How advanced analytics is changing the investment industry

Advanced analytics is changing the investment industry, with its ability to process billions of transactions in real-time.

Advanced analytics is changing the investment industry. We associate the multibillion-dollar industry with formal suits, complex rules and the 2008 recession. However, data analytics is changing the way the industry does its business because of its capacity to process billions of transactions in real-time. The sheer capability of big data opens new avenues for companies specialising in financial investment. In this blog post, I will explain what analytics does for the investment industry and what it means for financial investments.

What advanced analytics allows investors to do?

Advanced analytics brings several benefits to the investment industry.

Improve the research process

The research process is faster and more comprehensive than before thanks to advanced analytics, like NLP and machine learning. Analytics software analyses big data and flags points of concerns for asset managers to look at. The software can analyse large volumes of complex structured data in minutes. For example, analytics can analyse public filings to identify changes in sentiments and processing petabytes of data in short time. Furthermore, analytics opens up new sources of research that would not have been possible. For example, asset managers can study social media data to optimise investment and trading decisions. Data analytics software allows for a better, more comprehensive research process.

Debiasing investment decision

Bias has long been a problem in the investment industry because key decisions are made by humans who, despite their best efforts, rely on intuition over logic. However, advanced analytics promises to improve the decision-making process by eliminating bias. Analytics can collect and process data to reveal insights on a granular level while investors get an incredible, in-depth insight into how a company works.

Analytics can stitch together data from sources to discover trading patterns and a company’s history to discover their prospects for the future. With data providing detailed insight into company performance on a microscopic level, investors no longer have to rely on their intuition or other emotional cues that lead to biased investment decisions.

Risk management

The financial sector finds itself under pressure to tighten risk management procedures due to regulation, which means more time and resources dedicated to trading surveillance and risk management. Advanced analytics plays a huge role in this function because of its ability to scan communications for conduct breaches and build datasets from internal and external sources – making it easier to uncover instances of misconduct.

Asset managers using advanced analytics software found that they spend about 55 to 85 per cent less time on monitoring trades, while simultaneously improving risk management. The improvement occurs because machine learning algorithms were better at detecting risk than an experienced expert.

Optimise personalisation services

Investors and asset managers use advanced analytics to provide the right service to the right customer on the right channel. Previously, asset managers would look at the size of the client to determine how their services should be provided. Data analytics allows asset managers to fine-tune their service strategy to cater to the needs of their client and the specialities of their asset managers. The result is an increase in sales (because of better customer service), while also freeing up sales force capacity.

Automating time-consuming tasks

Transferring information, devising solutions to policy breaches and other operations are incredibly time-consuming. However, advanced analytics solutions in the form of NLP allow investors to automate many tasks that would otherwise consume a lot of time. Asset managers can now upload hundreds of documents to repositories using NLP to significantly improve the efficiency of core operations within the industry.

Customer Lifetime Value Prediction

The value of an organisation’s relationship with a customer is becoming more important than ever before. However, calculating this lifetime value is a complicated task because there are so many sources to draw upon. Demographics, use of diverse banking services and cost of acquisition are just some of the variables to consider. This is where advanced analytics programs like Generalised Linear Mode (GLM), regression trees (CART) and stepwise regression come into play. These analytics models allow asset managers to assess the value of a customer’s relationship over time.

The investment industry is changing

The financial industry has become more competitive and more stringent in its regulation than ever before. To overcome these challenges and enjoy benefits such as time-saving and cost-cutting, the industry has turned to advanced analytics. There is a lot of potential for data analytics to transform the industry into something that is more transparent, easier to grasp and more efficient to operate.

Of course, this is just the beginning – as data analytics becomes more advanced, the possibility of what it can do also expands leading to even more possibilities in the future.