The Security Hazards of Using AI in Insurance

• by Anthony Orjally
• Advent IM Blog

Artificial Intelligence (AI) has revolutionised many industries, including insurance. By leveraging machine learning, predictive analytics, and automation, AI offers the insurance sector enhanced efficiency, improved customer service, and more precise risk assessments. However, along with these advantages come significant security hazards that must not be overlooked. The increasing reliance on AI in the insurance industry brings with it complex challenges that require careful management to prevent potential risks to both businesses and consumers.

1. Data Privacy Concerns

One of the most pressing security hazards in using AI within insurance is the risk to data privacy. AI systems in the insurance sector often rely on vast amounts of personal data to function effectively. This data can include sensitive information such as medical records, financial details, and personal identification information (PII).

While AI enables insurers to process and analyse data more efficiently, it also creates more vulnerabilities in data storage and transmission. Hackers may target AI systems to gain unauthorised access to these sensitive records, which could lead to identity theft, fraud, and a breach of trust between customers and insurance providers.

According to data from the Information Commissioner’s Office (ICO), the UK insurance sector reported over 100 data breaches in 2023 alone, with the majority linked to cyberattacks. The main cause of these breaches was phishing attacks, where cybercriminals trick employees into disclosing sensitive information or login credentials, giving them access to internal systems. Phishing attacks are largely human-enabled, meaning that the success of these attacks often stems from training and awareness issues within organisations. Employees may inadvertently click on malicious links or fall for fraudulent emails that appear legitimate, thus bypassing technical security measures.

This highlights the importance of robust cybersecurity training programmes within the insurance sector. Without adequate training, even the most advanced AI systems and security technologies can be undermined by human error. Improving staff awareness and equipping them with the tools to identify and report phishing attempts can significantly reduce the likelihood of successful attacks. In addition to phishing, other common causes of data breaches in the insurance sector include unauthorised access by insiders, poor data security practices, and mis-delivery of emails or documents containing personal data. Mis-delivery is normally due to the data being, posted, emailed or faxed to the wrong recipient which is almost entirely a human error.

The highly regulated nature of the insurance industry means that any compromise in data privacy can result in severe legal and financial consequences, including fines under the General Data Protection Regulation (GDPR), which can reach up to £17.5 million or 4% of global annual turnover, whichever is higher.

2. Bias and Discrimination

AI algorithms are only as good as the data they are trained on. Inaccurate or biased data sets can lead to AI making decisions that unintentionally discriminate against certain groups of people. For example, AI systems might use historical data to assess risk and make decisions about pricing or eligibility for insurance policies. If that historical data includes biases, such as systemic discrimination based on race, gender, or socio-economic status, then the AI could perpetuate these biases, resulting in unfair pricing or rejection of claims for certain individuals.

This is a particularly significant hazard in the insurance industry, where fairness and non-discriminatory practices are critical. Unchecked, biased AI systems could not only lead to legal challenges but also damage the reputation of insurance companies, causing long-term harm to their business.

3. Cybersecurity Threats

AI in insurance is highly dependent on digital systems, making it vulnerable to cyberattacks. The integration of AI into critical insurance processes like underwriting, claims processing, and fraud detection increases the attack surface for cybercriminals. These threats can take various forms, including data breaches, ransomware, and denial-of-service (DoS) attacks.

Moreover, AI can be exploited by cybercriminals to automate sophisticated attacks, making them harder to detect and defend against. For instance, hackers might use AI to carry out “adversarial attacks,” where they subtly manipulate input data to trick AI systems into making erroneous decisions. In the context of insurance, this could lead to wrongful claim approvals or denials, ultimately resulting in significant financial losses for the insurer.

4. Lack of Transparency and Accountability

Another hazard associated with AI in insurance is the lack of transparency in how decisions are made. AI models, especially those based on deep learning, are often seen as “black boxes” because their decision-making processes can be difficult to interpret. This opacity creates a challenge for accountability, as it may not be clear why an AI system has made a particular decision, such as rejecting a claim or determining premium prices.

The lack of transparency raises concerns about fairness and trust, particularly if customers feel they have been treated unfairly by an AI system without a clear explanation. In addition, regulators may demand more accountability from insurers to ensure that AI decisions are in compliance with legal standards, further complicating the situation.

5. Over-reliance on Automation

Finally, the increasing reliance on AI and automation can lead to an over-reliance on technology at the expense of human judgment. While AI can streamline many processes, it cannot always account for the nuances of individual cases. In the insurance industry, where complex and personal circumstances are common, relying solely on AI could result in poor decision-making, and ultimately, harm customer relationships.

While AI offers transformative potential for the insurance industry, it also presents serious security hazards that must be carefully managed. Insurers must take steps to ensure data privacy, mitigate bias, defend against cyber threats, and maintain transparency and accountability. By balancing the benefits of AI with proactive risk management strategies, the insurance industry can harness the power of AI without compromising on security.

Organisations need to ensure that culture, policies and procedures are in a fit state for automation of any kind and that any new AI is always Risk Assessed properly and ensure it is within Risk Tolerances. For support with Information Governance, Risk and Compliance (or Assurance), contact the experts today  0121 559 6699 | sarah.richardson@advent-im.co.uk