Home / Companies / Start-ups /  How startups are using AI for fixing challenges in traditional financial services

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.

Improved Lending

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.

Preventing Frauds

A vast array of machine learning algorithms is designed to prevent fraudulent transactions and for detection of anomalies. The traditional methods of fighting fraud involved sets of hardcoded rules which were designed by domain experts, which then could be discovered and exploited by fraudsters. AI-based solutions can evolve over time and adapt to new patterns found in data pertaining to transactions, such as past behaviour, location, spending patterns and much more, allowing a more secure and protected system which can be used to identify anomalies and trigger warnings.

Personalised Banking Experiences

Chatbots have become increasingly popular over the past two years, and banking institutions are harnessing the power of AI and advanced NLP techniques to understand the needs of customers. Additionally, banking institutions are also leveraging vast amounts of data to analyse consumers’ spending behavior, while providing tailored financial advice to help them achieve their financial goals. These include tips on how to reduce monthly expenditures, predict recurring transfers in the near future, and much more. The possibilities for improving customer experiences remain endless with the use of AI and ML.

Process Automation

Automation has been one of the most crucial aspects offered by AI. Simplification of extracting information from documents, digitizing them, and processing forms are just a few examples of what AI-based systems are capable of providing, while optical character recognition can significantly increase efficacy of time-consuming processes. The benefits of using AI-systems can greatly enhance regulatory, supervisory and oversight capabilities for financial institutions.

AI, ML and data science are thus providing the financial industry a newfound robustness and resilience, helping them reduce the cost of credit underwriting, adding an extra layer of complexity to algorithmic trading, and providing secure, efficient and transparent ways of functioning. The integration of such innovative technologies is advancing the potential for financial inclusion in the country, bringing forth several opportunities for people to gain financial independence, while improving the quality of products and services available.

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