Hello and welcome to The Buzz, a bank automation news podcast. My name is Whitney McDonald and I’m the deputy editor of bank automation news. Joining me today is Naveed Anwar, Managing Director and Global Head of digital for city treasury and trade solutions. He’s here to discuss how financial institutions can use AI, machine learning, and RPA to stay on top of data analytics to deliver results.Naveed Anwar 0:26
So Whitney, thank you for having me. So I’ll say that for many of our clients across different industry, the world is really changing and the pandemic accelerated the demand for E commerce, particularly around the offerings that they had, as well as the digital experiences that they wanted to give. And now customers expectations are, they want to be informed by the last great experience, no matter what the industry is, right? Like it’s no different in the treasury world and what it is in the consumer world. Over the last few years city, particularly TTS has invested significantly in data for our clients. Because in the world of increasing complexity, decision makers need data to drive decision making. And these are head of treasuries who want to be able to have data at their fingertips to be able to make a decision. For city this has enabled us to identify growth opportunities, as well as areas in which we need to be investing in having this data readily accessible for us to identify, forecast and manage our risk exposure. For example, in the recent events that have been taking place in the market, TTS was able to leverage real time account balance and data to migrate and mitigate any of our risk exposures, and make informed decisions to avoid current market instability impacting our balance sheet as well as looking for the best interest for our clients. And then a better example would be we leverage data to analyze transaction flows, to help stakeholders proactively manage any inherent risks around activities that involve our clients, including regulatory compliance as well. So this positions TTS, as a trusted advisor to many of our clients, and effectively really helped us manage the relationship with them. So I hope that gives you a bit of a picture of how we actually leveraged data within city.Whitney McDonald 2:35
Ya know, if we can talk a little bit deeper about the tech side of it and how you dive into the data analytics have it can you talk a little bit through how AI and machine learning and robotics even help you stay on top of this analysis? You know,Naveed Anwar 2:51
this, this this space is, you blink in a new technology is pushed out, so you got to be really careful on like, the investments that you do so as our commitment has been in the industry to really transform the technology that is being utilized at Citi. What drives our commitment to innovation and data analytics is the fact that in the current environment, clients must have the tools to better understand the needs of their own customers as well. So our platform technology is able to mine a vast body of data to help TTS understand client preferences, needs as well as predict them, ultimately, providing clients with relevant and competitive insights into today’s what we call digital economy. So we use innovative technology to ensure we are best understanding our own customers. For example, at Citi, we leverage NLP which is natural language processing models to capture client feedback, so we can better understand what our clients are looking for and what the clients sentiment is, our product and client support teams use these outputs to guide product development and really manage our roadmap so that we are actually capturing that feedback coming from the clients. Again, from our vantage point, we’re also seeing market leaders utilize RPAS, which is robotic process automation, to reduce expenses and turnaround times for range of processes that they have on their side. Customer service is a prime example simple robotics can manage many low value, touch points with customers by working through predefined processes 1000s of times at the same high standard, right? Like where we would have had manual people or manual intervention, we’re automating that piece. Aside from higher customer satisfaction, it’s the consistency in the quality of execution reduces the human error. Such is also reducing the risk and exposing sensitive data to human operators as well. Like so when you get machines involved, you can actually get through that much faster. Once RPA processes are embedded within operations, enhancing this process through predictive modeling is relatively straightforward. So there is an opportunity for having AI to learn from those models. And so this then has a bigger impact of what what we are actually producing. So though that is a few ways that how we’re using NLP rpa, AI and automation in the way that we actually look at analytics within the city.Whitney McDonald 5:35
Now with having access to all this data, and you just talk through the technology in place in order to understand all of this data, how can kind of bigger picture here financial institutions, leverages technology and their own data in order to see growth?

Naveed Anwar 5:52
Look, FIS are redefining the way that technologies used within their environment as well, right? Where if these institutions didn’t exist, innovation wouldn’t be coming to the forefront. And we’ve seen that for many years in the industry. In the current environment, it is critical that financial institutions also transform alongside with their customers, right like so they are at the cutting edge. And those who don’t do so will fail. And, and those who fail to understand that role that data plays in this transformation may not be able to operate at the speed that today’s leading companies, particularly the FinTech, industry, and E commerce firms expect. Prioritizing self transformation helps financial institutions keep pace with innovations in data management and analytics, making it easier to launch new offerings while remaining compliant with regulators for city data. And analytics play a major role in understanding how customers operate. This enables us to both identify solutions that may be applicable, which are now and ideate for new offerings for the future. An example on this one would be the way that we use internal data, like transaction data allows us an FYI, to drive numbers for crucial insight about their clients, right. So when we give that insights over to the businesses and clients, such a business cycle information concentration, will allow us to have less risks, and geopolitical exposure as well. So this is where we’re helping the FIS use the data to derive some insights. And then from an external data source, such as credit data, information about markets, we can go into demographics, competitors, and industries, at Enterprise enterprise level information, we can also create a rich vein of insights that can be redefined to both identify where opportunities might be there in there in the market that they can grow, or where their risk exposures might be, for NFS. level at an epidemic level. So that’s where we kind of like try to optimize by working with data with them.

Whitney McDonald 8:18
Now, if we could talk a little bit about the flip side here of missed opportunities, if advisors aren’t leveraging their data that they do have access to can we talk a little bit about what they would be missing out?

Naveed Anwar 8:33
Look, if if we did not expose the data to the FIS, they would not be able to make the predictive analytics for for their customers, they will not be able to kind of leverage that data to create the insights for a better user experience as well, as well as not keep up with like what’s happening in their competitive landscape as well. So I think it’s important for them to leverage this data, those are the kinds of opportunities that they miss. And sometimes, when you don’t have those insights in place, that can lead into you not being relevant. And none of these advisors want to be in that space. So they will always want to be relevant. So having that data as as an asset, and available to them on their fingertips is something I think these clients really want to have in and work with Citi to make sure that they have that readily accessible.

Whitney McDonald 9:36
Now speaking of data as an asset, wondering how you approach all this data without getting overwhelmed, and then if you could tie in a little bit, the heightened regulatory scrutiny around data and what you’re seeing in that space, Could you could you talk about how you’re approaching data in that front?

Naveed Anwar 9:55
Yeah, look, everyone in the industry wants to utilize the But really, what you have to start thinking about is what is the data lifecycle, right? Like from the moment that you actually have bursts of data to the moment that you really have to destruct or delete the data, right? There’s a whole lifecycle of data management that has to be in place. And particularly around when you talk about data management, data governance is a key aspect here. Avoiding the duplication of the data reporting and relating related processes can be difficult to enforce in large organizations where there are so many people. So you really have to put the checks and balances in place, through a producer and consumer model, like who can subscribe to the data who can actually build the data. And then who has access to that data, a centrally mandated approach to data governance and data management in the wider sense also means that regulatory requests and requirements are easier to address, right? Like when you have that data management and governance structure in place in the organization, it’s easy to kind of like, address any needs that are coming from the regulators, it becomes really crucial in an organization at the size of what city is that everyone is clear what our data management policy dictates the standards that we put in place to support those requirements. And where we have controls embedded in the data lifecycle to ensure policy adherence across our federated footprint. Because what we don’t want to do is become a bottleneck by having a governance organizations, we want to make sure that people in the organization have access to the data access is given to the people that really need access to the data. You you correctly allude that look, the fact of regulatory pressure on data driven processes are even higher now. Which means that data leverage for decision making must be of the highest quality, right? Because those decisions that we’re making in real nearly real time, have to make sure that the data that we’re actually utilizing for analytics is of the best quality. And to address this reality. It is critical for FIS and organizations to invest both in infrastructure that can handle large amounts of data in safe and encrypted manner, right? Like, you want to make sure that the data is not being exposed in clear text, there are some encryption put on it as well. And then to top it all off, you need to make sure that you have the talent that has the ability to end the willingness to challenge every initiative around its data source as well, right like because you can’t just expect what has been given to you is actually correct. Like unless and until you understand and utilize data, you can take someone from one job family and move them over just to take a look at that data. This for us helps ensure the highest quality, quality of data that is being used in all our decision making. And with the large volumes of data automation is also a key right? As it’s simply impossible for corporations or larger FIS to manually monitor all transactions that might be violating regulatory requirements. So ultimately, it’s leveraging data is to really empower for us for better decision making a lot of the brands who need to succeed in the digital economy, they need to make sure that they need to keep in mind that the best practices around data management data policy is being put in place within their walls, so that they can have better insights for their customers and for their customers, while meeting the highest levels of the regulatory requirements. So it’s a complex topic, but I think you can throw the right data management policies and governance in place manage it to the highest level as well.

Whitney McDonald 13:49
Now before we close things out wondering if there’s any way that you can quantify or share some improved client experiences or monetary savings or time savings that you’ve seen, because of the best use of data.

Naveed Anwar 14:06
Look in the last year city, particularly city TDs reported about a 34% increase year over year in revenue, more than 12 billion in 2020 2020 22. Our TTS business, growing revenue to work by investing significantly to modernize a platform so we can better leverage here, right so we’re investing back and forth from a technology point of view. Our data driven insights have assisted driving actionable client insights for supplementing business development opportunities through cell service through tools as well. For example, leveraging data driven origination can activate possible incremental revenue and increase client confidence through customized solutions that we give them that utilizing data campaigns is an efficient method to proactive really identify sales leads and will, will will leads into revenue generation as well. data insights help drive action towards open items that the customers are giving us feedback on so that we can actually proactively work on fixing those issues and resolve them for a client executives so that they know that city is on their side, and it’s listening to them. And we’re acting on their feedback, because that’s very important for us. And then last but not least, look, there have been major advancements in AI, and machine learning in the past few months. More so, in financial institutions are constantly assessing new innovations and ideas. But when we work in a regulated environment, we have to be very careful on what technologies that we’re implementing with the right regulatory oversight in place. So that we can implement the right solutions for for our clients use our data analytics for a better customer experience. And it would behoove me if I wouldn’t speak on one particular topic. As we think about your your your podcasts are on automation, specifically around how we onboard customers within city, right. Whitney, I know that you’re especially interested in automation. But one area that we have been really, really looking and investing deeply is around the onboarding of our clients onto a CD platform be that from an account opening point of view be that from a product integration point of view, be that from the way that they actually operate and do things from a self service manner. In particular, we’ve looked to add value for our customers by creating opportunities where we’re looking at from an end to end client interaction point of view. And so we’re looking in automating many of these processes so that this can be done in a very timely manner, where the account opening along with the KYC pieces are done in a way that the client doesn’t feel that this is a very long and cumbersome process. So automation really helps us generate measurable metrics. At the end of the day, a city believes that we need to think more like a tech company versus a traditional bank. And that’s the approach that we’re using across city, particularly or in TTS, as we think about automation, in this crowded space of banking for a treasury, the bank that is going to really win who’s really have the client experience at the top of their priorities and imperatives. And that’s something that we live in breed on a daily basis that we obsess about client experience, we obsess about the success of our clients. And we have a very servant, mentality within city that we are here to do what we can to make our customers successful. So that’s one message that I would give out to your listeners as well. That city is a bank which is on your side, and client experience is the utmost importance to us.

Whitney McDonald 18:22
You’ve been listening to the buzz, a bank automation news podcast, please follow us on LinkedIn. And as a reminder, you can rate this podcast on your platform of choice. Thank you for your time and be sure to visit us at Bank automation news.com For more automation news,

Transcribed by https://otter.ai

Hello and welcome to The Buzz, a bank automation news podcast. My name is Whitney McDonald and I’m the deputy editor of bank automation news. Joining me today is Naveed Anwar, Managing Director and Global Head of digital for city treasury and trade solutions. He’s here to discuss how financial institutions can use AI, machine learning, and RPA to stay on top of data analytics to deliver results.Naveed Anwar 0:26
So Whitney, thank you for having me. So I’ll say that for many of our clients across different industry, the world is really changing and the pandemic accelerated the demand for E commerce, particularly around the offerings that they had, as well as the digital experiences that they wanted to give. And now customers expectations are, they want to be informed by the last great experience, no matter what the industry is, right? Like it’s no different in the treasury world and what it is in the consumer world. Over the last few years city, particularly TTS has invested significantly in data for our clients. Because in the world of increasing complexity, decision makers need data to drive decision making. And these are head of treasuries who want to be able to have data at their fingertips to be able to make a decision. For city this has enabled us to identify growth opportunities, as well as areas in which we need to be investing in having this data readily accessible for us to identify, forecast and manage our risk exposure. For example, in the recent events that have been taking place in the market, TTS was able to leverage real time account balance and data to migrate and mitigate any of our risk exposures, and make informed decisions to avoid current market instability impacting our balance sheet as well as looking for the best interest for our clients. And then a better example would be we leverage data to analyze transaction flows, to help stakeholders proactively manage any inherent risks around activities that involve our clients, including regulatory compliance as well. So this positions TTS, as a trusted advisor to many of our clients, and effectively really helped us manage the relationship with them. So I hope that gives you a bit of a picture of how we actually leveraged data within city.Whitney McDonald 2:35
Ya know, if we can talk a little bit deeper about the tech side of it and how you dive into the data analytics have it can you talk a little bit through how AI and machine learning and robotics even help you stay on top of this analysis? You know,Naveed Anwar 2:51
this, this this space is, you blink in a new technology is pushed out, so you got to be really careful on like, the investments that you do so as our commitment has been in the industry to really transform the technology that is being utilized at Citi. What drives our commitment to innovation and data analytics is the fact that in the current environment, clients must have the tools to better understand the needs of their own customers as well. So our platform technology is able to mine a vast body of data to help TTS understand client preferences, needs as well as predict them, ultimately, providing clients with relevant and competitive insights into today’s what we call digital economy. So we use innovative technology to ensure we are best understanding our own customers. For example, at Citi, we leverage NLP which is natural language processing models to capture client feedback, so we can better understand what our clients are looking for and what the clients sentiment is, our product and client support teams use these outputs to guide product development and really manage our roadmap so that we are actually capturing that feedback coming from the clients. Again, from our vantage point, we’re also seeing market leaders utilize RPAS, which is robotic process automation, to reduce expenses and turnaround times for range of processes that they have on their side. Customer service is a prime example simple robotics can manage many low value, touch points with customers by working through predefined processes 1000s of times at the same high standard, right? Like where we would have had manual people or manual intervention, we’re automating that piece. Aside from higher customer satisfaction, it’s the consistency in the quality of execution reduces the human error. Such is also reducing the risk and exposing sensitive data to human operators as well. Like so when you get machines involved, you can actually get through that much faster. Once RPA processes are embedded within operations, enhancing this process through predictive modeling is relatively straightforward. So there is an opportunity for having AI to learn from those models. And so this then has a bigger impact of what what we are actually producing. So though that is a few ways that how we’re using NLP rpa, AI and automation in the way that we actually look at analytics within the city.Whitney McDonald 5:35
Now with having access to all this data, and you just talk through the technology in place in order to understand all of this data, how can kind of bigger picture here financial institutions, leverages technology and their own data in order to see growth?

Naveed Anwar 5:52
Look, FIS are redefining the way that technologies used within their environment as well, right? Where if these institutions didn’t exist, innovation wouldn’t be coming to the forefront. And we’ve seen that for many years in the industry. In the current environment, it is critical that financial institutions also transform alongside with their customers, right like so they are at the cutting edge. And those who don’t do so will fail. And, and those who fail to understand that role that data plays in this transformation may not be able to operate at the speed that today’s leading companies, particularly the FinTech, industry, and E commerce firms expect. Prioritizing self transformation helps financial institutions keep pace with innovations in data management and analytics, making it easier to launch new offerings while remaining compliant with regulators for city data. And analytics play a major role in understanding how customers operate. This enables us to both identify solutions that may be applicable, which are now and ideate for new offerings for the future. An example on this one would be the way that we use internal data, like transaction data allows us an FYI, to drive numbers for crucial insight about their clients, right. So when we give that insights over to the businesses and clients, such a business cycle information concentration, will allow us to have less risks, and geopolitical exposure as well. So this is where we’re helping the FIS use the data to derive some insights. And then from an external data source, such as credit data, information about markets, we can go into demographics, competitors, and industries, at Enterprise enterprise level information, we can also create a rich vein of insights that can be redefined to both identify where opportunities might be there in there in the market that they can grow, or where their risk exposures might be, for NFS. level at an epidemic level. So that’s where we kind of like try to optimize by working with data with them.

Whitney McDonald 8:18
Now, if we could talk a little bit about the flip side here of missed opportunities, if advisors aren’t leveraging their data that they do have access to can we talk a little bit about what they would be missing out?

Naveed Anwar 8:33
Look, if if we did not expose the data to the FIS, they would not be able to make the predictive analytics for for their customers, they will not be able to kind of leverage that data to create the insights for a better user experience as well, as well as not keep up with like what’s happening in their competitive landscape as well. So I think it’s important for them to leverage this data, those are the kinds of opportunities that they miss. And sometimes, when you don’t have those insights in place, that can lead into you not being relevant. And none of these advisors want to be in that space. So they will always want to be relevant. So having that data as as an asset, and available to them on their fingertips is something I think these clients really want to have in and work with Citi to make sure that they have that readily accessible.

Whitney McDonald 9:36
Now speaking of data as an asset, wondering how you approach all this data without getting overwhelmed, and then if you could tie in a little bit, the heightened regulatory scrutiny around data and what you’re seeing in that space, Could you could you talk about how you’re approaching data in that front?

Naveed Anwar 9:55
Yeah, look, everyone in the industry wants to utilize the But really, what you have to start thinking about is what is the data lifecycle, right? Like from the moment that you actually have bursts of data to the moment that you really have to destruct or delete the data, right? There’s a whole lifecycle of data management that has to be in place. And particularly around when you talk about data management, data governance is a key aspect here. Avoiding the duplication of the data reporting and relating related processes can be difficult to enforce in large organizations where there are so many people. So you really have to put the checks and balances in place, through a producer and consumer model, like who can subscribe to the data who can actually build the data. And then who has access to that data, a centrally mandated approach to data governance and data management in the wider sense also means that regulatory requests and requirements are easier to address, right? Like when you have that data management and governance structure in place in the organization, it’s easy to kind of like, address any needs that are coming from the regulators, it becomes really crucial in an organization at the size of what city is that everyone is clear what our data management policy dictates the standards that we put in place to support those requirements. And where we have controls embedded in the data lifecycle to ensure policy adherence across our federated footprint. Because what we don’t want to do is become a bottleneck by having a governance organizations, we want to make sure that people in the organization have access to the data access is given to the people that really need access to the data. You you correctly allude that look, the fact of regulatory pressure on data driven processes are even higher now. Which means that data leverage for decision making must be of the highest quality, right? Because those decisions that we’re making in real nearly real time, have to make sure that the data that we’re actually utilizing for analytics is of the best quality. And to address this reality. It is critical for FIS and organizations to invest both in infrastructure that can handle large amounts of data in safe and encrypted manner, right? Like, you want to make sure that the data is not being exposed in clear text, there are some encryption put on it as well. And then to top it all off, you need to make sure that you have the talent that has the ability to end the willingness to challenge every initiative around its data source as well, right like because you can’t just expect what has been given to you is actually correct. Like unless and until you understand and utilize data, you can take someone from one job family and move them over just to take a look at that data. This for us helps ensure the highest quality, quality of data that is being used in all our decision making. And with the large volumes of data automation is also a key right? As it’s simply impossible for corporations or larger FIS to manually monitor all transactions that might be violating regulatory requirements. So ultimately, it’s leveraging data is to really empower for us for better decision making a lot of the brands who need to succeed in the digital economy, they need to make sure that they need to keep in mind that the best practices around data management data policy is being put in place within their walls, so that they can have better insights for their customers and for their customers, while meeting the highest levels of the regulatory requirements. So it’s a complex topic, but I think you can throw the right data management policies and governance in place manage it to the highest level as well.

Whitney McDonald 13:49
Now before we close things out wondering if there’s any way that you can quantify or share some improved client experiences or monetary savings or time savings that you’ve seen, because of the best use of data.

Naveed Anwar 14:06
Look in the last year city, particularly city TDs reported about a 34% increase year over year in revenue, more than 12 billion in 2020 2020 22. Our TTS business, growing revenue to work by investing significantly to modernize a platform so we can better leverage here, right so we’re investing back and forth from a technology point of view. Our data driven insights have assisted driving actionable client insights for supplementing business development opportunities through cell service through tools as well. For example, leveraging data driven origination can activate possible incremental revenue and increase client confidence through customized solutions that we give them that utilizing data campaigns is an efficient method to proactive really identify sales leads and will, will will leads into revenue generation as well. data insights help drive action towards open items that the customers are giving us feedback on so that we can actually proactively work on fixing those issues and resolve them for a client executives so that they know that city is on their side, and it’s listening to them. And we’re acting on their feedback, because that’s very important for us. And then last but not least, look, there have been major advancements in AI, and machine learning in the past few months. More so, in financial institutions are constantly assessing new innovations and ideas. But when we work in a regulated environment, we have to be very careful on what technologies that we’re implementing with the right regulatory oversight in place. So that we can implement the right solutions for for our clients use our data analytics for a better customer experience. And it would behoove me if I wouldn’t speak on one particular topic. As we think about your your your podcasts are on automation, specifically around how we onboard customers within city, right. Whitney, I know that you’re especially interested in automation. But one area that we have been really, really looking and investing deeply is around the onboarding of our clients onto a CD platform be that from an account opening point of view be that from a product integration point of view, be that from the way that they actually operate and do things from a self service manner. In particular, we’ve looked to add value for our customers by creating opportunities where we’re looking at from an end to end client interaction point of view. And so we’re looking in automating many of these processes so that this can be done in a very timely manner, where the account opening along with the KYC pieces are done in a way that the client doesn’t feel that this is a very long and cumbersome process. So automation really helps us generate measurable metrics. At the end of the day, a city believes that we need to think more like a tech company versus a traditional bank. And that’s the approach that we’re using across city, particularly or in TTS, as we think about automation, in this crowded space of banking for a treasury, the bank that is going to really win who’s really have the client experience at the top of their priorities and imperatives. And that’s something that we live in breed on a daily basis that we obsess about client experience, we obsess about the success of our clients. And we have a very servant, mentality within city that we are here to do what we can to make our customers successful. So that’s one message that I would give out to your listeners as well. That city is a bank which is on your side, and client experience is the utmost importance to us.

Whitney McDonald 18:22
You’ve been listening to the buzz, a bank automation news podcast, please follow us on LinkedIn. And as a reminder, you can rate this podcast on your platform of choice. Thank you for your time and be sure to visit us at Bank automation news.com For more automation news,

Transcribed by https://otter.ai