Decision intelligence & AI: supercharging the claims process

By Julio Pernía Aznar
Julio Pernía Aznar, CEO of Bdeo, explains how decision intelligence can supercharge the insurance claims process and benefit insurance businesses.

Decision intelligence refers to the use of transformative technologies to align processes and models for decision-making. It allows businesses to hone in on data central to their objectives, analyse it, and solve critical problems at the core of their business.

A key aspect to note is that decision intelligence models can ingest information in any format, irrespective of its origin. Therefore, stakeholders can access information, fit it into existing frameworks, and automate analysis. The model is trained continuously using real-time data, enabling accuracy in decision-making, which is particularly important in the insurance claims process.

Gartner has named decision intelligence one of the top strategic technology trends of 2022. Here’s a look at why decision intelligence is a prerequisite for insurance businesses in today’s day and age, and how, coupled with artificial intelligence (AI), it is helping insurers make informed decisions that are both accurate and context-specific – a must-have in the industry.

Addressing challenges in decision-making throughout the claims process

According to a report by Signal AI, a staggering 96% of business leaders and decision-makers believe that AI will transform the way business decisions are made.

This statistic alone shows that stakeholders at various enterprises are warming up to the idea of AI. However, there’s still a lot to be realised regarding the potential that AI holds for their business.

With the multitude of data available today, manual analysis is challenging – and this is precisely where AI can prove beneficial, assisting underwriters in automating the analysis. By doing so, they can focus on better supporting their customers and processing their insurance claims more quickly.

Navigating the abundance of data

Data can be extracted from a plethora of sources both online and offline. However, things are a little more complex in the insurance sector because underwriters require more than just basic personal information. In most cases, they need access to a customer’s personal history, financial history and dependency, extensive tax information, a list of assets and liabilities, location and environmental information, and more.

Using AI, the consolidation and analysis activities can be automated, making claims processing a lot more efficient. It helps underwriters determine a customer’s ability to pay and give a reasonable rate while accounting for all the potential risks their profile can pose.

Striking a balance between humans and AI

McKinsey predicted that by the year 2030, claims processing will be the most important function in the insurance industry. Even then, the expectation is that most of it will be automated. It has led to lopsidedness in the industry, with claims professionals finding it extremely hard to play catch up with this system.

AI-led decisions severely lack context, but human-led decisions are prone to errors. Here, the question is not about who is more important but rather about who needs to do what. Combining the best of both worlds is vital.

Striving for a holistic claims process

Traditionally, claims processing is time-consuming. Because of this, professionals merely look at the extent of damage and provide their customers with a fixed number. The actual context lies in the data – text, images, historical accounts, financial reports.

Still, this approach is highly subjective. Therefore, using AI to tackle data processing and analysis can add a degree of semblance, driving a holistic approach.

How insurance businesses can benefit from decision intelligence & AI

The primary goal of decision intelligence is to make decisions that are backed by facts and context. Without either of them, the claims process could result in several biases and inaccuracies.

It’s critical to identify processes that require automation, human intervention, or both. For example, assessing the damage to vehicles and properties does not require human intervention, as visual intelligence models can handle that. Similarly, identifying what else can be automated and implementing that is also a part of the process.

Using the reported data, the assessor can make more accurate decisions in a shorter period, increasing customer satisfaction with the claims process.

Democratisation of data

Data is ubiquitous, but that does not mean it’s always accessible. Based on the data type and its source, insurance processors can find it hard to analyse. Most of this data is unstructured and stored in specific repositories, leaving employees dependent on the IT desk to help them out.

Passing this data through a central system powered by decision intelligence makes things easier. It enables smooth processing, analysing, and structuring of information into a more digestible format. Besides, it also gives stakeholders control over the information they need and allows them to access it whenever they desire.

Stay ahead of the competition

The best way for insurers to stay ahead is to offer incredible customer service. But, to provide excellent service, the right systems must be in place. In claim processing, automating the process of decision-making can prove exceedingly beneficial as it saves insurers investment in terms of time and effort and ultimately passes those savings on to the end customer.

The insurance industry's future heavily relies on the acceptance and adoption of automation throughout the customer lifecycle, so it’s critical to stay ahead of the curve by exploring these solutions now.

About the author: Julio Pernía Aznar is CEO of Bdeo, a technology company that provides visual intelligence for motor vehicle and home insurers. A fast-growing insurtech AI startup with proprietary 'visual intelligence' technology, Bdeo has increased its international presence by 30% over the past year and now boasts offices in Europe, Latin America, and Africa.

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