Unstructured data
Insurers often have access to far more unstructured data than the structured data they hold in databases and process in their normal workflow.
Utilising this unstructured data (anything from a letter from a customer about their accident to an industry report about security weaknesses) is often labour intensive so only done when there is a specific requirement.
Machine Learning can process this data and determine its relevance to risk assessments, claims handling and more.
Machine learning has revolutionized the insurance industry by enabling the creation of accurate predictive models that can identify and assess risks at the beginning of the underwriting process.. By leveraging large datasets and advanced algorithms, machine learning models can ingest and analyse vast amounts of data from various sources, such as policy applications, claims histories, and third-party databases, to identify patterns and trends. This allows underwriters to quickly identify high-risk policies and focus their attention on those cases that require closer scrutiny. Another significant benefit of machine learning in insurance is the improvement in accuracy and consistency. Traditional underwriting methods rely heavily on human judgment, which can be influenced by factors such as fatigue, bias, and variability. In contrast, machine learning models are designed to identify patterns and relationships that may not be apparent to humans. By removing human subjectivity from the equation, insurers can reduce errors and inconsistencies in underwriting decisions.
Claims Handling
From using OCR to read claims forms to predictive models for claims costs, the claims handling process can be sped up dramatically for the vast majority of claims, with less costs and more accuracy, and/or allowing human employees more time to investigate the more ambiguous claims in more detail.
Fraud Prevention
It is estimated that claims fraud costs the Insurance Industry upwards of $30,000,000 per annum! Machine Learning can quickly classify those that are low risk, clearly fraudulent or requiring detailed investigation.
Other Uses
Chatbots and automated call handling can improve the customer experience, ML can be used to recommend policies and products and there are potential benefits in other areas such as analytics, customer retention and even audits.