With the development of financial services, there have been constant efforts to develop more secure and easy transaction systems.
Table of Content-
- Fintech: Overview
- Fintech Market Size
- How Fintech companies are using AI
Fintech : Overview
Fintech refers to any technology or innovation used in the financial sector to compete with traditional financial methods for providing financial services to customers.
Fintech has a long and illustrious history that extends back to the nineteenth century and even before. In 1860, a gadget known as the PANTELEGRAPH was created to help banks verify signatures. Historians consider 1866 to be the first year for the imprints of Fintech.
Fedwire's establishment of electronic financial transfers via telegraph and Morse code in 1918 was the initial baby step towards the digitalisation of money.
The pioneer stage in the evolution of fintech is marked by Diner's Card. It was the first sincere venture to enable cashless payments in 1950. Heeding this, Amex brought up the Credit Card in 1958. With Quotron's debut of screen-based stock data in 1960, the financial industry took a giant leap forward.
The second stage of the development of fintech is supposed to have started with Barclay's launch of the ATM in 1967. With the establishment of NASDAQ as the first electronic stock market in 1971, substantial fintech growth occurred. Fintech 2.0 marked the modernizing of the IPO process, the introduction of swift, development of electronic trades and online banking systems, online banking, the evolution of e-commerce, and the launch of PayPal. Fintech 2.0 finished up with the financial crisis of 2008.
The advancement of stricter regulatory compulsions for traditional banks, cutting down the operational cost using technology, and the advent of new technologies like wallets, bitcoins, and P2P was all the development that made Fintech 3.0 very special and important in the steps towards a more sophisticated financial sector.
India and China were the only two countries where the financial sector glimpsed notable growth after the year 2014. These two countries moved away from the domination of the western world and acknowledged the magnitude of the improvement of digital marketing. The year 2017 marked the end of Fintech 3.5.
Blockchain and AI are at the heart of Fintech 4.0. Whether it's for money transfer, processing and payments infrastructure, wealth management, or consumer lending, there are a slew of decentralized apps in the works.
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Fintech Market Size
The world has noticed extraordinary technological betterment and, as a result, widespread application of technology in one of the most essential industries - banking.
According to a forecast published by BLinC Insights, the Indian financial services business was worth $500 billion in 2021, with the FinTech market accounting for $66 billion of that. The rising digitalization has aided the rapid expansion of the FinTech sector
The Indian fintech industry has total funding of over US$27.6 billion, according to Amitabh Kant (CEO, NITI Aayog), and is estimated to be worth over US$150 billion by 2025.
In India, there are over 2100 Fintech companies, with more than 67 per cent having been founded in the last five years. Fintech funding in India has risen at an exponential rate. There are currently 10 unicorns in the Indian FinTech business, including Razorpay, CRED, Pinelabs, PolicyBazaar, and 52 unicorns (soon to be unicorns), including Navi, Mswipe, and Lendingkart.
How Fintech companies are using AI?
AI use in fintech is driven by the need to forecast user behavior and better service clients. Artificial intelligence (AI) is utilized in FinTech for a wide range of applications, including lending decisions, customer service, fraud detection, credit risk assessment, insurance, wealth management, and more.
Modern FinTech firms are employing artificial intelligence (AI) to boost efficiency, precision, and query resolution speed. The fintech market is predicted to increase as an outcome of this aspect. Because of the increased usage of AI interfaces and chatbots for efficient customer support, the AI category will give a lucrative potential for growth in the fintech market share.
Below is the list of 6 areas where AI is being used intensively by the companies:
- Lending Accurate Decisions: Insurance executives and future banking agents will ask the proper questions to robots rather than human experts as a result of data-driven management decisions at a reduced cost. Machines will then analyze the data and provide recommendations, which will aid leaders and subordinates in making better decisions.
- Customer Service: Text chats, audio systems, and Finance Chatbots are examples of customer-facing technologies that can provide human-like customer care or expert assistance at a cheaper cost. Automation and chatbots allow Fintech companies to save time and money: As your Finance Assistant, chatbots can help.
- Fraud Detection & Claim Management: Analytics software collects and analyses evidence necessary for a conviction. Artificial Intelligence technology then analyzes and tracks user behavior trends to detect fraud attempts and outliers. At many phases of the claim handling process, AI can be applied to improve claim management. Insurers can use it to automate handling procedures and manage vast amounts of data in a short amount of time. It can even expedite certain claims, reducing overall processing time and handling costs while improving client satisfaction. To assist in the detection of fraudulent claims, these algorithms hunt for trends in data.
- Credit Risk Assessment: The use of AI in credit risk management is still in its early stages, but the combination of an exponential increase in the amount of data accessible and improved machine learning algorithms to digest it has the potential to have a significant influence. Early warning signals are often utilized in credit risk management to identify organizations at higher risk of default before bankruptcy. Traditional early warning systems rely significantly on expert judgment and require a large number of experimentally defined indicators. AI is particularly good at detecting patterns in big amounts of data with a high velocity that can be utilized to generate credit default alerts. With enough computing capacity, AI algorithms may provide early warning signals based on signs from a variety of sources, as well as improve the indicator's accuracy.
- Insurance: Insurance management using AI technologies will streamline the underwriting process and make better recommendations for consumers by utilizing more unrefined data. Users can calculate their insurance needs with the help of online, automated agents. After a loss has happened, insurance frequently enters the scene. By integrating many relevant data sets, including external ones not available in the medical records, automatic underwriting can drastically speed up the process and often eliminate the need for costly tests. Rather than paying for expensive insurance treatments, it is preferable to recognize hazards and diseases early enough to prevent them. As a result, one can use the data that was previously used to assess risks to reduce the likelihood of losses occurring to the insured.
- Wealth Management: Artificial intelligence-powered smart wallets track and learn from the users' actions and behaviors. These instruct consumers on how to limit and change their financial spending to save money. Artificial intelligence systems aid in the identification of a client's financial preferences and provide customized and curated recommendations. It informs the wealth manager about the client's risk tolerance and by analyzing data and presenting it in the context of relationships between customers, markets, products, and client profiles, it saves time for wealth managers. Furthermore, no suggestion or conclusion is instinctive while working with an algorithm. Such software is heavily regulated, and it is required to offer appropriate justification for any recommendations.
Because of the tremendous potential benefits, the Financial Industry will see major growth in automation, Artificial Intelligence being used more frequently. In finance, artificial intelligence, machine learning, and bots are no longer the stuff of science fiction. They can expand talents, reduce expenses, and improve customer experience.