Key Challenges on AI, Machine Learning

Artificial intelligence has opened up a multitude of transformative opportunities for insurers to leverage within nearly every part of the value chain. This includes everything from risk management and fraud prevention to the development of new personalized products and enhancing customer service. Machine learning still has a long way to go before enabling the capabilities of Star Wars’ gangly droid C-3PO. Even in machine learning's current form, there are adoption challenges that prevent some insurers from moving forward with business initiatives based on AI. Insurers must recognize these challenges and address them head on to start taking advantage of the technology. Top Machine Learning and AI Challenges Currently, many insurers hesitate to move forward with AI and machine learning initiatives because of potential job losses, data management and limited time and skilled resources. Job Replacement. A significant percentage of an insurer’s investment and cost is staff. As the insurance industry continues to adopt more AI solutions, there is a valid fear among insurers that the livelihood of their agents, underwriters and other professionals is at stake. While commercial AI is not advanced enough to replace humans altogether, it can be a valuable tool today to enable and enhance humans. In fact, the AI solutions being built should have the perspective – “How to get 1+1 = 3?”, combining human capital with AI solutions. This can be observed in the use of intelligent chatbots. With NLP (natural language processing), machine learning and integration with back-end services, chatbots can be a great complement to a human agent. The chabot can provide insights to the agent for a more contextualized conversation with the customer. This allows the agent to deliver an empathetical and enhanced customer experience. See also: 4 Ways Machine Learning Can Help AI solutions should be viewed as opportunities to think outside the box to offer customer-centric solutions and not just to replace a current process with an automated AI solution. Data Management. Digitization and automation will create significantly greater amounts of data, which are necessary for a successful ML solution. The key, however, lies in the quality of data – whether one-time events or a continuous stream. The insurance industry is no stranger to using large volumes of data in developing insurance products, establishing premiums and better managing risks. To have a successful machine learning solution, insurers must combine traditional expertise with data management processes and harness the power of mature products that manage and cleanse data. Limited Time and Skilled Resources. Today’s insurers are working with full plates. As priorities often compete for time and resources, it is difficult to pick and choose from equally essential initiatives. While many are aware of the benefits that machine learning can bring to the table, insurers continue to grapple with the time, personnel and tight budgets to implement these new technologies. The other challenge is access to skilled resources who could implement AI/ML solutions. Unfortunately, these challenges create a “wait and see” attitude, pushing insurers further behind other industries and competitors that act to secure the first mover advantage. To take advantage of this new technology now versus later, insurers are partnering with innovative Business Process as a Service (BPaaS) firms that have made ML their focus to stay at the forefront of technology and innovations. Apart from leveraging the capabilities from the BPaaS and their partner ecosystem, this approach allows the insurers to free management and technical resources to focus on AI/ML Initiatives. See also: Strategist’s Guide to Artificial Intelligence   Conclusion The AI and ML technologies are mature enough and accessible for insurers. However, it is essential to view these technologies as enablers of new business capabilities and opportunities that might not exist today. For insurers to future-proof the way they do business and remain competitive, they must address these challenges and leverage existing foundational data management capabilities or BPaaS relationships to deliver customer-centric solutions.