Artificial intelligence is transforming the asset management industry by enabling fundamental analysts to research and extract more information faster so they can uncover accurate investment insights. Analysts spend hours and sometimes even days manually researching hundreds of sources. This process is extremely labor-intensive, and it’s easy for analysts to miss critical pieces of information. Analysts can use AI and natural language processing (NLP) to detect and extract the most relevant facts from unstructured datasets.
One of the ways AI has evolved is with its accessibility. Following the no-code movement, AI is now accessible by both technical and non-technical end users such as analysts, data scientists and engineers. This significant innovation can now be used by financial services.
Using AI And Big Data For Investment Research And Analysis
Asset management firms that harness AI and structured and unstructured data can gain a competitive advantage as rich insights can be drawn more quickly and accurately. By applying NLP to investment research and analysis, AI can extract the most important insights, generate summaries and create potential actionable steps from data for asset managers to use within their investment decisions. Asset management firms are beginning to realize the value that NLP techniques can bring to front-, middle- and back-office operations.
Analyzing massive amounts of unstructured data is also known as big data analytics. Unstructured data is undefined and comes in less recognizable text forms. Based on my experience as the CEO of a company that provides no-code AI platforms for financial services, I’ve found there are three main challenges that must be addressed to gain insight from massive amounts of text-heavy and not easily obtainable information:
• Data variety: The vast majority of digital information is unstructured. Unstructured data can be detected and analyzed manually by an analyst, but this can take hours or even days to go through all of the content and normalize the text. Alternatively, AI and NLP can be used to search through specific documents, identify relevant data points and then convert them into natural or human language.
• Data volume: The more meaningful information you can add to your analytics models, the more accurate your results. Automation enables data to be ingested frequently and at scale.
• Data velocity: Digital data is generated every second from various external news outlets and internal company news, social posts, reviews on companies and more. Analysts cannot monitor the news 24 hours a day to ensure that the most up-to-date and important information is tracked.
Using AI, data can be mined in real time from brokerage reports, news, corporate filings, social media and trade journals. You can also specify the type of data you want to retrieve by adjusting the data sources, changing the report formats and refining analytic strategies. In this way, AI and automation can help manage the variety, volume and velocity of critical data.
A No-Code Approach To Data Analytics
With the rise of AI and ML tools and advancements, existing fintech firms are growing and new firms are making an entrance. Fintech firms are providing innovative solutions, such as no-code AI, so businesses can easily implement AI and ML within their data sourcing processes.
A no-code platform enables users with or without a technical background to build AI models for data analytics. With a simple, yet interactive user interface, AI can be used to perform NLP on the unstructured text and automate many of the manual procedures. Once the right data is detected and pulled, the analysis can be displayed on a dashboard with a data visualization tool that monitors market trends in real time.
No-code AI tools make the ability to build analytics models more accessible to financial end users. Financial teams can then identify, analyze and track events that can impact a company’s portfolio such as early warnings of credit migrations, supply chain issues, ESG concerns and more to help asset managers make smarter investments.
No-code AI tools can also save time and money while improving overall efficiencies, due to the lack of expert subject-matter technical intervention needed to use AI.
Getting The Most Out Of Your AI
Four ways financial service teams can get the most out of their AI include:
1. Ensuring there is enough data. To make accurate predictions, AI platforms require large quantities of data to ingest. Without a large volume of data, the AI will not work as intended.
2. Along with a large volume of data, financial teams must ensure clean, accurate and relevant data to develop accurate algorithms. High-quality data can drive the value of the data and impact several aspects of the business outcome, such as risk management, customer satisfaction and accuracy.
3. Having a clear strategy to implement AI by identifying the areas that need improvement and where AI can help, setting clear objectives and ensuring a continuous process for improvement. For example, many firms choose to integrate their no-code AI platform with their customer relationship management (CRM) system to immediately extract information from the data there.
4. Ensuring data compliance as data leakage and misuse is a major concern of AI within financial services. Financial firms can start off with a small set of complex data to build AI systems and then add subsequent ones after monitoring the results.
The best way to begin the AI adoption process is by ensuring the quality and quantity of data, having a clear strategy to implement AI and guiding transparent conversations on AI to break any industry resistance toward AI. Analysts, data scientists and engineers can use AI to compile data so that asset managers can form relevant and valuable insights. These insights can be used to make strategic decisions and build stronger portfolios.
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