Finance and Insurance
Artificial intelligence can help in the finance and insurance industries
In finance and insurance today there is a huge amount of time-series data that one needs to use in order to stay competitive. It is the perfect match with our focus on uncertainty estimation, as we can extract a lot of learnings from very small amounts of data while at the same time scale to very large datasets.

The applications are almost endless
  • Predict the probability of something happening at a point in time
  • Decompose the drivers of a KPI
  • Understand the impact of the drivers, and the uncertainty surrounding their impact.
Customer Case: Sustainable Growth
Identification of actionable growth opportunities in +10 countries across five products.
There is a wide range of areas where our models adds value, here is an example of some of the models we support.

Sales Forecasting
Investigate the impact of drivers such as macroeconomic factors, seasonality, trends, weather data, distribution channels, media investment, etc. on the sales. This makes it possible to better plan and predict various changes to see what will have the biggest impact.

We support predicting future sales based on given input data, picking what variables can be changed and optimizing those to maximize sales, and comparing various scenarios to see how they compare to each other.
Customer Churn
Customer churn is the percentage of customer that stops using a service within a specific time frame. Given that there is usually a cost of acquiring new customers, it is vital to minimize churn. There are a lot of factors that impact churn such as brand loyalty, price, customer satisfaction, etc. By building a model that analyzes customer churn, it is possible to understand what impacts it and how much. When one knows that we make it possible to simulate various different strategies to mine customer churn and pick the best one.
New customers
New customers are always nice to have, but what drives new customers may be different from what minimizes customer churn as it comes down to establishing a new relationship not maintaining one. That is why it is key to be able to decompose the various drives of new customers in order to focus on how to acquire new customers while at the same time minimizing Customer Acquisition Cos(CAC)
Demand forecasting
In demand forecasting, one aims to forecast future customer demand over a defined period using historical data and other information such as seasonality, trends, macroeconomic factors, etc.

By knowing the demand for products one can decrease inventory costs by reducing or eliminating redundant and obsolete inventory and at the same time reduce the inventory carrying costs. Without demand forecasting one is at risk of making poor decisions that may increase inventory holding costs, poor supply chain management, and not having enough inventory all negatively affecting customer satisfaction and profitability.
Customer Case: Budget Optimization
Sales budget savings of up to 25% compared to existing spending.

How to proceed

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