How is Data Science used by investment banks? Check out our data science course in Delhi with data science certification to kick-start your data science career. The various areas are made up of banks. They have retail banking, which is about supplying the general public with loans, bank accounts, and other services. There is business banking, which provides small businesses with loans, bank accounts, and other services. Investment banking is a much more dynamic and riskier division of a bank than retail or business banking. There are many different areas in it, such as proprietary trading, Market making, mergers & acquisitions, corporate events, and structured products. People who have an interest in investment banking or the stock market know that banks have been using algorithms, known as algorithmic trading, to conduct trades for years. Algorithmic trading utilises a computer program that follows a given set of instructions to position a trade at a speed and frequency that cannot be performed by a human trader. High-frequency trading (HFT) is also possible, a subset of algorithmic trading where stocks are purchased and then sold in fractions of a second. More and more trade is carried out by machines over the years rather than by individuals. A trader's average cost is $500,000 a year, but now one computer engineer can replace four traders. About 30 per cent of Goldman Sachs staff were computer engineers in 2017. The world of investment banking has changed, and it is becoming more advanced technologically. The recruiting of computer engineers, artificial intelligence specialists, data scientists and other computer experts is now the priority of investment banks and hedge funds. After the 2008 global financial crisis, the use of data analytics in the banking industry began, where the industry is data-intensive with usually large graveyards of unused and unappreciated ATM and credit processing data. The banks recognised the need for risk management to recognise and forecast business dynamics by using all these data to identify consumer forecasts, fraud detections, and financial advisories. Banks have been better positioned to handle market volatility, mitigate Fraud and monitor the risk of exposure with the aid of advanced data science techniques. Banks can obtain insights that cover all forms of consumer activity, including channel purchases, account opening and closing, default, Fraud and customer departure, by applying data mining and predictive analytics to derive actionable intelligent insights and quantifiable predictions. Birth of Jupiter: An activist investor is an investor who purchases a significant number of public company shares. Then their shareholding and function are used to shift the pressure within the company. These changes appear to be detrimental to the organisation's long-term plan. They drive the leadership of the company out and interrupt growth operations, all in favour of boosting shareholder payouts. Therefore, defending against active investors is critical for publicly listed companies. It takes days for a team of analysts to evaluate the susceptibility of a company to attacks from active investors. Goldman Sachs, however, has built an automation tool called "Jupiter" that does all this work and provides a response to the customer in 30 seconds. Detect and Avoid Fraud: In order to protect the interests of consumers and employees, fraud detection is now becoming a vital operation. There are many external regulatory standards that banks must adhere to prevent fraudulent and illegal activity, as this is a highly regulated sector. Some of the approaches undertaken by banks to check fraudulent activities are check for duplicate entries if any in an account or as a bank as a whole. Keep a track on all transactions' inflows and outflows by following statistical methods such as variance estimates, transaction value anomalies or a number of transactions, cross-validation of account numbers entries and criminal record names. Risk Modeling: This helps banks to anticipate how their loans will be repaid and to forecast a background and credit report-based default. Risk modeling measures the risk rating for each case, and on the basis of this ranking, the Credits Team only sanctions loans. In investment banking, risk modeling is also used, where risk-reward ratios are measured for risky investments. This helps to provide clients with investment advice as well as to make the correct internal investment decision to produce a fund's income. AI-powered virtual assistants: JP Morgan is the first major bank to introduce an AI-powered virtual assistant that makes it easier for its corporate customers, whether for regular payroll or multi-million-pound M&As, to transfer money across the globe. Artificial Intelligence enables JP Morgan to have a seamless customer service experience that is multi-channel. The AI-powered virtual assistant will respond to the behaviour of customers and make intelligent suggestions using machine learning. Predict future Revenue: Banks need to estimate future profits based on past inputs. This is best achieved in order to measure the potential values of each client using predictive data analytics. This helps to segregate clients, recognise those with high potential value, and spend more money on customer service, discounts, and reduced rates on them. Generalised linear models (GLM) and Classification and Regression Trees are the primary data science instruments used for this purpose (CART). To reduce the risk of losing customers: Because of inadequate programs, there is always a chance of losing clients. In order to minimise this risk, banks have implemented data science strategies based on their business and transaction records, to organise them into different sectors. The K-means algorithm is a common process that is used here. Banks may follow various marketing strategies and services on the basis of these segments, such as offering better credit rates, annual fee reductions, etc. Conclusion: It is clear that banks no longer see themselves as just banks, but they also see themselves as technology firms. Banks have tried and tested systems, have a need for high security, are data-rich, highly customer-facing, and are cash-rich, making them the ideal technology advancement industry. As a consequence, banks are now vying for customer interest and employee expertise with top-tier technology giants. In the banking industry, data science is a rising choice, with more data being collected to meet various consumer needs, as well as to increase the market value of banks.