Automated data collection for credit score calculation based on financial transactions and social media

Financial credibility of a person is a cardinal factor in the approval of loans or permitting credit transactions. Today, this credibility is based on the `credit score' of the person which is calculated from the person's past performance on debt obligations. This paper provides a unique alternative solution to collect this data. Harnessing the fact that almost everyone has smartphones today, there can be a smartphone application that collects all such data and submits it to the official body. Financial transactions are not the only parameters that can determine credibility of a person. This paper proposes accessing social media data to get insights into general social status of a person. Today, all transactions are conveyed by banks and other institutes to the users via SMS. Hence, having access to SMS will result to getting data related to all such transactions. The proposed solution will have smartphone application that will capture bank transaction data and data related to online purchases through SMS. Use of artificial neural networks will enable calculating the final credibility score based on the various data parameters collected. The primary contribution of this paper is that it provides a system to automatically collect all data required to calculate the credit score. What is unique in this approach is the involvement of data from social media. This approach is better than the existing solutions as it will collect data other than just transactional data thus enabling calculation of a more effective credit score.