Ascent of AI in Financial Services

Introduction of Shameek Kundu:

Adel Nehme: Good day. Hello, my name is Adel Nehme, & gladly accept the invitation to Data Framed, a talk show about information and its effect on organizations around the world. The finance sector, including commerce, insurance, and commercial banking, is undoubtedly one of the most content-rich-rich industries. Big data and deep learning have numerous applications. Even so, that does not imply that deep learning is being utilized to its full potential, as there are innumerable roadblocks in the way.

Adel Nehme: I'm therefore thrilled to have Shameek Kundu, TruEra's chief strategist and head of financial institutions, and an erstwhile team CTO at Standard Chartered, on the board. Shameek has spent the greater part of his career in the industry of financial services trying to drive liable predictive analytics and AI acceptance. He is a participant in the Bank of England's Intelligence public-private forum and the OCD global AI partnership, and he served on the Singapore Monetary Authority's Steering Committee on AI fair treatment, morality, responsibility, and openness. Shameek was most recently team chief information executive at Standard Chartered Bank, where he aided the bank in exploring and adopting AI in a wide range of areas, as well as shaping the company's methods known as responsible AI.

Adel Nehme: Shameek goes on to discuss his back story, the condition of information extraction in finance, the deepest part versus the broad scope of deep learning operationalization and banking sectors today, the obstacles to easily accessible Adoption of ai in the economy, the significance of information reading skills, the confidence as well as commitment struggle of Artificial intelligence and machine learning, the perspective of big data and financial services, and more across the show.

Shameek, it's wonderful to have you join the show. Adel Nehme: I'm looking forward to discussing the condition of big data and financial services with you, as well as your career leading big data at major organizations and your present job at TruEra. Can you, nevertheless, provide us with a brief context on your back story and also how you ended up getting through into the dataset before we begin?

Shameek Kundu: First and foremost, thanks for the opportunity. This is an honor to be on your talk show. To address your question, I am a trained engineer. I then decided to pursue an MBA in financial management and devices. Presume it or not, my 1st task was to help with the creation of an internet shopping investment company in India over two decades ago. Then he joined McKinsey and spent 8 years guiding European consumer finance clients on advanced technologies and processes issues. Then, in 2009, right during a financial crisis, I decided to join Standard Chartered Bank, an international bank centered in Asia, Africa, and the Middle East. As well as I spent the preceding 11 years there, concentrating on technology and data positions before actually having joined TruEra in the year.

Adel Nehme: That's fantastic. Given your broad experience, I'd appreciate it if you would explain how you perceive the condition of transformation of information in banks and how it has developed during your time as a market data chief.

Shameek Kundu: Your account balance is information. Everything within financial services is information. So it has always been business centered on information processing, storage, protection, and movement.  To use all of this data, private information must be protected. I can accomplish a great deal more now for my customers, my company, my associates, and so forth. So that's one transition from strong defensive too, let's say, a defense and offense play.

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Science of Data, Analytics, & Machine Learning in the Financial Sector

Adel Nehme: Would you mind explaining where these regions of worth in financial institutions are currently residing?

Shameek Kundu: Many of the early analytics and data science collection use cases, especially machine learning, focus on increasing the efficiency of risk management within financial services companies.

The first one is efficiency in risk management. The second, and perhaps most obvious, is incremental revenue growth. The third step, of course, is to improve efficiency in the center and backend procedures. The fourth is tightly linked to the foregoing and is rarely visible, but as clients, we commonly consider the end-to-end digital experience.

Finally, and most notably, analytics and information are facilitating transformative leadership business strategy improvements.

Adel Nehme says: So how would you characterize the acceptance status of several of these utilization instances?

Shameek Kundu: I believe it is crucial to differentiate between what I'll call "merely information as well as predictive analysis," which also contains a few forecasting analytics but not deep learning, as well as the machine studying closing of things. If you simply mean conventional data-driven- driven insights, which include the comprehensive use of data, analyze the possibility, visualization techniques, and predictive modeling without the use of machine learning.

But the tale is different when it pertains to machine learning specifically.

One, between 50 and 65%, or half to two-thirds of traditional financial institutions, have begun to use deep learning in a non-trivial manner, indicating beyond a pilot or demonstration of a concept. They're making use of it. There is some value to be gained from it. In general, conventional data and analysis acceptance seems to be quite significant and has a massive effect on many organizations. Machine learning acceptance is widespread, but it is still mostly brief throughout all but one or two of each ten organizations.

Adel Nehme says: What do you presume to be the most major obstacles to the mass acceptance of artificial intelligence and machine learning in the industry?

Shameek Kundu says As you stated, there are clusters of these obstacles. There are technological barriers, institutional factors, and skill barriers in the company that you are in.

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Transformation of Talent

Adel Nehme says: Describe your opinions on the skill transition challenging task that the finance sector must face gaining optimized worth from big data, machine learning, and AI.

What do you picture a data-literate organization and industry looking like?

Shameek Kundu: Be it data crisis management, data management, or data visualization, we've covered it. I believe that there are individuals who can and cannot do data visualization.

However, it does not work unless you merge big data knowledge and experience with a fundamental knowledge of the realm in question. So one region that I believe even experts who work in this field should concentrate on rising the financial services quotient.

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The Future of Machine Learning and Artificial Intelligence in Finance

Adel Nehme says: Given starting to emerge understanding of research methods and tools as well as advancement throughout this area, what do you believe our deep learning or AI utilization cases in financial products will be future that we are unable to operationalize today?

Shameek Kundu: Build up to the point where you're able to ask intelligent questions. Don't attempt to persuade yourself that "I understand how to program." It's okay. It's fantastic if users know how and when to code. Understanding how well that code is working and also how information is utilized to train a model is probably more than being able to program yourself. So, my advice is to get to a point that allows you to ask the right questions.

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Required Action:

Adel Nehme says: Shameek, provided that we'll be closing on such a brighter note, are there any final suggestions for us before we consider it a day?

Shameek Kundu: Instead of chasing the excitement, let us recognize the possibility and focus on making a real impact with deep learning, data science, and predictive analysis.

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