The impact of AI can already be felt in the insurance industry as it tends to make processes more efficient and effective. Especially for this industry, insurance claims processing has been redefined by AI into a faster process that is low in errors and high on quality. The interactions between computer systems and humans has never been better and AI is expected to spread its influence in the insurance sector even further. Here at Sagacify, we value the importance of AI in insurance companies as illustrated by our previous contributions and attempt to prepare ourselves and our clients for what AI has to offer in the future.
Currently, AI techniques such as machine learning, deep learning and reinforcement learning enabled companies to be more efficient. It led to the models which are able to classify emails, process invoices, estimate and predict damage, detect anomalies, treat customer questions automatically and many more. Thanks to these technological advances, the use of AI translates to better decision-making, risk management, fraud detection and customer satisfaction.
Despite these improvements, the future will further increase the pace of change that will continue to impact the insurance industry. With new waves of deep learning waiting to be developed, insurers should prepare themselves for the trends that will shape the future landscape of the industry. With the growing amount of data, presence of physical robotics, improvements in cognitive technologies and use of open source and data systems, everyday life will not be the same anymore. Insurers should take into account new variations of risks and customer behavior as to take necessary actions for maintaining customer satisfaction.
For the time being, insurers face three big challenges:
These challenges might be solved with AI-powered solutions of the near future such as predictive and visual analytics, automated claim support and interactive chatbots.
When it comes to healthcare insurance, AI algorithms not only drive customer preferences but also go through large volumes of data to unveil patterns that will anticipate on certain health risks. The system takes on a proactive approach for encouraging healthy lifestyles and in doing so, it reduces health risks.
The appliance of tracking devices and Internet of Things to determine premiums has paved the way for dynamic and intelligent underwriting algorithms. Experts predict that the process of underwriting will be reduced to a few seconds resulting in the extinction of manual underwriting. Both internal and external information is gathered through API’s, outside data and analytics providers allowing for making ex ante decisions. The shift from a detect-and-repair approach to a predict-and-prevent is apparent. An example is deciding which car insurance premium to offer to clients. This can be personalized through installing an AI-driven device on the car that keeps track of its driving behaviour and to determine, based on the results, the right premium to be assigned.
The insurance claim process can be automated with the use of AI solutions where they remove steps that no longer require human intervention. AI ensures a model that autonomously is able to report the claim, estimate damage and risk, providing the system with updates and the customer with information. As a result, losses will diminish since people are aware of the risks of their action according to the advice given by the system. Feeding the model with standard claim reports will only improve its performance through the years discovering new patterns along the way.
Interactions with chatbots will support the customer in his search for an answer to their question regarding the insurance policy of the insurer. One purpose is for chatbots to act as a front line service desk in the hope to relieving the customer support team from all frequent and typical customer requests. That way, insurance can focus more of their human time interacting with customer that really need it. Another intent of customer interaction is to avoid potential loss by maintaining a solid relationship with the client through, for instance, real-time alerts or quick treatment of issues. Chatbot have been fairly limited until recently. But by combining Natural Language Processing with some kind of sentiment analysis, the AI-powered chatbot will screen the request and answer by means of his best possible answer.
With all this new AI-technologies in the pipeline, insurers should prepare for this new wave of innovation that will definitely disrupt the industry. Hence, developing an AI-implementation strategy to counter the upcoming challenges in the industry is not a luxury. Adopting AI to develop innovative products, derive insights and redefine processes has to be considered when planning to retain your competitive advantage in the future.
It is more important for your first few AI projects to succeed rather than be the most valuable AI projects. A first success will created visibility, generate internal buy-in and build confidence.
Success should come fast. Put in place an AI team (outsourced to go much faster) to partner with your domain knowledge team and build AI solution that can show traction in 6-12 months.
The project should be technically feasible. Too many companies are putting the bar too high for their first project which may lead to disillusion. Surround yourself with trustworthy AI expert and perform together a due diligence on what feasible and what is not.
You can then capitalize on the previous outsourced projects and execute a long term sequence of AI project.
Once the insurer understands the value of AI, he should start building the AI roadmap consisting of four core elements: data capabilities, organization and talent, models and tools, and change management. The roadmap should focus on creating value and must stimulate the company to keep on differentiating themselves from the competition. On top of that, it should include different checkpoints and milestones where possible modifications to the plan are evaluated based on what is happening in terms of AI and how this can affect the current operations at that moment.
Based on this first experience with a few AI projects, insurers can start thinking about AI strategy. Gathering high volumes of data for your AI models to analyze and improve the incorporated models is essential for preparing your organization for what comes next. In other words, try to fit your business model into a virtuous cycle of AI. Data is the new oil that will power futur AI strategy.
Finally, insurers are advised to lay the groundwork for an IT infrastructure including the necessary partnerships with external sources or integration of new roles (data engineers, cloud computing specialists, data scientists…) in the organization. Embracing AI technology is the only way to guarantee the right decisions and investments to be made in order to arm themselves for the future.
At Sagacify we accumulated the necessary know-how and skills to serve your needs now and in the future. Previous and upcoming projects spread over several sectors, but insurance activities are not unknown territory for us. We have proved our capabilities already through developing models that increase customer happiness, classify emails, detect anomalies and many more. As we value continuous improvement and learning, Sagacify will certainly ensure it is ready for what comes next.
