The quantity of digital text data has grown exponentially in recent years, and it will continue to grow at an exponential rate for the foreseeable future. ICD estimates for example that unstructured data will make up an astounding 80 percent of all digital data in the world by 2025. There are billions of structured and unstructured data points that offer vital insights into market performance for those who can separate the relevant information from the noise. But making sense of it all is the challenge.
Users should be able to digest all of this data in one place and determine what matters and what doesn’t to properly inform investment decisions.
In light of this, Kumesh Aroomoogan, co-founder and CEO at Accern shares his thoughts on what no-code technology means for the future of finance.
When you factor in social media posts, corporate financial filings, GlassDoor, Yelp, and Reddit, not to mention surveys, chats, emails, and more, financial analysts’ biggest challenges lie in manually monitoring various sources and extracting the most relevant data. In the traditional coding world, data scientists would step in with coding tools like Python to program machines to analyse the text from the unstructured data. Once the relevant information is extracted, experts can determine what actions to take. However, the time from data retrieval to insights can take months, sometimes even longer.
Studies show that about 80 percent of a data scientist’s time is spent on cleaning and reorganising huge amounts of data, while only 20 percent is spent on the actual data analysis. In recent years, the question about whether there is a better, more efficient way to find and extract relevant data from the noise and analyse the data to gain insight has risen. With technological innovation at its peak, are coding and programming still the most efficient way for the financial services industry to guide properly informed investment decisions?
Financial services firms have given significant attention to AI and NLP across several applications. AI can be used for text mining or text analytics to extract and analyse information from a vast array of documents. Social media posts, internal and external documents, emails, articles, and online forums, among others, are a few data sources that can be used in text analytics models. Text analytics requires Natural Language Processing (NLP) to translate large volumes of unstructured text into quantitative data to uncover insights, trends, and patterns.
This process has attracted the attention of finance professionals as NLP can cut down in days the time it takes to research and analyse unstructured data. However, training these models requires coding skills and experience to develop, but a no-code approach improves usability.
No-Code
No-code technology makes AI and NLP even more accessible to professionals. With no-code AI, non-technical finance and banking professionals can essentially do what data scientists can do while cutting down on the time to insight. This technology also allows users to access data in one place and determine what matters most, largely due to the fact that it provides access to the key advantages and insights that alternative data can offer, without needing a team of software engineers – which are often in short supply.
In the world of financial services, there are millions of structured and unstructured data points that can be acted upon, and understanding real-time trends is vital. It’s especially important that financial firms are able to sift through these massive amounts of data and be able to quickly make decisions accordingly.
The ability to easily access financial data researched by thousands of professional analysts – and then use the research to deploy AI models – not only provides faster processing and more prevalent use of data but is changing the day-to-day responsibilities of the rank and file within banking and finance.
No-Code Use Cases
In addition to accessing the world of financial data, no-code technology offers a library of use cases that are ready-made for financial services professionals to use immediately. Use cases provide specific insights to specific data sets. At Accern, the most in-demand use cases are ESG and Credit Risk. With the ESG use case, financial professionals can click to specific companies or industries to identify how they’re contributing to environmental, social, and/or governance issues and their sustainability risks. The no-code process cuts down on the time it takes to generate immediate insights into investment risks and opportunities with the click of a mouse.
Lastly, a major misunderstanding about no-code technology is the assumption that there is a lack of customisability when it comes to building AI use cases. Users are often misled especially when it comes to ready-made use cases. However, these use cases are created as a “quick start” tool for financial teams. No-code technology enables users to retrain these ready-made AI models with an intuitive model trainer that creates an efficient way for financial professionals to solve specific problems within the financial services industry.
The process of researching, extracting, and analysing data and then plugging it into AI models and generating insights with no-code technology is accelerated, which gives financial services a major competitive advantage. As the quantity and sources of data grow, the manual processes of analysing data will become obsolete. Quick and accurate data analysis is crucial for financial teams to stay ahead of their competitors. While new challenges are arising in data management and analysis, no-code is the innovative solution that helps the financial services industry maximise the potential of data.
