We analyse financial transactions and supplement them with different data on the dealer and the purchase. Different machine learning algorithms categorise and search through the information, comparing it with sustainability indices and public information in order to finally determine the transaction’s effect on the environment.
Ah ok, and what does that mean exactly?
Example: Let us assume that you have bought a t-shirt at a cheap discounter for just 10.00 €. You will receive an alert on your smartphone right after your purchase. This will describe the purchase and explain the production process of the t-shirt, the environmental effects resulting from the chemicals in the t-shirt, if the t-shirt was most likely produced through child labour, if the cotton is cultivated by peasants who are still using pesticides that have completely contaminated ground water in their region etc. At the same time, a calculation is performed showing that by donating approx. 1.50 € to organisations that strive to ensure children of nearby families and cotton farmers get appropriate school education, or another organisation that deals with the treatment of drinking water, you can contribute towards “compensating” for the damage caused by the production of your t-shirt.
The donations are debited right away, and the t-shirt costs you 11.50 € instead of 10.00 € but is now “environmentally friendly”. The advantage is obvious: The consumer is enlightened and has a chance to make the purchase “environmentally friendly” without having to change the complex supply chains and production processes. It goes without saying that these manufacturing processes and supply chains have to be changed in the long term, but the mere clarification and possibility to play a part through targeted donations is the first and, most importantly, concrete step in the right direction!