Machine Learning & Insurance
Nowadays, insurance companies can hugely benefit from the use of Machine Learning (ML) on their daily basis. Some of these benefits are:
- Risk appetite.
- Risk analysis.
- Premium leakage.
- Expense management.
- Fraud detection.
- Among others.
According to Accenture, “most of the insurance companies process only 10-15 percent of the data they have access to”. Considering these numbers, the amount of valuable data not being used is enormous. Undeniably, there is a lot of space to grow on these matters. With that being said, insurance companies are starting to mature in this field as the potential for growth is massive. Unquestionably, the advantages are great.
However, Machine Learning also presents some of the following challenges:
- Training requirements – creation of a proper training environment with plenty of data that can cover all possible scenarios, which ensures the ML algorithm is well trained.
- Right data source – quality of data as important as the amount of data: data should be representative and balanced in a way that avoids biased decisions from an algorithm.
- Difficulty in predicting returns – difficulty predicting the cost of an ML project and the revenue brought in by it.
- Data security – risk increase of a connectivity breach since there is the need to send data through more applications.
- Regulations – existence of specific country regulations related to data processing, which can limit the way of using some machine learning algorithms.
To sum up, the insurance business will undergo a great evolution in the future. The evolution of ML algorithms and the improvement of the available databases of the insurance companies are two strong factors that will contribute for this evolution. This will lead to the creation of innovative products and improvements in the business processes. Consequently, the results will vary between lower costs and to provide a far better experience to customers through personalised offers.