Leveraging Artificial Intelligence (AI) for Automatic Image Analysis

By Christian van Leeuwen, FRISS

Utrecht, Netherlands (Apr. 3, 2018) – Insurance companies consume terabytes of information every day in the form of digital data. Valuable data is being provided by more and more sources, which quickly paints a reliable picture of a risk or a submitted claim and makes it possible to filter out unwanted risks and fraudulent claims. State-of-the-art technology is required to process this influx of information and convert it into applicable knowledge and insights. This calls for intelligent software capable of processing information quickly, learning independently, drawing smart conclusions, and making recommendations – similar to a human, but then smarter and more efficient.

We’re talking about artificial intelligence (AI).

3 types of Artificial Intelligence

We can identify roughly three types of artificial intelligence. The first benefits everyone and is known as ‘general’ AI. Examples of this type of AI include natural language processing, facial recognition, and augmented reality. In the latter, the physical world is overlaid with elements from the virtual world. We use elements of general AI to support specific process in the other two forms of AI.

The second form – product-based AI – involves specific products, such as systems designed to identify and deactivate computer viruses, programs that filter e-mail spam, and programs that detect fraud patterns. The latter is extremely valuable to us. The more we feed AI with information about fraud cases, the better it becomes at this specific task. The systems learn by doing and by drawing on feedback from many different users.

The third type of AI is custom or domain-specific AI. In short, AI that is trained for usage in a specific niche. An example from within the insurance sector is AI that was trained to identify fraud patterns and is linked to Straight-Through Processing (STP) and the characteristics of a single insurance company: the specific products, target groups, distribution channels, and claims processes. This allows us to determine how AI can best be integrated into the business operations of an individual insurance company.

AI and Image Analysis

Combining all three types of AI often leads to the best results; for instance, using Artificial Intelligence to analyse images in an STP environment while simultaneously checking for fraud. Insurance companies receive huge amounts of visual material, which is used to illustrate claims and support the claims process (e.g. determining the legitimacy of a claim). The photos are submitted by policyholders, intermediaries, repair shops, and experts to illustrate damage (e.g. car damage, glass damage, or fire damage) or as evidence that lost or stolen items were indeed in the possession of the owner (e.g. jewellery, clothing, or cameras).

Claim Assessment

In order to optimize the use of photographs, it must be determined that the right photos were submitted and that the damaged object was photographed from every angle. It’s also important to determine that the object in the photo is indeed the insured object, that older damage is not being included in the claim, and that the photo has not been used before, downloaded from the internet, or photoshopped. The next step is to determine the damage and the costs of repairing the damage. Is the car a total loss? How bad is the fire damage? Are there any visible indications of fraud?

These analyses can be made with the help of the three AI types listed above. General AI is trained with millions of images to identify general objects like cars, windows, buildings, etc. The huge quantity of data and the impressive computational power means these analyses are carried out at lightning speed and with great accuracy.

Product-based AI is trained using specific claims (e.g. glass damage) to determine the amount of damage and to identify potential fraud.

As a final step, AI can also be trained with various machine learning algorithms in the specific application domain: in this case, the process used by the relevant insurance company. Combining these three types of AI ensures smooth and efficient claims processing. The longer AI is used and the more data and feedback it receives about the claims process, the better and faster it will work. This will speed up the claims assessment process and may even lead to a fully automated process in some cases, such as STP. An added benefit is that fraudulent claims are filtered out more efficiently.

In addition to image analytics, there are dozens of AI types currently available and under development. Insurance companies can use these technologies to improve and streamline their risk analysis and fraud identification processes. In the coming period I will elaborate on this subject using some of our fraud prevention cases as an example.

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FRISS has 100% focus and dedication to fraud detection and risk mitigation for non-life insurance companies worldwide. FRISS helps insurers to improve their combined ratio in order to achieve profitable portfolio growth and enhance the perception in the market as a trustworthy insurer. FRISS believes in honest and fair insurance premiums, for everyone.

FRISS is live in 8 weeks and has a ROI within 12 months, due to the experience with 60+ implementations at insurers. Customer specific configuration is the main part. Rather than general purpose analytic software or homegrown systems that have to be built from scratch, FRISS is largely prebuilt and therefore a ready-to-use business solution.

As a proven standard the FRISS® Score enables better decisions. It is the core of the solutions and indicates the risk for each quotation, policy or claim. The FRISS Learning Cycle supports a journey of continuous improvement to stay ahead in dynamic times and to sustain for the future.

Source: FRISS

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