Source Link: https://analyzingalpha.com/history-of-ai-in-finance
Artificial Intelligence (AI) has become integral to the finance world. Aside from its automation ability, it also can understand complex mathematical equations. In the technologically-advanced world, AI has become a cost-effection solution for financial service providers.
AI History Timeline
Artificial Intelligence can be traced back to when classical philosophers tried to relate human thinking to a symbolic system. It has existed for a long time but only received official recognition in the 1950s.
- 1956 – The term Artificial Intelligence (AI) was coined at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI).
- 1960s – Research focused on Bayesian Theory was used for machine learning. The Bayesian Theory was used in auditing, helping auditors make more accurate assessments by eliminating misjudgments caused by the auditor’s emotions. AI continued to grow until the 1970s.
- 1970s to 1980 – It was known as the “AI winter.” It was the time when the interest of the government had died down.
- 1982 – The presence of AI was felt in the financial services industry when an award-winning mathematician, James Simons, founded the quantitive hedge-fund Renaissance Technologies.
- 1983 – The interest surrounding AI resumed when Alvey, a British research project, was funded GBP 350 million to challenge the Japanese Fifth Generation Project.
In the 1980s, AI gained prominence. It was also during this time when more than two-thirds of Fortune 1000 companies invested in the development of at least one AI project.
The greatness of AI once again had diminished. But recently, there has been a renewed interest because of the increasing need for cost-effective solutions in the finance industry.
Expert System: The First Artificial Intelligence Software
The 1980s was a significant time in the world of Artificial Intelligence. It is when Expert System was launched and used in finance.
The Expert System was among the first successful forms of AI software. The software’s function is to emulate a human expert’s decision-making ability. Using rules and facts, the Expert System was widely used in financial institutions in making market trends, financial analysis, business development, and more.
More Expert Systems were built in the 1980s. The PlanPowerOne Expert System helped create financial plans for those with an annual income over $75,000. The Client Profiling System was also meant for those earning between $25,000 and $200,000 a year.
In the succeeding years, more Expert Systems were created to address several issues in financial institutions. Expert Systems were designed for fraud detection, underwriting decisions, stock market trends, and professional data security.
The Importance of Natural Language Processing (NLP)
In the financial world, aside from the massive numeric and quantifiable data, a huge amount of text needs to be analyzed for better insights. This is the reason why Natural Language Processing (NLP) was created.
NLP is an Artificial Intelligence that plays a significant role in finance. NLP has the capacity to read and interpret texts like humans. It can understand and interpret audio and textual content like press releases, sector reports, transcripts, industry jargon, currencies, and product names.
In finance, NLP reduces mundane tasks, analyzes tasks, builds portfolios, speeds up deals, comprehends financial issues, and automates accounting and audits.
Usage of Artificial Intelligence in the Finance Industry
Financial institutions worldwide have been using AI for mundane tasks and important finance-related functions, including financial modeling, financial analysis, and decision-making.
Here’s a list of the areas in finance that have been using AI to optimize and streamline complex functions and processes:
AI-based solutions help credit lenders and banks to make smarter decisions. During the underwriting process, the machine will look into different metrics and asses the borrower’s credit history and risk. With this, decisions are made accurately and quickly.
Claims Management and Fraud Detection
During the process, AI tools look into the user’s behavior and determine transactions that are fraudulent. The AI systems can adapt to newer cases using advanced machine learning techniques. It will help improve the detection power over time.
Experts in the industry suggested that human errors cause 95% of cyberattacks and data lapses. Thus, it is important to set up a detection system. The AI system will help you determine loopholes in the security system. Using complete data analysis, you can also create solutions to these loopholes.
Credit institutions have credit score systems that look into the customer’s base, demographics, etc. However, there are also other factors to look into in order to make a decision. With an AI-based scoring system, the algorithms will analyze the customer’s financial behavior, previous loan applications, debt situation, marital status, and more. This advanced credit scoring system will also help you target the right customers by designing the perfect campaigns.
Quantitative trading uses a lot of data, and this massive information needs to be analyzed to make strategic trades. This is where AI-powered computer comes in handy. The AI can analyze complex and huge data efficiently and quickly. As a result, you can automate the trade and save time. It will also help you see the trends and patterns in the financial market.
Predictive Analytics involves a huge number of data to determine trends and make predictions for the business. With the help of an AI system, trends are determined more accurately, allowing the business to make predictions on the outcomes as well as new opportunities and possible risks.
Advantages of Using AI
- Speed and Efficiency – Handling huge volumes of data and information becomes more accurate and fast. Real-time information also helps financial institutions to do budgeting and forecasting efficiently.
- Reduce Error – Because of computer algorithms, AI-based applications can significantly reduce the level of human errors.
- Better Data Visualization – AI has the capacity to understand and respond to information just like humans. This will help businesses to analyze key performance indicators to get more result-oriented information.
- Cost Effective – AI helps business target customers effectively, resulting in high ROI. With the help of sales intelligence, businesses can analyze the behavior of buyers with increased precision.
Disadvantages of Using AI
- Data Quantity and Quality – The data’s quality and quantity significantly affect the AI’s accuracy. Thus, the input data should be of the highest quality for the AI to function efficiently.
- Black-Box Effect – Because the AI depends on the input data, hidden biases and anomalies could affect the AI’s efficiency. Unfortunately, it is challenging to identify these biases because the algorithms are not transparent. The information is stored in the black box, an impenetrable system. The AI black box exposes the business to ethical and economic risks.
- Lack of Accountability – When a machine is performing and making decisions, there is no accountability. Because the behavior or the algorithms are unpredictable, human supervision is needed to monitor and validate the machine’s decisions.
- Lack of Safety and Regulation – AI is constantly evolving and entering new industries fast, so it becomes challenging for the governing agencies to regulate them. Also, as AI evolves, security procedures must also improve. If the data is misused, the could be massive losses in the business.
- Limited Function Scope – Algorithms are backed by pure logic, and they are smart, but they lack empathy. If you have a program to identify fraud, it will do that, but it may fail to detect any other suspicious activities.
Artificial Intelligence: The Future of Finance
Artificial Intelligence has become vital in the financial industry. Financial institutions believe AI will continue to give financial gains by unlocking new growth opportunities while developing cost-effective solutions.
According to a recent study by Technavio, the AI market share is expected to increase by $76.44 billion from 2020 to 2025.
Virtual assistants and digital apps will continue to get better in terms of cognitive computing and developing deep learning. Having the mundane tasks automated, employees and consumers in the finance sector can focus more on what matters – formulating strategies and making sound decisions.
Given all the benefits, there is no doubt that Artificial Intelligence plays a significant role in finance. It helps financial institutions analyze data and automate several functions. It also helps customize investments based on individual goals.
While most financial service providers have implemented AI-enabled functions and products, a massive chunk of the industry is cautious about the economic and time commitment needed to implement AI in digital financial services. It will take time, but they will soon realize that investing in these innovative technologies will offer long-term positive results.
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