Jeremy Ung, Chief Technology Officer at Blackline chats about the importance of embedded finance and the future of fintech in this GlobalFintechSeries interview:
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Hi Jeremy, take us through your fintech journey so far and your time as Blackline’s CTO.
Hi, Paroma. My career has always been about building scalable, data-driven platforms that help organizations tame complexity and make better-informed decisions. I’ve spent my career at the intersection of cloud, AI, and finance, leading large-scale transformations at AWS and Microsoft. Now, as CTO at BlackLine, I’m working on solving problems around AI, financial data integration, automation, and real-time insights. The financial world is changing at a breakneck pace, and the capacity to unify systems, break down silos, and deliver actionable intelligence is more vital than ever.
What are some of the most exciting fintech trends from the global fintech landscape that’s grabbed your attention of late and why?
There are several trends that particularly interest me. In finance, AI is moving beyond automation into agentic experiences that enable true augmentation—allowing finance teams to shift from manual work to strategic decision-making. With predictive analytics, AI-powered anomaly detection and automated reconciliations, finance operations are getting smarter and more efficient.
Embedded finance is a paradigm shift that allows businesses to natively embed financial services into their operation, whether through payments, lending, or real-time reporting. Additionally, there is a continued drive toward interoperability—a growing number of institutions are adopting API-first architectures to dismantle data silos and facilitate a more connected, real-time financial world.
I also see a growing demand for real-time finance activities. Decision-makers have abandoned sole reliance on historical information; they now require real-time information in order to effectively manage cash flow, minimize risk, and optimize working capital.
Despite access to several unified fintech platforms that can enable CFO data and decisions, why do modern teams still struggle to connect different financial data points and systems for better insights and overviews?
The biggest challenge is data fragmentation. Many organizations still rely on legacy ERP systems, spreadsheets, and disconnected tools that weren’t designed to work together. Even with APIs and integration platforms, there are still issues around inconsistent data formats, governance challenges, and a lack of standardization.
Another factor is that finance teams often don’t have dedicated engineering resources to optimize integrations. That can lead to manual workarounds, duplicate data entry, and reconciliation headaches. Ultimately, fintech solutions are only as effective as the underlying data they work with. Without a unified approach to data management, even the best tools will struggle to deliver accurate, real-time insights.
To meet this challenge, modern fintech platforms are embedding low-code and no-code features that enable finance teams to automate workflows, integrate disparate systems, and process financial data more effectively, with less dependence on IT or engineering resources. By lowering technical hurdles, these technologies enable finance professionals to be more self-sufficient in managing data connectivity and financial processes, leading to more seamless decision-making and improved financial outcomes
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How can CFOs and CTOs in these cases work more collaboratively to fill these gaps?
It starts with a shift in mindset. Today, most organizations view IT and finance as separate functions, when they could achieve significant value from seeing financial data as a shared business asset. CFOs and CTOs must agree on a common data strategy—one that is centered on integration, governance, and automation.
Much of this relies on selecting systems that are designed to be interoperable. Too often, companies invest in technology that solves one specific problem but creates new integration issues. Instead, the focus must be on API-first architecture and platforms that allow for seamless data exchange across the organization and consistent data architecture.
Another key element is automation. Manual repetitive work—whether reconciliations, reporting, or compliance checks—should be automated where possible. That not only makes it more efficient but also allows finance teams to perform more value-add work.
As I mentioned before, cross-functional collaboration is key. Finance and engineering teams brought together early in the decision-making process ensure technology investments are business-driven, rather than an afterthought.
What negatively impacts seamless integrations when teams deploy and adopt new fintech systems?
There are a few frequent roadblocks. For many, it’s incompatibility with their legacy systems that I mentioned earlier—most financial platforms weren’t designed for real-time, API-driven connectivity, so integrations are more complex.
Another issue is inconsistencies in the data. If different systems contain financial information in different formats, it is difficult to consolidate all of that into a single source of truth. Poor data governance can also be a major issue, which can lead to duplicate records, reconciliation problems, and compliance risk.
Security and compliance are elements that companies must prioritize, but it is not at all simple. Financial data is highly regulated, and any new integration must meet strict security requirements, which can slow down implementation.
Lastly, resistance to change is real. Finance teams have very established processes, and new technology—especially if the benefits are not readily apparent—can be met with suspicion. That’s why user adoption and training must be a top priority with any fintech implementation.
What tips would you share with finance teams evaluating new fintech stacks for adoption in 2025?
Always start with the end goal in mind. Commonly, companies evaluate technology based on features rather than business outcomes. Before looking at solutions, define what you’re trying to achieve—whether it’s improving cash flow visibility, reducing manual reconciliation, or strengthening compliance.
Second, test integration capabilities. Whatever fintech solution you adopt must have robust APIs and pre-built connectors so that integration overhead remains minimal. If a platform makes it difficult to access data or requires heavy customization, that’s a cause for concern.
We must not forget one of the most important aspects of new technology: the user. Even the most advanced technology won’t deliver value if teams aren’t utilizing it effectively. Choose solutions that are simple to use, minimize manual effort, and have clear ROI.
And finally, be iterative. Instead of a big-bang release, start with high-impact scenarios, measure results, and build from there. This allows teams to learn and refine their approach without overwhelming the organization.
A few thoughts on where fintech is headed with the impact of AI?
AI is transforming fintech from reactive to predictive. Far from merely automating existing workflows, AI is enabling finance teams to anticipate risk, optimize cash flow, and make data-informed decisions in the moment. An exciting long-term vision I have is the possibility with agentic experiences, where finance professionals focus on handling exceptions rather than repetitive work. This transition allows organizations to operate with greater efficiency while empowering teams to engage in higher-value decision-making.
One of the most important areas of impact is AI-based forecasting. Traditional financial planning has relied on static models, but AI can analyze large amounts of historical and real-time data to create more accurate, dynamic projections. This is especially valuable in periods of economic volatility.
Another trend is the use of large language models (LLMs) to make financial analysis more accessible. Instead of running complex queries, finance teams will be able to ask natural language questions—”What’s our Q2 cash flow forecast?”—and receive instant, AI-driven insights.
That said, as AI adoption accelerates, companies will need to double down on ethical AI frameworks and data governance. Financial decisions have real-world implications, so it will be necessary to make sure AI models are unbiased, explainable, and transparent.
In general, fintech is moving towards a future where finance teams spend less time on manual processes and more time on strategic, high-value decision-making. AI is one of the key facilitators of that shift, but success will ultimately depend on how well organizations manage their data and integrate AI into their broader financial operations.
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[To share your insights with us, please write to psen@itechseries.com ]
BlackLine is the intelligent financial data platform that powers the modern Office of the CFO. As the central nervous system for financial data, BlackLine seamlessly connects systems, automates workflows, and orchestrates the complex flow of financial information across the enterprise. By transforming raw transactions into strategic insights, BlackLine empowers finance & accounting teams to achieve future-ready financial operations that are accurate, efficient, and intelligent.
Jeremy oversees BlackLine’s global technology direction with an emphasis on enhancing BlackLine’s solutions for the Office of the CFO through connected data and AI-powered platforms that will accelerate the company’s ability to scale and continuously deliver customer value.
