Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Integrating gender into index-based agricultural insurance: a focus on South Africa

Index insurance is an agricultural risk management tool that can provide a safety net for smallholder farmers experiencing climate risk. While uptake and scale-out of index insurance may be slow among smallholders, we can learn from experiences that demonstrate where crop insurance can protect smallholders’ livelihoods from climate risk. Integrating gender into climate risk management is necessary to ensure that the benefits of index insurance are experienced by both men and women. A dedicated intention to integrate gender may be required. Taking South Africa as a case study, the potential for gender-sensitive index insurance scale-out among smallholders is investigated

Trust me, I'm an entrepreneur! Can trust help SMEs to gain the credit they need?

Research on relationship lending focuses attention on economic factors which influence the relationships between SMEs' owners/managers and banks but no previous work has focused on the role of trust. Trust is expected to reduce transaction costs and agency costs, reduce the perceived credit risk and, thus, influence credit availability. Trustwor-thiness is associated with three attributes of SME owner managers' namely; ability, be-nevolence and integrity. It is hypothesised that lending managers' assessment of the trustworthiness of SME owner managers affects the ability of SMES to gain the credit. Trustworthiness is hypothesised as positively associated with credit access in contrast to lower trustworthiness which is associated with credit constraint. Use of overdraft is con-sidered here as indicator of credit constraint. The data were obtained from a survey of lending managers from banks in North East Italy. Control variables and a vector of trustworthiness factors were collected on a random sample of borrowers, resulting in a sample of 535 firms. Results from regression analysis found evidence that firms enjoy-ing high level of trust are able to access the credit they need and therefore are less credit constrained. Some implications of these results for banks, owner managers and future research are discussed

Hidden gems and borrowers with dirty little secrets: investment in soft information, borrower self-selection and competition

This paper empirically examines the role of soft information in the competitive interaction between relationship and transaction banks. Soft information can be interpreted as a private signal about the quality of a firm that is observable to a relationship bank, but not to a transaction bank. We show that borrowers self-select to relationship banks depending on whether their privately observed soft information is positive or negative. Competition affects the investment in learning the private signal from firms by relationship banks and transaction banks asymmetrically. Relationship banks invest more; transaction banks invest less in soft information, exacerbating the selection effect. Finally, we show that firms where soft information was important in the lending decision were no more likely to default compared to firms where only financial information was used