The implications of big data in the finance industry
Innovative technologies including artificial intelligence and machine learning are transforming financial businesses and Big Data sits at the core of these new industries.
With the support of AI, businesses can efficiently assess data for insightful and relevant information in a fraction of the time. AI can identify specific trends and patterns and display this information clearly for businesses to digest. In the financial market, banks have a considerable amount of data related to their customers. Information on specific behaviours enables banks to determine what financial services to offer. Big data can be used effectively to measure investment options and also plays an important role in risk management. Financial products can then be created to effectively fit the requirements of their customers and essentially generate better returns.
As industry professionals point out, even the highest quality of information is not the same as knowledge. Investors make their decisions and act on knowledge, but cannot make decisive actions based simply on the information. Big data has huge potential, but can be rather useless if individuals cannot collect, process and understand the information.
What are the benefits of Big Data in Finance
The rise of digital technology in the finance industry provides a range of benefits to customers. Financial businesses are utilising big data to meet customer expectations and create a wider range of bespoke solutions. Some of the benefits include:
More Customer Satisfaction
Insightful and higher volumes of data mean financial businesses can provide more personalised investment choices. Big data enables quicker and more efficient services, saving vital time for customers.
Improved Security Measures
Security measures like fraud detection and unauthorised access can prevent potential cyberattacks. Financial institutions have security systems to notify customers of any suspicious activity.
New Research Tools
Many financial businesses provide tools to allow in-depth and technical analysis. These tools provide opportunities to predict changes in prices based on historical data trends.
Automated Investment Plans
Applying algorithms to improve returns on investments is becoming more popular. Data systems driven by AI have enabled investors to automate a lot of their investment strategies. While an automated investment cannot necessarily guarantee better results, it is a trend that has attracted several new investors to the industry.
What are the challenges of big data in the finance industry?
While there are many benefits, there are some challenges linked to implementing big data in finance.
Rising costs related to innovation
Big data requires a high volume of infrastructure. The data generated from businesses is stored in warehouses and the costs associated with new servers to store data can spiral quickly. There are also other expenses related to cooling systems and other ongoing maintenance. Financial businesses have to continue paying an ongoing fee to subscribe to big data services but may also be forced to continue paying additional fees related to upgrades.
A significant amount of data collected from personal devices is predominantly unstructured and the overall quality of this data can greatly vary. It can be challenging for financial businesses to determine unrelated data points and understand the business value of this data. It can also be equally complicated to understand which data to utilise and which to ignore.
Government measures have become stricter in terms of the regulations on big data. Financial companies have been forced to adhere to stricter rulings on collecting information on customers and their behaviours.