The financial sector as a whole is going through some disruptive times, with competition from new Fintech companies forcing traditional banking institutions to upgrade their architecture and legacy systems. Customers are also more tech-savvy these days, and they are also demanding that their banks do more to become part of the growing digital ecosystem.

Artificial Intelligence (AI) has become the poster child for the improvement of efficiency and productivity at a reduced cost for industries across all sectors – not least the financial industry. The financial industry is prone to risk, as the institutions involved handle sensitive information on a daily basis. Using AI and machine learning in finance, therefore, is the perfect way to help streamline the management of these risks in what is a competitive and growing industry.

What is artificial intelligence (AI)

Francois Challot, the AI researcher at Google and creator of the machine learning software library Keras, defines AI as “a system’s ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios.”

What this simply means is that machines use data to spot patterns and behaviours to help identify potential future actions. Some good examples of AI in action include Amazon’s recommendations of new products for you, or Netflix’s shows you might also like.

AI and credit risk

One of the largest risks involved in the financial sector is credit risk. This is money loaned to either businesses or individuals who may then default on their obligation to repay. Now, of course, there may be circumstances outside of the bank’s control that lead to these defaults, such as an individual suddenly losing their job, but there are ways in which AI can be used to help negate these incidents.

Experian, the credit reporting company, recently reported that there was a default rate of 3.68% on most credit cards. In this example, AI could be used to better assess customers’ credit histories, catching patterns that may be easily missed by the human eye and revealing additional vulnerabilities. In fact, Zest AI (a Fintech software company) recently advertised that they could reduce default rates by around 20% through the use of their AI models.

AI and fraud risk

Fraud risk is another large risk associated with finance, with malicious acts such as money laundering and identity thefts being very common.

As AI models can analyse massive volumes of data, they are able to spot patterns from several different channels quickly, and then send alerts about potentially fraudulent activity for a limitless number of banking clients at once.

Some of the larger financial institutions are now using multiple layers of protection within their applications, including biometric and fingerprint identification, to protect customers.

AI and market risk

AI is also perfectly placed to help reduce the risks associated with market trading, as machine learning algorithms are able to analyse vast volumes of data in seconds – as compared to the days it would take humans to review.

The insights that can be gained from this data can give traders optimal price points, relative to the risk of losses, giving them the ability to forecast with much greater accuracy. Trading firms who use AI can therefore achieve higher returns while mitigating more risk.

AI and underwriting risk

One of the largest tasks faced by underwriting companies is spotting fraudulent claims. To do this, they must take into account various risk factors associated with a client’s likelihood to claim such as:

  • Age
  • Credit score
  • Domicile
  • Previous claim history

If the data points collected are not sufficient enough to determine the likelihood of a customer claiming, then this could potentially be very costly. AI can be used to collate data points from a wide variety of sources and give the insurance company a more rounded risk profile.

The challenges associated with AI in risk management

As you can see, AI can bring many benefits to the area of risk management, but there are still many challenges to be overcome. For example, many traditional financial institutions are using incomplete and inconsistent data sets that are hard to extract, and sometimes data is only shared for compliance and regulation purposes. There is surely a long road ahead if financial services are to reach their full potential and utilise AI applications for risk management.

If you are interested in risk management or are looking for a role in Data & Analytics or Risk Analytics, then Agile Recruit may just have the role for you. Take a look at our latest jobs or get in touch with one of our expert consultants to find out more.

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