Activities and societies: Legal Discourse, English Economic Discourse, Political discourse, Introduction to Text Analysis, Computer-Assisted Translation (CAT) Tools, British Studies, Interlingual communication, Literary Theory, Principles of Text Production, Culture and Translation, Translation Strategies, Contrastive Lexicology, Literary Translation.
Many people struggle to get loans due to insufficient or non-existent credit histories.
To make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data -including telco (telecommunications provider) and transactional information -to predict their clients' repayment abilities.
In the US alone, around 7500 yearly cases of mushroom poisoning are reported.
To avoid expenses for hospitalization and in some cases pointless deaths, we have been hired by US National Health Service to create a machine-learning model, that can recognize mushroom types.
Being able to accurately assess the risk of a loan application can save a lender the cost of holding too many risky assets.
Without having a credit score or credit history we will try to predict how profitable a loan will be compared to our loans database, accumulated over the years.