Today's New-Age Technologies in the BFSI Industry
How are Machine Learning and Artificial Intelligence driving digital innovation and technology-enabled transformation?
In today’s world, the way businesses are conducted has evolved a lot. Whether it's about understanding the consumer/customer, gauging the market conditions or even doing a particular transaction, the entire ecosystem has become digital in nature. And, more than ever before, we have been seeing the use of data, machine learning and AI.
Let's take the use case of financial inclusion in India. Suppose I know how often a merchant receives payment through a UPI payment service. In that case, I know exactly how much money they are making and can help that merchant with targeted financial supporting products that help them grow and scale their business. And they don't need to rely on external data sources to collate and construct a merchant, consumer or borrower profile. The data itself will provide the merchant with the ability to make decisions.
Another great example where this has happened is Grameen Bank in Bangladesh.
Machine learning and related algorithms can help channel the correct information, products, services and support to the individual for who it is meant to be instead of going with a broad-based approach.
On the other hand, think about AI in terms of digital transformation. It is used in robotics and other fields where creative thinking is required. Whether or not the machine has previously learned, if a situation is presented to them, the machine will make a decision free of emotional bias. So that is currently transforming our industry.
As a digital products and solutions company, Tavant is placed right in the center of this digital disruption. Tavant works with some of the most innovative and transformative businesses that are cutting edge in terms of the technology they use.
Today, AI can automate low-value loan approvals and assist in the evaluation of larger transactions, such as mortgage applications. Please explain how AI has aided in the transformation of the fintech industry over the past few years.
There are several examples of how people in the fintech or financial services industries have used machine learning and AI to make quick decisions on low-value transactions. Whether it's about small payday loans or microfinance style loans, buy-now-pay-later loans or credit card options, the evolution of machine learning over the last decade or so has enabled consumers to make instant transactional decisions.
However, today we are witnessing the use of AI even in the case of more complex transactions. How can a loan default or prepayment be predicted? How can you tell if a customer is going to churn and refinance their loan with another lender? How can we anticipate fraud in the equation? Today, AI is assisting in answering these questions.
Over the last few years, Tavant has solved a very complex problem. We discovered a way to predict whether a house has things that are not captured in the report, such as damage to the exterior structure, by looking at a property appraisal report, including the data and pictures on the report.
As a result, image and character recognition have become extremely important, particularly when dealing with unstructured documents requiring natural language processing, such as past mortgage deeds or letters of explanation in a specific mortgage or borrower scenario. This is especially true when some of these documents contain more images than data. That is where machine learning has become extremely important in the decision-making process for complex loan products.
How will Metaverse help business leaders reconsider their strategies? How can today's businesses anticipate and shape change to gain an advantage by utilizing the metaverse platform?
The whole Metaverse ecosystem is about immersive experiences.
It's still at a nascent stage.
The most common application is in the gaming or travel industries, such as an Oculus Quest 2 or experiencing scuba diving while sitting at home or feeling the thrill of walking a ramp hundreds of stories above the ground while still sitting in your living room.
Another example relevant to our industry is the ability to visit our bank from the comfort of our own homes today. And by banking from home, I don't just mean mobile or web-based banking on a computer. With Metaverse, we can communicate with our financial advisor as if we were in the same room, having similar immersive experiences.
Alternatively, instead of having to step out and visit multiple sites when purchasing a home, we can experience walking into the listed houses in the Metaverse and conversing with the realtor and mortgage broker at the same time.
Businesses can extend their physical or brick-and-mortar store experiences to their customers and offer concierge-like services without spending time, money and effort on physical presence in cities. This would really bring a revolution in increasing the reach to rural locations and reducing the load on existing business branches.
Retail itself could change dramatically because of Metaverse. The whole shopping experience can be brought to us in the comfort of our homes.
How banking in the Metaverse will affect the banking Industry?
We talked about it in the earlier question as well. However, taking that thought forward, and I don't know who will bring this revolution; but assuming I have a Merrill Edge account and instead of just being able to trade stocks using their platform if I could experience the Wall Street in Metaverse, that would be game-changing.
Another instance is while you are buying a car. At one moment, you are at a car dealer's place in Plano, finding your car, test driving it in Metaverse, and the next moment, you are in the bank in the mid of getting a loan to buy the car and then have it delivered to your doorstep. And all of this happens while you are sitting on the couch in your living room. So, Metaverse will play a big role in providing instant gratification to consumers.
These illustrations are just two examples of how the Metaverse can affect the banking industry and impact our experiences. Things that have already started disrupting the banking industry are Virtual Currencies that are increasing transaction speeds and eliminating intermediaries and geographical boundaries, Non-Fungible Tokens that are running blockchain networks and getting validated by nodes spread across the globe, and Metaverse Real Estate.
How machine learning plays a major role in the proptech and fintech industry?
In the proptech industry, real estate transactions on their own are complex and time-consuming in nature. Let's take the example of residential real estate. Very few people buy their properties site-unseen. There are a lot of site inspections that happen, paperwork understanding that is included, bank procedures that need to be taken care of and so on. However, with machine learning or Metaverse, we are moving toward changing this experience in the future. Already there are some companies that provide three-dimensional imaging of the home through augmented reality and video-based walking tours of the house. But with machine learning, the whole real estate transaction process can become much easier and simpler than it has been in the past.
Whether it is about receiving the most accurate and correct data about the property or doing an affordability analysis for the planned investment, machine learning will play a key role.
One such example is Tavant's premiere AI-powered product suite, Tavant VΞLOX. Tavant launched its patent-pending Affordability Advisor product that leverages deep learning models within its consumer-direct portal to advise borrowers. A borrower begins a mortgage application through the Tavant VΞLOX platform from a financial institution. Using the Affordability Advisor interface, the solution automatically provides affordability information and credit factors, enabling the borrower to modify their application if needed.
When it comes to fintech, machine learning can predict customer behavior, allowing financial institutions to make better decisions about customer lifetime value and association. It can assist them in developing new customer acquisition strategies. The machine learning algorithms will then be able to detect fraud, detect anomalies in the transaction, and assist and augment the decision-making process for the actual decision makers, such as mortgage underwriters. And so forth.
So, definitely, machine learning is already bringing and will continue to bring a massive disruption in the proptech and fintech industries, owing to its ability to make decisions without emotional bias in a standardized and optimized manner.
.jpg "In conversation with Sarvesh Mahesh, CEO, Tavant")
In today’s world, the way businesses are conducted has evolved a lot. Whether it's about understanding the consumer/customer, gauging the market conditions or even doing a particular transaction, the entire ecosystem has become digital in nature. And, more than ever before, we have been seeing the use of data, machine learning and AI.
Let's take the use case of financial inclusion in India. Suppose I know how often a merchant receives payment through a UPI payment service. In that case, I know exactly how much money they are making and can help that merchant with targeted financial supporting products that help them grow and scale their business. And they don't need to rely on external data sources to collate and construct a merchant, consumer or borrower profile. The data itself will provide the merchant with the ability to make decisions.
Another great example where this has happened is Grameen Bank in Bangladesh.
Machine learning and related algorithms can help channel the correct information, products, services and support to the individual for who it is meant to be instead of going with a broad-based approach.
On the other hand, think about AI in terms of digital transformation. It is used in robotics and other fields where creative thinking is required. Whether or not the machine has previously learned, if a situation is presented to them, the machine will make a decision free of emotional bias. So that is currently transforming our industry.
As a digital products and solutions company, Tavant is placed right in the center of this digital disruption. Tavant works with some of the most innovative and transformative businesses that are cutting edge in terms of the technology they use.
Today, AI can automate low-value loan approvals and assist in the evaluation of larger transactions, such as mortgage applications. Please explain how AI has aided in the transformation of the fintech industry over the past few years.
There are several examples of how people in the fintech or financial services industries have used machine learning and AI to make quick decisions on low-value transactions. Whether it's about small payday loans or microfinance style loans, buy-now-pay-later loans or credit card options, the evolution of machine learning over the last decade or so has enabled consumers to make instant transactional decisions.
However, today we are witnessing the use of AI even in the case of more complex transactions. How can a loan default or prepayment be predicted? How can you tell if a customer is going to churn and refinance their loan with another lender? How can we anticipate fraud in the equation? Today, AI is assisting in answering these questions.
Over the last few years, Tavant has solved a very complex problem. We discovered a way to predict whether a house has things that are not captured in the report, such as damage to the exterior structure, by looking at a property appraisal report, including the data and pictures on the report.
As a result, image and character recognition have become extremely important, particularly when dealing with unstructured documents requiring natural language processing, such as past mortgage deeds or letters of explanation in a specific mortgage or borrower scenario. This is especially true when some of these documents contain more images than data. That is where machine learning has become extremely important in the decision-making process for complex loan products.
How will Metaverse help business leaders reconsider their strategies? How can today's businesses anticipate and shape change to gain an advantage by utilizing the metaverse platform?
The whole Metaverse ecosystem is about immersive experiences.
It's still at a nascent stage.
The most common application is in the gaming or travel industries, such as an Oculus Quest 2 or experiencing scuba diving while sitting at home or feeling the thrill of walking a ramp hundreds of stories above the ground while still sitting in your living room.
Another example relevant to our industry is the ability to visit our bank from the comfort of our own homes today. And by banking from home, I don't just mean mobile or web-based banking on a computer. With Metaverse, we can communicate with our financial advisor as if we were in the same room, having similar immersive experiences.
Alternatively, instead of having to step out and visit multiple sites when purchasing a home, we can experience walking into the listed houses in the Metaverse and conversing with the realtor and mortgage broker at the same time.
Businesses can extend their physical or brick-and-mortar store experiences to their customers and offer concierge-like services without spending time, money and effort on physical presence in cities. This would really bring a revolution in increasing the reach to rural locations and reducing the load on existing business branches.
Retail itself could change dramatically because of Metaverse. The whole shopping experience can be brought to us in the comfort of our homes.
How banking in the Metaverse will affect the banking Industry?
We talked about it in the earlier question as well. However, taking that thought forward, and I don't know who will bring this revolution; but assuming I have a Merrill Edge account and instead of just being able to trade stocks using their platform if I could experience the Wall Street in Metaverse, that would be game-changing.
Another instance is while you are buying a car. At one moment, you are at a car dealer's place in Plano, finding your car, test driving it in Metaverse, and the next moment, you are in the bank in the mid of getting a loan to buy the car and then have it delivered to your doorstep. And all of this happens while you are sitting on the couch in your living room. So, Metaverse will play a big role in providing instant gratification to consumers.
These illustrations are just two examples of how the Metaverse can affect the banking industry and impact our experiences. Things that have already started disrupting the banking industry are Virtual Currencies that are increasing transaction speeds and eliminating intermediaries and geographical boundaries, Non-Fungible Tokens that are running blockchain networks and getting validated by nodes spread across the globe, and Metaverse Real Estate.
How machine learning plays a major role in the proptech and fintech industry?
In the proptech industry, real estate transactions on their own are complex and time-consuming in nature. Let's take the example of residential real estate. Very few people buy their properties site-unseen. There are a lot of site inspections that happen, paperwork understanding that is included, bank procedures that need to be taken care of and so on. However, with machine learning or Metaverse, we are moving toward changing this experience in the future. Already there are some companies that provide three-dimensional imaging of the home through augmented reality and video-based walking tours of the house. But with machine learning, the whole real estate transaction process can become much easier and simpler than it has been in the past.
Whether it is about receiving the most accurate and correct data about the property or doing an affordability analysis for the planned investment, machine learning will play a key role.
One such example is Tavant's premiere AI-powered product suite, Tavant VΞLOX. Tavant launched its patent-pending Affordability Advisor product that leverages deep learning models within its consumer-direct portal to advise borrowers. A borrower begins a mortgage application through the Tavant VΞLOX platform from a financial institution. Using the Affordability Advisor interface, the solution automatically provides affordability information and credit factors, enabling the borrower to modify their application if needed.
When it comes to fintech, machine learning can predict customer behavior, allowing financial institutions to make better decisions about customer lifetime value and association. It can assist them in developing new customer acquisition strategies. The machine learning algorithms will then be able to detect fraud, detect anomalies in the transaction, and assist and augment the decision-making process for the actual decision makers, such as mortgage underwriters. And so forth.
So, definitely, machine learning is already bringing and will continue to bring a massive disruption in the proptech and fintech industries, owing to its ability to make decisions without emotional bias in a standardized and optimized manner.
