The introduction of Artificial Intelligence (AI) made a revolutionary impact in the field of finance and trading. Last decade witnessed a comprehensive change in the financial sector by the invention of Natural Language Processing (NLP), Machine Learning Algorithms and Big data Analytics. From daily record keeping to intricate investment plans, AI is restructuring the financial system.
Here in this post we discuss the influence of AI in trade and finance with its widening applications and try to analyze the challenges and considerations of AI in the current socio economic situation.
Simply Artificial Intelligence is trying to recreate human intelligence with the support of computer systems. It includes decision making, reasoning, data analysis, language learning and problem solving. AI in finance understands and analyzes large amounts of data and predicts the outcomes with more advanced algorithms.
AI action in finance;
• Fraud Detection
• Algorithmic trading
• Market predictions by sentimental analysis
• Automated customer service
• Robo advising
• Risk management
1. Fraud Detection
Fraud detection is always a hazardous task for financial institutions. Modern technological advancements displaced the traditional ways of scam detections. Machine learning and deep learning became an alternative to conventional rule based systems.
• Pattern Recognition and Anomaly Detection
AI has the capability in pattern identification and anomaly detection from large databases. This potential helps to pick out doubtful transactions and suspicious account activities and also alert if a customer exhibits an unusual spending behavior.
• Adaptive learning
AI is always updating. That is it always learns from the new data collected and adapt with the new threats.
2. Algorithmic trading
Through Algorithmic trading, AI can execute trade in a more quick and accurate way with a pre planned pattern, based on the market data. By analyzing the past data and patterns AI can predict the future movements more professionally.
• High Frequency Trading (HFT)
It is a type of algorithmic trading by which more orders are bought and sold at a highspeed.AI has a notable contribution in HFT by understanding the price variations within the market in nanoseconds. Immediate data analysis in price trends and news accelerates the decision making process in a much better way.
• Reinforcement Learning in Trading
It is a trial and error process that is learning from experiences. Here strategies of trading are formulated based on the continuous market feedback and thus can maximize the return.
3. Market Predictions by sentimental analysis
Predictions with the support of sentiments is one of the latest contributions of AI in the finance sector. By analyzing social media, news articles or textual data, AI is trying to understand the market sentiments and reach a conclusion on how investors or public acknowledge about a particular company or market.
• Predictive Power
Investors and traders use sentiments as an influential tool. If a company got positive feedback from a social media platform or news article it will push the demand of its stock. As it was a negative comment, the demand of the company will decline. Through a proper strategic analysis investors can reach more productive and profit oriented decisions.
• Combining sentiment analysis with fundamental analysis
We can make a conjunction of sentimental analysis with traditional analysis for a more effective decision making in investment portfolio.
4. Automated Customer Service
Customer support is one of the key features of the service sector. As part of this AI powered chatbots are becoming more common in banks and other financial institutions. This AI transformation helps to deal with customer inquiries, financial advances and other account management.
• Natural Language Processing (NLP)
Natural Language Processing (NLP) helps chatbots to give more personalized advice to customers. They recognize the complex tasks and reply to customers more naturally.
• 24/7 Availability
AI is able to give a full time customer service to regular inquiries like balance checking and transaction history. These chatbots reduce human work and thus can avoid certain financial burdens of banks.
5. Robo Advising for investments
Robo advisors provide immense support in financial matters. These AI systems understand and analyze each person's investment goals, money management skill and financial status for personalized advice.
• Cost effective
Robo advisors are more cost effective by providing efficient money related advice to individuals as compared to traditional advisors. Less time with less human capital is the feature of this service and is also attainable to a large section of people.
• Customization
By understanding individual preferences and market conditions robo advisors can offer personalized strategic plans. These machine learning algorithms provide a comprehensive idea about market updates without any traditional financial experts.
6. Risk Management
Risk dealing is always a crucial part of financial management. Traditional risk management is not always accurate. In this situation AI developed a more advanced technology for identifying, assessing and predicting risks by examining a broad amount of real time data.
• Market Risk Prediction
Nowadays AI has an increased potential to predict risks due to varying socio economic conditions and price fluctuations. It will help financial institutions to avoid possible money losses.
• Credit Risk Assessment
Finance institutions currently use AI tools to determine the credit risks. Machine learning models estimate borrowers spending patterns, credit history, social media activities and even personal interests to decide the credit given. This will help the lenders to avoid potential risks in credit management.
Contribution of AI in finance and trading is indisputable. Along with its numerous benefits there also have some challenges that we must deal with.
1. Data Privacy
Financial data is always sensitive in nature. Securing data is an essential task of privacy concerns. Make sure AI usage never steals or misuse any one's personal data. Financial institutions try to follow data protection regulations by taking the General Data Protection Regulation (GDPR) or California Consumer Privacy Act (CCPA) as a guideline for formulating their own laws.
2. Displacement of jobs
Job displacement will remain a byproduct of AI intervention. Automation will definitely replace the traditional trading jobs. Roles like financial analysts or customer service representatives will be displaced. Large opportunities will get in Data Science, AI governance or AI development. Regular upskilling and training is essential to cope up with new AI trends.
3. Biased Algorithms
AI algorithms work on the basis of historical data we are provided. If the data given are biased in nature the end result too is in a biased format. This will affect credit risk assessment, lead to unfair lending practices and discriminations.
The future contribution of AI in finance and trading will be unpredictable. A promising advancement is occurring in quantum computing, machine learning and block chain technologies to accelerate the efficiency and accuracy in a personalized manner.
Upcoming AI contributions are in;
• Quantum Computing in Finance
Quantum computers have the ability to deal with complex financial models at a high rate which will open up new opportunities in trading.
• Decentralized Finance (DeFi)
AI will increase its influence in various DeFi activities like peer to peer lending or liquidity improving by proper automation services.
• AI Driven ESG investing
By analyzing ESG (Environmental, Social and Governance) factors AI can articulate plans and decisions of investments in a more sustainable way.
Without any doubt, we can say AI is altering the traditions of finance and trading by bringing more competitive algorithms. These advancements accelerated the efficiency and reduced the time consumption. From fraud detection to sentiment analysis the scope of AI is varying which brings a more personalized approach. In this rapidly evolving market system it is the responsibility of financial institutions to protect the data of individuals in a more ethical way. Only by making a balance between innovation and responsibility, we can enhance the scope of AI in the finance sector. Amore secure and efficient approach is required for a more inclusive society.
