AI and ML in insurance: How organizations interpret the data is the difference-maker between hitting a growth plateau and reaching new levels of success.
The insurance industry has radically evolved over the years, and data has come a long way from what used to be a manual process involving writing down on paper to storing in databases and processed by applications. Data has always played a vital role in empowering insurance industry leaders in driving decisions on facts, figures, and patterns. Insurance-like any business has witnessed an exponential rise in the quantum of data generated from various sources.
Artificial Intelligence and Machine Learning in the insurance industry
AI and ML make it easier to successfully leverage the data- the way organizations need them and at the pace they desire. According to Gartner, Artificial intelligence (AI) and machine learning (ML) are at the peak of the hype cycle. Most insurance organizations have access to data that is structured and stored in traditional databases. However, analyzing unstructured data and deriving insights and trends is fast becoming a problem for insurance providers. ML and AI present a valuable opportunity for insurance organizations to gain trusted, timely, actionable insights from structured and unstructured data.
Benefits of AI and ML in insurance
- Integrating AI and ML into the insurance ecosystem allows for a multitude of benefits, including saving a tremendous amount of time and money by automating manual tasks and optimizing routine tasks and overall processes.
- Helps increase productivity in the workplace and drives operational efficiencies
- Helps remove the guesswork out of decision making and improving the outcome by combining AI techniques for maximum flexibility
- Improve customer interactions and customer engagement
Primary use cases of AI and ML in insurance
Traditional claims processing is manual-intensive. The entire process is inundated with paperwork, manually written correspondence, follow-ups, and outdated legacy point solutions. AI and ML allow insurers to reduce the time and money spent on routine claims settlement processes. A fine example of cost-cutting is the automation of application processing that involves extracting valuable information from a large volume of documents. By leveraging AI-driven document capture technology, carriers can automatically capture relevant information from documents. This results in reducing human errors and the cost of processing while expediting the overall claims process.
Risk management through machine learning
Another problem is predicting premiums and losses for their policies. Identifying risks early enables insurers to develop plans that take action before the risk occurs. Identifying risks using machine learning helps organizations make better use of underwriters’ time and make the whole process much more straightforward. The ever-changing risk environment poses significant challenges to insurers. Gaining deep insights into emerging frauds, credit risks, and fast-changing regulations requires more than ordinary efforts. With rich analytics and pattern prediction capabilities to help you get the job down, your team can detect credit risks with higher accuracy.
AI and ML can reshape the fraud claim detection process
AI and ML can reshape the fraud claim detection process. There are a variety of issues, including odd claims or employees making huge questionable financial transactions. However, there is a good way to reduce such fraudulent claims by employing advanced fraud detection, prediction, and prevention technologies. AI-driven fraud detection is overcoming one of the most significant barriers to the detection of fraudulent claims. The right AI-driven solution can
- decrease false positives significantly
- ensure minimal fraud run-time
- enhance fraud detection
- help quickly gain business-critical insight
Improving operational efficiencies with AI and ML
Internal processes of insurance companies still rely on manual-intensive practices; as a result, errors occur regularly. Repetitive errors can often be traced back to the claims cycle (due to extensive human intervention). AI and Ml algorithms can be used to continuously assess and find errors present in the system much faster than underwriters. If done right, these AI and ML applications can be a game-changer in improving operational efficiencies.
Increasing customer engagement and improving customer experience
The old-insurance style of connecting with customers relies on lengthy and complicated processes widely considered outdated and replete with confusing questions. But today, conversational AI chabots or virtual assistants seamlessly connect with the customer, answer queries instantly and give empathetic responses. Thus, while being a great investment for reducing average handling time, these AI chabots also play a vital role in increasing customer engagement and improving customer experience.
Challenges insurers typically face when implementing AI and ML
AI and ML is only as good as the data
Building out AI systems can be difficult when high quality and reliable data is unavailable. AI and ML systems are only as good as the quality of the data. AI and ML are promising technologies, however high quality data results in optimal benefits.
Lack of skill poses a significant barrier to the adoption of AI and ML
Skills recruitment is a challenging barrier for insurers. Business and IT leaders are aware that acquiring skills will be essential to accomplish AI jobs. However, attracting the right talent in the wake of ‘cool’ startups has been the Achilles heel in insurance providers talent hiring. The recent advancements in the insurance industry require fresh skill-sets in Artificial intelligence and Machine Learning, compounding the urgency to hire skilled talent. There is a need to balance leveraging emerging technologies on one side and updating skills and training on the other side, which could enable faster & more efficient preparedness during challenging times.
The urgency to leverage AI and ML technologies and transform obsolete systems has increased as insurance companies adapt to new processes in response to the changes in the business environment. ML and AI are intuitive systems, but it often takes a trained eye to ensure implementation is done efficiently and successfully.