Hi Folks, As promised here is the Blog on the Previous Podcast on Financial Sector in Industry 4.

Good morning.

So, as I promised, I’ll be recording a podcast on the financial sector related to Industrial Revolution 4.0 or the Industry 4.0, which is currently happening, right? What I’ll be covering is basically overview, majorly covering the United States and also the developments in the financial sector be it be related to the crypto banking which is slightly in transition across the world but basically not adopted globally right so crypto banking and we’ll also cover the other things related to insurance mortgages foreign exchange and stocks to an extent and I’ll try to cover blockchain related things as well on how industry 4.0 will mature as it goes further

The first part is let’s take a use case of IoT in finance sector right it can be as simple as uh if we check the most of the new automobiles which are coming in especially in states they do a profiling of the driver basically how he or she uses the automobiles basically their maintenance profile their risk profiles the way they drive right i know there is um automated driving system which is coming into existence at this part of the time with Tesla.

But also there are other factors as in managing, maintaining their vehicle, the servicing part or even to a greater extent, they maintaining it and servicing it, the service records related to it. And if they’re driving on their own, it builds a driver’s profile, right?

So based on this, there are certain insurance requirements payment modules or the subscription models which gets built and tailored for for example an insurance cost might be completely different for a driver who was taking risks was not maintaining and servicing the vehicles in the proper way or a fast driving is also one of the examples.

So in this scenario, the insurance company gives a different pricing mechanism based on each profile of the driver, inclusive of the service industry. I mean, not the service industry, the service records basically on how the automobile is being maintained. Like on the back end what happens is there’s something called as OBD devices, which is in the automobile. And there’s a lot of data gathered from these day-to-day activities in the automobile, right?

If you see the internet is inside the automobile now, everything what we do gets tracked not only from how good we are in driving, but maybe on the type of car, what we own, the way we maintain, the way we feel, the way we manage the batteries related, the charging, battery servicing, and other things. So the smart sensors basically has a lot of potential. data gathered from a top which gives goes as an input to the machine learning right and there are a lot of AI’s acting it acting on the data what we have given the several factors related to this one is the data when we compare one automobiles the data is immense and we compare the data what is holistically automobile companies gathering or even the transport department is gathering from this smart devices, smart sensors which are enabling these automobiles is immense.

What happens is on the back end there is big data also comparatively and this big data is structured using data science and fed into a AI-based machine learning software which act on this data and connect the dots and make it meaningful, profiler and give inputs based on various data points which is available from the smart sensors. So this not only goes to your insurance, but it also categorizes your driving credit points. And on the other side, it also kind of works on the credit underwriting and kind of it basically recommends a specific uh automobile type for you based on various factors that is collected from the smart sensors there is one use case we have this already in china we already have it in china they they do everything the cameras are all installed and there is a autopilot mode also for the cars there so

There’s a driver profiling done, there’s upselling done, there is insurance models built. There are various other factors which come into picture as in how you manage the pedestrian traffic, even the room occupancy data is kind of collected based on these automobile usage right to an extent so all this gets fed and used in different ways this is the mature industry 4.0 this is not 5.0 i’m not talking anything about 5.0 or industrial revolution 5 in these series of podcasts I’ve specifically called it out that this is a series uh my favorite number is 23 so i’ve kept this as 23 uh series of podcast and the episode being uh

This is being the first episode of series 23. all right so this is this series 23 is dedicated to industry 4.0 and the current one is all about uh financial sector right so we covered a little bit on the automobile aspects relating to how the usage is uh categorized and how it is fed into financial models the end outcome is maybe upselling of the insurance upselling of different automobile types profiling the driver profiling the number of people in the family All this gets categorized from the smart sensors which we just use in the automobile, right? Most of these automobiles come with the sensors. They are inbuilt. So I’m not talking about any additional gadget which you need to use in automobile which is bought directly from a showroom.

And especially this is in states in China and other developed countries. I wouldn’t consider it in India because we are far behind. We don’t even have proper cameras on the road which captures many of the, what do you say, many of them who are kind of not… following the rules, right? There are many of them and this itself is not being tracked autonomously in India. We are dependent on the public to capture the photos and report it to the government. So the government puts charges for unlawful activities within the traffic negligence or even though even not abiding to the traffic rules, right? We are dependent or even as developed as Bangalore maybe compared to other cities, even here we are dependent on the public to report it. traffic cops are different league altogether.

I don’t want to comment on that in this podcast, but we are far away from industry 4.0 maturity is what I would like to say at this part of the time. And other than this, this one use case is covered. Next is the automation of trading and investment, right? Now, we are dependent on financial consultants to go ahead and capture those big data, analyze manually, right? You know, let’s be straightforward and talk about the stock market, right?

Or even the mutual funds or even the, you know, what do we call, maybe the crypto trading right all this uh even in most of the countries across the globe this is being manually done right this is being manually done and there are a lot of errors which is happening there are a lot of market fluctuations currently going on and these all needs certain uh analysis and so far this is manually driven and there are a lot many things which the AI or the machine learning can’t take on its own because they’re not that mature as of now.

For example, let’s take what happened with Iran or Ukraine, right? These are things which the machine learning or the AI can’t handle on its own, right? So, okay what i’m saying is there are a lot of data points right when when is the right time to sell the stock when is the right time to buy a stock which stock to sell which stock to buy or which what is the right time to do a crypto trading what time is it what is the right time to sell the currency what time is to All this is manually analyzed based on lots of political and regional events which impacts the market value of these financial aspects which is in the question as per the use case, right?

So now the AI or the machine learning is not that capable enough to analyze what is the next event which is happening what is the strategy i need to use to buy or sell a stock or do a crypto trading right though ups and downs or all those are external factors which is triggered due to a human event it may that’s where I gave an example of Iran or Ukraine right these are external events which can’t be just predicted with data points Right. Rest of the data points, we have a natural graph and that can be identified and maybe we have different ways to analyze the ups and downs. But when it comes to geopolitical situations and even created events, there is very less what AI and machine learning algorithms can do. right so at the maturity of industry 4.0 this will be automated right it will be the ai algorithm will be able to track the different probability and combinations and basically our strategized plan ready for event A, event B, event C and so on and so forth. Right?

That is the maturity which we will be seeing in trading and investment. This is about automation of the trading and investment and yes, industry 4.0 at a mature state will see that the complete automation of the trading and investment will be achieved using ai big data as well as what was the other one data science right big data data science what is big data big data is just a collection of huge data data science puts a view for it make it understandable profiling ai puts the events and strategize strategies by in line with the uh excellent events happening and it actions it also beat the selling beat the buying of stocks or cryptos it can be achieved via automation right that is the second use case third use case is mostly uh kind of a thing which we are kind of scared to touch upon about the transaction security right there are various tools already to secure payment payment transactions but there are uh various uh negatives also as we explore which is coming to light uh maybe as simple as NFC being targeted by phishing or a QR code being replaced digitally by hacking or even though eye scanner how protective iris scanners can be I mean Wherever there are digitalized systems, there’s always a negative option or a threat of being compromised to hackers, right?

This we are yet to come up with a solution, right? So that is an ongoing thing. Maybe Iris scanners are not yet implemented. right we are still with the qr in india that’s good nfcs are there but um that’s been there from some time but nfcs are easy threat uh qr codes are a easy threat right so maybe what i see next is a iris scanner which is already in china right you can make a payment just by looking at it but what are the i mean there are threats for iris scanners or iris recognitions also to be at risk because it can be easily hacked so how robust we make our equipment to safeguard the iris scans and ensure that uh the identity of an individual is kept at the most uh secured environment makes a lot of difference this i don’t see any of the solutions being aimed at securing these personal for personal data in India for example there’s something called as other now they are coming up with the new citizen ID also but all this is already out in the public domain For example, when I go for a trip with the family,

sometimes I’ve given my Aadhaar card Xerox copy, which they take a photocopy and keep it with them as the record that I visited there. So the Aadhaar card was already out, even it was… It was part of hacking activity and billions of records are already out into the black market. This, I would say, is something which has to evolve more. I’m not sure if Industry 4.0 or Industrial Revolution 4 will be able to address the risk associated with it. But transaction security in the financial industry or the financial sector is something which will continue to add risk. And maybe the iris scanners would come into picture, at least if not in India, in US, in states, it should certainly come.

And I see China has already adopted it. And even in China, there are ways to pay using the palm scan, as well as the iris scan. So there’s more to happen at this part of the time. Let’s see how it matures. I don’t have an answer of how transaction security would mature because I see that in the pipeline, even in the research, what I have seen, I’m not seeing any other places or any other research or innovations being done with respect to transaction security so that is where we are lagging a little and third is the customer services in the financial sector now see for me as the end user to make quick decisions and to be able to have inputs for decision making is something which I look for from a bank. Now we have several other apps which is in bits and pieces. For example, there’s an app in India called Cred, right?

In Cred what happens is it auto It is connected with the banking. All my bank accounts are connected with CRUD and CRUD analyzes on different expenses what I’ve been doing. When over a period of time, this access is given to Cred. It starts reminding me of the payments. It helps me analyze where my money is going out. What are my investments? What are my regular payments? What are the ad hoc payments? So it supports my, it basically helps me manage my financial decisions better, right? Now this I expect from a banking service.

I don’t want to rely on a third party app to give me my money. that way i’m selling my data to a third party app right they’re using my data i’m not sure where else what other places this will be sold to but i would expect such services from my own banking institution right so these kind of improved customer services would be enabled uh at the maturity of industry 4.0 so banking sectors would start giving you more decision making capabilities it would analyze your financial spendings it would analyze your regular payments what you have to make, it would not only remind you, but also help you make decisions in a proper way, right? That is one part.

So that’s where the big data and cloud comes into picture in financial sector. So to make this happen, to have a WoW experience as a customer, I need to have I mean as a banking institution you have several thousands or millions or even billions of users who have bank accounts with you. So you have each account has many transactions and all these data points contribute to big data and there’s a cloud storage involved which is more secure and supposed to be secure from hacking and the security related threats right those uh will be in place which we already see but the next point is here is uh just like thread what it was doing we will see globally that the banking institutions will start giving you a summary of your expenditures right there is no need to have excel sheet for you to make a note of all their expenses manually written down to calculate where what it is going on so the banking sectors would come up with specific spending narrative, your credits, your debts, everything will be captured and a meaningful view will be given to you to make you better equipped on the financial decisions you want to take at any given point of time.

This is one thing. Second thing is you would have certain investment portfolios right so there instead of relying on external consultants who make the decisions there would be certain uh ai based solutions which would come into picture where you give the data of what your investment portfolio is the banking sector would give you uh personal financial consultant who would say what is the right time to liquidize your assets what is the right time to invest and where to invest how to invest these all will be recommended to you part of your banking services that is again creating a wow factor for the investors ruling out the financial consultants, third party financial consultants from being part of your portfolio and taking a amount of your total investment.

So basically you would get more of an AI based consultant. which will help you streamline your investment portfolio to a greater extent. This will also happen in the near future. I’m sure most of the financial institutions, especially the banking institutions, are working on AI-based solutions. But at this part of the time, the threat in having a security flaw within this AI-related system options are high we have not yet found a way to this goes back to the same i spoke about security six six transactional security right that will be at a threat even if industry 4.0 matures because as of now we have not found a way to get rid of the threats involved in the information security part of it, right? So this will be an ongoing process.

We have to educate the users. We have to normalize alternatives to, have checkpoints so that the threat is normalized. At least the impact is less. While that is being done, the financial institutions were working on many AI-based solutions which would have access to your transactions and which would enable you to make better investment decisions. So you would certainly have a… I mean before agentic AI I would say which which would be private to you your account it analyzes all your transactions and market conditions not only suggests you or feeds you with the right decision to make on one particular transaction but on an overall like on a yearly basis it helps you to strengthen your investment portfolio it helps you to uh segregate your portfolio and widen your portfolio to a greater extent with the right recommendations that being said the risk is always there and it is going to be there and there will be certain checkpoints which the financial institutions will put across so that our customer is not highly impacted Okay.

And with all this, the flexibility is in the flexibility. for the customers to a greater extent, the scalability is to a greater extent. I mean, the more the money you have, the more wider scalable your portfolio will be. And the more access you have to better decision making, your funds will automatically increase. And it helps you have a better overview of your institution. your portfolio anytime whenever you want and it it’s more like efficient uh because everything is right at your hands like maybe a mobile phone or a laptop you’ll be able to access all these features all right with that said and done uh this has been an amazing discussion for me and um i see they are almost on the verge of completing the industry for in the financial institutions but the threats are something which are unforeseen at this part of the time because there is very less uh experiment real-time experiments that is being done on agent tki being involved in the banking transactions right so a lot to look forward to maybe this itself for you to have uh agent tki on your phone recommending your investment portfolio and also helping you to decide whether to buy a car or not or maybe as simple as whether to buy a normal short or not Right. All those kind of very small to very big purchases, very small to very big investments, recommendations, all those are at a fingertip.

That’s what industry 4.0 or industry 4 matured financial sector would look like. With that, I wind up this podcast. Thank you for listening. Stay tuned and do subscribe. This is on Apple Podcasts, YouTube, Spotify, and many other places. I would be continuing this series 23 for a long time now. I have around 16 podcast scripts on various industries written. And again, calling it out, this is only about Industry 4.0. I’m yet to start on Industry 5.0. I will have next series dedicated for Industry 5.0. Industry 4.0, for India to reach there takes a long time. China is already at least very near to the maturity point of industry 4. With that, I end this podcast. Thank you. Stay tuned. Bye-bye.