Financial innovation in recent years have prominently contributed to the growth of Peer-to-Peer lending marketplaces allowing individual and businesses to secure loans on a common internet-based network. Similar to the ‘bricks and mortar’ banking system, online lending is coupled with the problem of information asymmetry. Borrower risk assessment has henceforth become the major concerns of various platforms that aim to reducing information imbalance towards mitigating credit risk. In this article, authors compared two learning algorithms – Logistic regression and Artificial Neural Network to classify borrowers based on loan repayment schedule. We revealed that both approaches were robust in classifying late borrowers with logistic regression being 0.02% more robust than Neural Network. Regarding variable relative importance, gender is considered the least important variable whereas terms-of-repayment is the most important variable affecting borrowers’ intention to pay off loans. Even though our study contributes to existing literature, it is however not limited to determining factors that may affect lenders’ investment decision in social lending.

The possibilities for companies to reach out more people to get in-depth understanding about brand, products, and services is through social media pages. We examined effects of social media on performance and customer relations of companies in Ghana. We obtained data from 390 respondents through structured questionnaires, and was analyzed with statistical package for social science (SPSS). The findings indicate increased awareness and usage of social media by companies in Ghana. However, customer’s desire for a products could be influence by company’s advertisement through social media post. We established that, managers are expectant with the use of social media enhancing customer’s relationship. Therefore, managers should modify their website to complement the social media strategies, identify the actions, wants and demands of customers to improve performance. We discussed several managerial recommendations.