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AI shakes up Data Science: Financial institutions lead change into ML revolution

AI shakes up Data Science: Financial institutions lead change into ML revolution

The integration of AI, or artificial intelligence, across different sectors of the industry is natural for two reasons. One, it enhanced efficiency. Two, AI aims to streamline some of the most complex activities that otherwise take a lot of manual labor. The latter can be diverted into something more productive, probably in a way in which AI and manual supervision complement each other.

Finance, for one, has seen massive growth in AI. In other words, the integration of AI with financial institutions is deep enough to conclude that a dramatic revolution is coming up in the future.

AI’s rapid rise in Finance

Visa, for one, uses artificial intelligence to create Visa Protect. It uses cutting-edge technology to reduce fraud in transactions that take place on or off its native network. Similarly, American Express has admitted using AI to support data science, risk management, and legal, to name a few.

The rise is further backed by notable statements from the likes of Jamie Dimon, the Chief Executive Officer of JPMorgan Chase. Jamie has highlighted that organizations can expect AI to have a significant impact on their businesses, citing its past performance as a crucial role. He has further stated that the implementation will embed data and analytics into their decision-making, irrespective of the management level.

Artificial intelligence has certainly found a way to integrate with the core functions of the banking industry. There is a chance, as highlighted by industry leaders, that it will go deep into every possible solution and aspect in the days to come.

The data monetization imperative

Many areas continue to attract skepticism, underscoring the need to define the breadth of AI implementation. This means that AI could be restricted in some sections but not in the basics.

AI can potentially introduce a new revenue stream from its data and analytics offerings. While the technical language takes it to another level, the simplicity explains that the accumulated data has to be analyzed, and findings can be used to grow the industry. It entails certain aspects, like buying trends, spending limits, etc.

The commercialization of external and internal applications has already gained momentum. This comes at a time when an article by MIT Management School has cautioned executives about using AI better. It clearly says that AI has to be used to match business solutions to business problems, not merely to stay ahead of the curve. The thought is based on the fact that allowing resources to fail brings out creativity, something that AI may take away if fully implemented.

Building the AI-first enterprise

The thought is better presented by enterprises that are heavily dependent on AI. A culture has to prevail wherein humans are open to experimenting with solutions and collaborating with machines only for assistance, like possible outcomes after implementation or analysis of past mistakes.

Next, enterprises must identify the problem they are looking to solve and implement AI in a way that aligns with the problem. This comes with the possibility of a reduction in problem accumulation and faster achievement of targets.

Innovations are built on human minds. Seeking AI to replace humans entirely would possibly stop innovations or bring a halt to developments.


In the end, it will all be about how enterprises integrate AI into their operations. AI for corporate banking automates tasks, improves customer service with chatbots, identifies fraud, optimizes investment, and anticipates market trends. Undoubtedly, banks are leading the way, but all sectors must drastically reconsider how they use data and analytics.

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ToAI Team
Fueled by a shared fascination with Artificial Intelligence, the Times Of AI journalists team brings together various researchers, writers, and analysts. We aim to provide a comprehensive knowledge of AI for a broad audience of the Times Of AI. Through in-depth analysis of the latest advancements, investigation of ethical considerations around AI development, AI governance, machine learning, data science, automation, cybersecurity, and discussions about the future impact of AI across various sectors, we aim to empower readers with the details they need to navigate this rapidly evolving field.

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