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AI combined with big data and analytics, is an invaluable tool for finance functions that want to better understand their organization’s customers or model future scenarios. Risk management assesses the following financial risks involved with AI.

Credit riskEdit

Credit risk and revenue modeling

Machine learning supports more informed predictions about the likelihood of an individual or an organization defaulting on a loan or a payment. It can also be used to build variable revenue forecasting models.

Hacker attacksEdit

Hackers who want to steal personal data or confidential information about a company are increasingly likely to target AI systems. If they take control of algorithms that make high-stakes decisions, such as driving cars or controlling robots, the impact of an attack could be devastating.

Fraud detectionEdit

For many years, machine learning has been successfully applied to the detection of credit card fraud. Banks use systems that have been trained on historical payments data to monitor payments for potential fraudulent activity and block suspicious transactions.

Supplier riskEdit

AI-based analytics platforms can manage supplier risk by integrating a host of different information about suppliers, from their geographical and geopolitical environments through to their financial risk, sustainability and corporate social responsibility scores.

Surveillance conductEdit

Surveillance of conduct and market abuse in trading

Financial institutions use automated systems to monitor their traders by linking trading information with other behavioral information relating to the traders, such as email traffic, calendar items, office building check-in and check-out times and even telephone calls.

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