Insurance companies are increasingly turning to digital business models to position themselves for future success, with emerging technologies acting as significant drivers of efficiency and effectiveness in various key business areas.
Rapidly evolving technologies, such as artificial intelligence (AI) and machine learning, are becoming more prevalent in the industry, with functions such as underwriting being increasingly underpinned by cutting-edge technical capabilities to model risk and provide insurance products and services for the future.
However, along with improving various areas of operations, AI also has the potential to revolutionise the insurance industry in South Africa by enhancing skills transfer, assisting in addressing skills gaps, evolving roles and upskilling practices. When leveraged properly, AI can add value to workforce data to manage talent risks.
This is according to Thokozile Mahlangu, CEO of the Insurance Institute of South Africa, who explains that human resources (HR) platforms that integrate AI can effectively close skills gaps and prevent them from occurring in future.
“By leveraging technology such as AI, insurance companies can evolve roles within their organisations and improve training approaches, which can be pivotal to boosting the employee experience and helping attract new talent,” she says.
Mahlangu notes that this essentially means that large data models can be used to collect employee self-assessment data and skills requirements tied to roles, teams and functions, which enables an organisation to get a detailed view of their skills landscape.
“An AI-driven (HR) platform can track and flag various skills trends, including unusually high turnover rates in certain positions or demographics which allows HR to address and even reverse the trend. Additionally, with the help of AI, HR managers can use future skills gap predictions to pre-empt a skills shortage, including that caused by retirement-driven attrition.”
AI-powered tools and solutions can be instrumental in helping prevent critical knowledge loss due to baby boomers retiring from their positions – a very real problem faced by the insurance industry today.
“Simply stated, AI can be harnessed to help record what employees do to help develop training materials and document procedures and processes. Interestingly, this type of data can be more detailed relying on an employee to write everything down,” says Mahlangu.
“Alongside being key to assisting with the transfer of decades of institutional knowledge from outgoing employees to their replacements, technology can also be leveraged to help new employees find their feet. Chatbots, for example, can be deployed as training assistants to supplement human instruction.”
Generally, AI can personalise training programmes for insurance professionals by analysing individual learning patterns and preferences. These intelligent systems can recommend relevant courses, webinars or resources.
“Insurance professionals can learn from AI by actively participating in these personalised training courses, which is particularly important as continuous learning and upskilling are essential for adapting to industry changes.
She points out that bespoke AI solutions have the potential to reduce learning curves and fill the knowledge gap between highly experienced retiring underwriters and new recruits who will be taking over their positions.
“Crucially, AI algorithms can be leveraged to analyse claims data, assess risk and determine the validity of claims. By automating routine tasks, such as verifying policy details and calculating payouts, insurers can free up human resources for more complex tasks,” says Mahlangu.
“At the same time, insurance professionals can learn from AI-based systems by understanding the decision-making process behind claims approvals or rejections. This knowledge transfer will enhance their expertise and ensure consistent and efficient claims handling.”
Mahlangu adds that AI algorithms can be used to predict customer behaviour, identify potential fraud and optimise pricing strategies. By analysing patterns and historical data, insurers can make data-driven decisions.
“Insurance professionals can learn from AI by understanding the factors that contribute to accurate predictions. This knowledge transfer can potentially improve their ability to anticipate evolving market trends and customer needs,” she says.
However, she cautions that while AI can offer significant benefits, it is crucial to address ethical concerns that are associated with the use of this technology. Insurance professionals need to understand the limitations and potential biases of AI models. Learning about AI’s strengths and limitations will ensure responsible use and prevent unintended consequences.
“AI can greatly facilitate skills transfer in the insurance industry by providing personalised assistance, automating processes, enhancing risk assessment and promoting continuous learning. However, it is key to adopt a collaborative approach that combines AI capabilities with human expertise for optimal results,” Mahlangu concludes.