Greater Use of Artificial Intelligence and Machine Learning in Finance

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

Using machine learning to find reliable and low-cost solar cells

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

The influence of AI on trust in human interaction

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

港須完善法規 立足數字經濟


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。 






















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) 




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。 


WEB3技術推動公開數據 改善金融界信用評級困難



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





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。





Chinese AI firm SenseTime launches ChatGPT rival SenseNova, joining giants like Alibaba and Baidu in chatbot race

  • 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
The SenseTime office in Shanghai. The firm’s potential clients include internet firms such as e-commerce operators and video-game developers, co-founder and CEO Xu Li says. Photo: Reuters

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