Artificial intelligence (AI) has revolutionized financial technology (FinTech), introducing a wide range of new tools that are transforming the business of finance across departments and functions.

This article gives a brief intro into three areas AI is applied in finance:

1. Credit risk
2. Fraud & compliance
3. Wealth & asset management

Making credit decisions relies on careful analysis of all relevant information. Artificial intelligence (AI) systems can help make the process of credit risk analysis more efficient, accurate, and effective.  

While AI has been adopted enthusiastically by the trading, marketing, fraud management, and customer service departments of financial institutions, its adoption on the credit side has been patchy.

AI usage is common in some areas of credit risk analysis, but other areas – such as commercial lending – have been slower to adopt AI tools.

Broadly speaking, however, AI can be a powerful tool for enhancing operational efficiency, fairness, and accuracy in lending and credit risk analysis.

For financial companies, identifying fraud and managing compliance processes are key tasks that must be performed efficiently and effectively. Fortunately, AI systems have the potential to make these tasks easier, cheaper, and better.  

Fraud detection involves classifying transactions into those that are genuine and those that are fraudulent.  

As such, fraud detection can be performed using rules-based algorithms or systems.  

Such systems have clear rules for identifying potentially problematic transactions – for example, unusually large transactions may be flagged as suspicious, or transactions that take place in different geographies on the same day.  

So, if there is a small supermarket transaction on a card in London, immediately followed by a very large jewelry purchase in the south of France, a rules-based system would raise a flag, and the transactions would then be investigated or verified by the cardholder. 

AI and ML can improve fraud detection systems, making them more efficient and reducing costs. Compliance issues such as anti-money laundering (AML), however, pose a different set of challenges. 

Wealth and asset management firms increasingly rely on AI and ML systems to support their operations and make their services more efficient and effective. Technological tools can help wealth managers lower their client acquisition and servicing costs and improve their ability to provide personalized portfolios. They can also help asset managers with asset selection, portfolio optimization, and other core functions. 

Wealth managers use technology as a differentiating sales tool and a way of discovering the individual needs of clients and matching them to the wide variety of funds that are available – in other words, to support portfolio construction using a set of funds or other products created by institutional asset managers. 

The main uses of AI in wealth management are gathering client data and classifying clients into the correct groups. Any process that can lower the client acquisition costs (CAC) while providing data about the client that will enable customized solutions allows the wealth management firm to sell itself as being more customer-focused than its rival (while maintaining lower CACs). 

Institutional asset managers are in the business of generating positive returns. To deliver a positive return to their clients – which may be wealth managers, individuals, or institutions – they need to identify and invest in assets whose value will appreciate over time, such as single stocks, bonds, commodities, and FX. They also need to build risk-controlled portfolios of assets to mitigate against periods when their selections are wrong and to allow enough time for their asset selection skills to generate positive returns.

If you would like to learn more about the applications of artificial intelligence in finance, Intuition recently released three new tutorials on this topic.

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