Use of AI in Investment Banking – A Detailed Exposition
Could AI be among the most significant things to happen to us? Did you hear that artificial intelligence can complete the most complex tasks at a much faster speed than humans? Is it the turn of artificial intelligence to take control even before your expectations? Nowadays, AI has entered every field, including the finance market, to create skills, make the machine use of brains, and provide the best customer experience without fault. Machines “learn” how to do something or operate through artificial intelligence. This article on using artificial intelligence in investment banking will cover how AI disrupts investment banking and what it means for the industry. In the article below, we’ll discuss what’s missing and potential issues that may arise regarding deploying artificial intelligence in finance. However, we’ll also examine how AI pushes investment banking tech further.
With the power of AI, machines can understand and interpret new information, fix mistakes on their own, and behave similarly to humans. Artificial intelligence (AI) applications include expert systems, natural language processing (NLP), speech recognition, and machine vision.
It is time to explore the massive rise of the global AI marketplace. This market expansion has been growing at a CAGR of 36.62% per year, and the projected figures of this market were projected to reach 190.61 billion USD by 2025. As per studies, by 2035, including artificial intelligence in businesses and industries can create an economic impact, constituting nearly 15% of India’s GDP. The government of India has significantly invested in building artificial intelligence (AI) capabilities.
What is Investment banking?
The services provided by investment banks include mergers and acquisitions (M&A), insurance, resource management, trades and exchanges, and private banking. Investment banking is a crucial component of the financial system, assisting governments, firms, and many other entities in acquiring necessary cash.
Financial institutions receive securities from diverse firms, afterward redistributing them to investors, thus stimulating market activity and enhancing its overall efficiency. Investment banks offer various services, including mergers and acquisitions (M&A), insurance, asset management, trading, and exchanges.
Investment banking services include giving advice to clients and attracting consumers like businesses, nonprofits, and government agencies; running research projects to help the bank and its clients make sound money management decisions; working with mergers and acquisitions (M&As); and endorsing or raising capital for their clients by selling debt or other tradable assets, like stocks or bonds. These banks make it easier for groups not open to the public to do so through initial public offerings (IPOs). They also help the risk authority keep the money safe and teach clients about the risks of making certain financial moves.
Areas of Artificial Intelligence
Machine learning: AI creates algorithms that no human can afford to make. If AI keeps learning more about the problems, it can help machines learn to think, predict, group, and sort. Thus, robots with artificial intelligence try to understand the same way people do. With the help of AI technology, spam is caught, strange trends are found in financial apps, and voice recognition is used to make personal suggestions on websites or mobile apps. Big companies like Google Maps, Gmail, Netflix, and Facebook use it daily. There are four types of AI, and their learning can be directed, unsupervised, partially controlled, or helped.
Robotics: Robotics combines science, engineering, and technology to make machines that can copy or replace human actions. It helps train and improve machine learning algorithms by giving them data and events from the real world. Robotics is the study of how to make intelligent, efficient robots that can do jobs without human help.
Robots are used in many businesses to speed up processes, increase productivity, improve safety, and make the user experience better. Its purpose is to copy or replace what people do. These are the six most common kinds of robots:
- Self-driving mobile Robots (AMRs)
- AGVs are vehicles that drive themselves.
- Robots with moving parts
- People who look like humans
- Hybrid Cobots
For applications such as automatic gearboxes and 4-wheel steering, fuzzy logic systems are used in automotive systems. It is used in intelligent highway systems, traffic control, defense, aerospace, business, finance, automotive, domestic goods, and environmental control systems.
Fuzzy logic has several benefits. Fuzzy logic provides the best solution to complex problems. A fuzzy logical system is simple and easy to understand. It helps resolve engineering ambiguities, and with fuzzy logic, you can improve performance by making quick changes to the system.
Expert systems: AI expert systems are computer programs that try to mimic the way experts think and act. An expert system can help with decisions, processes, and jobs you do repeatedly. It can store a lot of information, cut down on the cost of training employees, centralize decision-making, make things run more smoothly, solve problems faster, and combine the brains of different human experts.
An expert framework has four parts: an information source, an inquiry or derivation, an information acquisition framework, and a UI/correspondence framework. Master tools are made to make it easier for professionals to do their jobs. For example, DENDRAL, the name of a professional system, can predict how atoms will be built so scientists can study them. PXDES is a specific way to determine what kind of cell damage someone has in their lungs and how common it is. Google Search is a professional system that uses AI to get better at what it does. There are five types of artificial intelligence master frameworks: rule-based, frame-based, fuzzy, neural, and neuro-fuzzy frameworks.
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Natural language processing: Robots can understand human speech using NLP, a text analysis method. NLP helps machines understand and process human language automatically to do repetitive jobs. It is about making machines understand written and spoken words like people do. Examples include machine translation, summary, ticket sorting, and spell check. This interaction between humans and computers makes it possible to do real-world tasks like automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, connection extraction, stemming, and more. An excellent illustration of NLP in a search engine is search autocomplete.
The first step in NLP is syntactic analysis, which looks at the words you can understand in a language. The syntactic research shows how the words and phrases of a language are put together to make sentences. The following steps are semantic analysis, integration of the discourse, and pragmatic analysis.
Below is the process of NLP for reading and understanding human words.
- Sentence Segmentation
- Tokenization in Word
- Stop-word analysis
- Dependency parsing
- Tagging the Part of Speech (POS)
Neural networks: An AI approach called a neural network trains computers to think and reason like humans do. Machine learning is a form of deep learning that mimics the way the human brain works by using networks of interconnected neurons organized in layers.
Realizing a highly simplified human brain model is the primary goal of artificial neural networks. Artificial neural networks attempt to learn tasks and solve problems in this fashion by modeling their operations on the human brain, which consists of a complex web of interconnected structures and cells with specialized functions called neurons.
A neural network can be one of nine different types:
- Modular Neural Network
- Recurrent Neural Network
- Network for Feed-forward
- Long-Short-Term Memory (LSTM) Learning
- Convolutional Neural Network
- Multilayer Perceptron
- Sequence-to-sequence models
- Radial basis functional neural networks
Neural networks have the benefits of being able to handle high-dimensional data, perform parallel processing for efficient computing, and reveal previously undiscovered insights and patterns.
Neural networks are used most frequently for the following purposes:
- Face Detection.
- Stock market forecast
- ANN (Artificial Neural Network)
- Social networking sites.
- Handwriting Analysis and Signature verification
- Space travel
- Medical care
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Use of AI in Investment Banking
#1. Risk Management: Use of AI in Investment Banking
Managing risk is the most significant use of AI in investment banking. For managing risk, AI can help reduce potential risks, identify and detect patterns, and provide timely insights to make decisions as soon as possible by scanning large volumes of data in less time.
Financial institutions utilize AI to mitigate risks with far-reaching consequences for the industry. Here are a few illustrations:
Credit risk is the probability of facing losses while investing in bonds, loans, or any other asset recognized as secure. AI will analyze borrower-default risk, which is how likely borrowers will have trouble keeping up with the loan terms. Artificial intelligence-based models would explore multiple data types, including Credit Reports, Bank records, Market Trends, etc.
- Foreign currency, interest rate, commodity price, and stock price risks are examples of market risk.
- Next, we have liquidity risk, which is the danger that a bank will not have enough money to pay its bills when they come due.
- Another type of operational risk is the risk of financial loss due to faulty internal procedures, employee mistakes, unexpected events, or malfunctioning equipment.
#2. Fraud Detection: Use of AI in Investment Banking
AI can help find and stop fraud by monitoring transactions, looking for patterns and suspicious behavior, and informing the authorities. Detecting fraud with the most famous technology of today is among the best use of AI in Investment Banking. AI and machine learning help banks find scams, reduce risks, find holes in their systems, and make online finance more secure.
By leveraging AI, banks can identify real-time suspicious activities, like money laundering or fraudulent transactions. The system highlights high-risk transactions for manual review by human experts, enabling proactive risk management and compliance with regulatory requirements.
#3. Reduced Cost and Increased Productivity: Use of AI in Investment Banking
The use of AI in Investment Banking has also led to cost reduction and a boost in productivity. In a poll of 500 finance industry experts worldwide, 36% reported cutting annual expenses by more than 10% thanks to AI software. Almost as many people (46%) also think it has enhanced their overall customer service.
One of the most disruptive innovations for the finance sector could be generative artificial intelligence (AI). According to Deloitte’s estimates, the 14 largest global investment banks’ front-office efficiency can increase by 27–35% with the help of generative artificial intelligence.
#4. Cyber Threat Detection: Use of AI in Investment Banking
Artificial intelligence in banking allows for constant cyber-attack surveillance, allowing banks to respond quickly to upcoming intrusions before they impact their customers, workers, or infrastructure. Malware detection by machine is now possible through supervised machine learning.
A device-learning-powered application will continuously learn about malicious files using fresh parameters. A cybersecurity AI can detect abnormalities in data transmission patterns. Artificial intelligence uses machine learning algorithms to monitor networks, detect malicious software, and prevent data breaches.
Banks can benefit from using AI to handle cyber threats. The bank’s capacity to detect fraud grew by 50%, while the number of false positives dropped by 60% due to the deep learning technique. The AI-powered fraud detection system also mechanized several other essential conclusions.
One of the AI systems, called the “Black Forest” system, examines financial dealings and keeps tabs on unusual occurrences. The AI eventually becomes capable of accurately categorizing transactions and only writing down those that pose a genuine security risk.
#5. Portfolio Management: Use of AI in Investment Banking
Machine learning and natural language processing are used by artificial intelligence to analyze and understand the data and then offer helpful portfolio management recommendations. As an example, an AI portfolio automated technology can determine what asset classes to purchase and what ones to divest from. Regarding the goals and interests of an investor, what ones should they hold in their portfolio regarding the level of risk they’re comfortable with?
AI-enabled systems would enable the system to view a lot of information, identify examples, and determine what they think, depending upon the client’s desire. This feature makes forecasting and selecting the right choices more probable, considering positive returns on investment.
#6. Chatbots and Customer Service: Use of AI in Investment Banking
Chatbots run by artificial intelligence and managed by computers help clients with banks by quickly answering their questions, giving them personalized funding ideas, and moving the conversation forward. As robots learn more, they get better at sorting and solving their clients’ problems.
Chatbots and other tools run by artificial intelligence can help businesses give their customers the answers they need. Erica, a coworker robot, can help you meet your Mastercard obligations and provide extra protection.
With the help of artificial intelligence, the Know Your Client (KYC) process could go further. Onfido verifies ID because of the way it works. It depends on how well you know a person’s face and eyes. The financial management business can benefit from chatbots because they can make customer service more accessible, cut down on legal tasks, and give clear instructions to customers.
#7. Anti-money Laundering: Use of AI in Investment Banking
Money laundering is when a lot of stolen cash looks like it came from a legal source. Many cybercriminals steal money by using this scam.
Artificial intelligence’s risk assessment abilities provide tremendous benefits in anti-money laundering. For example, AI helps apply a method based on probabilities that let banks combine information from a single customer, which improves customer due diligence and knowing your customer (CDD/KYC). Rule- and pattern-based methods can also model the consolidated data to spot activities that point to money laundering.
For instance, HSBC has implemented a cloud-based AI-first approach as its principal AML transaction monitoring system in its important markets, with Google Cloud’s AML AI as its central component.
Challenges of AI in Investment Banking
In financial services, one of the biggest obstacles presented by AI is the need to establish additional security measures due to the large volume of data collected that contains sensitive and secret information. Since banks gather enormous volumes of consumer data and AI algorithms need access to this data to function successfully, data privacy and security are the most significant barriers. Protecting sensitive client information and preventing data breaches require strict adherence to data privacy and security protocols.
To successfully apply AI in the financial sector, we must overcome the significant obstacle of poor data quality. High-quality training data is crucial to the success of artificial intelligence models. According to the report, poor data quality is one of the top three reasons AI programs fail to provide results. As a result, incorrect forecasts and outcomes may result from poor data quality.
Biased AI Models:
In banking, artificial intelligence (AI) makes models that are not impartial. The caliber and representativeness of the data, the modeling features, and the methods can all limit AI models. Research shows that AI models can repeat data flaws. Even when reliability was considered, check and test models were likely to be turned down or given higher interest rates than white buyers. To solve this problem, we must check and test models daily, train them with the right traits, and make a collection that includes everyone.
Trust and Responsibility:
You must know and trust your customers to make the correct AI models. Who is responsible when something goes wrong with an intelligent machine? Customers will want to know that their data is being collected, handled, and stored correctly since it may be used to make these models. Financial companies do not want to give robots complete freedom because they still determine what they will do. Validation and reworking can show how well your models predict customers’ wants and make them happy.
Financial companies often use a human manager to check the machine’s decisions for essential jobs like releasing or stopping payments or certifying deals. This defeats the purpose of using technology. As technology improves, meeting compliance and operating security rules will be easier.
Cybersecurity risks can happen at different stages of AI adoption, such as when integrating systems, gathering data, and making models. Most of the time, people get in without permission and steal information. For example, a rival with bad intentions could go after a financial institution’s data process to change the data used to train AI, making the models less reliable. Its answer is to check and update systems regularly, create a robust cybersecurity plan, and put data protection steps into place.
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Frequently Asked Questions About the Use of AI in Investment Banking:
Q. Is AI changing human intelligence?
Artificial intelligence can never outsmart human intelligence. AI-supported devices and tools offer an aid that would allow for everyone’s arrival at the goal, but we need some artistic touch to make it there! The use of AI in Investment banking is increasing rapidly. However, humans become innovative and develop great new ideas and creativity when they do these manually. Because of the complexity, using emotional experiences and memories from the past to generate creative ideas is beyond AI’s capability. But when we delegate tedious tasks to artificial intelligence, AI can spare humans some time, and humans turn out to be more imaginative and priceless.
Q. What is India’s rank in AI?
The use of AI in Investment Banking is at its peak. India is now 32nd out of 181 countries on the AI Readiness Index 2022, a jump from last year. The government’s AI website says that India’s National AI Strategy, which came out in 2018, is already affecting the rest of the world.
About 32% of banks already use AI technologies like prediction analytics and voice recognition to stay one step ahead of the competition, according to an analysis by the National Business Study Institute and Narrative Science in 2020.
Q. What is the future of AI in investment banking?
In a world where AI dominates each sector, the future of finance is exciting and worrisome. Banks use AI differently to change how they do business and how things work. Business Insider says that banks operating AI apps will save $447 billion by the end of 2023. The primary purpose of AI in banks is to help customers by giving more weight to their opinions. AI supports the bank in determining what its customers want.
Investment banking is changing because of AI, machine learning, and expert systems. AI lets tools work independently, making them more efficient and accurate. Machine learning uses intelligent, efficient robots to answer challenging tasks. The automotive and defense businesses use fuzzy logic to mimic how people think. Expert systems help with choices, jobs, and processes by acting and thinking like people. These systems keep track of information, lower the cost of training, and move decisions to one place.
Banks can respond quickly to cyber threats because of AI monitoring. Chatbots enabled by AI may provide instantaneous responses to consumer inquiries, offer personalized recommendations, and walk users through the investment process. By combining customer records, AI aids in the fight against money laundering. Data privacy and security are still major concerns, however.
By examining a person’s spending, saving, and investment patterns and making specific suggestions based on that data, artificial intelligence (AI) is reshaping the financial planning industry. With AI’s help, banks may one day be able to provide around-the-clock service and cut fees for customers.