Why Leveraging Machine Learning is the Key to Reducing Claims Risk

According to a survey by McKinsey, artificial intelligence (AI) is expected to drive at least 25% of the processes in insurance by 2025. To remain competitive, exploring technology options for claims management is crucial. One key technology, machine learning (ML), holds immense potential for optimizing risk reduction and improving outcomes.

Importance of Machine Learning for Intelligent Claims Management

Machine learning refers to the ability of computer systems to learn and adapt their operations without following explicit instructions. This allows data patterns to be analyzed to identify problems and anomalies. Examples like natural language processing (NLP) and ML-augmented optical character recognition (OCR) deliver meaningful results in claims programs.

Why are these technologies beneficial for risk management? Consider the initial adopters of intelligent technologies: financial institutions. They have a wealth of information in the form of clean, transactional data with their customers. Now think about an insurance claim. Much of the information is in text, reports, and discussions – in other words, dialogue rather than data. Medical information is another component of a claim that does not break down into clean data. Making sense of this type of information without human involvement requires the ability to read and digest the context from voice communications.

Analyzing and tracking psychosocial information benefits industries with high levels of person-to-person interaction, including healthcare and claims management. Machine learning’s ability to interpret language means it can accurately process information much more rapidly than a human. For the first time, psychosocial information can be automatically interpreted and made actionable.

Here’s an example of machine learning’s value in claims management. Many injured workers are engaged in recovery and eager to return to work and everyday life. Now consider other employees who may not be looking forward to returning to the job for various reasons. Their attitudes can make a significant difference in their recovery. How do you identify these differences early on to take proactive measures to improve a claim’s outcome?

ML can spot the behaviors that indicate a claim’s trajectory and alert adjusters to take immediate action as needed. ML-augmented optical character recognition (OCR) can even spot patterns in speech to discern clues about the injured worker’s attitudes and emotions.

What Prevents Companies from Adopting ML?

Misconceptions about ML hinder adoption, with many believing it is expensive and labor-intensive. However, “plug and play” models offered by tech companies such as Google, Amazon, and Microsoft reduce costs by allowing other companies to benefit from this technology via a lower cost of entry.

The need for more high-quality data poses another challenge. However, by partnering with firms implementing ML and OCR, organizations will gain access to all the essential data streams, enabling real-time trend identification without independent system development.

Also, contrary to the belief that machine learning will reduce the need for employees, ML frees employees from mundane tasks, allowing them to focus on more meaningful work and making the company more competitive.

Meeting that 2025 Prediction

ML-powered solutions are valuable innovations that are becoming necessary for remaining competitive in nearly all business sectors and efficiently managing the processes that some form of AI will power within the next two years. Those who effectively apply machine learning will provide increasingly superior value to their client partners and customers and achieve a competitive advantage.