has grown exponentially, becoming one of the substantial financial service sectors. This growth is noteworthy, particularly in emerging sub-sectors like insurtech, which has had a 34% compound annual growth rate. This growth is largely attributed to the increasing adoption of smartphones and high-speed internet accessibility in India. This, in turn, has created new opportunities for insurance firms to engage with customers through mobile apps, social media platforms, and other online websites/portals. With the government’s Digital India initiative, the country is witnessing accelerated growth in digitisation across sectors, including the insurance sector.
While digitisation offers many benefits, it also presents certain challenges. Akin to other sectors, the Insurtech ecosystem in the country is also prone to key challenges like cyber-attacks and cyber security threats. Multiple frauds within the insurance industry have been a challenge despite significant technological strides. According to the Deloitte Insurance Fraud Survey 2023, life and health insurance fraud incidents have increased in India. Furthermore, studies in the Indian insurance industry have revealed that insurance fraud costs the industry more than Rs 30,000/-crore every year.
On the other hand, insurance companies face an uphill battle against identity theft, insurance hoppers, and fraudulent or forged claims. If these challenges weren’t enough, the fact that India’s insurance industry processes billions in premiums annually while collecting a lot of customer data makes it imperative for insurance firms to stay ahead of cyber fraudsters with a proactive approach towards fraud prevention and data analysis.
The Role of Artificial Intelligence (AI) in Cybesecurity
AI is changing that paradigm by providing insurers with the tools they need to accurately model myriad cyberattack scenarios and develop more precise risk assessments. Following are some of the specific areas where the most rapid advances are seen among firms that are adopting this approach:
Generating synthetic data: AI algorithms are being used to generate synthetic data that resembles real-world cyber threats, including malware samples, phishing emails, and network attack patterns. This synthetic data can then be used to train machine learning models, enhancing their ability to detect and classify new and evolving threats.
Anomaly detection: Insurers are currently deploying AI to detect anomalies within network traffic, system logs, and user behaviour by establishing baselines from normal patterns. By generating synthetic data that mimic legitimate network traffic or user behavior, any deviations from these patterns can be identified as potential indicators of a cyber-threat.
Simulating attacks: Simulated attacks, mimicking real cyber attacker behaviour, are being reconstructed with AI models to help security teams proactively search for vulnerabilities within their systems, networks and applications. By analysing the generated attack scenarios, organisations can identify holes in their current security and develop appropriate countermeasures.
Threat intelligence sharing: AI is also being used to anonymise and aggregate sensitive cyber threat data, allowing organisations to share information with trusted partners or security communities. By generating synthetic data that conceals the sources, organisations can contribute to a collective knowledge base without compromising their security.
Malware detection and analysis: AI analyses a wide range of features, such as disk access, APIs, bandwidth usage, processor power, and internet data transmission, to identify and analyse various types of malware, including viruses, Trojan horses, worms, exploits, botnets, and ransomware.
AI can help transform cyber risk insurance operations include providing a consolidated score for an organisation’s overall cybersecurity posture, incorporating various parameters including technology stack, risk signals at an internet scale, topology, threat level, business priorities, regulatory obligations, and historical insights.
AI-based fraud detection systems can work in real time on large data sets. These systems proactively detect fraud, helping insurers save money and resources. Among the many advantages of AI, preventing fraudulent claims is undoubtedly one of the groundbreaking applications. It empowers firms to modernise their existing processes, have accurate pricing, lower overhead costs, and enhance operational efficiency. With the right mix of solutions, training, and technologies, insurers will have a competitive edge, ensuring increased customer satisfaction and trust. AI enables them to provide more secure and reliable services.
