Fintech Underwriting using Machine Learning
Bogdanova, Mariia (2019)
2019
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2019102820288
https://urn.fi/URN:NBN:fi:amk-2019102820288
Tiivistelmä
This thesis aimed to research and develop a FinTech Underwriting solution and to prove that FinTech Underwriting solution using machine learning is possible to create. This thesis was commissioned by the company WeBuust Oy. The main objective was to prove that it is possible to create a rating system using machine learning that will check the potential of the startups to become successful so that the potential investor can determine if the startup is worth investing into and to prototype and implement the system like this.
The company is aiming to develop a platform that will connect the investors and startups, and this FinTech Underwriting system will be implemented within the services of the company as a feature. Supervised and unsupervised machine learning algorithms are used to prove that developing this system is possible and to achieve the best results and the best approach is selected.
As the result of this study it was proven that it is possible to develop FinTech Underwriting solution using machine learning. The best implementation approaches were selected. This thesis aimed to research and develop a FinTech Underwriting solution and to prove that FinTech Underwriting solution using machine learning is possible to create. This thesis was commissioned by the company WeBuust Oy. The main objective was to prove that it is possible to create a rating system using machine learning that will check the potential of the startups to become successful so that the potential investor can determine if the startup is worth investing into and to prototype and implement the system like this.
The company is aiming to develop a platform that will connect the investors and startups, and this FinTech Underwriting system will be implemented within the services of the company as a feature. Supervised and unsupervised machine learning algorithms are used to prove that developing this system is possible and to achieve the best results and the best approach is selected.
As the result of this study it was proven that it is possible to develop FinTech Underwriting solution using machine learning. The best implementation approaches were selected.
The company is aiming to develop a platform that will connect the investors and startups, and this FinTech Underwriting system will be implemented within the services of the company as a feature. Supervised and unsupervised machine learning algorithms are used to prove that developing this system is possible and to achieve the best results and the best approach is selected.
As the result of this study it was proven that it is possible to develop FinTech Underwriting solution using machine learning. The best implementation approaches were selected.
The company is aiming to develop a platform that will connect the investors and startups, and this FinTech Underwriting system will be implemented within the services of the company as a feature. Supervised and unsupervised machine learning algorithms are used to prove that developing this system is possible and to achieve the best results and the best approach is selected.
As the result of this study it was proven that it is possible to develop FinTech Underwriting solution using machine learning. The best implementation approaches were selected.