Monica Millares: [00:00:00] Hello Ara, how are you today?

Ara Abrahamyan: Hello Monica, I'm very well, thank you. How are

Monica Millares: you? I'm really good, thank you. Very looking forward to our conversation. Me too. Yeah. Thank you. Thank you for coming to the show because like just to add context for everyone, Ara and I met a few months ago in Amsterdam.

Like I was going to Money 2020. Then before Money 2020, there was another conference from the Banking50 and both of us. We're speakers in that conference and we just happened to sit next to each other, . And then he was giving this talk about ai and I was like, oh my God, that is like so interesting. . And at the, at that day, I I was like, oh, ARA can I take a little video of you talking?

And I did, but it doesn't look that good. So we never posted that video , because it's like impromptu not looking good. So instead we were like, let's jump into, proper podcast camera mode, and that's why we have [00:01:00] Ara today, so thank you, Ara.

Ara Abrahamyan: There was a signal from top so that we would happen to sit next to each other.

Exactly. Looking forward to it, Monica.

Monica Millares: Likewise. Okay, so to get started for context. This podcast is all about how can we create more purpose driven fintechs and have more impact as such. So that's a very important question. So in your opinion, how can we design and build fintechs that are more purposeful?

Ara Abrahamyan: I think the idea of financial technology and its purpose is built into the very nature of the small scale solution to the problems or Solutions that are helping people in various aspects of their life in various [00:02:00] situations by decomposing the whole idea of financial services or the whole idea of whatever, insurance, banking, business, and so on into much more affordable, much more accessible, much easier to use, much more convenient, commodity tools, and I think, making it more purposeful is effectively a part of the DNA.

Of a FinTech? Yes. By disentangling the financial services per se.

Monica Millares: Can you expand on what do you mean with the DNA by disentangling financial services?

Ara Abrahamyan: I think, look financial services, industry, banks, insurance companies, and so on and so forth are very large, are very complicated, are not very customer centric, not very friendly people friendly and so on and so forth.

[00:03:00] Normally. People tend to have distant, if not cold relationship to financial services industry and by cutting this complex infrastructure, financial regulated, heavily regulated infrastructure into smaller pieces and making them more understandable, more problem bound, more accessible, available easier to use, Helps achieving the, idea itself of what financial services, with a capital S is is supposed to be, right? And and I think that decomposition, which is The role of fintech by cutting the big, elephant into pieces is, part of their DNA, part of the idea [00:04:00] of actually fintech solution.

Yes. Otherwise, if you glue them together, you will become the old, same, big, complex bank.

Monica Millares: Exactly. And that's what we don't want to do. Hence, I do agree that Fintechs have a DNA that's different to banks, and probably both of us we've worked in big banks and Fintechs, so there's a comparison point that you're like yeah, they are different.

They are not better than the other. They are just different DNAs.

Ara Abrahamyan: Absolutely.

Monica Millares: Absolutely. Yes. And then just building exactly on what I said about working in both fintechs and banks, like you have a very interesting career, like you've worked across the big banks now just sit in the advisory board, both big banks fintechs, you also teach.

Can you tell us about your journey, your career journey, and then what led you to do advisory work at this point in [00:05:00] life?

Ara Abrahamyan: With pleasure. Yeah. So I'm an engineer. My background is an engineering background. I did whatever, 25 plus years ago, my PhD in, in computer science, apparently part of the artificial intelligence at that time, which was called decision support systems.

And and after back in Armenia, where I am originally from and then in year 2000, I moved to, Germany and started to work for a startup, funny enough, which was doing payments, EBP, electronic bill presentment and payment technology. After 9 11, the startup went bust and I had to look for a job and I ended up being in a bank, in a big bank, in the biggest bank here, Frankfurt and Deutsche Bank, where I spent 15, almost 15 years, yeah, 14 years of my career.

Which was [00:06:00] very successful from my perspective because I had a pleasure to meet extremely talented and clever people who taught me a lot. I had no clue about financial services, no clue about banking. So I was genuine, pure information technology profession. But I've been building then systems ID for banks for 15 years at Deutsche.

And then, when, I was responsible in my last job, there was, I was responsible for risk technology here and I moved to Austria to Vienna. And five years later, or three and a half, four years later, I became a member of the board responsible for the digital transformation of Esther group, biggest financial services provider in central and Eastern Europe, headquartered in Vienna.

And then six years later, I, returned back to Frankfurt and started to [00:07:00] do different things. Redirected my. Career from the corporate world to the fintech world, because I realized it was probably a midlife crisis as well involved in that there is a huge world outside the corporate world, the world full of innovation, the world full of, fast paced development world full of Great innovative entrepreneurs and so on and support.

And then I started to do my consulting business where I do consulting for startups. And that is the bit where I help, in the advisory function cognize and also another startup with profit but also do some consulting work to incumbent banks. Area bank is one of my clients.

And, I teach and teaching is a part of me that kind of [00:08:00] tells me that, okay, now you've been around for quite some time, you've seen quite a bit in the corporate world and now in the entrepreneurial world. How about trying to share your experiences with with younger generations? And I teach in executive education courses.

I teach leadership, I do some parts of the digital transformation curriculum and so on and so forth.

Monica Millares: Awesome. You know what? What's very exciting about this episode is exactly that mix of your career. That you got to a board level in a big bank, e. g. You understand like big banks financial services system and at the same time you are doing like Innovation and fintechs and working in one of those companies that it's AI based so we can talk about digital transformation from both angles With someone that understands both which is [00:09:00] awesome So before we get into this conversation as such, currently you are in the board of AmeriBank and you're also doing advisory work in Cognize.

One is a big bank. The other one is an AI startup, both in financial services. Can you tell us what's the role of purpose in these two companies? One is big, one is small, and what's purpose for each

Ara Abrahamyan: of them? AmeriBank is One of the leading banks in some aspects, the leading bank in Armenia, and, my

willingness or my intention and or my motivation. To help them is to make the most modern up to date financial services available to my home country, to the people in my country, [00:10:00] which is something they do very, well. They embarked on journey of digital transformation and and it fits very well my experience and skills from.

From my career to support them on that journey, whereas cognize is another crazy engineering part of my of my heart and my maybe my brain where were the most advanced technologies, right? Often connected technology, which is that the leading edge of research is being implemented and put into the purpose of helping organizations to become more efficient.

To become more accessible, to become more customer friendly, to become, yeah, helpful, useful, right? This gives me an opportunity to jump forth and back between my past and my future [00:11:00] and, transform information or effectively build bridges between these two very interesting, very valuable, but totally different worlds.

Yes.

Monica Millares: And I guess like I want to dig a little bit deeper into that about building bridges, but the thing that is the common theme is digital transformation, whether it's a big bank doing a project of digital transformation, or if it's a fintech, because we ourselves as fintech, sometimes we do not see ourselves as doing digital transformation.

But actually we are like, it is not like changing state to school, big bank to a digital thing, but it's, we need to disrupt customers behaviors, customers lives via a digital experience as well. And it takes. Transformation from different angles and same as a start to becomes a little bit more [00:12:00] mature. It has habits that we need to break to then go to the next level.

So there's an element of transformation as well in fintechs. So I want to expand on that. But first start with the big organizations. How do we breach this world between like big organization and innovative companies and deep tech while still being able to stay Okay. In the mind and with all the constraints and priorities and pressure from investors and regulators,

Ara Abrahamyan: and that's a very good question.

That's actually probably the million dollar question, right? I completely agree. Let's start with a simple part of it. Yes, are simple in quotes part of it. And the digital transformation in the context of fintech industry is a digital transformation off of the real world, if you will, of the customer or taking the reality with you on the journey of the digital [00:13:00] kind of product offering service, et cetera, et cetera, it's much less about actually changing the way the company works itself.

Whereas if you think about the digital transformation of an incumbent company, that is much more. Transformation that looks in words, right? It looks into the organization itself. It looks at the way it does things and so called how and it looks at what it does, right? What do they do in terms of the implementation of?

Modern technology, whether they move their applications into cloud, whether they implement DevSecOps as their implementation methodology, whether they employ AI, whether they do RPA and so on and so forth, right? So they take their processes and procedures and products and kill everything they know.

About the way these [00:14:00] problems, if you will, are solved and start from scratch. Yeah, first principle. Applying the most modern technology to the solution to create a solution of that problem. So that is why I see the digital transformation of a big financial services company, right? An incumbent company, not always, not necessarily financial services company.

By splitting into two pillars, what you do in terms of the technology itself and how you do it in terms of ways of working, collaboration, innovation, agility, and so on and so forth.

Monica Millares: I love that. And I want to dig a little bit deeper on the what? That is taking the most modern technology and applying it to your company.

What are the challenges that big banks or the incumbents face when they are trying to implement these new technologies?

Ara Abrahamyan: There are many. [00:15:00] Let me few, let me name a few. There is one that becomes increasingly apparent to me. And in the context of what I do and how I do in my kind of Mixed world, mixed reality is the fact that the digital transformation or any kind of transformation is a long process, right?

And this process is usually consisting of many, small steps that in its aggregate contribute or end up being the big shift from the old world to the new world. Is. Not in a good match with the corporate governance. Usually people who are responsible for this kind of transformational programs are board members who have short, reasonably short, three years, four years contracts, [00:16:00] which is not always the term of such a transformational program, right?

And management often is simply Reluctant or afraid of starting this kind of transformation because it's bearing huge amounts of risks and their terms to not give them the opportunity to address them and actually see the results or harvest the results of this transformation. That's why I think it would be fair to say that most of the incumbents in a financial services industry still struggling.

In actually making big and badly necessary steps towards the new digital reality. And that is why the fintech world is so aggressively biting up all the tastiest pieces of that kind of big service pipe. [00:17:00] Yes,

Monica Millares: but this is it's a challenge because exactly the fintechs are trying to get ahead.

While the banks have the customers and the banks have the processes and the banks have the. know how and the funds to do it. So how could banks overcome this challenge? Because probably we're not going to change the three year tenure of the board.

Ara Abrahamyan: I wish we could, but probably not here in this very podcast.

I think it takes leadership. It takes courage, and it takes, an understanding of decomposition of a big problem into smaller pieces, and it takes acceptance that partnering with Innovative smaller companies on resolving individual bits and pieces will bring you that kind of mutual benefit where you as a bank have [00:18:00] to trust and the proximity to the customer and the FinTech and new technology company has the speed, has the high tech, know how, has a product that is badly required on the market.

So For me, the key word here is a partnership. And, partnership is not procurement in a classical

Monica Millares: sense of a bank,

Ara Abrahamyan: because if you want to kill a startup, you can put the startup into a procurement process and they will go bankrupt. They will go bankrupt. Yeah. So, it's about actually, creating meaningful trust based partnership between the two worlds where the best of both can come into play.

Monica Millares: Awesome. And we are going to go into deeper into that topic. But first I want to go back [00:19:00] because at some point you talked about the what and the how, where the how it's all about ways of working and culture in general. So the more I do this work, I'm like yeah, the what is very important, but the how means humans.

And that's more challenging. So what are the biggest challenges that we face when it comes to culture in all this digital transformation process? If

Ara Abrahamyan: you.

There are many. One of the biggest ones is the lack of skills, of course, and the lack of the talent that is hardly excited to go and start for a large incumbent financial services organization when they full of creativity and [00:20:00] full of energy and so on and so forth.

So one of the biggest challenges in the transformation of financial services industry is In terms of the culture, it's the talent itself. You hardly get the people you need. But there are also some technical challenges as well. There is frequently outdated infrastructure, there is frequently outdated architecture, technical, technological architecture.

There's lack of data, which is one of the kind of key ingredients, key components of the digital transformation, especially of what we do. In the AI space. And the last kind of third important pillar of the challenges or important force that apply that imposes challenges on this is the regulation, the [00:21:00] aspects of security, safety, fairness.

bias and so on and so forth. Data protection. This is another huge component and block of impediments to the digital transformation that creates huge amount of ethics to the corporate officers.

Monica Millares: Yes. And I could add also to FinTechs, maybe not such a big headache, but it's something that as I've done all these episodes with people and talk to other people outside, when I ask, Hey, what is it that you would like to see change?

And everybody says like regulation it's like people see regulation in general, obviously generalizing as, Something that slows you down rather than something that enables you to go faster and innovate better. So we need to have the [00:22:00] balance.

Ara Abrahamyan: Yeah. Maybe if I can add one sentence to that my corporate life has been full of interaction with regulators, right?

I was responsible for risk technology, both input bank at and then at some stage at the beginning of my work at Airstep. Which means, that you regularly undergo a regulatory audit and you address findings and you have, and my experience is following, you have to consider them as a key stakeholder of your change process, you have to engage into open and honest conversation, you have to ask for help if you need help, because, My experience showed that if you base your collaboration with the regulator [00:23:00] on this kind of vectors or on this principles, if you were, then, it brings another level of kind of trust and collaboration, which is helpful for both regulators are keen for you to build systems that are making sure that you're stable safe, And so on and so forth.

And you want to make sure that the regulators understand the challenges and sometimes even help you to overcome the challenges to address their concerns and or to provide the best service to the market. So even there, a collaboration and communication is key.

Monica Millares: Yes. And that comes back to what you said at the beginning about trusted partnerships, including the regulator.

Exactly. Awesome. So as we move more to the other side [00:24:00] of your career, we're going to the startup side of the fintech side. You are working with an advisory role in an AI company. Can you tell us a little bit about Cognize and what they do? Before we go into the whole A. I. Conversation just to have a little bit of context.

Ara Abrahamyan: Sure, with pleasure. Cognize this is a very interesting company. It's an amazing company that does something which is reasonably simple on its surface, but hugely complex in its details. It extracts Information data from unstructured documents, right? It's a platform. It's a SAS company that provides tool to help people organizations, companies or clients and so on and so forth to [00:25:00] extract valuable information like reports, PNL report data.

Balance sheet data and so on and so forth from PDFs, pictures, photos and so on and so forth. Simple problem, very complex solution.

Monica Millares: Yes, that's what I was going to say because it seems that this is just, well to me, we're just at the beginning of the AI revolution, right? And this is just a very specific use case that just on that use case we can see the complexity.

Oh yeah, you extract data, it's not that, it's not that easy, no, it's not that easy. So if we go a little bit into high level and we say AI financial services, what do you think are going to be the biggest challenges in the next five years when it comes to adopting AI technologies?

Ara Abrahamyan: First is skills, [00:26:00] second is data, and third Is probably regulation.

Monica Millares: What can we do about it right now?

Ara Abrahamyan: Skills is very easy. Very easy in quotes. So I'm sorry. I'm being cheeky. You I think you have to acknowledge and understand that there are things that you can do as a as a company undergoing a digital transformation. There are things that you can do yourself and the things that you need to partner with.

If you look at the partnership landscape of the most advanced digital companies of these days, Google's, Amazon's and so on and so forth. They are partnering with hundreds and hundreds of the smaller companies to to get their services done. And so this you have to accept at a first place.

Second is the [00:27:00] probably the most challenging one, data, and this is the bit where we come to help, right? We help companies to recreate or restore the data they have lost on the way of by, by killing the data itself into making them PDFs and putting them into kind of endless archives where the data is effectively being lost.

And there is a lot, a huge amount of homework that organizations need to do in order to implement all the kind of data governance framework, data quality framework, and so on and so forth, so that's a big amount of, just a big amount of work that has to happen. Yes,

Monica Millares: because in my head I'm like, data quality just on its own.

Ara Abrahamyan: Yeah, it's a huge problem. It's a huge problem. And the third is, as I said, if you go back to the regulator, I think, Close, kind of collaboration, and it's in terms of if you look at in this [00:28:00] triangle of a kind of service provider, a startup company, innovative company, the client, let's say a big bank and more of an insurance company or whatever, and the regulator that kind of carefully observes that space.

It's in interest of all three, and it's Effectively to make sure that, the solution that is being built by one and implemented by the other is happily accepted by the third, by the independent observer.

So I see it as a

Monica Millares: collaboration. Think about yeah, I'm like, oh, that is a little bit more complex than just, like you as a fintech and the regulator, now it's you, the fintech the fintech, the regulator, and the third party.

Ara Abrahamyan: Yeah and the good thing is that I, think we just have to, we just have to accept that's the new kind of normal way of doing things and, try to make it work for [00:29:00] for the industry as a whole by.

Making sure that the collaboration on this triangular collaboration is seen as such.

Monica Millares: Yes, so you just said something very true that it's like this is the new normal. That's it. AI is not going away. And then this time of the year Just happens to be that it's like Q4. We're all getting ready to do our strategic planning for next year

When it comes to AI, let's say that we have our strategy and roadmaps for the 95 percent of the business. How do we go about thinking our AI strategy? Whether that is for next year or for the next five years

Ara Abrahamyan: I think probably the first statement I would make in that context is maybe [00:30:00] a bit outdated, but I'll still make it because it's important.

It's what you just said is the fact of acceptance that AI is here, right? Our discussions with our clients two years ago or one and a half years ago were totally different. We were starting from explaining what is AI and why do we need it and so on and so forth. Now, I think this is done, right?

Everybody is, okay, good. AI is here. It's reality. That's the first step. And the second is

accept the fact that this Either with you or without you is going to transform every single industry in one or the other way, and it's the job of the leadership is to prioritize the capability of actually implementing different types of basic machine learning or generative AI or image processing or text, large language mode, whatever, right?

You name it [00:31:00] in many, different kind of Spaces and subspaces of AI implementation project by prioritizing, putting resources onto that, and making sure that you keep an eye on it by exploring, assessing, implementing something, assessing, collecting the results, and starting from scratch, explore, implement or experiment assess the result, and so on and so forth.

Is not always fitting into the logic of a large corporate where you have a project, where you have a benefit cost benefit case and you do it because frequently you will find out that some things just don't work the way you expect it do not have that kind of ROI on your kind of investments and so on and so forth, but not being engaged, not [00:32:00] being involved in that game is not a solution.

Yeah, so you better start playing it now,

Monica Millares: so if I summarize what you said that it's yeah It makes a lot of sense. It's number one ensure that you start building your AI capabilities Whatever that looks like for your business, but it's like getting to the game. Now it's about building capabilities because we don't have that knowledge, that expertise, the process.

We don't have that. Most fintechs or banks don't have it because it's new. So one, it's build the capabilities and two. The process that you explained, it's what I call, use the experimentation approach. Correct. It may not work, it will work, but then you go with a hypothesis, you have a very specific thing that you're going to do.

You roll it out, then you step back, you learn, and then you see what happened, and then you make the next decision, and you do the next experiment. [00:33:00] Yes, exactly. Awesome, I like that. Capabilities plus experimentation.

Ara Abrahamyan: Listen, I will I think it may sound too simple, or even simplistic, but in reality it is the simple things that we struggle to accept and implement and we tend to overcomplicate things By making them dysfunctional.

And if we, just do this, okay, let's, if we translate it into real practical ways of doing it, what would I recommend my client? I say, okay, good. Hire 10 people, put one lead AI engineer on top of it and establish three partnership AI service providers in your industry and start it. Take 10 projects, prioritize top three of them, try them out.

Two will not work, one [00:34:00] will work, fine. Discard the two, put another two on the agenda, and so on and so forth. So it's, it is as simple as that.

Monica Millares: Practical, yes. Yes, because that's the way, right? To me. As we, let's say in the next one year, three years, five years, we start building all these capabilities.

And we start learning as an industry, if we fast forward 10 to 15 years from today, what do you think was the, in the future? What we, when we look back, what is the impact of AI in the industry when it comes to building more purpose driven fintechs?

Ara Abrahamyan: It's a very good question. I think we should probably start with a fair assessment of, actually what AI can do. And when we talk about most of the [00:35:00] tools that we categorize in the bigger big AI spaces, we to, we talk about the tools that bring efficiency and bring speed that bring sp scale and in some cases that bring, fairness into the process.

By removing some of the judgmental issues out of it by automating them or by digitally transforming them. Now, what does it mean in terms of the implementation of purpose driven technology? So by making processes efficient, you make them more accessible to a broader public. If something is much more cost efficient, products are cost efficient.

If they're cheap and so on and so forth. Obviously larger community can make use of them, can benefit from them. And the second thing, by eliminating [00:36:00] our flaws our biases, by actually acknowledging them. Identifying and eliminating them from the data that we put into the training of our models by actually the backtesting and validation of our models by making sure that we comply to the regulatory demands of standards of ethical standards and so on and so forth of our model.

We will make them better for the, world, right? So you start, if you use tools that are assessing candidates, right? We know this classical case of a high bias based on the training information that is implied or applied by the to the existing batch of CVS will permanently choose a specific subset of people which is correlated to the historical best of them, but by knowing it, acknowledging it and actually explicitly eliminating that and many other biases out of it, [00:37:00] you can make a fair, transparent and useful, efficient process.

Monica Millares: So we need to become very good at finding those biases, such that we train the machine to take to, not be biased, basically, but it's, interesting, right? Because a bias is a bias.

Ara Abrahamyan: Yes, but in reality, I think the problem is not finding them. It's the problem is actually accepting, right? So we, as humans, right?

The catalog of our biases is known, right? If you list them there's confirmation bias this bias, it's on and so forth. So they, there is a reasonably comprehensive list of them. It's just, we are not good. We. Humans are not good at avoiding them. There are ways of handling them, right?

There are ways of [00:38:00] handling them. And, as a manager or as a leader, you have responsibility in order to make sure that in your decision making process, you have all the support that you need to avoid them from the technological perspective. If you had a catalog, you just need to make sure that data is representative.

That your biases are being incorporated into your, or have been taken care of, and, then it just becomes the matter of routine, right? You validate your data set against whatever set of tests, if you will, is, and then you are good to go. You will not be perfect from the scratch, of course, that's normal, right?

As you go, you will identify the more and more sophisticated way of identifying and eliminating biases [00:39:00] the same way as just implementation of the product.

Monica Millares: Yeah, and I think I'm seeing a theme here that it's like, of course, we have. The machine AI. But then we have the human side that it's exactly the biases as such.

And you use this word in the conference in Amsterdam. And it's also part of cognizes proposition. So cognize and you have talked about hybrid intelligence. And I think you were just getting into that. Can you explain what is hybrid intelligence?

Ara Abrahamyan: With great pleasure. We actually write the concept of hybrid intelligence on the big, kind of slogan of what and how we do.

Recognize and, it's about it is in a simple way. It's about a combination of strength [00:40:00] and hedging weaknesses off each other. So if you are a good leader, right? And you are good in numbers and or are you are good in actually doing the motivational speak, but you are not good in actually checking the details of the project.

And you will normally hire a person into your team who is very good at that detail to each to hedge your weaknesses to offset your weakness. Thank you. So in reality, the A. I. And I. Q. Or each you are very good. How should I say partners? Yeah, and they are very complimentary night and the things that are done well by humans, are not so easy for technology.

There's actually theory that the more of X. Paradox That explains that actually robots and machines are much better and doing some things that are complex and difficult for [00:41:00] people, analyzing huge amounts of data, doing big calculations, so on and so forth. And on the other side, they struggle with doing something simple as a picking a cup and having a drink, right?

Because there's so much technology many, kinds of technology involved in it. So by accepting that fact, we think that the hybrid intelligence, so effectively a partnership between AI and a person, will make that person much more efficient and will help the person to get rid of the routine. Waste of time, waste of effort and make much more productive and efficient.

Monica Millares: I like that. You know what, like one of the things that I love about the podcast, somebody asked me the other day what's the most surprising or what did you love about the podcast? And I always say, Oh, the unexpected things. So in this specific podcast, I'm like, all we talked about [00:42:00] has been like not all, but like the theme of partnerships is so present.

It's just just partnerships, but with different parts of the ecosystem as such. So as we start to wrap up the episode, just following on that theme that it's like partnerships, but also culture. And we have the incumbents, we have the established fintechs. And now we have the rise of the AI startups.

How,

can we all work best together? Because in some cases, like AI startup, big bank incumbent, somehow the definition of each of them, it's a little bit incompatible. So what's your take on how do we make? This partnership a successful one.

Ara Abrahamyan: My instinct [00:43:00] tells me I'm also, as I said, I was said in the beginning, I'm also teaching and one of the master classes I do is about the trust is about the trust has enabler of digital transformation.

And I talk about the trust in the, in the relation of working environment in the relationship between leaders and stakeholders. And their team members in relation in the relationship between different parts of the organization's trust between, different departments, divisions and so on and so forth.

And I accept and understand that it's a multidimensional problem. There's millions of puzzle pieces that all contribute and add up to trust. I think this logic works very well, with, the way I think the collaboration between the big corporate world and and innovative [00:44:00] startup world is going to be successful.

It's, first the acknowledgement that they're different, that they're diverse, right? And acknowledgement as they are. Yeah, and, by accepting this, having the courage to put the trust as a key component of partnership between between both parties, right? I learned over my career in the most painful way that the contract is the worst possible place you need to look up for the solution.

With your relationship between you and your provider, the best way are the people that are sitting next to you around the table. So the partnership alignment on the common goals, understanding the purpose of what it is that we are going to [00:45:00] achieve together, helps us go big, Chunk of the way that we need to get into order to achieve and there are multiple ways of partnership, right?

Some companies have the client service provider relationship. Some are investors. Some are just I don't know It was advisory relationship and so on and so forth. But the core idea is Are we in agreement about the common? Mutually beneficial goals of what we're trying to achieve and if you spent 90 percent of the time on alignment on that very specific everything else works out automatically.

So, I think in the age of AI and artificial intelligence, a human aspect of the relationship between two parties and, and understanding of what it is that we are [00:46:00] jointly addressing is the key to the solution of the problem.

Monica Millares: I love that. I have this saying that the future of Fintech is human, and that's exactly what it refers to.

It's it's not about AI, it's not about technology. It's about being more human across all the touchpoints in the ecosystem. Not just product, but like you said, partnerships. It needs to be the human element. It's trust, it comes back to human. It is. Yeah. Yeah, I'm not sure. I'm not sure.

Ara Abrahamyan: I'm not sure if that's if that was the conclusion that an AI topic, a podcast as an AI topic would come to.

But it is. Yes.

Monica Millares: Yeah. Yeah. It's been. It's been genuinely a very interesting conversation. And probably They will need a part two. I could keep like talking and asking and asking and asking [00:47:00] more questions. But just to keep like this part one, as we wrap up where can we find more about you and Cognize?

Ara Abrahamyan: Oh, we are. Obviously our web. We're starting a series of podcasts actually ourselves. The announcement will come soon. Probably by the time this podcast, is published, we will be already live ourselves. We frequently go on to conferences, talk about ourselves, present our product. Our webpage and this is there with totally the shipped articles and so on and so forth.

So we're pushing all the channels to make sure that our ideas are heard and seen. Good

Monica Millares: show across everywhere and I'll include those everywheres in the show notes. Thank you very much. So as, thank you. As a [00:48:00] very last question. If you were to change one thing in fintech that can improve the lives of customers, staff and shareholders, what could you change?

Ara Abrahamyan: That's a tricky question. I think the one thing that needs change or is necessary is to understand that they're different. It's the understanding of their difference or acknowledgement of their difference. Fintech is not a bank. Or an insurance company. A bank is not a fintech and an insurance company.

And by understanding, it's like with humans, by accepting who you are,

you've actually achieved everything. I think we, as stakeholders, and in many different roles of what, what financial what fintechs we do, we need to understand that they're different [00:49:00] and treat them as such. And I think that is the key to Mutually beneficial relationship, partnership, success,

Monica Millares: understanding that we're different. And then, like you said earlier, looking at our strengths, looking at our complementary weaknesses slash how we can work best together. Awesome. It's been an absolute pleasure having you in the show, Ara. Thank you so much.

Ara Abrahamyan: Thank you. The pleasure was mine. I really enjoyed it.

Thank you very much, Monica. Thanks for having me. Thank

Monica Millares: you. Thank you. Thank you, everyone. See you next week. Ciao.