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"The Monetary Authority of Singapore has started using an advanced data science tool named Apollo to help enforcement officers detect misconduct in financial markets, the central bank said in its inaugural enforcement report."

In China, online lenders are filling the credit gap, leveraging cutting edge technology including artificial intelligence (AI) to make lending decision in just a second. Alibaba's MyBank and Tencent's WeBank make their lending decisions using AI, processing vast amounts of user data and information gathered from the mobile payment services they operate.

Last year, JP Morgan began using machine learning to hedge a portion of its vanilla index options flow book. Next year, the bank plans to roll out similar technology for hedging single stocks, baskets and light exotics.

Public authorities, and central banks in particular, are increasingly realising the potential of big data sets and analytics - with the development of artificial intelligence and machine learning techniques - to provide new, complementary statistical information. Yet the question remains: how should institutions organise themselves to benefit the most from these opportunities?

ML is increasingly being used in UK financial services. Two thirds of respondents report they already use it in some form. The median firm uses live ML applications in two business areas and this is expected to more than double within the next three years.

Suncorp ran Watson silently alongside its consultants for six months, with the AI returning the most accurate insurance claims assessments.

This paper discusses potential strengths and weaknesses of ML-based credit assessment

This paper proposes a new framework for supervisors to measure the risk-adjusted performance of machine-learning (ML) credit assessment models, harnessing the process for validating internal ratings-based (IRB) systems for regulatory capital to detect ML's limitations in credit default prediction. From a supervisory standpoint, having a structured methodology for assessing ML models could increase transparency and remove an obstacle to innovation in the financial industry.

New agencies are trying to widen the range of financial data that's used to assess loan applicants. They hope to extend credit scoring to the unbanked, gig economy workers and young people with little credit history. These challengers use machine learning techniques to plough through financial transaction data, or to scrutinise online questionnaires. Machine learning is controversial in this area, as it could give rise to bias and discrimination in lending decisions. Nevertheless, large credit card companies such as Capital One and established agencies like Experian are showing interest in these new methods.

An article by a couple of Banco de España economists proposes a framework to assess how reliable machine learning (ML) technology is to credit assessments. It is based on generating datasets intended to resemble typical credit settings, in which the relationship between the variables is defined. It uses non-interpretable ML models on these generated datasets, and explain their results using two popular interpretability techniques. It then calculates to what extent the explanations given by the interpretability techniques match the underlying truth.