In this article, we have discussed how data science is helping startups to fix traditional finance services. Read to know more about it in detail.
The advancement and integration of artificial intelligence, machine learning, and data science in technology automation have resulted in the development of some of the most advanced, efficient, and secure financial services systems available today. These technologies have impacted and improved operational capabilities across industries, ushering in a period of superior performance backed by an increase in the availability of high-quality information and data.
This technological revolution has had a significant impact on financial services, which has seen the rise of several startups that use AI and data science to predict cash flow events, assess creditworthiness, and detect fraud. To gain a competitive advantage, startups using AI are making better data-driven decisions, increasing profits directly through efficient targeting, automating repetitive tasks, and much more. As a result, we have made significant progress in financial inclusion, significantly increasing penetration to previously unserviced populations. Furthermore, global AI spending is expected to exceed US$110 billion by 2024, nearly doubling the total from 2020.
Credit scoring is a critical application of AI in the financial industry. AI is being used by large banks, fintech, and NBFCs to speed up processes and accurately assess potential borrowers. To determine a customer’s creditworthiness, AI and ML use advanced classification algorithms with a variety of explanatory variables such as demographical data, income, savings, transaction history, past credit history, digital footprint, and more. The use of these technologies gives institutions the added benefit of making unbiased decisions, while also allowing borrowers without extensive credit histories to access capital and demonstrate their ability to repay loans.
Advanced Algorithmic Trading
The use of AI techniques such as evolutionary computing, deep learning, and probabilistic logic has been an intriguing development in algorithmic trading. These techniques assist traders in systematically implementing and strategizing upcoming trades, exponentially increasing predictive capacities. AI systems are also assisting traders in tracking risk exposures, adjusting or exiting positions based on the needs of the user in a fully automated manner, removing the need for any human intervention. Furthermore, AI and ML systems provide traders with competitive pricing, liquidity management, optimization, and streamlined execution, while Natural Language Processing (NLP) techniques used in AI systems allow traders to accurately identify sentiments from sources such as Twitter, Reddit, news articles, and so on.
A plethora of machine learning algorithms are designed to detect and prevent fraudulent transactions. Traditional methods of combating fraud involved hardcoded rules designed by domain experts, which could then be discovered and exploited by fraudsters. AI-based solutions can evolve and adapt to new patterns discovered in transactional data, such as past behaviour, location, spending patterns, and much more, allowing for a more secure and protected system that can be used to detect anomalies and trigger warnings.
Personalised Banking Experiences
Chatbots have grown in popularity over the last two years, and banks are leveraging the power of AI and advanced NLP techniques to better understand their customers’ needs. Furthermore, banking institutions are using massive amounts of data to analyse consumers’ spending habits and provide tailored financial advice to help them achieve their financial goals. These include suggestions for reducing monthly expenses and forecasting recurring transfers in the near shortly more. With the use of AI and ML, the possibilities for improving customer experiences are limitless.
Automation has been one of the most important features provided by AI. AI-based systems can simplify extracting information from documents, digitising them, and processing forms, to name a few examples, while optical character recognition can significantly increase the efficacy of time-consuming processes. The advantages of using AI systems can significantly improve financial institutions’ regulatory, supervisory, and oversight capabilities.
Thus, AI, ML, and data science are providing the financial industry with newfound robustness and resilience, assisting them in lowering the cost of credit underwriting, adding another layer of complexity to algorithmic trading, and providing secure, efficient, and transparent ways of operating. The incorporation of such innovative technologies is advancing the country’s potential for financial inclusion, providing numerous opportunities for people to gain financial independence while also improving the quality of products and services available.
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