The Financial Services Industry (FSI) has always been at the forefront of technological innovation, and ML adoption is no exception. In recent years, we have seen a range of ML solutions being implemented across different business functions within financial services. For example, in capital markets, ML is being used across front, middle, and back offices to aid investment decisions, trade optimization, risk management, and transaction settlement processing. In insurance, companies are using ML to streamline underwriting, prevent fraud, and automate claim management. While in banking, banks are using it to improve customer experience, combat fraud, and facilitate loan approval decisions. In the following sections, we will explore different core business areas within financial services and how ML can be applied to overcome some of these business challenges.
In finance, the front office is the revenue-generating business area that includes customer-facing roles such as securities sales, traders, investment bankers, and financial advisors. Front office departments offer products and services such as Merger and Acquisition (M&A) and IPO advisory, wealth management, and the trading of financial assets such as equity (e.g., stocks), fixed income (e.g., bonds), commodities (e.g., oil), and currency products. Let’s examine some specific business functions in the front office area.
In sales trading, a firm’s sales staff monitors investment news such as earnings reports or M&A activities to identify investment opportunities for their institutional clients. The trading staff then execute the trades for their clients, also known as agency trading. Additionally, trading staff can execute trades for their firm, which is known as prop trading. As trading staff often deal with large quantities of securities, it is crucial to optimize the trading strategy to acquire shares at favorable prices without driving up prices.
Research teams support sales and trading staff by analyzing equities and fixed-income assets and providing recommendations. Algorithmic trading is another type of trading that uses a computer to execute trades automatically based on predefined logic and market conditions.
The following diagram illustrates the business flow of a sales trading desk and how different players interact to complete a trading activity:
Figure: Sales, trading, and research
In the domain of sales trading and research, there are several core challenges that professionals in this industry face on a regular basis. These challenges revolve around generating accurate market insights, making informed investment decisions, and achieving optimal trading executions. The following are examples of these challenges:
Sales trading and research offer numerous opportunities for ML. By leveraging natural language processing (NLP) and, increasingly, large language models (LLMs), key entities such as people, events, organizations, and places can be automatically extracted from various data sources such as Securities and Exchange Commission (SEC) filing, news announcements, and earnings call transcripts.
ML offers significant opportunities in sales trading and research: