Artificial Intelligence (AI) and Machine Learning (ML) are already impacting customer expectations, service options, and how professionals operate. Recognizing the potential and understanding the impact of AI and ML is the first step to maximizing your organization’s use of the technologies.
As the industry continues to grow and transform, life insurance companies and professionals can leverage emerging technology to remain relevant and successful in a fast-paced, service-driven marketplace. Demystifying the technology and embracing the huge potential it offers for insurance are essential as we continue to deliver faster, more adaptable, and more efficient insurance solutions.
Once you recognize and understand the value it can bring, how can you make AI and ML work for your organization? Last year, we began testing how AI and ML can be used to enhance the accuracy and speed of delivery of our treaty review services and have learned a lot so far. Stay tuned for more on this initiative, in the meantime, continue reading for guidance on incorporating AI and ML in your life insurance organization.
Focus on the people behind the technology
ML and pattern recognition principles really stem from our “common sense” understanding of how humans learn and behave. If they are used correctly, they can be leveraged to transform the insurance industry into a convenient, service-driven experience for clients and more creative, strategic, and successful career for professionals.
While it can save on time and labour costs – it is important to maintain focus on the people behind the technology. Some people hear about automation and instantly think of jobs being lost. However, the people behind ML and AI will be the ones driving the direction of usage, training, implementation, quality assurance and more.
Align business and IT teams
To successfully employ ML strategies, it is crucial that organizations establish a Data Department that merges Business and Applications/IT departments. Closely aligning technical capabilities with business objectives will produce the best results and ensure the teams and tools are living up to their full potential. Whether you want to drive customer service with on-demand approvals, design new products based on past claims, or empower advisors with instant answers about business protocols – the team and the ML system both need to be working toward a unified goal!
Understand that teaching the machine requires programming and industry skills
Teaching the machine requires technical programming skill and industry expertise to get the most authentic and reliable outcome from the system. For technical programming, end-to-end considerations and input of data scientists and engineers will be required. Invest in talent that has proper knowledge of how these tools are used and the vision to generate the desired results. The industry expertise, will more than likely come from internal resources. Especially in reinsurance, as the industry is experiencing a loss in expertise due to retirement.
You can also consider utilizing a reliable ML/AI solutions provider to deliver expertise on the technology side. Combining their knowledge with your internal industry and IT expertise can result in a strong partnership to bring a solution to fruition.
Data preparation is necessary for success
Another key element of machine learning is preparing the dataset being used in the technology. This involves a set of procedures that helps make your dataset more suitable for machine learning and establishing the right data collection mechanism. Not only is this an important part of machine learning, it can also be the most time consuming.
This phase usually requires the involvement of a data scientist, but in the reinsurance world it would also require industry professionals who are familiar with the data involved. If you are using TAI reinsurance software, lean on industry experts that are familiar with the dataset and can help accelerate and drive your reinsurance AI/ML initiatives.
Continue to follow our experience with AI and ML
When it comes to making the first move towards machine learning and artificial intelligence, remember that people, planning and data will all play a big role. Given the novelty of the technology, education will also be necessary to get your organization on board. Make sure to subscribe to our blog as we continue to share more about our experience with AI and ML.