Financial service companies are constantly looking for ways to gain an edge in capital markets and surpass competitors. To do this, firms are moving away from legacy systems and adopting more agile business models that encompass Artifical Intelligence (AI) and Machine Learning (ML) techniques.
In particular, firms are deploying AI and ML projects in concert with Robotic Process Automation (RPA) technologies to meet client requests, speed up financial market analysis, and detect risk exposure in the trade process more accurately.
Let’s take a look at three ways automation and AI are shaking up capital markets.
Automating Business Processes with Robotic Process Automation
Robotic Process Automation (RPA)–a type of automation software that handles repetitive tasks in place of a human–is abundantly used in capital market firms and is applied across front, middle, and back offices.
In the middle and back office, where RPA implementation has historically been most beneficial in practice, firms use RPA for updating client data in record systems, error reconciliation, and reviewing legal contracts. Particularly as the cost of offshoring middle-and-back office operations rises, RPA provides financial institutions with a cheaper alternative.
Within the front office, RPA is applied across trading, investment research, and portfolio management functions. For instance, automated investor reports can streamline the time it takes for financial professionals to read, extract, and analyze financial data.
How does it work?
Say you’re a trade analyst and you need to compile a daily report of over-the-counter (OTC) market transactions. Without RPA, you’ll likely manually compile and transfer data across various sources (your email, CRM database, online brokers, etc.).
RPA utilizes bots to read and extract structured data from email attachments and spreadsheets and then automatically update the information in the relevant database. By replacing the labor-intensive process of copying and pasting information with bots, an analyst can provide internal and client updates in near real-time.
Nowadays, investment firms are leaning toward applying more advanced forms of automation in their workflows. However, successfully scaling RPA within an organization is a strong indicator that a firm is prepared to handle advanced automation.
Building Market Intelligence with Alternative Data
With the rise of Big Data, it’s no surprise that financial institutions are using AI to sift through unstructured text data, an ordinarily labor-intensive process.
A report by the Alternative Investment Management Association found that the most common alternative datasets used by hedge fund managers were sentiment from social networks, consumer spending and lifestyle data, data extracted through web-crawling, and data sourced from expert resources networks.
With AI, financial institutions can manage to ingest high volumes of unstructured text data sources to build powerful investment signals. For instance, AI bots can index web pages to notify financial professionals about relevant datasets.
With Natural Language Processing (NLP), financial advisors can quantify consumer sentiment towards a particular market using data from social media posts.
The latter application can be helpful for financial analysts that apply fundamental analysis to pick stocks. Financial institutions can use NLP to create predictive models on future company stock performance from historical and real-time company-related news. However, current trends in sales and trading tell us that firms are moving from applying AI for predictive analysis (i.e. what is likely to happen) to prescription analysis (i.e. what should be done, based on likely outcomes).
Financial institutions are incentivized to use alternative data to gain insights their competitors may not have through traditional data. In fact, most financial advisors regarded as market leaders leverage multiple sources of alternative data to generate alpha, manage risk, and inform investment decisions.
From an ethics and regulatory standpoint, some types of alternative data are tricky for financial institutions to navigate, particularly in cases where the customer is unaware that their information is being used. But the widespread use of alternative data shows no signs of slowing down.
It is anticipated that by 2028, the global alternative data market size is expected to reach 69.36 billion U.S. dollars. Uncovering findings from alternative data sources has become an integral part of the market research process for investors.
Improving Pre- and Post-Trade Risk Analysis with AI
According to a 2018 report by the World Economic Forum, AI and advanced automation can assist financial service companies in deploying advanced capital and risk-management solutions.
The application of AI tools offers support across the trade process:
- Pre-trade risk analysis
- Continuous risk analysis
- Post-trade risk analysis
Pre-Trade Risk Analysis
AI and advanced automation can support compliance with current and emerging regulations. For instance, under phase 5 of initial margin (IM) requirements for uncleared derivatives, financial institutions with over EUR/USD 50 billion need to exchange IM with their counterparties. With later implementation phases of the regulation involving the buy-side in addition to sell-side participants, more players in the financial services industry will need to prepare for these changes.
Financial institutions can use AI/ML techniques to support the calculation of initial margins (a form of collateral exchanged between parties to minimize risk exposure).
One approach to computing initial margins is using a deep learning-based method, a form of machine learning that simulates the human brain to learn from large amounts of data and make accurate predictions based on these learnings.
Using deep learning, financial institutions can access high-quality information on the initial margin trajectories for a portfolio over its lifetime.
Continuous Risk Analysis
AI is also driving improved compliance with the implementation of continuous risk models. Continuous risk models inform financial institutions about changes in risk exposure in real-time and allow them to recalibrate capital levels.
With AI-led risk management tools, firms can improve compliance (e.g. adjust how much capital to keep on hand) and increase revenue.
Post-Trade Risk Analysis
Detecting deviations that result in post-trade failures requires sifting through high volumes of data.
Using AI/ML predictive modeling, firms can identify patterns in incident data to forecast future incidents. Because the post-trade process is more susceptible to errors across trading and operations, firms that can identify these patterns can reduce costs and apply better incident management practices.
Pros and Cons of AI in Capital Markets
So, what’s the verdict?
Are capital markets better off with RPA and AI?
At a sector level, capital market firms are seeing:
- Increased gains from implementing AI predictive analytics platforms, AI trading technologies, and RPA across analysis and administrative activities.
- Improved capital efficiency and enhanced risk management, thanks to the speed at which these technologies can generate insights on company health and risk exposure and carry out tasks like financial reporting.
The benefits of using AI to exclusively steer investment decisions tells a different story.
Many maintain that AI algorithms can’t beat the market or necessarily outperform humans, particularly when pitted against professional investors. However, being capable of forecasting up-to-date industry trends or detecting patterns on company financial health can provide early adopters of AI technology with an edge over firms that don’t utilize AI-led market intelligence strategies. Using AI can also help financial professionals deal with increasingly high volumes and varieties of data.
Ultimately, capital market firms affect the acceleration of AI just as much as AI is affecting capital markets. As more financial institutions adopt AI-aided investment strategies, firms should maintain confidence in the quality of their data to ensure decision transparency with their clients and capital providers.
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