What they wanted
Our client is a fast-growing service provider. In fact, their growth is limited by their ability to hire and train underwriting specialists. They therefore want to increase their STP rate to uncouple their business growth from hiring and labour efforts. Also, they are looking for new ways to monetize the data they have been storing since the start of their digital operations.
What we did
We believe successful underwriting decision models start and end with the specialist, her expertise and the business process. Our consulting approach to data science enables us to develop models that will also be accepted and successfully integrated in the business process. So far we have proven the concept that machine learning helps to develop their underwriting decision model based on a proof of concept. We are now planning full rollout, during which we will work closely with the client’s BI team to simultaneously develop a new data science capability with them.
What we achieved
The AI decision models will drastically reduce manual underwriting activities for selected products by an estimated 75%. On top of this, decision models are more consistent and can be more accurate than the specialists. The increased profitability and reduced dependency on scare talent will enable their business growth to accelerate. Through the introduction of in-house data science capabilities our client will also be able to further develop valuable insights and data products from their data assets independently.
What they said
“We can’t wait to start full rollout”