While in practice for decades, “machine learning” has become a major buzzword in today's rapidly evolving business landscape. From optimizing marketing campaigns to enhancing customer experiences, machine learning is a subset of artificial intelligence (AI) that has the potential to revolutionize virtually all aspects of your business operations. But what exactly is machine learning and how can insurance distribution professionals take advantage of its potential? In this post, we'll demystify machine learning and explore its applicability to insurance distribution.
What is Machine Learning?
At its core, machine learning is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed. In essence, it allows computers to analyze data, identify patterns, and make decisions or predictions based on those patterns. This powerful tool can be used to speed time to market, reduce costs and produce better outcomes on a day-to-day basis. As you will see, the potential applications of machine learning for insurance carriers and marketers are broad and far-reaching.
How Does Machine Learning Work?
Unlike traditional programming, where rules are explicitly defined by humans, machine learning algorithms must learn from data to make informed decisions autonomously. Machine learning algorithms process large amounts of data, known as training data, to identify patterns and relationships. These algorithms then use these patterns to make predictions or decisions when presented with new, unseen data. Additionally, the algorithm “learns” from its own results (its experiences), becoming more accurate as time goes by. The process typically involves several key steps:
1.) Data Collection: the first step in any machine learning project is collecting relevant data. This data can come from various sources, and should include both internal and external data sources such as customer interactions, sales data, demographics, and more.
2.) Data Preprocessing: once collected, the data often needs to be cleaned and preprocessed to remove noise, handle missing values, and standardize formats. This step ensures that the data is suitable for analysis by machine learning algorithms.
3.) Model Training: during the training phase, the machine learning algorithm learns from the training data to identify patterns and relationships. This involves adjusting the model's parameters to minimize errors and improve performance.
4.) Model Evaluation: after training, the model is evaluated using a separate dataset, known as the validation set, to assess its performance. This step helps ensure that the model generalizes well to new, unseen data.
5.) Model Deployment: once validated, the model is deployed in production, where it can make predictions or decisions in real-time. Continuous monitoring and refinement is necessary to maintain optimal performance over time.
It’s easy to imagine multiple potential applications for machine learning in insurance distribution, but the question remains – how can you best assess whether a given business problem represents a good fit for a machine language solution? MIT Professor Thomas Malone recommends a disciplined approach of assessing three requirements to determine the viability of machine learning as a solution to any business challenge.
Requirement 1, Can the business issue be formulated as a machine learning problem?
The first requirement outlined by Professor Malone is the ability to formulate the issue as a machine learning problem. Essentially, this means identifying a specific task or objective that can be addressed using machine learning techniques. Whether it's predicting customer behavior, classifying data, or optimizing processes, defining the problem accurately lays the foundation for a successful machine learning solution.
Requirement 2, Is the relevant data available in enough quantity?
Training data is the lifeblood of machine learning. Without sufficient and relevant data, machine learning algorithms cannot learn patterns or make accurate predictions. It's essential to have access to a diverse dataset that encompasses the factors influencing the problem at hand. This data should be clean, well-organized, and representative of the real-world scenarios the model will encounter.
Requirement 3, Does the system have enough regularity (i.e., not chaotic and unpredictable)?
The third requirement emphasizes the importance of system regularity, implying that the problem should exhibit a degree of predictability and consistency. In other words, the underlying patterns governing the problem should not be chaotic or entirely unpredictable. Machine learning thrives in environments where there are discernible patterns and regularities that can be learned and exploited by algorithms.
Machine Learning in Health Insurance Distribution
The Health Insurance value chain is rife with data, but can machine learning turn it into strategic value? As previously discussed, it is important that AI initiatives tie back to an organization’s strategic goals. We believe every insurance carrier and marketer should be striving to lower customer acquisition costs while increasing customer lifetime value (or put another way, optimize LTV/CAC). The question is then, do you have data within your organization and from the outside that can help achieve this goal. By following the requirements outlined above, it is easy to imagine multiple ways machine learning could help advance this goal.
Example - Agent Compliance
A not-necessarily obvious area where machine learning could add huge benefits is agent compliance. Sometimes, improper agent behavior can be improper due to lack of training or it can be a sign of ill intent. Either way, bad actor agents can mislead or defraud customers leading to chargebacks, rapid disenrolls and ultimately regulatory complaints. This kills LTV and can have devastating consequences for insurance carriers. Following Dr. Malone’s three-requirement paradigm, this seems like an excellent application of machine learning: 1) The formulation of the problem is simple - can ML learn to spot the behavior of non-compliant agents early, during the sales process, predicting regulatory issues well before they happen? 2) The data is readily available - combining historical data with real-time monitoring of phone calls and email exchanges. 3) There is potential for regularity in the system - it seems likely that regular patterns will emerge that will allow ML to identify bad acting agents after just a few interactions, allowing human intervention long before actual compliance problems arise.
Conclusion
The world of machine learning holds immense potential for revolutionizing insurance distribution, offering a myriad of opportunities for insurance carriers and marketers to optimize their operations and enhance customer experiences. Machine learning presents a transformative opportunity for insurance distribution professionals to drive innovation, improve operational efficiency, and deliver superior value to customers. By embracing machine learning and integrating it into their strategic initiatives, insurance companies can unlock new opportunities for growth and success in today's competitive marketplace.
Want to learn more about the future of AI in insurance distribution? Get in touch here. For prior posts in this series, click here or below: