If deep learning will definitely play a crucial role in automation and improvement of the daily insurer operations involving image, text, and audio processing, it is not expected in the mid-term to deeply impact the core insurer business related to risk management.
The Eldorado in Insurance?
Deep learning has been and is definitely giving a new boost to Artificial Intelligence. However it won’t be the solution to any problems for insurers! Let’s dig into a few details in order to understand where one can expect low or high added value of deep learning in the insurance sector.
As of today, no one is able to understand nor clarify why deep learning is performing so well on numerous use cases, therefore deep learning is not natively conductive to interpretation.
On the other hand, one of the main tasks of the insurer is to understand the risk and its drivers, in order to best prevent and manage it.
The interpretability is required from several perspectives: regulation, audit, decision making and stability. Interpretability is actually a field of investigation in the insurance sectors, in big groups and also in InsurTech start-ups. The idea is to take the outcome of Deep Learning and then to explain it by an interpretable model…But this is just the beginning of the story.
This explains briefly why deep learning has not any straightforward uses in the pure “risk” applications that insurers face.
However, there are many other applications in insurance, and this is a very active field. Why?
Insurance is an information business, therefore with flows of information and data. And today a big part of this data is made of images, texts, and audio. As mentioned above, the added value of deep learning is clear for these types of unstructured data. The main areas where deep learning is impacting the insurance business are probably:
… already in the short-term:
- Claims: e.g. assess claim cost based on images
- Fraud: asses fraud in images, speech, texts
- Customer experience and new services: satisfaction, quick quotation
- Augmented intelligence in Robotic Process Automation (which may lead to a shift of operational risk)
- Parametric insurance for agriculture, farming, or any weather sensitive activities
…. and even more in the longer-term:
- All IoT data generated: time-space series for instance for connected health or telematics businesses.
These applications are numerous, they are designed to improve the service and therefore customer satisfaction. They are also designed to improve efficiency.
To conclude, it is quite clear that deep learning technologies will not serve in the short term all data analytics needs of insurer carriers, whose job is mainly to understand risks and interpret related models and algorithms. As of today deep learning is not understood and therefore does not lead to such desired models
However, there will be in short & long terms a massive use of deep learning technologies in insurance, everywhere there are images, texts, audio, and IoT generated data.
There is currently a big underlying debate on the future and the role of human employees in this context. What will be the real impact on insurance jobs? Will employees be massively replaced? Will there be a shift of roles played by humans?
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