This informal CPD article Claims in Insurance and Machine Learning was provided by The Tesseract Academy, offering consultancy services to help your company become data driven, whether you are an entrepreneur, a start-up or a corporate.
Any property and casualty insurer, no matter how big or little, will rapidly learn that claims may mount up. Adjusters for insurance claims are under increasing pressure to boost client satisfaction without increasing spending. Insurance businesses can benefit from using AI to process complicated claim data in order to address common bottlenecks. It has been predicted by Juniper Research that the insurance business can save up to $2.3 billion thanks to AI in the year 2024.
Unstructured Big Data: A Challenge
For insurance firms utilizing Big Data, dealing with unstructured data presents one of the largest challenges. Unstructured information cannot be easily integrated into a company's pre-existing structured data since it does not follow a predefined schema. It's possible that this information relates to:
- Images
- Video
- Audio
- Sensor data
- Invoices
- Written text
Sorting through unstructured data can be a time-consuming process. Instead, insurance companies that have invested in AI can process this data more efficiently. Artificial intelligence (AI) has many uses, but one of the most promising is Machine Learning's ability to enable solutions to learn on their own and improve their accuracy and efficiency as more data is fed into them. The potential for more savings is unlocked by this development.
The Effects of Machine Learning on the Claims Process
Newly submitted claims require immediate attention. According to Accenture, the speed with which claims are settled is the most important determinant in the happiness of policyholders. If a claim is settled, it frees up resources for both the policyholder and the insurer.
In 2016, Lemonade, a single insurance, proposed an AI system that could settle a claim in 3 seconds. Claims settlements typically take several days, indicating a huge window of opportunity. Insurance companies use a balanced scorecard to track claims management metrics like total claims, average time to close, customer satisfaction, and more. Limitations in the claims management process are addressed by AI solutions, allowing for better, more well-informed business decisions.
Image Recognition
Even a simple claim analysis is complex and time-consuming. When anything happens to a house or car, it's the insurer's job to assess the damage and come up with an estimate for fixing it or replacing it. Tokio Marine, an insurance company based in Japan, employs a computer vision system to analyze photos submitted as part of auto insurance claims. Tractable's AI product has handled vehicle insurance claims totaling more than $1 billion.
Text Recognition
There have been recent advancements in artificial intelligence's ability to comprehend human speech. This progress is visible in Google's search algorithm, which now provides accurate results across languages. Using Machine Learning, AI can be trained to interpret written material, and this has practical applications in the insurance claims industry. For example, the insurer may use AI to quickly analyze this content before importing it into their P&C insurance product. There are many other types of documents that can benefit from text recognition processing, including police records, insurance adjuster notes, and policyholder emails.
Key Takeaways
The insurance sector is rapidly adopting AI systems. When properly implemented, the right technology in claims management can:
- Reduce claim resolution times dramatically
- Help the insurance company save lots of time and effort
- Find ways to make customers happier
- Accumulate your earnings more quickly
We hope this article was helpful. For more information from The Tesseract Academy, please visit their CPD Member Directory page. Alternatively please visit the CPD Industry Hubs for more CPD articles, courses and events relevant to your Continuing Professional Development requirements.
