AI Financial Tools: Risks and benefits of AI in the financial sector

Today, we are witnessing the continued rapid development of artificial intelligence (AI) and its implementation in various fields. Modern software solutions in the financial sector already include blockchain technology and digital currencies, but can they be extended to incorporate more contemporary developments?

In this blog post, experts at Grapherex discuss the role of AI in improving the efficiency of financial management and the potential risks associated with implementation.

AI tools in the financial sector

The concept of artificial intelligence has been around for a long time, and now it is being actively implemented in financial services. Areas impacted by AI include robo-advisory, algorithmic trading, fraud and compliance, credit scoring, and predicting financial distress. AI can execute processes faster than humans and make inferences that humans might miss.

There are several examples of AI tools that can be used in financial institutions. Let's talk in more detail about the use cases for artificial intelligence.

AI tools such as ChatGPT and Bing Chat have demonstrated impressive efficiency improvements because they can process large amounts of data quickly. The robots are so good that, according to the company's CEO, 7,800 of IBM's jobs have the potential to be replaced by AI within a few years.

ChatGPT is a language model created by OpenAI. It generates conversational responses in the form of chats. ChatGPT is used in various applications to answer questions and provide explanations, just like the staff of the support center. While the software is advanced, it may not always produce accurate responses. Its knowledge deadline is also September 2021.

Bing AI is a new feature of Microsoft's Bing search engine that uses AI to provide better search results, more complete answers, and the ability to generate content. Bing Chat uses GPT4 and is free. It is also connected to the internet, which means it can receive information beyond 2021. Bing provides confirmation links for almost all generated papers.

Machine learning has huge potential in finance. ML is a subset of artificial intelligence that learns from data and has the ability to mimic human decision-making. The more the system learns, the better the answers it gives and the more accurate its predictions become. In a 2022 report, trading platform Robinhood noted that its machine learning models are already very advanced and add value to many business opportunities.

AI assistants within the cryptocurrency space provide automated support and provide users with information on topics they are interested in. They can tell users about various cryptocurrencies, blockchain technology, trading methods, and more.

In March 2023, cryptocurrency exchange Crypto.com released a generative AI user assistant called Amy. Binance launched an AI-powered NFT token generator that minted more than 10,000 tokens in less than three hours.

Important success factor

Let's talk about the limitations of modern development. But let's start on the positive side. To reduce potential errors in the operation of AI, we must be careful with the materials from which it is trained. There are several aspects that affect the performance and efficiency of AI.

Here are some of the factors highlighted in the Payments Association's AI guidelines:

1. Clean and aggregated data
2. Effective AI regulation
3. AI accountability
4. Authorized supervision

The most important factor is the use of clean, reliable and comprehensive data during training; This helps ensure that AI makes accurate predictions. Data should come from a variety of reliable sources and be properly structured. Once the data is deemed adequate, it needs to be aggregated so that AI can analyze and draw conclusions from multiple internal and external data sets.

Second, continuous monitoring and testing is necessary to improve decision-making, eliminate bias, and ensure confidentiality and security. Verification is also essential for regulatory compliance. Third, the distinction between "black boxes" and "white boxes" is important to ensure accountability. White-box AI algorithms that promote explainability and accountability are preferable to black-box algorithms.

Finally, for AI to work, we need laws that enable it to be embedded in the financial sector. The European Union has proposed an AI bill to regulate AI and address bias, discrimination, privacy and human rights violations. The proposed rules would particularly affect banking and finance. However, implementing these regulations could prove costly, potentially exceeding $30 million, according to estimates from the Center for Data Innovation.

Limitations and risks of AI

Now that we've talked about everything related to success factors, let's move on to the future and the potential obstacles and limitations facing AI.

AI tools cannot fully support individuals in difficult economic times. AI-based vendors may lack the empathy, understanding, and decision-making capabilities that human advisors or support systems can provide. Algorithmic bias is another legitimate concern, as AI may inadvertently bias or disadvantage underlying ideas based on the biases generated by its models.

It is necessary to monitor, supervise and adjust AI in the financial sector according to market dynamics. It includes measures to simulate hazards, consider cybersecurity and data privacy. This should be done to improve the stability of the system and reduce vulnerability to failure.

The risk of massive job transfers may seem overestimated, but it is also relevant. This is confirmed by the above assertion from IBM's CEO. However, just as computers and smartphones are not replacing humans, but rather prompting them to explore the topic and join in, so will artificial intelligence. There may be (and are already emerging) a large number of occupations related to the training and maintenance of AI systems.

Will AI help with financial management?

AI has transformative potential in finance. Here's a short checklist of how algorithms can help accountants with their work:

1. Risk assessment and management
2. Detect and prevent fraud
3. Effective credit and trading decisions
4. 24/7 customer support service
5. Automation of recurring processes
6. Reduce human error

Robert Quartly-Janeiro, chief strategist at cryptocurrency exchange Bitrue, said that modern AI tools are part of the future, far beyond their current applications. He added that if these tools save money, businesses will use them. In his view, AI can help transform the retail finance industry if it manages to provide fairer credit solutions, a more open approach to consumer lending, better risk management, and increased access to finance.

Chris Ainsworth is CEO of Pave Finance, a service that uses artificial intelligence to monitor market conditions and personalise portfolios. AI financial tools are not currently ready to be used without oversight, he said. Current AI tools are likely to lag, he said, so it will take a lot longer than people think. Ainsworth added that oversight is necessary to ensure models are set up correctly to account for volatility and market changes.

Overall, AI tools could become a paradigm shift in trading, banking, and financial advisory. In addition, future AI tools will help reduce the cost of managing and maintaining products in the financial industry.