Can AI solve the property data dilemma?

In the world of property insurance, data is crucial. However, despite undergoing a major digital transformation over the past few years, insurance companies still face numerous data-related challenges.

Accessing and properly managing quality data, especially for an extensive portfolio of properties, is still extremely complicated. Without high-quality data and insights, insurers can struggle to identify risk, reduce costs and make good decisions at scale.

One of the biggest issues is the lack of a single, consistent way to identify property data and an individual building’s corresponding records. Therefore, it’s not uncommon for property data to be outdated, incomplete or contain errors. This leaves insurers with the strenuous task of managing, storing and extracting value from an ever-growing volume of data from disparate sources. Forrester reports that between 60% and 73% of all data within an enterprise is never analyzed at all.

The good news is that artificial intelligence (AI) is emerging as a powerful tool to help insurers deal with these data challenges. From automating processes to producing real-time insights for more informed decision-making, here is an overview of how AI is helping insurers conquer property data management.

1. Dealing with fragmented data

One of the most significant hurdles in the property industry is the fragmentation and lack of standardization of data. According to a Forrester survey, manual routing and process gaps complicate claims and underwriting processes — with 37%  of business and technology decision-makers reporting that their organizations experience these problems. Accenture reports that insurance underwriters are spending nearly 40% of their time on non-core activities, leading to an estimated loss of between $85 and $160 billion over the next five years.

Although online quote tools took the property insurance industry by storm many years ago, property data is often compiled from a variety of sources, including brokers, property managers and public records, and it can be inconsistent or incomplete. These tools can also require some waiting time, as many still require some additional data be added by a real person. AI solutions are addressing this challenge by automating the process of data consolidation and standardization. Through natural language processing (NLP) and machine learning (ML) algorithms, AI can identify, interpret and integrate data from disparate sources, and generate quotes in seconds. This enables insurers to understand risk better and make faster, more informed underwriting decisions.

2. Improving data quality and accuracy

Accurate data is crucial for underwriting and risk assessment. Inaccurate or outdated information can lead to costly mistakes, such as underinsurance. In the UK, 40% of commercial properties and four in five UK households (80%) are underinsured. This can result in underinsured customers having their claims rejected or only partially paid out. For insurers, it can mean being held liable for paying the difference.

However, accessing quality data is still a daunting process for many insurers. AI can step in to help insurers improve data accuracy by continuously monitoring, validating, and updating data automatically. Advanced analytics and ML algorithms can process large datasets quickly and efficiently, providing previously unattainable insights.

AI can also help identify errors, inconsistencies and anomalies, ensuring insurers can access the most reliable and up-to-date property information. Lastly, predictive modeling can anticipate market trends, helping insurers make proactive decisions and stay ahead of the competition.

3. Smarter risk management

Failure to accurately assess risk can result in financial losses and reputational damage for insurers.

Assessing damage after a catastrophic weather event is particularly important for all parties concerned, especially property insurers. Claims adjusters need to be able to focus on assisting those who were affected. For instance, to help them find temporary accommodations. Insurers also need to be able to set reserves, determine whether external assistance is needed with the adjustment of the claim, and plan for reconstruction costs.

AI and advanced analytics play an important role in facilitating risk management, helping insurers monitor regulation changes, analyze potential impacts, and automate compliance reporting. The technology can also be used to create predictive models to evaluate factors automatically and identify potential risks before they occur. For example, the effects of market volatility and climate-related events on a property’s valuation and condition. This enables insurers to create more proactive and accurate risk mitigation strategies.

Unlocking more value from property data

As property insurers continue to grapple with data-related challenges, AI is proving to be a game-changing solution. By addressing data fragmentation, improving data quality and accuracy, and supporting risk management, AI is bridging a new level of standardization to the property data landscape and helping insurers unlock exciting opportunities for growth and success.

Jakub Dryjas is the CEO of Tensorflight, a property intelligence platform powered by AI that combines ground-level imagery with satellite and aerial imagery to provide underwriters and insurers with accurate datasets.

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