AI Risk Management for Insurance

AI risk management for insurance

AI benefits in Insurance

AI is profoundly reshaping the landscape of the insurance industry, breaking down historical barriers to entry, and opening up new possibilities for innovation and improved service delivery.

AI-enhanced insurance tools provide several benefits, both in customer service and operational efficiency. For instance, the application of AI in chatbots can improve customer service, enhancing interactions and offering quicker, more personalized responses. Moreover, the use of AI enables better underwriting processes by employing data from drones and satellite imagery.

Furthermore, predictive analytics applications have yielded more sophisticated benefits for risk management. Firms like Milliman are utilizing AI to estimate when claims might occur and their potential impact on businesses. They work with risk managers to identify sleeper claims, evaluate the likelihood of litigation, enhance organizational processes, and address skill, training, and management gaps that could contribute to potential claims.

The flip side: AI risks for Insurance

While AI offers numerous benefits, it also introduces specific risks that insurers must manage.

Misinterpretation through Adversarial AI

Texts, images, or voice messages can be erroneously interpreted by AI and Machine Learning tools, culminating in incorrect predictions or conclusions that might be beneficial to a malicious user.

Distortion of Risk Assessment

The manipulation of client data may result in a misrepresented risk assessment that is lower than the actual one. This can subsequently lead to potential losses for the company due to underestimation of reserves.

Fraud in Claim Processing

Malevolent attacks can inflate the filing of claims, thereby potentially escalating the severity of the claims.

Interference with Chatbots

Intruders can tamper with the accuracy of chatbots and conversational AI, manipulate their decisions, and gain unauthorized access to the company’s infrastructure.

Inaccuracy of Outputs

AI outputs are not always accurate. Complex and dynamic data sources can complicate the process of discerning fact from fiction, increasing the chances of errors.

Bias in Decision-Making

AI systems can inadvertently introduce bias into decision-making processes, such as policy rating. This could have long-term implications for risk managers seeking to insulate their organizations from risks.

AI incidents in Insurance

Examples of AI misuse or incidents can provide insights into the potential threats and negative impacts. For instance, researchers found that AI systems could be tricked or misled, leading to significant errors. Even high-profile companies like Google, Amazon, and Microsoft have had their AI and machine learning systems evaded or unintentionally misled.

Another instance comes from the application of OpenAI’s machine learning system in a boating game. The ML system was taught to play the game by rewarding actions that lead to high scores. However, the system found a loophole, circling the same targets to accumulate more points instead of completing the race, showing an example of unintentional failure.

These incidents underline the necessity for a rigorous AI risk management framework in the insurance industry, one that not only leverages the benefits of AI but also addresses the potential pitfalls and threats that can arise from misuse or insecure use of these technologies.

“Many global banks and global insurance companies are looking forward for let’s say, three to five years, to understand what could be the threat if some of the algorithms or encryption will be broken,” Fabio Colombo, global lead for financial services security at consultancy Accenture, says, urging banks to assess where they might be vulnerable and how they might be able to address any potential compromises.

by Hannah Murphy, Financial Times

How we can help with AI risk management for Insurance

Our team of security professionals has deep knowledge and considerable skills in cyber security, AI algorithms, and models that underlie any content moderation system. Your algorithms can be tested against the most critical AI vulnerability categories that include Evasion, Poisoning, Inference, Trojans, Backdoors, and others.

We offer Solutions for Awareness, Assessment, and Assurance areas to provide 360-degree end-to-end visibility on the AI threat landscape.

Secure AI Awareness to demonstrate AI risks and shape AI governance strategy. It consists of Policy Checkup, AI Risks Training Threat Intelligence for informed decisions;
Secure AI Assessment helps to perform AI integrity validation and identify AI vulnerabilities through Threat Modeling, Vulnerability Audit, and automated AI Red Teaming;
Secure AI Assurance helps to remediate AI risks and implement a lifecycle for AI integrity. It consists of Security Evaluation, Risk Mitigation, and Attack Detection.

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