In today’s commercial world, the volume of data being captured and stored is growing exponentially. As companies continue to expand their offering and grow their businesses it is critically important that they have systems in place to be able to analyse the available information.
In order for you to be able to understand the challenges and strengths of your business, it is essential for you, as a decision maker, to be able to analyse your business in a critical way. In today’s fast-paced business world it has become increasingly important for this information to be available in real time in order to make decisions faster than your competitors. And the first step is to generate dashboards and provide this data in a way that can be easily analysed.
Many businesses already have a business intelligence functionality. The modern dashboard, however, does far more than just tell the story of where your business has been. It is vital to know too where the business is going. And it is through parameterisation and predictive analytics, that the modern dashboard allows you to do just that.
CHURN / LAPSE MODELLING
We assist you with all components of the risk management process used to manage your quantitative and qualitative risk exposures. This includes designing and enhancing top-down and bottom-up risk appetite processes to enable decision making, developing risk taxonomies and risk dictionaries to facilitate the identification of risk, as well as the design of risk registers and tools for measuring and monitoring risk exposures.
At QED we understand that current customers can constitute one of the greatest untapped sources of sales, and that current customers thus often form a portion of any target market for existing products. Using data that you already possess, our cross-sell models through the use of machine learning, can facilitate this sales process by identifying which of those existing customers are most likely to purchase a product they don’t have.
Using statistical models, and using available data, we can discern patterns that identify claims with a high probability of being fraudulent. Our models facilitate focused initiatives that identify a higher proportion of fraudulent claims at a reduced cost – a significant benefit for fraud departments, particularly if they are under-resourced.