Even the players in the insurance world, in the run-up to an inevitable and complete digital transformation, are increasingly focusing their attention on the advances in artificial intelligence (AI) and machine learning (ML), looking for new tools and new methodologies to deal with business challenges along the entire insurance value chain. The experience gained over years of developing ML and AI projects to support companies operating in the Insurance sector in achieving their business goals.
From Digital Technologies To Process Automation
The insurance sector is going through a difficult and exciting moment in the transition to digital: the Claims Process still rests wholly or in part on a large number of paper documents, and for this reason, it is often characterized by slow procedures and payments still slower. The new technologies developed in the ML and AI fields offer important help in this context because they can automatically perform the repetitive tasks that operators still carry out by hand. In addition to the need to improve internal operations, another factor is accelerating the abandonment of analogue practices: customers, too, are asking for levels of service comparable to those of sectors in which the Customer Experience now ensures quality, speed and personalization.
But it is not simply an inevitable opening to digital technologies. These are the new opportunities for process automation (or process modules) that open up particularly interesting prospects for the sector, revealing the possibility of freeing employees from all those low-value activities that machines could perform more efficiently. Insurers progressively recognize the potential of AI in its various applications in different areas of intervention: from underwriting forecasting to estimating the insured’s probability of loss, from product pricing to claims management, fraud detection, customer experience and sales support.
Chatbots And Data Processing Tools: The Future Of Insurance Is In Artificial Intelligence
Several technological solutions based on artificial intelligence appear destined to revolutionize the insurance sector, two in particular:
- Chatbots, which assist the consumer in receiving payments and complaints and managing claims reimbursements, analyzing the technical documentation, for example, relating to a user’s medical file, and reducing response times: conversational interfaces that enrich Search User Experience with new features;
- The image and visual data processing tools that use ML techniques to simplify and optimize the claim process, identifying the damages related to accidents directly through photographs and estimating any premiums to be paid to the insured. In addition to classifying the damage, these tools can predict a cost range for the claim through historical data on repairs (parts list, labor, black box telemetry).
For technologies linked to artificial intelligence to evolve in increasingly effective and efficient directions and positively impact the insurance space, the ability to detect, structure and interpret data will have to grow simultaneously.
The Centrality Of Unstructured Data
Data has always played a central role in the insurance industry, but more than ever, organizations are overwhelmed by enormous quantities coming from many sources. In a recent Accenture article, it emerges that most insurance companies process only 10-15% of the data they have access to, most of which is structured data stored in traditional databases.
Organizations fail to extract value from a significant “slush” of structured data and overlook the valuable insights hidden in unstructured data. One of the major advantages of ML is that it can be successfully applied to structured, semi-structured or unstructured datasets to formulate more predictively accurate assumptions about risk, complaints and customer behavior.
Some Use Cases: Claims Processing, Fraud Prevention, Document Management
Claims Processing
ML can be effectively used to improve operational efficiency by automating claims processes, starting with claims capture. This can reduce claims settlement times and improve the customer experience. Artificial intelligence models also allow insurers to assess the cost ranges of claims better, allowing them to carry out targeted surveys based on the more accurate indications provided by the algorithms.
Fraud Prevention
Insurance companies lose an estimated $30 billion annually to fraud. ML and AI techniques make it possible to identify potential fraudulent requests. Faster and more accurately by drawing on unstructured and semi-structured data, such as claims, documents, and structured data. Identifying risks early in the process provides insurers, among other things, with considerable savings on the costs associated with using consultants.
Document Management
Globally more and more companies are integrating fully automated methods in the value chain, including Insurance: world spending on this type of technology, which was worth 680 million dollars in 2018, is about to touch 2.4 billion in 2022. ML and AI are also growing in the claims management industry, especially in document flow management support activities.
The new tools allow for handling a large number of openings for each customer and the possibility of optimizing the entire process of managing the documentation produced during the settlement, including texts written entirely by hand. The role of technology in the ecosystem is constantly evolving, and to predict future developments, anticipate, direct and govern them, it is necessary to have acquired considerable maturity in terms of skills and experience.
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