IBS Intelligence Global FinTech Interviews
Go one-on-one with the innovators, disruptors, leaders, and decision-makers driving change in FinTech and financial services. IBS Intelligence delivers exclusive global interviews that uncover strategies, challenges, and the ideas powering the next wave of financial technology.
IBS Intelligence Global FinTech Interviews
EP1031: From Automation to AI-Led Banking Decisions
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Mohamed Moniem, Vice President – ME Region, nCino, speaks to Puja Sharma about how AI is moving banking from automation to predictive, insight-driven decision-making, covering its impact on lending, customer engagement, risk and banking infrastructure, while highlighting the need for trust and responsible AI adoption.
I'm Pooja Sharma of IBS Intelligence, and you're listening to the IBS IVUs podcast. With me is Mohammed Abdul Monem, Vice President, Middle East region of Encino. Mohammed manages the GTM growth and expansion strategies of Encino in the Middle East region. Welcome to the podcast, Mohammed.
SPEAKER_01Thank you, Pooja, and my pleasure to be with you in this podcast.
SPEAKER_00Mohammed, banks have spent years digitalizing processes. How is Encino helping financial institutions move from automation to predictive, insight-driven decision making?
SPEAKER_01Just before we go on, uh Encino, we are mainly focusing on credit portfolio management. So we are automating and digitizing the credit workflow process end-to-end, from the onboarding till the loan is booked, and even later on for the portfolio management. So this is what we do in Encino. We started from the cloud business, so we moved to we are cloud banking solutions, then we move from cloud to uh to automation, full automation process, then intelligence, predictive intelligence. And now we are talking about Agentic AI and digital partners. This is also uh one of the hot topics right now. Automation means that we are removing some certain steps from the lending process. Okay. And this manual steps, it's in front of us. So we are seeing this and we need to minimize uh or to add value into this by removing this manual tasks. However, predictive analytics or predictive intelligence or AI, they are telling us what are the next steps to take, even before we ask. Okay, so this is a mind shift in in the concept here of automation or digitization in general. Within Incino, we are focusing on adding value to the lending value chain, improving the decision-taking process through or using AI technologies here, AI capabilities and functionalities. All of our solutions, all of our software modules are AI ready, are AI enabled in order to give some more value, some more advantages to our customers here. So you can think about double workforce. The the human capital, which is the the bankers who are working in the bank, and also some parallel AI agents. So in the background, the AI agents are doing the complex, the complexity of tasks or the complex tasks. However, the the human beings or the bankers are focusing on relationships, are focusing on more value, more value tasks or and also cross-selling and and upselling with their clients. So this is what we are doing in Encino. We are mainly focusing on the complete lifecycle of the lending process from automation, predictive analytics, and also agentic AI.
SPEAKER_00What are the most impactful AI use cases Encino is seeing today across lending, customer engagement, and risk management?
SPEAKER_01We are not uh bringing or giving our customers classical or standard AI tools. We are giving we are giving our customers use cases that can fit into their operations, can add value to their operations. And as you know, the lending process consists of many processes, many tasks in sequence. It starts from documents exchange with the customers. And this is one of our main advantages here that we have very good use case based on AI capabilities. The credit team usually they receive documents from the customers and they need to start analyzing this. One of the documents, for example, is uh uh financial statements. So, usually credit analysis, we always do uh analysis of these financial statements in order to check the healthy status of the of the customer, build the covenants around this financial financial data, financial history. Uh, and this is a very lengthy process and always takes a long time. Okay, so this is one of our use cases here. So we have what's called locate and file capability that can extract data out of those financial statements with 99%, start building the covenant management around this extracted data and even roots the the documents and files into the next the next steps. So now we are eliminating hours of processing time from the credit analyst team in processing those uh financial financial statements. So this is one example, another example uh related to customer engagements today. Every bank is striving to deliver different different services to their clients. So here speed speed matters, speed matters a lot. So they need to respond to the client, to resp to respond to the potential customers. So whenever there is a request coming on the digital channels, digital banking channels, they need to react, they need to respond. So, for example, if I'm a customer and I'm requesting, I'm requesting to get some more information about one certain product, the credit team or the banker's team can easily generate a loan code or a proposal that includes the payment schedules, the CD rates, everything related to this loan in seconds, collect all of this information, put it in a very nice template, very nice response, and send it back to the customer. So now I am creating a good engagement with our customers or even with our potential customers. And this also reflect on reflect on a high level of customer satisfactions. Onboarding. So part of the lending process that I need also to be sure about compliance checks, about the the complete documents related to the onboarding. So within in scene also, we we already automated the onboarding process, starting from small clients till the large uh multi-complex entities with all the requirements, regulatory requirements, compliance checks. All of this are automated together and built together using AI technologies, AI capabilities, and also till we reach to the product origination. So we are completing the cycle from origination and onboarding till the product originations. One last point you're asking me about risk here. Very good that the customers or banks are giving credit to people or to businesses in order to enhance the lifestyle and enhance the financial inclusion in every country. But the good thing also, I need to monitor this. I need to monitor the health of my portfolio in the market today. This is one of our strong use cases within Encino, that we have what's called continuous credit monitoring that will give the bank some early warning messages about this use case using rich data from whatever data we are collecting within the system, uh, with explainable AI technologies, with explainable AI capabilities here. So this will give the financial institution or the banks a live view, live view of the health of the portfolio in the market today. And this this use case also will send some proactive messages, some early warning signals about any relationship, about any contract, about any any deal before it becomes a problem, not after. So using such type of use cases, we are we are giving a lot of good value to our customers, we are giving a lot of efficiencies, we are giving a lot of good insights, good risk mitigation in order to get better decision uh actions and better decision activities.
SPEAKER_00As banks accelerate AI adoption, how can they balance innovation with the need for trust, transparency, and regulatory compliance, Mohammed?
SPEAKER_01Let's agree on something, and this is our philosophy in Cino. Trust in AI, this is a design choice. We are we are building our solutions and our capabilities with this in mind. So it is design choice from the beginning, not something that we add on at the end. We want to be very transparent, we want to be we want our customers and our customers' customers to trust in our AI. Okay, so explainability is non-negotiable. So we need everything to be proven, everything to be exceptionable in a very nice way. And by the way, it's a very common, common question coming from regulators, coming from credit committee, even coming from borrowers. How did how did you reach to this decision, to this credit decision? So we need to be able, so we need to give our customers the ability to answer these questions. Today, every regulator, when they go to any bank, they always ask this question show me or prove to me how did you reach this decision. So this is what we do in in CN. So all of our AI capabilities or AI use cases are explainable. So we can give you a lot of good details about why we take this decision based on what? What are the documents that we use to take this decision? So everything is in a very transparent way, in a very trustable way, and this will create a lot of confidence and trust into our capabilities and into the data that the bank is giving either to the regulator or to the to the borrowers. This is our philosophy. And we always believe that financial institutions need to need AI to transform the way that they are operating today in order to be more agile, in order to be more responsive to the market, in order to be more competitive to the market. But financial institutions need to implement the right AI strategies, the right AI use cases that will fit their needs. It is not just a generic AI concept, no, it should be something related to their operations, related to to give some more value, some more trust that will create trust, will create confidence, it will create loyalty in the lending operations.
SPEAKER_00Many financial institutions still struggle with fragmented data and legacy infrastructure today. What foundations are critical for unlocking the full value of AI here?
SPEAKER_01AI without trusted data or without clean data foundation is meaningless. So banks today who are rushing towards implementing AI strategies without fixing or correcting or building the right data uh structure, they will reach to impressive demos, but uh disappointing results. So data is the key here. So the question is not what are is not what are the AI tools that I need to buy, but whether my data is ready to the AI framework or not. So this is a very basic uh question here. And within Incinome, we have our own analytics tools that we can pull together data from different fragmented data sources, structure this data, create the standard data sets for our AI, our AI engines, in order to be sure that the AI will run on top of clean and correct data. In addition to this, also we have also within NCINU, we have our integration gateway platform that can integrate with many of the standard and known core banking platforms and risk management solutions and financial services uh applications today, in order also to integrate and get data from those different systems in in order to build the data needed for AI. Do not forget that in CNU in the business for the last 15 years, we are trusted by more than 2,700 global financial institutions on the global level. So we are building our AI models on top of this. We have a history of 15 years of industry data. We are getting a lot of experience working with different scale of customers across different regions. So we got to know more, we got the experience, and we got to know more about the operation of every bank, whether it is tier one bank or tier four banks. So we got to learn and get the experience about this. And accordingly, we started to build and train our models AI monos based on this. So today, any customer who is working with Incinu can easily get some operational excellence, can do the benchmarks about their lending operations, about their loan cycle time, about bottlenecks even in their lending process, through benchmarking their current operation today versus other peers or other financial institutions who are doing similar business today. So, this is the concept within Incino. Our AI is built on rich data based on 15 years of experience, based on experience with working with different organizations across the globe. This is what we always promise our customers that you will get a lot of strong AI capabilities, well-trained models, and except that can create trust and loyalty into into our AI, using strong and rich data and correct data in place.
SPEAKER_00Looking ahead over the next three to five years, how do you see Encino and AI more broadly reshaping banking decision making?
SPEAKER_01We keep investing in our products and we keep come up with new innovative ideas and innovative solutions. And now we are more and more going into the AI capabilities. As I mentioned, all of our solutions are AI enabled and AI ready. So we are moving now from AI as a tool to AI as a colleague. We are building this, we are coming up with this new mindset. So we are giving AI a job title. So we are coming to the market now with five different AI digital partners, or you can call them digital colleagues in different domains. So if I name them quickly, executive digital partner, this is mainly for the C level in order to take strategic decisions, strategic that they will give them also strategic intelligence to take the right decision in the right time. This is mainly for the executive and sea levels. We have analyst partners for credit and risk team in order to do the proper analysis of any credit case. Again, this is a digital partner that will help them. We have service digital partners, mainly for the relationship managers, in order to collect the data, to get some more insights about every single deal, every single relationship with potential clients that will help them also for cross-selling and upselling. Processor digital partner, this is mainly for the uh loan officers who are dealing with different documents, compliance checks, so all of these regulatory requirements. One last client client digital partner to this will engage or will be plugged in into the digital channels of the financial institutions that will mainly interacting with their clients when they are logging into their websites. So all of this concept of digital partners, we are now adding values and we keep investing into this. So the future into this agentic AI. So we are keep investing into this thanks to the technology. Technology is helping us to create more and more, and this is what we are also come to into the near future here, that we will keep investing into this direction. We'll keep coming up with new digital partners with more capabilities that will help all people within the organization to be more agile, more productive, more efficiencies, will create a lot of good efficiencies to our customers. So this is the future, and we are we are focusing into this. Banks who will lead in the next from three to five years from today, they are building their AI strategies and AI foundations and strong data-rich foundation from today. So this is our advice to the customers. If you need to lead in the future or the near future, you need to start investing from today. And this is what in Sinu can can help uh financial institutions can help, can help uh potential customers in order to grow and be more competitive in in the banking domain.
SPEAKER_00Mohammed, any closing lines you have or if you want to conclude this uh interaction.
SPEAKER_01Thanks for your time. As a closing remark, we are one of the leaders in the credit portfolio management. And to stay the leader, to stay among the leaders, you need to keep investing into the products. And AI is one of our future uh strategies. We are not providing standard AI tools, we are providing mainly strong use cases. We are delivering use very comprehensive and explainable AI use cases that will help banks and financial institutions in building their AI strategy to deploy in confidence, to deploy and trust what they are going to take out of these uh solutions without the need of having a big army of AI team within those organizations. And this will create a lot of values in the sense of efficiencies, productivities, growing into the market, be competitive, be agile, be speedy. So, all of this this is what we are offering, and we will keep inventing and keep doing our innovation into this in order to add value to the lending value chain in the market.
SPEAKER_00Mohammed Abdel Munem, vice president of Middle East for Encino.