AI and risk management Innovating with confidence每日免费阅读行业报告1、每日星球内分享10+精品报告；2、不定时分享国际投行报告，脱水研报等；3、行业报告均为公开内容，版权归原作者所有，星球仅分享内部学习。扫描右侧二维码即刻加入【行业报告（免费） 】星球（此页只为更多需要行业报告的朋友提供便利，希望不会影响您的阅读，谢谢理解！）Contents1. Executive summary 012. A brief overview of AI 033. Challenges to widespread AI adoption in Financial Services 044. Embedding AI in your Risk Management Framework 075. What are regulators likely to look for? 186. Regulating AI – some reflections 227. Conclusion 24Contacts 25Authors 251. Executive summaryArtificial Intelligence (AI) is not a new concept, but it is only in recent years that financial services (FS) firms have started to learn about and understand its full potential. AI can drive operational and cost efficiencies, as well as strategic business transformation programmes, including better and more tailored customer engagement. However, limited availability of the right quality and quantity of data, insufficient understanding of AI inherent risks, a firm’s culture, and regulation can all act as real, and in some cases, perceived barriers to widespread adoption of AI in FS firms. EU and international regulators have also taken an active interest in AI, and while they recognise the benefits that AI can bring to financial markets, consumers, and their own work, they are also increasingly mindful of the potential risks and unintended consequences that the use of AI by regulated firms may have.This is particularly relevant given that, over recent years, the FS sector has been hit by a significant number of financial and other penalties in relation to the mistreatment of customers and market misconduct. The resulting focus on the fair treatment of customers and market integrity, together with the relatively untried and untested nature of AI in a regulatory context, have meant that FS firms have been understandably cautious about the adoption of AI solutions. Effective risk management, far from being an inhibitor of innovation, is in fact pivotal to a firm’s successful adoption of AI. To overcome these obstacles, reap the full benefits of AI adoption, and avoid problems further down the road, it is imperative for boards and senior management to develop a meaningful understanding of the technology, including its existing and potential uses within their organisations, and take a firm grip on the implications of AI from a risk perspective. In this context, effective risk management, far from being an inhibitor of innovation, is in fact pivotal to a firm’s successful adoption of AI. “The biggest challenge for firms is less about dealing with completely new types of risk and more about existing risks either being harder to identify in an effective and timely manner, or manifesting themselves in unfamiliar ways”.In relation to the latter point, which is the focus of this paper, we believe the biggest challenge for firms is less about dealing with completely new types of risks and more about existing risks either being harder to identify in an effective and timely manner, or manifesting themselves in unfamiliar ways. In this paper, we discuss how firms should review and adapt their existing Risk Management Framework (RMF) to reflect some of the important differences which need to be taken into account when deploying complex AI use cases.01AI and risk management | Innovating with confidenceFor example, the ability of AI to learn continuously from new data, and to make decisions which are driven by complex statistical methods, rather than clear and predefined rules, can make it challenging for firms to understand the decision drivers which underpin the final output. In many ways, this is not dissimilar from the challenges that organisations face in managing human resources. However, evolving AI solutions can make auditability and traceability challenging, and the speed at which they evolve can result in errors manifesting on a large scale, in a very short space of time. Firms will need to review and update their risk practices to manage risks through the various stages in the RMF lifecycle (identify-assess-control-monitor). The continuously evolving nature of AI solutions will require some of these activities to happen at shorter and more frequent intervals. Existing risk appetite statements will also need to be reviewed, and a number of new components, such as a fairness policy for example, may need to be developed to inform the various phases of the RMF. Evolving AI solutions can make auditability and traceability challenging, and the speed at which they evolve can result in errors manifesting on a large scale, in a very short space of time. In this paper, we use a simple conceptual RMF to draw out some of the challenges that AI introduces and bring the framework to life by working through a practical example of how a firm may manage model risk within an AI solution for policy pricing in the insurance sector. We also discuss how regulators are responding to AI solutions, and highlight their key areas of focus and e...
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