GenAI in insurance: Getting the governance, ethics right

With GenAI, insurers are presented with a compelling opportunity to streamline processes and unlock new opportunities for growth and competitiveness.
Vinoliah Martin
By Vinoliah Martin, Writes in her personal capacity.
Johannesburg, 15 Mar 2024
ITWeb Industry Insight contributor Vinoliah Martin.
ITWeb Industry Insight contributor Vinoliah Martin.

Insurance organisations have historically been too conservative to fully embrace the power of emerging digital technologies. This reluctance stems from various challenges, such as complex legacy systems, regulatory compliance, risk aversion and a culture that is resistant to change, among others.

However, as artificial intelligence (AI) continues to advance and with the emergence of generative AI (GenAI), insurers are presented with a compelling opportunity to streamline processes, drive efficiency, enhance customer experience and unlock new opportunities for growth and competitiveness.

According to Precedence Research, the global insurance market for GenAI is projected to grow at a compound annual growth rate of 33.11% during the forecast period from 2023 to 2032.

This significant growth underscores the increasing demand and transformative potential of GenAI within this industry.

It is crucial for organisations to formulate precise policies and operational protocols specifically tailored to confront ethical challenges head-on.

As insurance organisations embark on this journey, it is imperative for leaders to prioritise the understanding of the potential, implications and opportunities brought about by GenAI. This may involve attending workshops, seminars, or training sessions and actively engaging with experts through collaboration.

It also necessitates that leaders keep up-to-date with industry trends and developments, potential risks and cyber security concerns associated with the integration of AI technologies into insurance processes.

Additionally, identifying specific business objectives and potential use cases where the integration of GenAI could generate significant value is crucial.

Some of these use cases could include:

Risk assessment and underwriting: GenAI has the capability to swiftly and accurately analyse large volumes of data, such as historical claims and demographic information. This capacity can improve risk assessments and underwriting processes and lead to the creation of more accurate pricing models and fairer policy decisions.

Claims processing and automation: GenAI can streamline claims processing by automatically extracting relevant information from claims forms, images or other documents, which can reduce the need for manual entry and minimise processing time. As a result, insurers can expedite claims settlements, enhance accuracy, reduce operational costs and ultimately boost customer satisfaction.

Fraud detection and prevention: GenAI can play a pivotal role in combating fraud by identifying patterns and anomalies in claims data. Training fraud detection models can strengthen algorithms, increase accuracy and reduce false positives in identifying fraudulent activities.

Customer engagement and personalisation: By leveraging GenAI models, insurers can analyse customer data and predict behaviour patterns which can generate personalised recommendations, policies and communications tailored to individual customer needs and preferences.

Furthermore, chatbots powered by GenAI can be used to answer customer inquiries and provide personalised assistance at any time, which can improve customer experience. GenAI can also streamline back-office processes and automate customer service interactions, which can empower insurers to do more with less, driving cost savings and improving overall competitiveness.

For insurance organisations that are yet to commence the GenAI journey, Ernst & Young proposes three preliminary steps:

  • Establish a multidisciplinary team of business stakeholders, IT specialists and data scientists who are dedicated to tailoring GenAI solutions to the specific needs of the organisation.
  • Identify the operating model that best fits the organisation, particularly in ensuring a safe, successful and scalable deployment.
  • Develop the essential skills and capabilities among the workforce and start with easily manageable use cases, and then refine models over time, leveraging domain expertise and data sources.

With the potential and value GenAI can create, there are also various concerns around data security, privacy threats, regulatory compliance, as well as ethical implications and biases, among others.

According to a survey conducted by KPMG, 57% of CEOs worldwide cited ethical challenges as the top concern when implementing GenAI, followed closely by a lack of regulation.

Moreover, as the scrutiny surrounding AI continues to intensify, it becomes increasingly evident that stricter regulatory frameworks are essential to effectively manage potential risks. Consequently, organisations are faced with the imperative to adopt proactive strategies.

In doing so, it is crucial for organisations to formulate precise policies and operational protocols specifically tailored to confront ethical challenges head-on. These measures are not only essential for compliance with evolving regulations, but also for maintaining societal trust and facilitating responsible AI deployment.

Additionally, organisations must prioritise ongoing evaluation and adaptation of these frameworks to keep pace with the dynamic landscape of AI ethics and regulation. By integrating ethical considerations into core operations, organisations can navigate the complexities of AI governance, while fostering innovation and safeguarding against potential risks.

* The information I have shared represents my own personal views. I am speaking for myself and not on behalf of my employer, Microsoft South Africa.