Embarking on an AI strategy

Against this backdrop, companies will do well to devise on an effective AI strategy if they do not wish to miss out on the massive potential it offers.

For the uninitiated, I have a simple formula to recommend. One, think big picture and understand the real job-to-be-done; two, develop use cases that will support your strategy to offer premium customer experience; three, learn from others and leverage on technology. If need be, learn from tech giants and ask for their recommendations; finally, break the entire process into multiple phases. To start, aim to deliver a real business value to customers and business units, whether it is to the actuarial department or marketing team.

Access to data is key to building AI

To start with the technical implementation of AI insurers and other financial firms need to leverage big data. The key to unlocking this potential lies in making sense of the internal data you have accumulated across multiple sources and linking all of them together.

As a matter of good practice, you would need to combine in one location, which is usually Data Lake, multiple sources of data, both structured and unstructured data, and create de-duplication rules in order to create a single source of truth. As much as possible, try to establish a connection, create a lineage from the data you have.

Some companies can be inundated, or even overwhelmed by unstructured data, but having that is still better than not having any. Besides, there are tools to help us make sense of the data, including the likes of Amazon, Microsoft, Google, and Alibaba, who all have data centers and offer services to help corporates to take advantage of their data.

Another factor in Singapore’s favor – the Government adopted a multi-pronged approach to cloud computing by leveraging on commercially-available public cloud capabilities. Keeping data in the Cloud enables companies to build new machine learning models at scale and faster speed.

Things to keep in mind when building AI culture in your firm

The financial industry is highly regulated for the right reasons, given the sensitivity of the data insurers and banks possess. The challenge for companies, therefore, lies in striking a balance between staying agile to take advantage of the opportunities presented by technology such as AI and making sure the confidentiality of our data is not compromised, particularly when we are working with large sets of data.

To move efficiently, I recommend companies to source for best-in-class AI solutions instead of trying to build all of them in-house.

To this end, it is good to have at least one group dedicated to understanding business pain-points across the company, and from there, find the most suitable AI-based solutions for these problems. Similarly, sufficient technical resources should be allocated to allow companies to prototype with these found solutions to figure out whether it makes sense to integrate them.

Indeed, if the intent of adopting AI or any other form of innovation is to solve business problems, then it only makes sense if the remit to innovate does not reside with just a particular department. Instead, every member of an organization should be encouraged to adopt an agile mindset, stay informed about the latest technology, continuously learn new tools, and be unafraid to take risks with new projects, even if there is a chance of failure.

If there is one thing I learned from working in different tech companies, it is finding ways to try something quickly and iteratively is often better than investing months or years to develop something that might or might not work.