Unstructured data, whether it’s in raw form from news articles, reports or social content, is growing at a significant rate. Studies suggest that over 80% of all new data generated is in an unstructured format, yet only around 1% of this data is measured or applied to a business.

The sheer abundance of unstructured data is becoming a challenge for financial institutions. With a rising volume of incoming data, many businesses are struggling to understand where to start in utilising this information and transforming it into clear, actionable insights.

Decision-making potential is being lost simply because of the issue of data overload. As a consequence, may financial institutions are relying more on AI to support them in the decision-making process with unstructured data. New AI-driven tools can analyse, query and leverage unstructured data to deliver deep insights in record times. How can these tools provide value and support financial businesses to convert huge volumes of unstructured data into key decisions?

Extracting vital insights

Innovative big data analytics solutions that utilise machine learning can scan data and identify valuable sets of information. These tools enable financial businesses to discover vital insights that tend to remain hidden in unstructured data format, providing an immediate competitive edge over other businesses that are failing to utilise the power of AI.

These analytical tools can reveal new market developments, enabling teams at finance-focused companies to gain a deeper understanding of the market and make better financial decisions. HSBC recently launched an AI-driven investment index that measured unstructured data from multiple sources such as Tweets, satellite imagery, news content, or financial data. The ML-powered tool enables analysts to gain intelligent market insights considerably quicker compared to conventional methods.

Generate sentiment analysis

A ML algorithm focused on managing unstructured data can also explore sentiment analysis to gain a deeper understanding of the media’s feeling on a specific topic. Traditionally this process would involve highlighting and picking out certain words such as “great”, “poor” or “disaster”. The new process explores the context of synonyms and extracts the meanings, which is particularly important in the finance industry, where words and phrases have specific meanings. When these models are applied to news related to a specific company or sector, they generate qualitative information of the writer’s tone, informing you how positive or negative the stories are and how positive certain articles were in comparison. This is particularly useful in finance and investment, revealing certain aspects of a business that influence financial decisions, such as confidence in the market or a specific company.

Applying natural language processing and sentiment analysis tools in this format is an important way for financial businesses to generate value from huge volumes of publicly available data. According to a recent McKinsey report, quantitative funds that leveraged advanced analytics proved to perform better than others in terms of revenue.