How startups are dealing with issues with traditional financial services using AI and data science
The use of these technologies provides institutions the added advantage of unbiased decision-making, while also enabling borrowers without an extensive credit history to access capital and prove their ability to repay loans
prativad
The growth and integration of artificial intelligence, machine learning and data science in technology automation has led to the emergence of some of the most advanced, efficient and secure systems in the financial services today. These technologies have impacted and improved operational capabilities across industries, heralding a phase of superior functioning backed by the growing availability of quality information and data.
This technological revolution has markedly affected the finance industry, which has witnessed the emergence of several startups that are leveraging AI and data science to predict cash flow events, assess creditworthiness and detect frauds. Startups using AI are making better data-driven decisions, increasing profits directly by efficient targeting, automating repetitive tasks, and much more to unlock competitive advantages. Consequently, we have made great headway in financial inclusion being able to significantly increase penetration to the previously un-serviced population. Moreover, the global spending on AI is projected to be more than $110 billion by 2024, almost double of the total from 2020.
One crucial application of AI in the financial industry is credit scoring. Large banks, fintechs and NBFCs are leveraging AI for the purpose of speeding up processes and accurately assessing potential borrowers. AI and ML use advanced classification algorithms with diverse explanatory variables such as demographical data, income, savings, transaction history, past credit history, their digital footprint and more to decide the creditworthiness of the respective customer. The use of these technologies provides institutions the added advantage of unbiased decision-making, while also enabling borrowers without an extensive credit history to access capital and prove their ability to repay loans.
Advanced Algorithmic Trading
An intriguing development in algorithmic trading has been the use of AI techniques such as evolutionary computing, deep learning and probabilistic logic. These techniques are helping traders systematically implement and strategise upcoming trades, increasing predictive capacities exponentially. AI-systems are also helping traders track risk exposures, adjust or exit the position depending on the user’s needs in a fully automated manner, eliminating the need for any human intervention. Further, AI and ML systems provide competitive pricing, liquidity management, optimisation and streamline execution for traders, while Natural Language Processing (NLP) techniques leveraged in AI-systems allow traders to accurately identify sentiments from sources like Twitter, Reddit, news articles, etc.
