Wednesday, June 19, 2024
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How AI Is Transforming The World Of Finance

The AI apocalypse is here. It may not be obvious, but it is taking place across businesses, segments, and ways of life. The obscurity of the jargon, as well as the excitement around it, is much more pronounced. However, it is not without foundation.

There are numerous players in the race to the top, including research institutes, prominent corporations, educational institutions, and even states from all over the world. Artificial intelligence-powered financial businesses are expanding rapidly, as is venture capital investment for them. It is critical to adapt rapidly to digital and become an active component of the ecosystem. The new digital revolution is centered on network-enabled co-creation.

AI and fintech

AI has the potential to fundamentally alter everything—some more than others. The sheer efficiency of AI in processing huge data volumes and delivering excellent insights through pattern recognition has already been witnessed in the BFSI industry. AI has had a significant impact on the field of payments and financial services, which is plagued by identity fraud and impostor fraud.

With the requirement to provide better experiences and consumer conveniences, AI allows better and quicker fraud detection in a hyper-digitized environment where PII (Personally Identifiable Information) is available on the dark web for a few hundred dollars. Advanced risk monitoring and fraud detection allow for real-time prediction and detection rather than post-factum detection.

The world of payments has gone a long way, from barter to paper cash to plastic money to contactless cards. AI promises to bring in a new era of frictionless and seamless payments, rendering today’s omnipresent contemporary POS obsolete. Amazon’s attempts to eliminate retail checkout lines are an excellent example.

AI and investing

With its capacity to evaluate considerably more complicated aspects ranging from tax-loss harvesting to financial planning, AI-powered Robo-advisors are the ‘in’ thing.

AI applications include anticipating customer behavior and allowing successful purchasing of hyper-relevant and personalized items, providing huge potential to financial services marketers.

Digitization and decentralization are at the heart of the AI-enabled financial revolution. Aside from AI, other significant technologies influencing finance include blockchain, robotic process automation, and data analytics.

AI has altered the structure, delivery, and consumption of financial services, while new technologies offer an umbrella branding to the fintech transition. To capitalize on the potential that is being released, an enterprise-wide strategy must be developed, as well as the application of AI to revenue and consumer engagement prospects.

The most decisive early uses of AI in fintech are financial risk mitigation and management. It is assisting in the restoration of trust in financial institutions and promoting inclusion through digital money, bridging the gap between the formerly unbanked and the banked. The epidemic has accelerated the rate at which AI is becoming the standard in fintech.

The story of the fintech market

Mordor Intelligence forecasts the AI fintech industry will be worth more than $22 billion by 2025, with a CAGR of 23 percent. The role of technology-driven by blockchain and AI is recognized by the world’s top finance leaders. The worldwide fintech market is dominated by APAC and the Americas, with APAC expected to be the fastest-growing area.

Razorpay (AI-powered system Third Watch helps address fraud issues); INDmoney (AI-driven financial advisory); CogNext (regulatory tech company using AI through automated tech platform, Platform X); Capital Float; Credit Mate; Lending Kart, and Mswipe are among the frontrunners among Indian fintech companies using AI.

The Fourth Industrial Revolution will witness a convergence of technologies spanning physical, digital, and biological domains, allowing firms to be reinvented around data, connectivity, analytics, and AI, removing conventional constraints of scale, scope, and learning.



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