全球風險管理專業(yè)人士協(xié)會(GARP)致力于為風險管理條線上的各級人員，包括各大金融機構的風險從業(yè)者和監(jiān)管機構人員提供風險教育和最新行業(yè)資訊。GARP China微信公眾號將持續(xù)轉載“GARP Risk Intelligence”系列文章，介紹科技、企業(yè)文化與治理、能源等領域對操作風險、信用風險、市場風險和資產負債管理的影響。讓我們一起全面認識風險，防范風險，化解風險。
研究表明，日益依賴于數據的金融服務行業(yè)正面臨著新興風險和監(jiān)管問題。
根據世界經濟論壇(World Economic Forum)的一項研究，隨著人工智能和相關技術的日益普及，金融服務的風險、運營模式和競爭機制將發(fā)生根本變化。這份長達167頁，題為《金融服務新物理學(The New Physics of Financial Services)》的報告被稱為“世界上最大的關于人工智能對金融服務影響的研究之一”。該報告與德勤合作，是世界經濟論壇“金融服務未來”系列的一部分。
該報告深入探討了雖然人工智能技術在成本節(jié)約、效率提高方面的潛力巨大，但成功地應用此技術需要重新考慮風險管理假設，并顛覆傳統(tǒng)業(yè)務和運營模式，這非常依賴于從公司內外順利獲取數據。隨著金融機構采用全新的競爭機制，他們急需新的戰(zhàn)略和合作伙伴關系。
“As AI drives operational efficiency, economies of scale alone will not sutain cost advantages,” Rob Galaski, Deloitte Global Banking & Capital Makrets Consulting leader, said in an August 15 press release.
“In the future,” he went on, “financial institutions will be built on scale of data and the ability to leverage that data. Increasingly bifurcated markets are already emerging where data sharing is critical to competitive success and first movers are positioned to distinguish themselves by delivering better advice, constant presence, and curated ecosystems. Firms that lag behind are finding that their old strengths may not keep them as competitive as they once were.”
1、厘清定義
The paper is based on research over a 10-month period, consisting of more than 200 interviews and seven workshop discussions with subject matter experts, with both incumbent institutions and AI-driven challengers represented.
The authors – led by WEF financial innovation lead R. Jesse McWaters – note that there is no universally agreed upon definition of artificial intelligence and “a marked lack of clarity” around it.
“When business people talk about AI, they typically are not talking about a particular technical approach or a well-defined school of computer science,” the writers say. “Rather they are talking about a set of capabilities that allows them to run their business in a new way.” These amount to “a suite of technologies, enabled by adaptive predictive power and exhibiting some degree of autonomous learning,” which contribute to machine-driven advances in pattern detection, foresight, customization, decision-making, and interaction.
圖1：該報告稱人工智能不存在于真空中，它需要“與所有其他技術創(chuàng)新的發(fā)展交織在一起”。
2、轉型的意義
The premise of the WEF-Deloitte research is that financial firms must go beyond an understanding of AI's technical capabilities and start to analyze these technologies' strategic implications and long-term impact, which “may be even more radical and transformative than we first imagined.”
The transformative impacts “will necessitate a level of public-private commitment to understand and continuously shape the future of financial services,” McWaters said in the August announcement. “And while emerging questions about consumer protections and systemic risks remain the purview of regulators, effectively responding to these challenges will require collaboration between public and private stakeholders in order to resolve regulatory uncertainties and manage the risks and opportunities of AI in financial services.”
The report emphasizes that AI does not stand apart from, and will be intertwined with, such other innovations as cloud computing, blockchain, smart contracts and quantum computing.
“There are a number of technologies that will need to come together in order for AI technologies to work successfully, most notably the use of cloud along with technology to ensure data security,” says Dilip Krishna, CTO of Deloitte's Risk and Financial Advisory practice.
3、“新的重心”
The physics metaphor – a weakening of “the bonds that have historically held together financial institutions” and creation of “new centers of gravity where new and old capabilities are being combined in unexpected ways” – leads to several key findings about the financial services ecosystem's changing dynamics. These include the growth of collective solutions for shared problems and their consequences; the rise of uneasy data alliances; and the growing power of data regulators and their inevitable impact on the use of AI.
Collective solutions will arise, the report explains, to fulfill the relentless need for data sets that can be fed into AI platforms, facilitating pattern recognition and problem-solving in, for instance, anti-fraud and anti-money-laundering (AML). Such shared solutions, the document says, can increase the accuracy, timeliness and performance in non-competitive areas, spreading operational cost savings while improving the overall safety of the financial system.
4、集中化和“令人不安”的聯盟
When it comes to shared and centralized solutions, there are questions, not yet fully resolved, about accountability in the event of cyberattacks on shared data, and about responsibility for protecting sensitive customer data.
The rise of uneasy data alliances is said to be inevitable in an ecosystem where institutions are vying for new and diverse data sets to feed and optimize their data-hungry AI engines. In such an environment, the WEF report warns, managing partnerships with competitors and potential competitors will be critical and fraught with strategic and operational risks. It poses the question: “How will institutions protect the competitive value of their proprietary data in a world where that data must be shared with competitors to access minimum requirements of efficiency?”
One example of this trend: An employee health insurance partnership of Amazon, Berkshire Hathaway and JPMorgan Chase & Co. that aims to deploy big data and AI to align incentives. “Only time will tell if [such partnerships] drive sustained value,” the report says.
5、數據監(jiān)管
Another trend cited by WEF and Deloitte is the emerging power of data regulators, with a potential impact on AI usage in financial services, and the uncertainty of how such regulation may play out in different parts of the world.
The report notes that the U.K. has adopted Open Banking, which requires customer-data portability and can erode incumbents' data advantage. Australia, Canada and Singapore are among countries actively considering some form of Open Banking, while in the U.S., Congress has been taking testimony from large technology companies on the topic of privacy and data security. These discussions could lead to new rules.
Of more immediate concern to Krishna is the need for risk managers to start thinking about the internal risks associated with AI as such platforms are rapidly being deployed. “Given the speed with which AI usage could take root internally over the next three or four years, risk regimes need to catch up, in terms of understanding, analyzing and managing the range of potential risks.”
金程推薦，FRM一級智能***計劃體驗班 0元體驗。學習素材+人工智能完美結合，為FRM學員通過保駕護航;權威名師嘔心瀝血，傾情打造;小班制管理，班主任督學，1對1私教。
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