Time to Strike: Intelligent Detection of Receptive Clients : Predicting a Contractual Expiration using Time Series Forecasting

University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

Abstract: In recent years with the advances in Machine Learning and Artificial Intelligence, the demand for ever smarter automation solutions could seem insatiable. One such demand was identified by Fortnox AB, but undoubtedly shared by many other industries dealing with contractual services, who were looking for an intelligent solution capable of predicting the expiration date of a contractual period. As there was no clear evidence suggesting that Machine Learning models were capable of learning the patterns necessary to predict a contract's expiration, it was deemed desirable to determine subject feasibility while also investigating whether it would perform better than a commonplace rule-based solution, something that Fortnox had already investigated in the past. To do this, two different solutions capable of predicting a contractual expiration were implemented. The first one was a rule-based solution that was used as a measuring device, and the second was a Machine Learning-based solution that featured Tree Decision classifier as well as Neural Network models. The results suggest that Machine Learning models are indeed capable of learning and recognizing patterns relevant to the problem, and with an average accuracy generally being on the high end. Unfortunately, due to a lack of available data to use for testing and training, the results were too inconclusive to make a reliable assessment of overall accuracy beyond the learning capability. The conclusion of the study is that Machine Learning-based solutions show promising results, but with the caveat that the results should likely be seen as indicative of overall viability rather than representative of actual performance.

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