Why Leveraging Machine Learning is the Key to Reducing Claims Risk

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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.

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