Applying Artificial Intelligence Technologies in your Organization

The chatbot landscape has seen massive interest and change with many different players doing many different things. Even tech experts find it hard to distinguish solutions among platforms, frameworks, natural language processing technologies, messaging players and code-free bot builders. So, it’s even harder for businesses to figure out what is required to build a chatbot and how much they should pay for it (prices for chatbot projects can vary greatly, from $5,000 to $500,000).

Four key steps to enabling the success of a bot project include defining the right use case, designing the conversation flows, choosing the most appropriate tech stack and developing an agile methodology to improve bot performance based on collected analytics before scaling up. At Mercer, we have successfully piloted several chatbots, one of which was in a finalist in the Singapore Fintech Awards in 2017, and we are scaling up our chatbots initiatives globally.

• Algorithmic Decisions or Advisory

This group of applications includes scoring and risk-modelling, recommendation engines, virtual underwriter assistants, virtual agents and brokers, robo-advisors, and algorithmic models to extract knowledge and predictions from large and diverse data sets to mimic human intelligence in decision-making. With the ability to analyze “big data” (that is, analyzing very large volumes of data with high velocity, variety and veracity), AI can accomplish years of human work in moments. Therefore, with AI, personalized solutions for “segments of one” from millions of data points around customer profiles and behaviors are possible. AI-powered recommendation engines, or virtual agents, are able to understand customers’ needs at a much more in-depth level and can match them with the best insurance products. This capability is crucial in the insurance digital ecosystem model where insurance players try to curate solutions from ecosystem partners to provide users (many) choices. Such intelligence helps consolidate personalized choices. AI-powered recommendation engines and robo-advisors are also important for the “insurance on demand” model, as their intelligence helps insurance platforms deliver customized coverage for specific items and events.

Another area in which AI is assisting human decision-making is underwriting. AI prowess can create a powerful new paradigm for underwriting, creating competitive advantage for insurance players—especially important in the current environment of risk homogenization, margin pressures, and price-sensitive markets. AI is facilitating three distinct innovations in underwriting: predictive and dynamic underwriting, evidence-based underwriting, and automated underwriting, allowing more accurate and personalized underwriting using (almost) real-time information at a much faster speed. Searching medical databases globally or collecting and analyzing other evidence, such as electronic medical data from third-party providers can be automated and supported by the combination of many AI technologies (machine learning, natural language processing, etc.). AI models can learn and predict risks from large sets of data, including non-traditional underwriting dimensions such as consumer behavior collected in real-time from internet-of-things devices(for example, driving behavior captured through car telematic devices or health and wellness data from wearables), making highly dynamic, context- or usage-based pricing possible. Cognitive computing will push automated underwriting to a new frontier. Automation of AI predictive risk-scoring algorithms can therefore potentially help solve one of the traditional challenges of the insurance industry: the long, invasive, and complicated underwriting process. However, such intelligence is based on probabilities and past human decisions, which Organizations should not use such algorithmic decisions as a “black-box model,” but rather they need to have the capabilities to understand and scrutinize all predictions and decisions that the machine makes. Overreliance on the AI models that organizations do not fully understand could have significant consequences; companies should employ AI risk assessment and scoring systems as an advisory tool for human oversight.

• Further Applications of AI

The insurance industry is also using AI for fraud detection, image detection, and so on. Many forward-looking players are exploring AI applications across the value chain and operation stages, from product optimization, customer engagement, pricing and underwriting to claims and payment processes. While waiting for business stakeholders to identify use cases and approve budgets, IT departments can start experimenting and upskilling by applying AI to IT processes that are often intensive and sometimes too complicated for humans to do alone.

Three Fundamental Issues for Organizations to Consider Before Adopting AI

Three specific issues organizations need to think strategically about from the start of their AI journeys are:

• Creating a sufficient supply of AI skills

• Gaining better access to data

• Creating a pipeline of use cases

Because AI is a spectrum of technologies, organizations first need to know what problems they wish to solve with what technologies before acquiring and building the corresponding skills. AI feeds on data, and one key inhibitor to the further expansion of AI is the separation of data in silos and having limited access to different data sources. Organizations must think of data as an important class of assets and develop strategies to access, acquire, protect and capitalize on this asset. Last but not least, building trust in AI is an important overarching strategy that covers governance to improve trust across steps from data quality, to data-sharing mechanisms, to data protection, to model accuracy.