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

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

• 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