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AI & machine learning bring benefits and risk to financial services: FSB

  • Publish Date: Posted over 6 years ago
  • Author:by Alan Jarque

The Financial Stability Board (FSB) has highlighted the financial stability implications of the growing use of artificial intelligence (AI) and machine learning within the financial services, which could bring risks as well as benefits.

Financial institutions are increasingly using AI and machine learning in a variety of applications that span the financial system. These include; to assess credit quality, to price and market insurance contracts and to automate client interactions.

Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions, noted the FSB in a recent report.

Meanwhile, hedge funds and broker-dealers amongst other firms are using it to find signals for higher uncorrelated returns and to optimise trade execution. Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment and fraud detection.

Specifically for insurance, the report noted that the industry is using machine learning to analyse complex data to lower costs and improve profitability. Adoption of AI and machine learning applications in InsurTech is particularly high in the US, UK, Germany and China.

Many applications involve improvements to the underwriting process, assisting agents in sorting through vast data sets that insurance companies have collected to identify cases that pose higher risk, potentially reducing claims and improving profitability. Some insurance companies are actively using machine learning to improve the pricing or marketing of insurance products by incorporating real-time, highly granular data for example online shopping behaviour or telemetrics (sensors in connected devices, such as car odometers).

AI and machine learning applications can substantially increase some insurance sector functions, such as underwriting and claims processing. Machine learning techniques can be used to determine repair costs and automatically categorise the severity of vehicle accident damage.

In addition, AI may help reduce claims processing times and operational costs. Insurance companies are also exploring how AI and machine learning and remote sensors (connected through the ‘internet of things’) can detect, and in some cases prevent, insurable incidents before they occur, such as chemical spills or car accidents.

The FSB’s analysis reveals a number of potential benefits and risks for financial stability that should be observed as the technology is adopted in the coming years and as more data becomes available.

These are:

  • The more efficient processing of information, for example in credit decisions, financial markets, insurance contracts and customer interactions, may contribute to a more efficient financial system. The applications of AI and machine learning by regulators and supervisors can help improve regulatory compliance and increase supervisory effectiveness.
  • Applications of AI and machine learning could result in new and unexpected forms of interconnectedness between financial markets and institutions, for instance based on the use by various institutions of previously unrelated data sources.
  • Network effects and scalability of new technologies may in the future give rise to third-party dependencies. This could in turn lead to the emergence of new systemically important players that could fall outside the regulatory perimeter.
  • The lack of interpretability or auditability of AI and machine learning methods could become a macro-level risk. Similarly, a widespread use of opaque models may result in unintended consequences.
  • As with any new product or service, it will be important to assess uses of AI and machine learning in view of their risks, including adherence to relevant protocols on data privacy, conduct risks, and cybersecurity. Adequate testing and ‘training’ of tools with unbiased data and feedback mechanisms is important to ensure applications do what they are intended to do.