Technology has transformed transactional banking much faster than wealth management. Many wealth institutions still rely on combinations of legacy platforms, spreadsheets and specialised tools even as portfolios, regulation and client expectations become more complex.
Investbanq is addressing that gap with WealthOS, an AI-native, modular wealth operating system spanning onboarding, CRM and workflows, portfolio management, middle- and back-office processes, reporting and an intelligence layer built around artificial intelligence.
The Singapore-headquartered company is focused primarily on banks, asset managers and family offices. Rather than trying to replace established financial institutions, Investbanq aims to give them the infrastructure and AI capabilities to evolve into the next generation of WealthTech businesses.
Olzhas Zhiyenkul (aka Oz), CEO and Co-Founder of Investbanq, says that distinction is fundamental to the company’s strategy.
“We do not believe the opportunity is simply to build another consumer app and try to replace institutions that have spent decades earning their clients’ trust. The bigger opportunity is to give those institutions the technology, intelligence and operating infrastructure to become AI-native themselves.”
Its next phase centres on expanding its specialist AI copilots, developing its AI CIO orchestration layer, deepening institutional deployments and expanding enterprise distribution across Asia and MENA.
Key Takeaways
- Wealth technology remains fragmented: expensive legacy platforms sit at one end of the market and specialised point solutions at the other.
- Investbanq is primarily institutional: its core customers are banks, asset managers, wealth managers and family offices.
- Modularity is central: institutions can deploy individual capabilities before expanding into a broader platform rather than replacing every existing system at once.
- AI depends on the infrastructure beneath it: data, workflows, permissions and integrations must work before AI can create dependable institutional value.
- AI CIO is an orchestration layer, not simply a chatbot: it connects specialist copilots, institutional information, workflows and approved AI models through a governed conversational interface.
- The architecture is model-agnostic: commercial, open-weight and open-source models can be selected according to the use case and institutional requirements.
- Evolution matters more than disruption: Investbanq wants to help established institutions retain their trust and expertise while upgrading the technology through which they operate.
From Wealth Manager to Technology Builder
Before founding Investbanq, Zhiyenkul had already spent more than a decade across financial services, including insurance, banking, investment management, external asset management and financial technology.
His path into finance was preceded by a strong grounding in mathematics, an early influence that shaped the way he approaches complex systems and later contributed to his interest in technology and artificial intelligence.
Among his earlier ventures was Paladigm Capital, a wealth-management business he led for five years. There, Zhiyenkul experienced the limitations of wealth technology from the buyer’s and operator’s side. After exiting the business, he founded Investbanq in 2022, bringing together lessons accumulated across his broader career in financial services.
One problem kept recurring: financial institutions needed better technology, but the available choices often meant either large legacy platforms that were expensive and difficult to adapt, or narrower tools addressing only one part of the workflow.
“I spent years as a user and buyer of wealth-management technology before becoming a technology builder,” he says. “We genuinely tried to buy the systems we wanted. Too often the choice was a large legacy platform that was expensive and difficult to adapt, or a point solution that solved only one part of the problem.”
Payments, cards and retail banking have become highly digital. Wealth institutions face a more complicated operating environment: multiple custodians, sophisticated portfolios, private assets, suitability and compliance requirements, cross-border clients and highly sensitive information.
“Transactional banking moved very quickly. Wealth management did not move at the same pace,” Zhiyenkul says. “The problem was not that the industry needed another feature. The underlying operating infrastructure needed to change.”
That became the premise behind Investbanq.
Building a Modular Wealth Operating System
Investbanq’s WealthOS is designed to cover the broader institutional workflow while remaining modular.
Its capabilities include digital onboarding and KYC/AML workflows, CRM, portfolio and investment processes, reporting, workflow automation, client interfaces, market and investment information and specialist AI copilots.
An institution does not need to deploy everything at once.
“The architecture needs to be broad enough to cover the wealth-management chain, but modular enough for an institution to start where the business case is strongest,” Zhiyenkul explains. “A bank should not have to replace everything it already has simply to introduce one better capability.”
Deployment varies by client. A family office might use a cloud-based platform connected to several custodians. A bank may require private or on-premise deployment, deeper integration, data-residency controls and more sophisticated permissioning.
Investbanq began by working with family offices and independent wealth managers, where implementation cycles could be shorter, before moving progressively into larger financial institutions.
The company now works with dozens of financial institutions, including large regulated banks in Asia.
Software Before Intelligence
The rise of generative AI has made sophisticated models widely accessible. Zhiyenkul argues that it has also exposed a misconception: placing a language model on top of fragmented infrastructure does not make an organisation AI-native.
Financial information may still sit across portfolio systems, PDFs, custodian statements, data rooms and internal databases. Before an agent can work across that information, it has to be retrieved, structured, permissioned and connected to the relevant workflow.
“AI cannot compensate for broken plumbing,” he says. “You first need the data, systems and workflows to connect properly. Only then can an intelligence layer do useful work across the institution.”
\This is why Investbanq combines software infrastructure with AI rather than positioning itself simply as a conversational application.
Its specialist tools include Document or Parser Copilot, Advisor Copilot and Analyst & News Copilot. These can support work ranging from extracting data from unstructured documents to investment analysis, portfolio support and market intelligence.
From Copilots to an AI CIO
Investbanq’s broader ambition is to connect these capabilities through its AI CIO.
Zhiyenkul describes it not as another chatbot sitting beside the platform, but as the intelligence and orchestration layer running through it.
An adviser could request portfolio analysis, retrieve research, examine relevant client information or initiate an approved workflow through natural language. An authorised client could access appropriate portfolio information and reporting through the same underlying architecture.
The system is model-agnostic. Some applications can use commercial foundation models, while others may use open-weight or open-source alternatives. The model can therefore be selected according to capability, cost, latency, confidentiality and deployment requirements.
Investbanq’s proprietary value sits in the layer around those models: wealth-management domain logic, applications, workflows, integrations, orchestration, permissions, evaluations and controls.
“We are not trying to win by claiming one foundation model will be best at everything,” Zhiyenkul says. “The models will continue changing extremely quickly. Our job is to put the best available intelligence to work safely inside an institution’s real operating environment.”
Controlling Data, Models and Agents
That becomes particularly important in regulated financial institutions.
A useful AI agent may need access to client information, portfolio positions, research or operational systems, but it should not receive unrestricted access simply because the underlying model is capable of processing that information.
Investbanq’s AI CIO uses MCP-enabled orchestration to connect AI applications with approved tools and data sources. Permissions and institutional controls determine what information an agent can access, what actions it can perform and when human approval is required.
“For a regulated institution, confidentiality, authority and accountability cannot be afterthoughts,” Zhiyenkul says. “You need to know what an agent can see, what it is allowed to do, where the data sits and when a human has to remain in the loop.”
Private and on-premise deployment options also allow institutions to address different data-residency and confidentiality requirements.
Making the Economics Work
Model choice is also an economic question.
Using the largest frontier model for every task may be unnecessary at institutional scale. Routine extraction or classification can require far less computing power than complex investment analysis.
Zhiyenkul therefore expects financial institutions to use combinations of models rather than committing their entire AI architecture to a single provider.
“Open-weight and specialist models can be dramatically more economical for some tasks,” he says. “But cheap inference alone does not solve the problem. You still need the financial knowledge, workflow, orchestration and controls around the model.”
The broader objective is productivity rather than AI adoption for its own sake: reducing manual processing, increasing adviser capacity and allowing institutions to serve more clients without increasing operational complexity at the same rate.
Investbanq estimates that its combination of software and AI can reduce operational costs by up to 40% and increase scalability by two to three times.
The Next Phase
Product development remains Investbanq’s first priority.
The company plans to expand its agentic toolkit and deepen AI CIO so that more institutional workflows can be accessed through a common intelligence layer.
Commercial expansion is the other side of the equation.
Enterprise financial technology involves long sales and implementation cycles, local integrations and extensive procurement, security and compliance requirements. Investbanq is therefore expanding direct enterprise sales while developing relationships with systems integrators and value-added partners that already work with financial institutions in selected markets.
“A great product without distribution does not transform an industry,” Zhiyenkul says. “We have spent significant time building the technology. The next phase is making sure we can deploy it through the right partners and institutions in the markets where demand is strongest.”
Evolution, Not Disruption
Demographic change is adding further pressure to modernise.
A major intergenerational wealth transfer is under way, while younger investors bring different expectations around accessibility, digital experience, personalisation and communication.
Zhiyenkul expects this to challenge traditional service models but is sceptical of the assumption that trusted institutions will simply disappear.
“Younger clients will judge wealth firms differently. Brand and heritage will still matter, but they will not compensate indefinitely for a client experience that no longer works.”
For him, the objective is not disruption for its own sake.
“I do not want to disrupt traditional financial institutions simply because disruption sounds exciting,” he says. “These institutions hold people’s wealth and have built enormous amounts of trust, expertise and regulatory infrastructure. The more valuable opportunity is to help them evolve.”
That philosophy sits at the heart of Investbanq’s positioning.
Rather than asking whether AI will replace the wealth manager, Zhiyenkul focuses on what happens when established institutions combine their trust, expertise and client relationships with AI-native infrastructure.
“The winners, in my view, will be institutions that combine human judgment and trust with the productivity and intelligence AI can provide. We want Investbanq to be the operating system that helps make that possible.”
Getting Personal with Oz
Zhiyenkul’s career spans more than 15 years across insurance, banking, external asset management, investment and financial technology.
His early academic interests were rooted in mathematics before he moved into management and finance, studying Management at Bayes Business School. He later held roles with London-Almaty Insurance Company and Unicorn IFC before working as an EAM with Credit Suisse from 2013 to 2016.
He subsequently spent five years as CEO of Paladigm Capital and is also Founder of Tesla Capital. He holds the Certified Anti-Money Laundering Specialist credential and is a Forbes 30 Under 30 alumnus.
Zhiyenkul says the combination of mathematics, financial services and technology ultimately shaped the thinking behind Investbanq.
“Mathematics trained me to think in systems – to break complicated problems down and understand how the pieces connect. Finance then showed me how those systems operate in the real world. AI brought those two interests together in a very natural way.”
His experience operating wealth businesses provided the other half of the equation.
“Running wealth businesses showed me the problem from the inside,” he says. “Investbanq is really the culmination of those lessons – what worked, what did not, what clients needed and what we could never find in the market.
“With a wealth business, your impact is ultimately constrained by how many clients that business can serve. With infrastructure, if you build it well, you can enable many institutions to serve their clients better. That is what makes the opportunity exciting to me.”
