"Intelligent Decision-Making" Is the Future

An increase in digitization, the rise of AI and better value-tracking methodologies have paved the way for more advanced technology like "intelligent decision-making."

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Automating insurance business processes is certainly not a new concept. Many insurance companies have implemented rules-based engines and business process management software and continue to do so. However, insurance is complex and heavily regulated, with state and regional exceptions quickly overwhelming these process models and solutions. Ultimately, this leads to a massive repository of rules that is messy and difficult to maintain. To overcome this challenge, some insurers are investing in "intelligent decision-making."

What Is Intelligent Decision-Making?

Intelligent decision-making is fundamentally the ability for AI to ingest information and use it to make a decision or recommendation for the next best action. It can be used to automate and streamline everyday tasks to complement and enhance the productivity of skilled knowledge workers and customer service representatives. 

The first key component of intelligent decision-making is digitalization. The insurance industry is still a heavily paper-based industry, but times are changing, and insurance is amid a transformation driven by digital technologies. Advances in technology and digitalization have dramatically increased the volume, variety and velocity of data, which is the second key component of intelligent decision-making. Data enables insurers to make more informed decisions about risk and business processes. 

However, data on its own is worthless without the ability to use it for insights to transform the business. Hence, the third component for intelligent decision-making is AI. AI uses machine learning algorithms to analyze data to replicate human decisions, resulting in faster and improved decision-making processes. The key value proposition of an intelligent decision-making engine is the ability to integrate AI-driven machine learning models into core transactions systems.

The final component for intelligent decision-making is value tracking. Delivering value is crucial for the success of any AI and intelligent decision-making project, so it is essential to measure the business benefit and ROI from implementing AI and machine learning models. 

See also: The Evolution of Leadership Intelligence

Challenges of Intelligent Decision-Making

Intelligent decision-making is not easy, and insurance companies need to overcome many challenges to use it successfully. Some challenges arise due to the regulation of the insurance industry. For example, carriers and compliance officers need to prove to regulators how the model makes decisions. Doing so is not always straightforward; some types of AI, such as neural networks, are not transparent. There are also fears about model bias and fairness, especially when associated with age, gender and race.

Insurance companies must also consider data usage and privacy issues. Ethical use of data is becoming essential. It goes further than simply adhering to the rules and obligations imposed by regulators; insurance companies must also apply their own judgment in line with organizational values and commitment to building customer trust.

Lastly, to use intelligent decision-making successfully, companies must put in the work to improve their data quality. AI and intelligent decision-making are only as effective as the data used to train the models. Poor data quality leads to inaccurate and faulty algorithms.

Use Cases for Intelligent Decision-Making 

Companies are using these models to drive efficiency in several ways. Companies can detect point-of-sale and claims fraud by using AI to identify fraudulent behavior such as suspicious quotations, policy applications and claims. An intelligent decision-making engine can improve productivity and effectiveness in underwriting with instant information about underlying risk characteristics and by reducing manual dual entry of submission data into rating engines, underwriting workbenches and policy systems. 

Lastly, as consumers have become more familiar with digital tools and more comfortable with remote and virtual interactions, insurance companies are looking for better ways to engage with and enhance the customer experience. An intelligent decision-making engine can be used to analyze all customer and operational data to determine the next best actions along the customer journey.

Concluding Thoughts

Insurers are navigating a complex industry full of complex regulations and massive rule repositories, but implementing intelligent decision-making is helping some carriers respond to these hurdles more rapidly. An increase in digitization, the rise of AI, and better value-tracking methodologies have paved the way for more advanced technology like intelligent decision-making.

To learn more about how property/casualty insurers are using intelligent decision-making to bolster their processes, read Aite-Novarica Group’s report Intelligent Decisioning for P/C Insurance: How AI Is Automating Insurance Business Processes.

Stuart Rose

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Stuart Rose

Stuart Rose is a strategic advisor on Aite-Novarica’s P&C insurance practice. He is responsible for market research and delivering strategic advice on applying data, analytics and technology.

Rose began his career as an actuary at a leading global insurer in both its life and property and casualty divisions. Prior to joining Aite-Novarica, he worked for a variety of software vendors, including at SAS for nearly a decade. He has been responsible for go-to-market strategies, product marketing and application development. He has extensive experience working with insurance companies across the globe, including in the U.S., the U.K., continental Europe, Latin America, Asia and South Africa.

Rose graduated from the University of Sheffield with a B.Sc. in mathematical studies. He is a regular contributor to insurance publications, frequently speaks at industry conferences and is co-author of the book Executive Guide to Solvency II.


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