Ai In Risk Management : Artificial Intelligence Will Change Risk Management Forever

Ai In Risk Management : Artificial Intelligence Will Change Risk Management Forever Although AI has existed for several decades, financial services companies have only recently begun to realize its incredible potential.

Simply put, AI is a technique involving a machine’s simulation of human intelligence.

Machine learning (ML), a sub-field of AI, allows computers to learn via data. Advances in computing power have led to various machine learning algorithms, such as deep learning, random forests, gradient boosting algorithms, and cluster analysis. In fact, common procedures in machine learning algorithms, including regression, classification, and network formation, are all very suitable for risk management. 

In the current economic environment, the level of risk in banking is rising. The demand for automated risk prevention and improved control capabilities is growing larger. However, the traditional system of risk management lacks flexibility and the techniques for risk monitoring and control measures are all falling behind. Thus, AI technology, with big data coverage, rich dimensions, and high real-time feedback, applied to banks’ risk management has been particularly attractive.

What Risks Should We Prevent? 

The Basel Agreement mentions three pillars, of which the first pillar of the minimum capital requirement is at the core. This pillar clarifies the calculation method of capital adequacy ratio for different risks, including market risk, credit risk, and operational risk, which are generally recognized as the three major risks of the banking industry. I would include a source here!!

Market risk refers to the bank’s risk of losses due to changes in interest rates, exchange rates, stocks, commodities, and other prices. Credit risk, also known as default risk, refers to the borrower’s, or counterparty’s, inability to uphold the conditions of the contract for various reasons, which constitutes a default. Operational risk refers to the risk of losses caused by problematic internal procedures, employees, information technology systems, and external events.

How To Prevent Risk Using AI? 

Big data and AI initially serve to complement traditional bank risk control methods, such as information verification in account opening, black and white list matching, face recognition, etc. Through the determination and matching of simple rules, banks receive assistance in making risk-control decisions. 

Moreover, imagine a book of 500 loans, ai can monitor hundreds of data points for those loans, allowing the bank greater efficiency.

However, the creation of such rules relies on expert experience. Therefore, current models cannot automatically update for new risks; risk control rules may be susceptible to fraud. In general, the model at this stage relies on manual instructions that inform the model on how to distinguish between good and bad cases.

After big data and AI technologies continue to mature, relevant data is further available. In this advanced stage, the use of artificial intelligence technology to build and apply a risk management model to subdivided business processes. Such as credit pricing, pre-loan review, post-loan monitoring, and transaction fraud detection, is essential. By continuously feeding data to the algorithm, the algorithm itself learns how to identify the bad cases. In turn, the quality and applicability of the model will gradually improve.

Three-dimensional risk control

However, increased accessibility to the internet has brought up some new issues. Modern-day fraudsters use various tools, like special forces, to find every possible risk control loophole; they can break through the entire line of defense (I would reword this). Due to this “asymmetric” risk change, commercial banks should focus on building an integrated, three-dimensional risk control system. One that engages in pre-event warning, mid-event monitoring, and post-event analysis.

 Pre-event warning involves…  (I think that this needs to be elaborated on)

Mid-event monitoring involves gathering data in case of fraud? (This needs to be elaborated on). For instance, a system that is unable to respond to fraud in real-time will render the platform meaningless. With the rapid changes in the banking business and the emergence of new fraud technologies. Risk control rules also need to comply with changes outside the market in real-time, which in turn requires rapidly updated AI training models. 

Post-event analysis involves studying previous cases of fraud? (Needs to be elaborated on). Through complex network technology and cross-industry data, AI can realize automatic correlation analysis and the visualization of multi-scenario big data. An example of using AI for risk is the Amazon credit card fraud system. The AMazon team built neural networks by using the data obtained from hundreds of thousands of online credit card purchases.

Many experts believe that the future of risk management lies in big data and AI technologies. Banks will demand self-built AI infrastructure and applications, professional consulting companies, and direct use of third-party AI services. Among them, AI-aas (AIas a Service) in industries will play an important role.

Ai In Risk Management : Artificial Intelligence Will Change Risk Management Forever

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