Collaborating Authors


Bank IT compliance: how financial services can stay compliant


Financial services compliance is a big area. Prajit Nanu, CEO of B2B payments platform Nium, says it's in everybody's interest that payment transactions are as frictionless as possible, but many commonly used payment systems carry unnecessary layers of complexity, including when ensuring regulations and compliance. He says automation can help to resolve lags arising from risk and compliance checks, which can be a time-consuming and labour-intensive process, particularly for those dealing with cross region, cross country checks. An automated payment platform appropriately integrated with other business software can perform these checks much more seamlessly. Nanu says: "Digital tools, such as individualised transaction profiles, coupled with the output of machine learning processes, will be able to offer real-time solutions which significantly reduce the time required for risk and compliance checks, while still allowing effective identity verification and fraud detection checks."

The case for placing AI at the heart of digitally robust financial regulation


"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.

Forecasting: theory and practice Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Financial services take lessons from machine learning


Open Banking promises increased access to customer data, but with newfound transparency comes a greater need to understand how that data is utilised. Together with developments in machine learning (ML), markets are changing. So far, ML has been used in financial services to manage content and data, detect fraud and money laundering, facilitate biometric identification, trade via algorithms, and assist with regulatory compliance. A survey by the Bank of England, undertaken in the first half of this year, found that two-thirds of UK financial services companies are now using ML. Nineteen percent of companies surveyed have or are creating centres for promoting the technology internally.