We have seen a considerable surge in the usage of artificial intelligence (AI) and machine learning in the finance industry in recent years. These technologies are being adopted by financial institutions in order to automate and optimize their processes, eliminate risks, and acquire insights into client behavior.

AI and machine learning are transforming the way we do business and proving to be significant tools in the banking industry.

What Exactly Are AI and Machine Learning?

Artificial intelligence (AI) and machine learning (ML) are computer technologies that allow machines to learn from data, discover patterns, and make judgments. AI entails creating algorithms capable of performing tasks that would normally need human intelligence, such as language translation, image recognition, and decision-making. 

Machine learning is a branch of artificial intelligence that focuses on developing systems that can learn from data without being explicitly programmed.

The Application of AI and Machine Learning in Finance

AI and machine learning have several financial applications. Here are some examples of how these technologies are being used:

One of the most significant advantages of AI and machine learning is its capacity to detect fraudulent transactions. These technologies are being used by banks and financial institutions to examine vast amounts of data and find trends that may suggest fraudulent conduct. This enables them to detect and prevent fraud before it causes harm.

• Risk management: AI and machine learning can assist financial organizations in identifying possible hazards and mitigating them. For example, they can examine market data to discover trends that may affect investments or clients who are at a higher risk of loan default.
• Customer service: Artificial intelligence and machine learning can assist financial companies in providing better customer service. Chatbots, for example, can be trained to respond to consumer inquiries and resolve issues in a timely and effective manner.

AI and machine learning can be used to evaluate market data and find investment possibilities in investment management. They can also be used to automate trading operations, allowing financial organizations to make more accurate and timely trading decisions.

The Advantages of AI and Machine Learning in Finance

The application of AI and machine learning in finance has various advantages. Here are a few examples:

• Improved accuracy: AI and machine learning systems can examine massive volumes of data and uncover patterns that people would struggle to detect. This can lead to more accurate predictions and more informed decisions.
• Increased efficiency: Using AI and machine learning to automate procedures can help financial organizations save time and costs. This can result in shorter processing times, better customer service, and lower operational expenses.
• Better risk management: AI and machine learning can assist financial organizations in identifying possible hazards and mitigating them. This can aid in the prevention of financial losses and the reduction of risk exposure.
• Improved customer experience: Artificial intelligence and machine learning can assist financial organizations in providing better customer service. Chatbots, for example, can be trained to respond to consumer inquiries and resolve issues in a timely and effective manner.
• Competitive advantage: Early adopters of AI and machine learning can obtain a competitive advantage over their peers. These tools can assist them in identifying new opportunities and making better, more timely decisions.

The Difficulties of Using AI and Machine Learning in Finance

While the application of AI and machine learning in finance has significant advantages, it also has some drawbacks. Here are a few examples:

• Data quality: In order to produce accurate predictions, AI and machine learning algorithms rely on high-quality data. The algorithms may generate incorrect results if the data is wrong or incomplete.
• Insufficient transparency: Some AI and machine learning algorithms are sophisticated and difficult to comprehend. This can make explaining the reasons behind certain judgments difficult.
• Concerns about security and privacy: Financial institutions that employ AI and machine learning must ensure that the data they collect and analyze is safe and secure. They must also ensure that data privacy standards are followed.
• Concerns about ethics: AI and machine learning can make decisions that have ethical ramifications. Algorithms used to calculate creditworthiness or loan approvals, for example, may accidentally prejudice against specific categories of individuals.

Integration with current systems: Integrating AI and machine learning into existing systems can be difficult and may necessitate considerable infrastructure and training investments.

The Risks of Machine Learning in Finance

In finance, machine learning has been used for tasks such as risk assessment, fraud detection, portfolio optimization, and trading strategies. However, like any technology, machine learning in finance comes with its own set of risks that need to be carefully considered and managed.

Source from: financemagnates

Researchers at the University of California, Davis College of Engineering are using machine learning to identify new materials for high-efficiency solar cells. Using high-throughput experiments and machine learning-based algorithms, they have found it is possible to forecast the materials’ dynamic behavior with very high accuracy, without the need to perform as many experiments.

The work is featured on the cover of the April issue of ACS Energy Letters.

Hybrid perovskites are organic-inorganic molecules that have received a lot of attention over the past 10 years for their potential use in renewable energy, said Marina Leite, associate professor of materials science and engineering at UC Davis and senior author on the paper. Some are comparable in efficiency to silicon for making solar cells, but they are cheaper to make and lighter, potentially allowing a wide range of applications, including light-emitting devices.

A primary challenge in the field is that the perovskite devices tend to degrade way more readily than silicon when exposed to moisture, oxygen, light, heat, and voltage. The problem is to find which perovskites combine high-efficiency performance with resilience to environmental conditions.

Perovskites have a general structure of ABX3, where A is an organic (carbon-based) or inorganic group, B is lead or tin, and X is a halide (based on chlorine, iodine or fluorine or a combination). Therefore, “the number of possible chemical combinations alone is enormous,” Leite said. Further, they need to be assessed against multiple environmental conditions, alone and in combination, which results in a hyperparameter space that cannot be explored using conventional trial-and-error methods.

“The chemical parameter space is enormous,” Leite said. “To test them all would be very time consuming and tedious.”

High throughput experiments and machine learning

As a first and key step towards solving thesechallenges, Leite and graduate students Meghna Srivastava and Abigail Hering decide to test whether machine learning algorithms could be effective when testing and predicting the effects of moisture on material degradation.

Srivastava and Hering built an automated, high-throughput system to measure the photoluminescence efficiency of five different perovskite films against the conditions of summer days in Sacramento. They were able to collect over 7,000 measurements in a week, accumulating enough data for a reliable training set.

They used this data to train three different machine learning algorithms: a linear regression model, a neural network and a statistical model called SARIMAX. They compared the predictions of the models to physical results measured in the lab. The SARIMAX model showed best performance with a 90 percent match to observed results during a window of 50-plus hours.

“These results demonstrate that we can make use of machine learning in identifying candidate materials and suitable conditions to prevent degradation in perovskites,” Leite said. Next steps will be to expand the experiments to quantify combinations of multiple environmental factors.

The perovskite film itself is only a part of a complete photovoltaic cell, Leite said. The same machine learning approach could also be used to forecast the behavior of a complete device.

“Our paradigm is unique, and I am eager to see the upcoming measurements. Moreover, I am very proud of the students’ diligence during the pandemic” Leite said.

Srivastava is a 2021 National Science Foundation Fellow. Additional authors on the paper are Yu An and Juan-Pablo Correa-Baena, both from Georgia Tech. The work was supported by grants from the National Science Foundation and Sandia National Laboratories.

Source from: Science Daily

As AI becomes increasingly realistic, our trust in those with whom we communicate may be compromised. Researchers at the University of Gothenburg have examined how advanced AI systems impact our trust in the individuals we interact with.

In one scenario, a would-be scammer, believing he is calling an elderly man, is instead connected to a computer system that communicates through pre-recorded loops. The scammer spends considerable time attempting the fraud, patiently listening to the “man’s” somewhat confusing and repetitive stories. Oskar Lindwall, a professor of communication at the University of Gothenburg, observes that it often takes a long time for people to realize they are interacting with a technical system.

He has, in collaboration with Professor of informatics Jonas Ivarsson, written an article titled Suspicious Minds: The Problem of Trust and Conversational Agents, exploring how individuals interpret and relate to situations where one of the parties might be an AI agent. The article highlights the negative consequences of harboring suspicion toward others, such as the damage it can cause to relationships.

Ivarsson provides an example of a romantic relationship where trust issues arise, leading to jealousy and an increased tendency to search for evidence of deception. The authors argue that being unable to fully trust a conversational partner’s intentions and identity may result in excessive suspicion even when there is no reason for it.

Their study discovered that during interactions between two humans, some behaviors were interpreted as signs that one of them was actually a robot.

The researchers suggest that a pervasive design perspective is driving the development of AI with increasingly human-like features. While this may be appealing in some contexts, it can also be problematic, particularly when it is unclear who you are communicating with. Ivarsson questions whether AI should have such human-like voices, as they create a sense of intimacy and lead people to form impressions based on the voice alone.

In the case of the would-be fraudster calling the “older man,” the scam is only exposed after a long time, which Lindwall and Ivarsson attribute to the believability of the human voice and the assumption that the confused behavior is due to age. Once an AI has a voice, we infer attributes such as gender, age, and socio-economic background, making it harder to identify that we are interacting with a computer.

The researchers propose creating AI with well-functioning and eloquent voices that are still clearly synthetic, increasing transparency.

Communication with others involves not only deception but also relationship-building and joint meaning-making. The uncertainty of whether one is talking to a human or a computer affects this aspect of communication. While it might not matter in some situations, such as cognitive-behavioral therapy, other forms of therapy that require more human connection may be negatively impacted.

Jonas Ivarsson and Oskar Lindwall analyzed data made available on YouTube. They studied three types of conversations and audience reactions and comments. In the first type, a robot calls a person to book a hair appointment, unbeknownst to the person on the other end. In the second type, a person calls another person for the same purpose. In the third type, telemarketers are transferred to a computer system with pre-recorded speech.

Source: Science Daily

以下是雲端與流動運算專業人士協會會長陳家豪先生發表於明報財經的文章。

數據是新石油 港欠妥善管理 

大數據的出現徹底改變了社會對數據科學的接受程度，傳統的金融機構也可以通過使用金融科技來分析信用數據。當更多關於個人消費者和企業行為的信息，如信用紀錄、銀行對帳單、收據和帳單證明等可以被合法採集、儲存和分析時，愚公敢肯定社會將通過這種數字基礎設施創造出前所未有的價值。相反，如果法規不完善，銀行不能完善收集的信息和在此基礎上有效完成Know Your Customer (KYC)、Anti-Money Laundering (AML)及信用評級的運作的流程。因此，香港需要盡快制定一個適當的法律框架來規範公司對個人信息的內部和外部處理，否則其作為國際金融中心的聲譽將很難維持下去。 

現代化城市須達至公開數據 

推動公開數據是一個世界性的趨勢。對於數字經濟來說，公開數據是個重要元素，因為數據為創新和改進提供了基礎。開放數據有助於為城市發展創造有利條件，提高經濟運行效率，為基礎設施和人民福利增加價值。而為公開數據立法對一個城市的數字經濟至關重要。香港政府目前仍然沒有為公開數據法而推動任何研究及討論。然而，世界許多具有前瞻性的城市早已制定了相關的開放數據法，當中包括紐約、倫敦和中華台北。 

香港現行個人資料保護法極不合時 

近年來，各地政府提倡保護個人數據已成為一個極其重要的議題。儘管亞洲最古老的綜合性數據保護法之一，即香港的《個人資料（私隱）條例》（PDPO）於1995年完成立法並在1996年實施，但由於無法跟上歐盟的GDPR（通用數據保護條例）和中央政府實施的PIPL法令的發展步伐，香港對數據的相關保護法令已被遠遠拋離。在歐盟和中國大陸等地，法律規定了個人數據保護的權利，如訪問權，給予個人糾正數據的權利和刪除的權利。然而，在香港要通過法律手段落實這些權利，仍然未有方案及時間表。根據歐洲GDPR，個人數據必須從第一天起就受到保護。然而，香港目前的法律只是為了確保個人信息在需要時不被破壞，並通過合法的訪問和披露控制有效地發揮作用。目前仍然缺乏像歐盟和中國大陸那樣具有阻嚇性的法規和懲罰措施來保護個人數據的正確使用。 

政府以鴕鳥政策對應問題 

在過去的十多年，由於香港沒有完善的個人數據保護法，政府部門、金融監管機構甚至負責促進技術發展的稅務部門都採取了避重就輕的政策，逃避問題的根源。在某些情况下，市民甚至被教導用「山寨」的方式來保護他們的個人數據。你可知道，香港處理個人數據保護的方式與一些極端保守的國家保護婦女安全方法有共通點嗎？在那些仍然沒有很好的法律來保護單身女性的國家，最安全的做法當然是把她們關在家裡，當她們不得不出去的時候用黑布蓋住她們全身。 

落後法規扼殺港數字經濟正常發展 

個人數據保護已經成為一個全球性的議題，香港需要仿效世界發達經濟體，以發展數字經濟為目標，通過立法來保護個人數據。我們必須像解放婦女可穿上展露體態的時裝融入社會的心態一樣，把長年被困在家裡的個人數據融入社會。有了良好的法律和嚴格的執法，不僅年輕女孩能夠當上令人羨慕的模特兒專業，驕傲地以身體為時尚界創造價值；普通民眾也能夠放心地向政府和商業機構提供個人數據，為社會添加數字經濟火車頭的燃料。必須先讓香港躍過這堵舊牆，我們才能在數字經濟的道路上邁出第一步。 

數字人民幣國際化等願景成港數字金融改革動力 

然而，公開數據和個人數據隱私之間的關係複雜，與政策制定和實施有關。香港政府沒有像其他發達經濟體般有市長競選制度，好讓市政府履行競選承諾而整合政府各部門資源推動新政。香港要推動相關項目時，必須要靠新的推動力才能讓政府各部門及市場上的不同利益持分者妥協。未來幾年，中國內地及香港的央行數字貨幣發展，以及推動香港成為國際數字資產交易中心的願景，將成香港的數字金融改革動力，愚公認為香港應該先把個人資料保護法的更新及使用守則做好，繼而像紐約、倫敦等國際金融都會般推動公開數據立法，方為上策。 

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以下是雲端與流動運算專業人士協會會長陳家豪先生發表於明報財經的文章。

因為穩定幣（Stablecoin）日趨普及，已給金融系統帶來了潛在的風險，全球發達經濟體的監管機構目前正倡議甚至已經開始若干程度上監管穩定幣，穩定幣可以被用來促進洗黑錢、資助恐怖主義和其他犯罪活動，以及成為大規模逃稅及非法走資的渠道。此外，它們也可能被用來操縱市場，製造不穩定，並擾亂貨幣體系。而通過監管穩定幣，監管機構可以幫助確保這些活動不會發生，使金融系統更加安全和穩定。 

對症下藥全面監管運作 

香港金融管理局（HKMA）月初宣布打算引入一個立法框架來監管穩定幣。該框架旨在提供消費者保護和金融穩定，並促進本地數字經濟的發展。此舉被市場視為一個積極的步驟，預計將鼓勵香港甚至大灣區內的創新和投資。 

過去一年經歷穩定幣Luna/UST及知名交易所FTX兩個大型爆雷事件後，社會意識到單靠分散式信任機制去監察業界運作並防止爆雷發生明顯有不足之處。業界對這立法倡議反應正面無可厚非。金管局公布的文件顯示，該立法框架和其他國際市場的做法相似，將為穩定幣市場的所有參與者提供一個公平的競爭環境。該框架還將為消費者提供更大的保護，因為它將要求穩定幣的發行者滿足某些標準和規定。此外，此舉還將引入更大的金融穩定性，因為它將確保穩定幣發行者的活動受到監控，並採取適當措施應對任何潛在風險。

金管局亡羊補牢未為晚也 

在香港金融市場因為去年證監會（SFC）推出非常苛刻及「趕客」的VASP框架引致人才、資金及機構大量流失的教訓下，HKMA的新框架將鼓勵創新和投資為其中一個著眼點。它將會通過提供更清晰和確定的監管環境，預計將吸引更多的公司和投資者進入市場。這反過來就是一個創造就業機會並促進本地經濟的而主動出擊的政策。此外，它還將鼓勵新技術的發展，因為公司將能夠探索創新的解決方案來解決穩定幣帶來的獨特挑戰。 

面對遺傳性監管分工限制 

由於香港的財金監管框架是按市場產品分類而分成HKMA、SFC及保監（IA）等三大監管機構，而穩定幣卻是數字資產當中的一種，目前也有以下三大種類：

1. 法幣支持的穩定幣。法幣支持的穩定幣與基礎法定貨幣的價值掛鈎，如美元。它們由儲備的同等數量的法定貨幣支持。(目前由HKMA負責) 

2. 加密貨幣支持的穩定幣。加密貨幣支持的穩定幣由基礎加密貨幣支持，如比特幣或以太坊。它們旨在保持相對於它們所支持的加密貨幣的穩定價值。(目前由 SFC 負責) 

3. 演算法穩定幣。演算法穩定幣沒有任何資產支持，而是旨在通過演算法調整其供應量以應對需求的變化，從而保持穩定的價格。(目前由SFC負責) 

先不說目前虛擬資產交易是以智能合約模式不斷在穩定幣和一般虛擬貨幣之間不斷交易需要各監管機構以統一標準、指引及法則維繫市場運作，單從將要監管穩定幣的議題上，你應該已經意識到目前提出的框架裡仍然未能提供未來幾個監管機構的分工和合作模式，甚至是共同制定標準及操作指引的議題。 

將監管穩定幣和其他數字資產活動的任務分別交給HKMA及SFC將會遇到很多挑戰，甚至會導致混亂和工作重疊。它還可能導致兩個監管機構在其職責範圍和對數字資產活動的規則方面產生衝突。當加入更多機構如穩定幣交易保險這一類需要三個甚至更多監管機構參與的情況時，它更可能因為監管權責分散，缺乏統一的監督，使利益相關者難以了解監管情況的種種負面問題。 

按數字經濟新玩法整合資源 

總而言之，愚公認為金管局引入立法框架來監管穩定幣的舉措是一個積極的步驟。該框架預計將提供更大的消費者保護和金融穩定性，並促進香港甚至大灣區數字經濟的發展。此外，它還有望鼓勵該地區的創新和投資，最終導致大灣區內經濟增長和發展。由於中國內地短期內並不會接受任何虛擬貨幣及資金自由出入的財經活動，在一國兩制下香港將會繼續發揮一個重要的中介人角色。 香港政府必須馬上展開整合各相關監管機構在虛擬資產交易領域的分工和合作框架及運作模式，一方面成立跨部門工作小組針對未來發展以制定一致性的標準和指引給業界參考，另一方面必須全力投入橫跨多個監管機構使用的數據庫及人工智慧監管/合規科技平台，這樣才是實現「以結果為目標」的好方法。 

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以下是雲端與流動運算專業人士協會會長陳家豪先生發表於明報財經的文章。

過去7、8年間，政府發現香港中小企業無論在本地及外地的競爭力受壓抑，因被大型企業多方面佔據着有利條件，令該等企業發展愈來愈困難。儘管政府不斷動用庫房的資金協助中小企業在香港、內地甚至東南亞發展，但授人以「魚」只是權宜之計。政府必須盡快制定長遠政策，以治本的角度協助中小企業，從而在大企業及外地企業夾擊下衝出重圍。 

愚公過去十多年一直以不同身分參與香港的公開數據的發展， 最令人費解的正是香港政府現在仍然未有像其他發達經濟體一樣重視公開數據，自2017年發布了《香港智慧城市藍圖》以至到 2.0 版藍圖裡面也沒把整個城市的公開數據發展納入綱領及提供實施時間表。 目前政策只提及個別政府部門怎樣公開部分公眾數據，而沒有提及怎樣與其他城市看齊，推動整體的公開數據發展及長遠政策。 

相對星韓政策 港仍屬落後

新加坡在數據開放方面表現出了較高的水平，政府推動了「開放數據」計劃，開放了大量政府數據，鼓勵創新和發展。韓國政府也推動數據開放，建立了國家數據平台，提供各種數據和API接口，方便市民和企業使用和開發。香港政府在這方面的進展相對較慢，但政府已經推出了「開放數據」平台，開放了一些政府數據，並且逐步擴大開放範圍。 

在數據分類和格式方面，三地也有一些區別。新加坡政府將數據分為三個類別，分別是「開放數據」、「限制數據」和「內部數據」，並制定格式標準。韓國政府也將數據分為多個類別，並且提供多種格式的數據，方便使用和開發。香港政府在這方面的進展相對較慢，政府雖然已經推出了一些開放數據格式，但缺乏統一標準。 

在數據使用和開發方面，三地都鼓勵市民和企業使用和開發數據應用。新加坡及韓國政府也推出了包括政府及公營機構參與的「數據共享平台」，方便市民和企業使用和開發數據應用。香港政府在這方面的成功案例卻乏善足陳，尤其在對抗疫情而開發的APP應用上的落差就可見一班。 

科技應用紓緩數據共享問題

政府致力推動香港成為世界虛擬資產交易中心，正是引入區塊鏈及Web3技術去解決政府難以突破的數據互通問題的機會。 Web3和區塊鏈技術可以改善香港政府部門數據分散和穀倉效應的問題，而同時提供數據共享、數據安全、數據溯源三個理想效益。 

你可知道，愛沙尼亞政府早於2012年就推出了一個區塊鏈技術的數據管理平台集成不同部門的數據，實現數據的共享和開放。而新加坡政府在2018年也推出了基於區塊鏈技術的，還能夠實現數據的不可篡改性和安全性，從而保證數據的可信度和安全性的數據共享平台。 阿姆斯特丹市政府、美國愛荷華州政府、日本及澳州政府也相繼在2018年到2021年間推出不同基於區塊鏈技術的數據共享平台。 總的來說，區塊鏈和Web3技術已經成為全球政府數據管理和共享的新趨勢。

香港政府可以借鑑外地案例，積極利用新技術推動公開數據發展，社會就能依靠公開財務和經濟數據，如稅收、GDP、通貨膨脹率等數據，讓中小企業更好地了解市場環境和行業趨勢，從而更好地制定商業計劃和貸款申請。政府也可以讓企業信用數據變得更透明，如企業的信用評分、財務狀況等數據，讓中小企業更好地了解自己的信用狀況，從而更好地申請貸款和獲得融資支持。 

除了香港金融管理局推出的CDI平台，政府應透過公開產業數據，如行業結構、市場規模、競爭格局等數據，讓中小企業更好地了解產業環境和市場趨勢，從而更好地制定商業計劃和貸款申請。 

趕緊應用新技術推動公開數據發展，能讓企業可以在安全的環境下共享和交換數據，並可以確保數據的可信度和安全性，提高公共服務的效率和質量，有效促進香港中小企業的發展。 

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• Firm unveils SenseNova, a set of large AI models that cover key capabilities including computer vision, natural language processing and AI-generated content, during a live demonstration in Shanghai
• In China, AI bots will initially develop fast in B2B territory before B2C companies start using them, co-founder and CEO says

Chinese artificial-intelligence (AI) company SenseTime unveiled its answer to ChatGPT on Monday, jumping onto the generative AI bandwagon as mainland Chinese technology companies race to commercialise the so-called large language model.

The company unveiled SenseNova, its latest set of large AI models that cover key capabilities including computer vision, natural language processing and AI-generated content, during a live demonstration at its data centre in Shanghai’s Lingang free-trade zone.

“In China, AI bots will initially develop fast in B2B [business-to-business] territory before business-to-customer [B2C] companies start using them,” said Xu Li, SenseTime’s co-founder and CEO. “We need to improve our technological capabilities and fine-tune services to better commercialise AI.”

The launch of SenseTime’s AI models follows similar moves by Chinese search-engine giant Baidu and e-commerce giant Alibaba Group Holding, which owns this newspaper, after ChatGPT – an AI chatbot released to the public by Microsoft-backed OpenAI late last year – prompted Chinese technology companies to come up with their own versions. ChatGPT, which was updated with a latest version called GPT-4 last month, has gained widespread attention because of its ability to hold humanlike conversations.

Source: SCMP