real time commodity risk engine machine learning

#Real time commodity risk engine machine learning trial#

In a glib sense the public relations and fundraising functions of startups tend to use the more attractive AI term when they most often mean machine learning, but even in research there is a reasonably fluid distinction. The remainder of the chapter is devoted to the actual application of AI and machine learning to various forms of risk management, finishing with a forward-looking perspective on what is next for the role of AI in risk management and some challenges that need to be addressed.Ī first step is defining what we mean by AI and machine learning, and this is not necessarily a straightforward distinction.

The next section briefly details, in a non-technical manner, the core machine learning techniques which can be applied to improve risk management. Our chapter thus expands on this idea of how AI and machine learning can improve risk management-particularly around the techniques being used to make decisions based on such large volumes of atypical data. This new information is often atypical and non-numerical the type of data which machine learning is innately suited to analysing. A standard credit score is largely a linear calculation of a small number (about 50) positive or negative numerical characteristics about a person and thus misses out on a huge amount of additional personal information that can help to either reduce negative risk or accept positive risk. The ZestFinance example illustrates the essence of how AI and machine learning can improve risk management. This approach crosses the line from machine learning to AI due to the automated nature of the lending decision-making process. The actual clusters will be significantly more refined than the simple examples given above. While on the other hand, a history of online spending that supports the applicant’s reported income in their application to borrow, or maybe even data indicating upward career mobility, might cluster someone into a group of lower risk borrowers. Thus a search history indicating accessing gambling websites would cluster a potential borrower into a higher risk group. The exact nature of how ZestFinance makes these decisions is not disclosed except under the broad umbrella of machine learning and AI, but essentially what they use as a base is a core machine learning set of techniques around clustering and decision trees, and possibly deep learning.

#Real time commodity risk engine machine learning trial#

A reported trial in 2017 of their system led to a 150% increase in total small-item lending by Baidu with no increase in credit losses in the space of just two months. They use thousands of data points per customer and are still able to make lending decisions on new applications in seconds. ZestFinance (with permission) taps into the huge volume of information on members held by Baidu such as their search or purchase histories to help Baidu decide whether to lend. Lending to people who have either ‘thin’ credit profiles, or no credit profiles, is inherently risky as there is no history to draw on to check borrower reliability. Unlike most developed countries, the risk with lending in the Chinese market is that less than 20% of people have credit profiles or credit ratings. Baidu was particularly interested in making small loan offers to retail customers buying products from their platform. ZestFinance was founded by a former Chief Information Officer of Google and in 2016 partnered with Baidu, the dominant search engine in China, to improve Baidu’s lending decisions in the Chinese market. We further envisage the future role for fully AI solutions as the natural next step after the widespread adoption of machine learning in helping the organisation to manage risk.Īn example of ZestFinance serves to illustrate the potential for AI and machine learning in risk management. In this chapter we detail current machine learning and AI techniques being used and current applications of those techniques. Everything to do with understanding and controlling risk is up for grabs through the growth of AI-driven solutions: from deciding how much a bank should lend to a customer, to providing warning signals to financial market traders about position risk, to detecting customer and insider fraud, and improving compliance and reducing model risk. Artificial intelligence (AI), and the machine learning techniques that form the core of AI, are transforming, and will revolutionise, how we approach financial risk management.