Artificial Intelligence, Machine Learning and Predictive Analytics.
Multidisciplinary expertise in combining knowledge, science and data with the objectives of discovering useful information, communicating insights, automating decision-making and supporting business intelligence and strategy.
Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotel
Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels
As we walk, cycle or drive around Oxford, make telephone calls, send texts or emails and do our shopping, many of us are unaware of exactly how much data is being generated by our activities. "Big data" is a catch-phrase for describing the overwhelming volume, velocity and variety of this stream of information. Big data has the potential to provide many opportunities for the public and private sectors, offering a means of fusing different sources of information and supporting decision-making in real-time. Perhaps the most interesting aspect of big data is how it deepens our understanding of human behaviour seen through the collective actions of many individuals. We tend to consume services following the temporal cycles in our everyday lives. There are three evident cyclical patterns based around the hour of day, the day of the week and the season of the year. All of these patterns can be seen in electricity consumption, call centre activity, internet usage, financial tran