Vikas Agarwal, an expert in Artificial Intelligence, Machine Learning, and Cloud Computing, writes a special column for Deccan Mirror about the potential of MIT’s GenSQL in interacting with databases.
The world has become digital, and so have the financial operations. Emerging economies like India, too, have adapted to the system of digital payments, followed by many other countries. With a huge chunk of the world population operating the currency in digital transactions, the huge challenge of managing financial operations in the digital world is imminent. Here comes the data science into the play.
Banks can only store and manage currency, but not how users operate with the money in their bank accounts. It is going to be a humongous task for the bankers to oversee, check, and verify the various states of financial operations of the users in the bank, in terms of user behavior and identifying potential customers for their new schemes. Data science analyzes financial data to discover actionable insights, which uses multiple skill sets like statistics, mathematics, and AI. This will simplify for the bankers to analyze the statuses, loans, credit balances, payment behaviors, and potential abilities of customers who can in turn invest into their new ventures.
Data science can also be helpful in making the financial weather forecasting, which helps them prepare for the best and worst times in financial days. The chance of potential frauds can also be encountered with the use of data science by establishing certain threshold values and benchmarks that can point out irregularities in financial behavior.
At times, where loopholes in provisions and software are used to breach finance management systems, data science provides key insights into where things are going wrong exactly, a process that once needed manual checkups for years together. The same data science tools will help organize and arrange their payments, which reduces the stress and time taken during the auditing times of the companies and banks. One more advantage of data science lies in the optimization of data in investment portfolios, based on the historical data and market trends. Using these insights from big data and advanced analytics, the job of portfolio managers can be simplified.
The common technical problems, like privacy concerns, efficiency of algorithms, and technical failures, too mar the usage of data science. Still, the data science is already revolutionizing the finance sector with its manifold utilities.
