Risk analysis, to some extent, occurs throughout all industries. Even at the individual level, we unconsciously or consciously weight the risk of (future) events and engage in risk evaluation. Bigger risks, e.g., loaning someone your favorite car or lending them millions of dollars, require more data to to make an informed decision.
Likewise, financial markets need to compute different risk levels at any given time, and the input values are also varied: the weather (especially for commodities), competitors, political decisions, customer preferences, personal and business credit scores (creditworthiness), and so on. With so many different factors, automating risk analysis is an area where machine learning and data science can have a large impact.
At this point, algorithms can quickly provide a prediction for whether or not a customer is likely to repay a home loan, private loan, or credit card balance. Algorithms such as neural networks, random forests, or gradient boosting, are capable of producing recommendations at each stage of the risk analysis process. Preprocessing of the data, feature selection, risk classification, and mapping if-then scenarios to risk patterns are becoming more reliant on machine learning and AI algorithms.
With the recent rise in popularity of exotic assets among traditionally conservative investment funds (think trusts and endowments), there have been a few high-profile cases of non-standard investments (especially in foreign countries) falling apart due to total lack of monitoring.
For example, a handfull of foreign agriculture investments have fallen apart because, while fund managers thought farmers were farming, no one ever broke grounds on the farmland they bought. They learned too late that no one followed through on their agreements, and they had to take a huge write off.
Techier investors may ask, "Why weren't they monitoring satellite imagery of their land?" That's a good question, and, in retrospect, there was no reason not to monitor the farmland via satellite. It doesn't take deft machine learning skills to figure out wether or not a piece of land is covered in dirt or maize.