Financial Data Management – How Would AI Shape the Financial Services Industry?

Artificial Intelligence (AI) is increasingly incorporating sophistication and intelligence in various processes in the financial industry. Through its various tools, AI is simplifying complex financial tasks and making them less labor-intensive. Enhancements in AI would introduce more advanced methods and approaches, which would further help the industry boost productivity. Several financial firms have already adopted AI, and the technology is set to further penetrate this space.

AI has been redefining industries and sectors across the globe. Being the early adopters of AI, the financial services industry has been benefitting from its countless applications. AI has become a favored tool of stock market traders, wealth managers, insurance companies, and bankers.

Though AI has varied applications, it has limitations with regard to where and how it can be applied. Nevertheless, AI tools, such as machine learning (ML), natural language processing (NLP), and sophisticated algorithms that can perform complex mathematical tasks, are capable of creating financial models and running through huge data sets in a matter of minutes.

AI Tools

• NLP – NLP has the ability to run through vast data sets and find the relevant miniscule piece of information. Investment bankers, traders, and wealth managers use NLP software for data collection and analysis. Banks and insurance firms apply NLP to social media-related data for marketing and communication strategies.
  NLP can improve the speed and accuracy of investment research for stock traders and wealth managers. For example, NLP algorithms can browse through websites for news on M&As, corporate deals, or IPOs, and quickly share relevant information with a financial analyst. The algorithms can also perform sentiment analysis of certain stocks or companies to gain an understanding of how consumers are reacting to a financial news. With such specific data at their fingertips, financial analysts, stock traders, and wealth managers can gain insights on how the stocks would perform, and thus, take informed buy or sell decisions.
  Sentiment analysis can be used to gauge customer sentiments around a brand, based on comments and testimonials on social media platforms.
  Some financial institutions use NLP to determine an individual’s credit worthiness, business intelligence, and sentiment analysis for customer service.
• ML – Various ML models can aid financial companies automate and further digitize their tedious documentation processes through NLP. These can be integrated into existing workflows without disrupting the current processes. The NLP software automatically reads and understands documents that involve mortgage or loan processing.
• Predictive analytics – Wealth managers, investment bankers, and traders can use predictive analytics software to estimate stock performance. AI models can be designed to simultaneously run through thousands of stocks and corelate certain data points to predict the stock returns. The technology is also proficient in suggesting the top stocks to buy or sell at a given point of time. With the help of ML algorithms, AI models can also conduct risk analysis of stocks to make recommendations. Such technology would help managers and investment bankers in creating a portfolio for their clients on the basis of risk profiles.

AI Applications

Portfolio Management
"Robo-advisor" is a new term that is becoming increasingly popular in the financial domain. Robo-advisors are algorithms built to calibrate a financial portfolio based on the goals and risk tolerance of a user. For example, if a user enters his/her financial goal as INR10,000,000 in savings at the end of 70 years of age, the robo-advisor spreads the investments across different financial instruments and suggests alternatives to achieve this goal.

If the user changes the requirements or market scenario, the algorithm accordingly calibrates these and aims to find the best fit for the individual’s goals. Robo-advisors have gained significant momentum with Gen X and Gen Z consumers who do not want to interact with human advisors.

Document Digitization
A key challenge faced by insurance firms or banks or financial institutions while adopting AI is that large volumes of historical data are stored in paper documents, which need to be digitized. To achieve this, computer vision software can be used. Employees at financial institutions can scan and upload PDF documents on the computer vision software, which reads through the text using the optical character reader (OCR) technique to get the desired output in text.

Users only need to scan and upload the paper documents. The software will run the algorithm through the PDFs and “read” the content, thereby populating fields on a digital version of the document from the PDF. This kind of digitization further prepares documents for AI-based search functionality.

Search Key Information Through Large Database
Financial institutions often struggle to search for key information in large data sets; this is where NLP comes to the rescue. It can be used to identify and extract the exact text needed from a huge set of digital data. NLP uses context-based algorithms that scan the entire database to obtain the desired output.

Outlook
The financial services industry is evolving rapidly and using technologies to enhance the efficiency and accuracy of its processes. The industry has seen wide-scale adoption of AI, which would further penetrate into the space as the technology advances. AI has given the world of banking and finance a new way to service its customers and meet their demands. It is making the entire process of banking and investing smarter, more convenient, and safer to access.