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October 07, 2021

BTOES in Financial Services Live - SPEAKER SPOTLIGHT: Harnessing Digital AI-Infused Operational Excellence for Financial Services

Courtesy of Enhance International Group's Jim de Vries, Fusemachine's Carol (Cee) Bunevich and DataSpeckle Scientific's Dr. Bülent Uyaniker, below is a transcript of his speaking session on 'Harnessing Digital AI-Infused Operational Excellence for Financial Services' to Build a Thriving Enterprise that took place at the Business Transformation & Operational Excellence Summit in Financial Services Live.

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Session Information:

Harnessing Digital AI-Infused Operational Excellence for Financial Services

Driving Competitive Differentiation

The business transformation journey of operational excellence can be challenging, and digital tools and hype technologies can be overwhelming. Focusing on Financial Services, we walk through our roadmap to deploy a self-sustaining Resilient Digital Ecosystem™ based on people, process and data.  We will demonstrate how Enterprise Information Management, Process Mining, RPA and Hyperautomation are the building blocks to establish a self-sustaining AI-Infused ecosystem.  Start small and think big! 

We look forward to sharing case studies and look forward to your questions on how to infuse AI into your organization.

Agenda

  1. Financial Services Operational Excellence
  2. Enabling Digital Transformation through Process Mining to RPA to Hyperautomation
  3. Infusing Practical AI Applications to Financial Services

Session Transcript:

Either about our next speakers, we have a trio of cross industry experts on culture, business, digital transformation, excellence, innovation, in financial services.

So I'd like to welcome doctor Bill on your neck, or Jim ..., and C ..., who are going to be here with us for our next session, Quick bios on each one of this terrific leaders, doctor.

They'll let you go in, and Eric is a physicist, and the founder of Data Spackle Scientific in British Columbia. He focused on complex imaging and data science problems. He's a Senior AI Researcher and Director of Academic Affairs at Fuse Machine, and the founder of Data Speckle Scientific.

He has led technical groups and scientists, stablish Research Labs, Thoughts Physics, which he is my major as well, medical imaging, computer science and cosmology courses at the Max Planck Institute, Herzberg Institute, and the University of British Columbia. He is joined by ..., who joined Fuse Machines in 2015 after a long career in Finance as VP of partnerships.

Fuse Machine is an AI consulting, Talent and Education company, starving the financial services industry, with a social mission to democratize artificial intelligence.

C is currently expanding fuser machines, AI educational business in the Americas, and the she commute between New York City and Cape Cod and I understand she is like in Nantucket, Massachusetts today. So, we all should be jealous about where she has asked. Jim De Vries, our great Jim ... has been a longtime speaker, our keynote speaker, and host of this event in the past, has served as resilient enterprise, executive leader, an advisor for companies, from startups to Fortune 50. He oversees the global identification prioritization and execution of high value business improvements and innovations for the company's business partners, and customers in multiple markets that he serves.

He has held operational excellence, sales and supply chain and sales leadership positions in leading global companies, in the chemicals industry, with air products and chemicals in energy with GA Energy security with Tyco ADT, and supply chain with C H Robinson.

EIGThroughout his career, he has developed and refined Resilient Enterprise Eco systems to accelerate digital transformation, innovation, leadership development, strategy, execution, and value creation globally.

He believes that people make a difference, and it is in harnessing people's talent that companies can become resilient. Ladies and gentlemen, what a pleasure to have you here with us. Thank you so much for sharing your gifts, your expertise, and your experiences with our global community today.

Thank you, Josie, and the vetos team for providing us the time.

And to speak with you today about our passion about harnessing a digital AI infused operational excellence to drive your competitive differentiation.

Good afternoon, good evening, And good morning, the, the presentation, over the last two days, I've been very insightful providing many different sorts of perspectives on how the support and enable financial systems to move to that next operational performance level Time for doctor Boaler occur, and she bought a batch, and I have been working together, to provide, thought leadership, to applying, AI solutions, that we are excited to share with you.

Our experience on how to build that resilient, digital, AI, infuse, resilient, a digital ecosystem.

We will be sharing the building blocks, with case studies and examples, and how you can infuse AI in into your organization.

As we all know, in doing one thing, in doing this one thing to complete the AI project, it is quite another thing to integrate an AI ecosystem across your business and integrate it into your exo into your execution cadence. We look forward to your questions at the end of the webinar.

And so let's get started here.

Our agenda is three fold.

We're going to start with, you know, defining what financial services, operational actions in an AI world could look like.

Why do we need it?

What does it encompass, and where has there been success in the different financial service areas?

Then number two, we'll get into enabling the digital transformation journey. Will give you some of the building blocks, then Boulogne and C will provide an overview and practical AI approaches to support financial services with Roadmaps and Examples.

So let's get started here. So I just saw this the other day.

Just a real quick note here: we're not able to see your presentation just yet, so please make sure that you're showing the presentation on the Sharing tab, sorry, Perfect, we see it now. Thank you.

Thank you.

So, so getting started here on the financial services area, this is something I saw on in LinkedIn post by McKinsey, their dialog, ...

perspectives on re-imagining Operations for Growth and the workforce Malnourishment.

There was a hyper acceleration, of course, in this first fourth Industrial Revolution, which we've been talking about, probably for the last 10 years.

But more and more, folks are our understanding that digital solutions, along with their entire value chain, includes production networks, end to end value chains and support functions, including HR, finance, and IT.

In turn, ways of working are, are also evolving. So, we're changing, we're more remote.

But we still need those fundamentals of that end to end value chain.

We have lots of methodologies out there, agile, Lean six Sigma, at all different ways, too, drive cross functional teams and rapid iterative processes. The critical link lies in company's ability to upskill talent at the same scale and pace as technology.

So look at this as a foundation.

four, what's going on in the marketplace? But, you know, when we look at this, is this anything new that we've been talking about?

Btog CTAWe can go back to 1985 with the value chains, We're still talking about the same fundamentals that Porter did back in 1985.

And and that article could just be looked at as a complete replication of what Porter espoused back then, and, you know, Porter, identify the primary end to end value chain processes in inbound, outbound, logistics, operations, marketing sales services in those core competency areas, that McKinsey Article reference.

So all this is going back to the fundamentals a lot like Rios had mentioned before, we can't forget our fundamentals and, you know, we have to provide the background to understand where we've been able to gain the biggest bang for your buck and applying the resilient digital ecosystem.

Where are your bottlenecks and challenges?

Aligning your efforts to impact versus effort perspective will guide you to identify and prioritize the areas that are most it need to be addressed.

So, another survey from McKinsey, and what we can see here from this survey, is that, yeah, we're not able to see your slides again, I'm not sure why, but it stopped showing your screen again.

OK, thank you. Thank you.

The most proven areas are from McKinsey Report, where they've been doing digital uh, and Transformation work has been in the in the general areas of accounting, cash disbursement, revenue management, financial controlling and reporting.

The research indicated that 42% of the finance activities are fully automated all using currently demonstrated technologies and another 19% ARR mostly automated ...

so as we can see, there's a lot of movement here, and if you speak to most companies, they are focusing their efforts of digitization in these, in these spaces, so this will continue.

Next, we're gonna get into enabling the digital transformation and process mining to RPA, to hyper automation roadmap.

So this is a roadmap that we had put together a number of years ago the team Colin including Boulogne and see.

And what we found is, is it more of a maturity level model?

And, we call it the three pillars, Pillars of the resilient digital ecosystem.

And the first pillar, we have data, the second pillar, visibility, and the third artificial intelligence.

And you can look at it from maturity from bottom, left, top, right.

So building the capabilities from data mining, enterprise information management, through digital twins in the center, throw machine learning on the right.

It's it's really the whole, holistic ecosystem of building these capabilities together. That's going to get you where you want to be.

If you're trying to do just a computer vision project, but you haven't done process mining, or some of these basic tools on the left, you're not going to be able to sustain that change.

So, this is our backdrop as we move forward.

And the, the process that McKinsey brought up a number of years ago was, you know, we have Lean six Sigma process design in business process outsourcing and they said, well, we need digital, digitalization, hyper automation, advanced data analytics.

But what they left off was, again, this need for believable data with it and enterprise information management, and data cleansing and mining, and we'll get into those in just a few minutes.

So, how do you know that your data is, it can be used as a competitive asset?

one of the biggest challenges we have is, we're still using, making decisions, based on our experience, not on actual data.

So, the, this overview provides an overview, or this, on the Y axis, you have your competitive advantage.

On the X axis, you have analytical maturity, and it is going through each of these phases maturity that you're able to drive your you're competitive asset using data.

So most of us may be using data as A, in the descriptive analytics, where, where you're looking at data that's already captured, or are we using data that's more predictive, prescriptive, and cognitive, And that a cognitive as a learning system?

So I'd ask you to think about, when you're making decisions day-to-day across your enterprise, from finance, all the way to operations.

What type of data are you collecting? And how are you using that data to make your decisions?

And one of the fundamental areas is what Tom Davenport, another guy in the 19 nineties put out is this enterprise Information framework.

And even Rios, at the last presentation was bringing a lot of things up, and that were actually brought up by Davenport back in the in the nineties, And that is, know, it starts with risk management. Do you have trustworthy data? And that's coming together in spades. Today.

Then it's creating value out of that data.

And then it's making able to make decisions from that data.

So where are you on this, on this journey?

Are you able to build data mart's, to have a Digital Twin?

Are you using data as data? Are you using it for information? And then, how do you use that data from a knowledge perspective?

So, these are all things that are fundamentals that we would want you to think about as you look at the different areas within the financial services area.

On applying, so we from source to play to order to cash, which are the the bread and butter of financial services, to recording, doing your quarterly reports, treasuring, tax, all these are fantastic areas, two, uh, to apply these fundamentals, and on the top, we have all the different tools that can be used.

So it's really a question of when do you use a tool, or how you use a tool for each of these areas, and this is what we'll be covering in the next few slides.

So, if you're in finance, you're all familiar with Envoy to cache one of the core areas from services rendered through cache apps.

Uh, we're We're not espousing through using digitization that we throw away.

All the Lean six Sigma type of tools. We still need to map our processes, understand our inputs and outputs.

So, no, don't forget that. We will you'll still want to be able to do that. But there's some really cool tools to help you map the processes that we were not able to do in the past.

So probably the best tool out there right now, just to get an understanding of what's going on in your systems, is process mining, van Vanderwall.

It is the father of, of process mining, dutchmen, like myself, And he came up with this in around 2000, and it's fantastic process.

So both Davenport and Vanderwall, or are the guys that built some of these fundamental building blocks that we are just now starting to realize?

It's very important to understand these underlying processes, but it's also important to understand that the, whatever you enter in the process, mining each of these given stages, like create a promise order, price, or change goods, receipts, invoice booking, those are activities within your system.

28And so process mining is fantastic and needed, but it's only as good as the data in the system.

If there's other activities that are going on outside the, the system, you will need additional types of technology or process mapping, maybe even?

No, you still need to do the process map manually, and then only this only captures the inputs and outputs that the system captures.

You may not be capturing critical areas or critical axes in your process, that are preventing your process not to perform as it was originally intended, so the next area is hyper automation, which is made up of robotics process automation, machine learning, intelligent business process systems, and artificial intelligence.

So all of these areas are combined in this new term that was came out from our friends at Gartner.

So everybody keeps on renaming things to gather brownie points, I think.

So RPA, of course, is good for data entry.

In web based legacy apps and a knowledge worker completing high value tasks, AI can be brought in for data, collection, and intelligent extraction.

Data validation, notifications, and human.

Human approval, and knowledge worker. Again, knowledge workers completing highly valued tasks, of course, AI.

Then, intelligent business process management systems can help out in the data validation, in extending the process to the next level.

So, this is a, this is a an example that we have on, uh, driving, just data entry into account creation for our client.

And, you can see that, know, you can use the bots for the data application, the data entry into the legacy system, OCR for appraisal reporting, and account creation for the legacy system.

Then, for AI, the smart plots can be used to categorize small e-mails using natural language processing, automate data entry, from images via optical character recognition, computer vision.

Then, furthermore, ... has integrated predictive fraud analysis into their hyper automation platform.

So, this is the next, the next frontier.

And the nice thing about these types of onboarding for a targeted error, you can go in, and 2 to 4 weeks generate significant value by using these type of tools.

So, once you process mine, you know where you're going to focus in your organization, then, you know, this type of a case study can be done in a very short, upon a time.

So, there, they're monitoring over 2500 invoices in this, in this payment process, invoice, pavement for manufacturing, firm and, and they got a 90% reduction and total turnaround time, thousand staff hours saved per month and 100% reduction in manual errors.

So, a very significant impact. And they did this in just two weeks of on the ground focusing with that company.

So, with that, I'm going to turn it over to see, and she's going to bring us through the next area. See?

Sure, waiting for my, A picture to come on. Wait a SEC.

That's weird.

Can you see me, Jim?

OK, so we're going to talk a little bit about practical financial applications.

See, you are not able to see right now. You need to toggle your camera on the interface, Let me try it again.

OK, there you go, now we can see you, thank you.

So, Jim, why don't we turn to the next slide? We're going to talk about Ader, great.

So Ater is a scoping framework that we created at Fuse machines.

And this is how we speak to prospects and clients about identifying if the project they want to do is a good project for AI.

A stands for Algorithm Feasibility.

Um, when you work with a data scientists, he or she can help you right away, figure out, based on your problem, which algorithms are going to work for your challenge, in which ones are a waste of time. So, it's important to work with people who really understand algorithms, and don't waste a lot of time.

I stands for impact.

If you're going to take on an AI challenge, which will take you time and will, you will spend some money, you want to make sure that you have tremendous impact.

So, you really want to make sure that your return on investment is worth it.

D stands for Data.

Data is the most important part of the journey, and it's probably accounts for 80% of your success in any AI project.

So, we'll talk more about the data later.

But, I just want to re-iterate, data is the key to success and preparing your data.

It's something that we can all be working on in our institutions and making sure that data is, as I say, revered and people, um.

Properly organize their data, so it can be used, or AI challenges, and R stands for recurrence in our ader framework. Recurrence means.

We all know what it means, it says a recurring issue because you want something where you're collecting data constantly because you're going to be feeding your model.

The new data and the model's going to get better and better.

And that's one of the main differences between AI and other forms of analysis.

You're constantly improving as you feed the model new data.

Like, it's going to now share with us how AI can be used in financial services.

Lot, please join.

Thank you very much see, um, so, I'm going to provide some perspective from the AI.

And just try to, you have some examples of what kind of algorithms we use, what kind of branches that AI is tackling with for the financial services. The whole idea here is obviously extracting information and converting that information into usable insight. That's the whole idea behind using AI.

As the data sources and amount of data increases, it is becoming very difficult to use old and existing statistical methods to do that task.

So, we turn into AI on the right-hand side here. So, we do see two columns on the right-hand side. You see the branches that we are routinely using AI applications. Those are just not a complete list, but those are machine learning, deep learning, natural language, processing, and computer vision, as core pillars of AI, so to say. And among them, obviously, there are some topics that are intertwined with all these topics, basically, speech, natural, language, understanding, and natural language generation. And at the same time, expert systems, which is being an old system, but all of them, together, we can call as the umbrella of AI.

Screenshot (4)These approaches, and these branches, basically correspond to all these algorithms, or the applications that we see on the left hand side. And all of these groupings of regression, classification and predictive analytics. We can basically summarize under three groups.

What we do is, we try to understand the data and extract information so nuts, we usually have a bunch of data we would like to group together. Those are the algorithms that we simply summarize under classification group. So, basically, what we tried to do is we place the objects into predefined groups. And this is just, at the same time, called pattern recognition together. So, pattern recognition, and classification together is one of the most important things that we do see in, in financial applications, as well as in general AI applications, where these groups, we do see applications, like fraud detection, management, and prevention, and risk management. All these kinds of problems can be addressed using classification algorithms.

The next topic usually is the regression. Regression means that you have some input variables, and you do try to figure out the association between your input variables and the output variables that you're going to be getting. So those type of algorithms, or irrelevant of which method you're using called regression, and those regression analysis can be used for price prediction and algorithmic trading, and many other applications on the left-hand side. So we do see the associations of these algorithms with the corresponding branches of AI, regression, and classification. We do see a very much associated with machine learning, in general. Obviously, this does not mean that we cannot use regression and classification algorithms in computer vision.

The whole idea here is there are so many different sources of information that are coming to us. And we're trying to gather information, collect them, transform them, an extra information, and make them visible, if possible.

We do see here, is that the information coming from tax, that can be translated, that can be clustered, grouped, and used in regression analysis when the data is new in numeric nature.

Sometimes data comes from speech, and we need to really convert that into the understanding, the natural language understanding. So we need to extract information from speech, convert that into text, and go again for regression or classification.

Sometimes the information is embedded, images or video, and we need to use computer vision algorithms to extract that information into meaningful data and translate that into tags or numerical values, and, and process that period.

We do see a lot of applications that includes invoice, conversion, data automation, and process automation under the move of machine vision, and image recognition, object localization, sufficient explanation, and grouping of.

In the next slide, we're going to be talking a little bit about the details of the applications that we have built, Just to give you a taste of what kind of problems can be addressed using these automation, Metes and artificial intelligence methods.

Here is one example that we have just generated. This is an automated invoice processing system. The whole idea is, there are so many different sources of invoice. They're all formatted differently. And our goal is to extract that information from unstructured data, so to say, and try to figure out which field is where. So this is a combination of text mining, natural, language processing, and computer vision, at the same time altogether.

And here, the idea is to get that information from that embedded document, and convert that into meaningful numbers so that they can be processed.

We have built an algorithm for this one. And the collection of all these algorithms that is bringing natural language processing, computer vision, and text mining together. It will be call it as fuse extract. Which simply extracts all that information from all the sources and converts that into the data that can be proved process for the business purposes.

The next one is actually a little bit different. In the next example, we do see another example of computer vision. But at the same time, deep learning algorithms can be used together.

Most of the time, like in the example of an invoice, we do have an invoice into, in the past, and we do know which fields we would like to extract.

And the invoice is usually one or several pages, but suppose that you have billions of documents, and billions of pages. And you would like to extract some information, but you don't know where the, where the information is.

In this particular case, there are financial tables embedded in published yearly and through hundreds of these pages, and it's almost impossible to go and extract that information manually.

But at the same time, for the algorithm, it's very difficult to figure out where that information is.

That information usually is given in the cover page for the company name and people that are interested in the company and some computer related information. But, at the same time, given in those tables, those are documenting all these values, financial information and grass.

So, there, it comes actually, deep learning algorithms because you don't know where they are new terrain algorithm.

This is a computer vision deep learning algorithm that automatically browsers to billions of pages and figures out, what does the table, what does the graph, and what kind of information can be found, on which page there afterwards, we can return back to extracting that information, like we did in the invoice case, and extract that information.

Here, we just show one example, Use Case, which is just the Altman Z score, which is usually use, I'm not a financial person, but which is usually used for the risk assessment. How healthy A, company, where you would like to invest for this company, whether you would like to buy that company or whether you would like to work with that company.

That information can be quite easily extracted from deep learning algorithms that are extracting financial tables, putting all that information together, and making some calculations based on the expenses, the value of the company, And just calculating this is a very simple calculation for the financial people, obviously. But getting that information, putting that information into the formula that you usually use is the main challenge that actually AI is helping.

EIGSo, we have done this for this use case and in the next slide, we do see some examples of alpha ... Z score extracted from the the the documents.

Basically.

There are several applications that we can use, artificial intelligence or machine learning algorithms in order to gain insight or businesses, gather financial information or automate. Any process that we do not want to do ourselves.

Here, we have a small list not going to be reading all of them, but it's quite obvious that payment matters is optimizing invoice processing. Document automation is the key for for, for the payment.

Processing applications, the second one is the supplier onboarding. Obviously, so, many things can be done manually.

But, automated ways of screening, examining text details, credit scores, and all. And Finding all these groups associations between these parameters can be done using machine learning.

And AI algorithms, procurement is another area that we usually see the the automation, because many of these things are usually being done manually.

But processing those unstructured data, using AI and ML techniques, would expedite things, make things a lot faster, and a lot secure. Same thing applies for the session of the auditing process and gathering information, and obviously, providing at the same time, the security that comes with it.

The other area that we usually see is the cash flow monitoring to monthly quarterly cash flows. Collecting that data from different sources and putting all of them together is a difficult task. And at the same time, ensuring that this process is secure is just another task. Putting all of them together, AI, and machine learning algorithms, can make these things, or fessor, what secure.

And they can actually warn the, the human processors in this case, if there are any issues, if there are any problems that they need to tackle.

And, obviously, the expense management on the whole. We didn't even go for the AI chatbots and machine learning algorithms that discover patterns from all of them. We did not even mentioned them, but on the whole if we're going to be summarizing this slide in one centers, digitization is actually the essence of all AI applications. We need to have that analog information that is on paper, that is on speech, or sometimes video, that are embedded in some situation in different media, convert them in digital, and processing will form. Then, we just go to AI algorithms to extract information.

Here, I just hand over to Jim. Thank you very much for this opportunity.

Thanks. Go on and use a. I think that wraps us up. I want to thank everyone.

We're listening to us today, and we look forward to some questions coming in.

Fantastic! Fantastic presentation. Thank you so much, doctor Beal on Jim and Carol, far for the insights on Digital AI infused Operational Excellence.

You know, in the last couple of days, we have discussed the, the role of AI in the, in business transformation, operational excellence, certainly focused on financial service, and specifically, we had a AI expert from Dell who, doctor Bill Wong based in Toronto Trader, who talked about extensively about implementation of AI systems for value creation.

And I love the charge that doctor Bulent had on the, on the analytical approach is regression, and so on and so forth. And then, the real, deep learning approaches that, that happened with neural networks and then, for true AI.

Because the term AI is being overhyped, oversized misuse. Yeah, yes. Analysis.

And they talked about using artificial intelligence like Kyle, this is multivariate regressions, is not artificial intelligence.

So, thank you for are setting the record straight and then talking about this next level. So, you, you provided some really interesting use cases for artificial intelligence here. And, this market, this applications are evolving so rapidly. one of the themes that has a margin of questions has to do about what is reality right now, for a value creation of artificial intelligence. The data that, any B, what's evolving in time, is not the tech knowledge, but the use cases are evolving that you see evolve in financial services.

Poland, you want to take that, or?

Go ahead, please. Yeah, you go ahead, you go first.

Well, um, AI technologies and problems that we're trying to solve with AI, goes hand in hand with the development and complexity of the algorithms.

Some problems we were not able to solve using AI or anything statistical in the past, because of the lack of computing power.

So, now, we have the computing power, and we can tackle with the issues that we could not do before. Using these kind of algorithms. Need algorithms are not new by any means. AI is just a very old field. It's just it has the past proximately 50 years, even more. But now, we have the power, and we have the means of collecting data. I think collection of data is just one of the key aspects of the applications and use cases that we can you can do with AI.

28Previously, it was not thinkable to get deep learning algorithms, extracting information from video scenes.

It was impossible to think extracting, which browsing through billions of pages and getting all these financial information.

It was impossible to get all these information form, the Web, just web scraping and getting information about companies, your competitors. This was not all possible.

Now, we do have the data. So actually, presence of data is the first thing. The second thing is the power to process that existing data, those or reverse the evolution ising, the way that we're seeing the financial problems. If we don't have the tools, we can't even think how to approach the problem that we want to solve that, is my take on that.

Just to build on that, where pool onset is, today, we can know, 5, 10 years ago, bringing in multiple data sources for an overall picture profile.

What's going on, on the floor on our call center floor, or on a invoicing floor, or a call center?

That was very, very difficult, but today, we can bring all that data together into a data mart.

We can, we can.

And then, we can apply these AI algorithms because of this greater computer power to solve, and optimize things that people were talking about. But, they were having a hard time saying, now, we can do this, but we can not only do it.

No, Over 6 or 8 months.

Now, we can do it over 2 or 3 weeks, because of that, because of the systems that are available today.

I would like to add, we're using AI in money transfer, today, we're using it in identification, for security purposes of any.

any transfer. And this is done in seconds, not minutes, or even nanoseconds. In other words, it's instantaneous.

We couldn't have dreamed of doing this 10 years ago because it was too expensive, so it made no sense, and that's one of the things that is great about the fact that computing power has come down in cost.

It allows us to do all of these tasks.

We've been discussing at a cost that makes sense for people, right? Your cost has to fit the problem. Otherwise, it doesn't make sense to use it.

And, things such as non fungible tokens were not really thinkable 10 years ago today. We do use AI even just to own a piece of art.

Isn't that? Fantastic.

Robbery.

Correct, yeah, we have a couple of minutes here, and I want to make sure I addressed the number one question for our audience, and the related to this topic.

And that has to do with the fact that, you know you have to follow the field.

You know you have Google DeepMind, then now has solve the 50 year old problem with protein folding with, with a very robust AI algorithm. and it has made it available now to universities. I mean, that's, that's kind of the holy grail for biology now.

And, uh, and then, and then the other side, we have several organizations, including several organizations, who are watching us live, or will be watching us on video later on, where they, they don't even have basic data analytics, figure it out in their organization. And. And they're trying to, you know, to make sense out of all of this.

So there's such a diverse range of much maturity when it comes to data analytics. And then advanced technologies.

For someone who is trying to get it, started in the right foot, or someone who is hitting the reset button there, organizations, and saying, hey, we want to do this, right.

What is what are some of the basic things to get started, right, that people should look at? Maybe in terms of structure, I don't know, governance, is it about having a certain place technology platform? What are what is the key advice you'd have for someone who's getting started in their organization to get it right for value?

Well, let me just start with you need to have a chief data Officer.

You have to value data, and you'll have to elevate it. And I would suggest that that person be in the C suite.

In a large organization, if it isn't, at that level, you're not going to get the co-operation of everybody participating in the challenge.

So to me, that's number one, Jim, Watson, hashtag two.

Yeah, it's, it's, as you said, the leadership is number one, and then it's creating some kind of a task force, around that cross functional, in particular, areas, generally, internal areas, in the company, and in, and get it right. For those internal areas, like finance and Envoys to cash is a perfect area. That's where we started. We started in the procure to pay that invoice to catchwords lots of transactions.

And in start automating A lot of people don't even understand that.

We don't even attend to all the actions that we would want to in those areas.

We just don't it doesn't have the roi to do that with the manpower and now we don't have enough manpower. So it's a perfect area to get started.

To me, like in any scientific project, problem solving is the same in all areas.

I think the first thing is, you need to have data, and you need to have a very well defined problem.

If you can define your problem, well, that brings you the answers, and to do that, actually, the best thing to start with is to consult someone who can tell you what kind of data you need to collect.

That data is appropriate for your purpose, and just really trying to help you isolate your problem.

You should have someone like ... on staff, or very often.

Now, what I was saying when we talked about our eight are framework, 80% of the challenge is the data, and you don't want to waste a lot of time, so you need people who really understand what's good data, what's useless data, how to tag the data. So, you definitely need to someone who is a data expert.

So, that's the way to begin.

Screenshot (4)What a masterclass, and thank you so much for sharing this, this terrific insights, doctor ... of Jam and Carol. We're all better off from this session. Thank you for your generosity and taking the time to share your expertise with our global audience, terrific insights, and a lot of things for, for our audience to consider on their journey of excellence and innovation. Thank you so much for being part of our journey today.

Thank you.

Thank you very much.

Aye.

Ladies and gentlemen, there we have it a tremendous trio of cross industry leaders on the field of artificial intelligence and applications to financial services, and what, what, a gift, to have them with us, to wrap up, day three of the Beetle's Financial Services Live conference.

I am very grateful for everyone's engagements throughout this three days, for those of you who are going to be watching us later on, on video. I am also grateful for you to take the time, to, to learn, and, and, and, and, and share this content that, that such valuable content that was discussed with this global exports in the, in the past three days.

I want to thank our sponsors. None of this would have happened without C prime and sigma of you being behind us and allowing us to provide this quality of broadcast around the world and no cost. Thank you. See, prime. Thank you, ... for your industry leadership and and allowing us all, all of us to learn, to share and create this future together.

Now, big thank you also to Brian Raffle, our conference director, who does a masterful job of organizing all the spaces, pieces with global participants and making it a seamless experience for all of us. And to our CEO, the CEO of ... Digital VJ by Josh, for having the vision and executing on that vision of creating environments where great people and great ideas can connect.

So I say goodbye for now for all of you. The we have still the LinkedIn post on open. If you have questions, commentary, feel free to go there. You can look up on there. My name's Josie Theories on LinkedIn and look for beetles financial services live boasting. You're gonna see what participants are saying. You're gonna see the comments that are happening, and the, and the insights there are being shared there, including some questions that people may have that get answered there directly by our speakers. You also have links to every one of the speakers, and the conference, so that you can connect with that speaker directly. And ask questions directly to that speaker, if you choose to do so. So for now, thank you so much for a date for spending your time with us, and for collaborating and engaging in the conference. And whenever you are in the world, Have a great rest of your day and a great rest of your week.

Bye, Bye for now.

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About the Author

Jim de Vries-Oct-07-2021-01-24-18-46-PMJim de Vries,
Managing Partner,
Enhance International Group.

 

 

Jim de Vries is a skilled thought leader with more than 30 years of experience helping clients achieve their desired outcomes through his ability to facilitate teams and drive improvement. His experience encompasses financial, commercial, CRM, services, IT, call centers, security, transportation, automotive, power systems, oil and gas, nuclear energy, research and development, government, and electronics industries.

pillar%20page%20line%201

About the Author

Carol (Cee) Bunevich-1Carol (Cee) Bunevich,
VP Partnerships,
Fusemachines.

I am passionate about sharing my 20 years of sales and marketing experience with clients to help them drive growth, increase revenue and raise capital. I help companies identify how Artificial Intelligence can improve their business.

Throughout my sales management and leadership positions across global companies and start-up organizations, I have achieved a consistent record of top sales rankings. Whether creating innovative business development strategies, adapting to the changing sales landscape or building trusting customer relationships, I am able to increase revenue. Furthermore, I enjoy shaping company culture by training, coaching, and leading diverse sales teams who meet client needs and surpass goals.

Currently, I work with Fusemachines; helping them create and execute sales & partnership strategies. We are growing rapidly and targeting to increase our run rate to 500K monthly. I am expanding Fusemachines AI Education business in the Americas. We provide executive education, continuing education at colleges|universities and assist public and private high schools.

In addition to my consulting work, I helped launch the Enterprise Sales Forum and grew it from 1 group to 15 groups. Through this forum, I facilitate group meetings and secure speakers to educate members on effective sales strategies.

Specialties include sales management, coaching, revenue growth, consulting, culture, competitive positioning, new market penetration, event planning, consultative sales, sales negotiation, client support, big data, and artificial intelligence: Machine Learning, Deep Learning, Natural Language Processing & Computer Vision.

pillar%20page%20line%201

About the Author

Dr. Bülent Uyaniker-1Dr. Bülent Uyaniker,
Founder, AI Scientist,
DataSpeckle Scientific.

Experienced physicist trained in Data Science, signal processing, Algorithm development, magnetic fields, polarization, electromagnetics, medical imaging, medical image segmentation, satellite geodesy, machine vision and machine learning, complex measuring devices, receivers, radio antennae, interferometers and detectors.

Skilled in every aspect of Algorithm and Software development, signal processing, statistical analysis and handling large data sets and encryption. Big data applications with drone based remote sensing thermal cameras. Knowledgeable on IT management and operating systems. Managed projects, led research and technical groups. Strong skills in building effective teams.

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