Domino is a dashboard for ING employees from different domain to visualise their client’s complete portfolio at one go. It comprises of the company structure, current accounts, lending exposures, suppliers, customers and peers.
This application helps users get rid of the conventional data gathering from excel sheets and different other applications by providing all of them in a single place visualised in a way that is much more easier to grasp hence making it easy to take decisions and make strategies for their clients.
This is the home page for the new on-boarded users, intended to help them through a guided tour to get a basic understanding on how to use the tool. It also shows the highlights of the sprint specifying the new features added with respect to the users feedback we got.
As this is a data driven application, we think that users should be well informed about data source updates. This tells users on how new the data is in Domino to give them the confidence that they are looking at the latest data.
Once the user selects his client from the search results, he comes to this dashboard where he can see the entire portfolio of his client in one shot.
This is a dynamic dashboard, which means, the user can make selections in the company structure tile and all the other tiles gets updated as per the selection made. This makes it very easy for the users to deep dive within a company and get insights.
Each tile has a detail page which gives more precise and detailed information unlike the snapshot view in the dashboard.
Converting the data in excel in a well visualised form makes it easy for the users to come to an inference without digging deep.
The cash flow shows one of the Data- scientist algorithm called Name-matching. In terms of design, making this visible in a productive way so that users can be benefited with the extra valuable information was the intension.
The users can see the trend of the top 10 suppliers and customers, their seasonal patterns and any changes happening. This view specifically helps when a company goes bankrupt. The suppliers and customers dependent on this company gets affected too, so this network helps users to make better decisions on the dependent clients also hence saving on the bank side.
Getting good peers is another Data- scientist work. This page justifies the hard work of data scientists by allowing users to add peers and also give feedback on the good or bad peers. These feedback in return improves the algorithm bit by bit each day making it smarter in providing the best peers to the users.
The users can then select the peers they want to compare.
