Louisa PavisHead of Consulting Pro Global, delves deeper into algorithmic underwriting’s developing world, looking at what AU means, the importance of clean data, and the biggest challenges to its implementation.
The insurance industry is experiencing algorithmic underwriting (AU). However, mainstream products like car and house insurance are already seeing AU used well. Specialty insurance is relatively new. Data plays a part in the success of algorithmic underwriting (AU). As a form of computerized automated decision-making, AU relies on data to follow a set of instructions, the most basic of which is “if X happens, carry out this action; if Y happens, carry out that action”. Unlike artificial intelligence (AI) – which can adapt instructions depending on the data or results of a sequence – AU doesn’t have this level of machine learning, so if you put unusable data into the system, your results will be unusable too.
Insurers need to ensure that data is properly formatted and cleansed in order for AU to function. A leading Insurtech report highlighted the importance of having the right infrastructure. This includes cloud-based services and the necessary underwriting knowledge.
The importance ecosystems
Standardization and data cleansing are the first steps to delivering appropriate data for AU. While this can be tedious and costly, it is also prone to error. This is particularly true for specialty insurance lines and risk analysis. Data cleansing and standardization can be time-consuming and prone to errors. This is a slow process that must be done for clients on an individual basis.
In order to reap the full benefits of AU, insurers need system(s). These systems can automate many tasks, such as geocoding, audit trails, identifying new lines, and even just storing information. These processes work best when implemented in conjunction with other technologies and systems. This can be accomplished through strategic partnerships with subject-matter experts.
The best partnerships allow the insurer to focus on what they do well (i.e. Underwriting, while external experts assist in areas they are best at (i.e. From a technology perspective.
Take the next step
There is an abundance of data currently in insurance, and with new innovations around sharing data and streamlining processes, such as AU, it is becoming even more vital that data is cleansed properly, is in a consistent format – and above all, actually usable to get results.
Garbage in equals garbage out, as the old saying goes. If the underlying data are not formatted, structured and cleaned properly, advanced tools such as AU will not be able to help with the underwriting process. Increasingly we are seeing insurers turn to advanced new augmented intelligence tools to help them get to grips with these data fundamentals – tools that learn to sweep through their data schedules and find, fix and structure data before they flow through to the next stage. Insurers can then use AU with clean data. This allows them to provide faster quotes and estimates, as well as reducing the need to clean up data.
Clean data deployment is just half the battle for AU. It is the other half that you need to be able to attract the right talent and skills to help build the necessary frameworks to support automated underwriting decisions. Because at the end of the day, it is all about good data, not data for data’s sake. Clean data allows underwriters to not only properly apply these technologies but also have a better understanding of their portfolio. This means they can write more quality business with confidence, and can apply the most accurate pricing models.