Southeast Asia’s retail wealth ecosystem is now being driven by a young, mobile-first demographic and a rising middle class. Retail investment participation has exploded across the region, exemplified by Indonesia, where combined equity and crypto investors surged to nearly 40 million in just five years. However, this massive appetite for wealth creation is colliding with a highly fragmented regulatory environment and a steep financial literacy curve, leaving regional platforms to grapple with the high-stakes challenge of scaling technology without sacrificing institutional trust.
Companies like Pluang, a multi-asset wealthtech brand, have doubled down on a strict, compliance-first architecture. They now have over 13 million users in Indonesia by capturing licenses across multiple regulatory bodies. The platform is now aggressively expanding into the Philippines via the SEC’s regulatory sandbox.
We explore why AI governance is becoming a go-to-market issue for Southeast Asian startups
To better understand Pluang’s next-generation strategy, we speak to Claudia Kolonas, co-founder and CEO, Pluang. In this interview, we aim to understand what lies in the balanced integration of artificial intelligence, leveraging tools like their Aura AI to accelerate market literacy while maintaining rigid guardrails against algorithmic hallucinations. Rather than relying on developed-market data feeds, the platform advocates for deeply localised context to genuinely serve the nuances of the Southeast Asian investor.
How do the Indonesian market and the Philippines market differ from each other when it comes to wealthtech?
Indonesia has seen explosive retail investor growth. Equity investors went from 3.8 million to 18 million in five years, and crypto investors from under one million to 19 million. The infrastructure is more developed, the regulatory frameworks are more established, and competition is intense. Pluang has been operating here since 2019 and we’ve built a multi-asset platform with over 13 million users, so the market has depth.
The Philippines is earlier in that journey. Retail investment participation is still relatively low, but the trajectory is similar: a young, mobile-first population, a rising middle class, and a growing appetite for wealth-building tools beyond traditional savings. We entered the Philippines last year through an SEC regulatory sandbox, offering US stock investing in Philippine pesos. The demand signals have been encouraging, but the market requires a different approach. You can’t just copy and paste your Indonesian playbook. The regulatory environment, payment infrastructure, and user behaviour are all distinct.
Southeast Asia has an extremely mixed set of investment regulations. How do you plan to scale in markets that have a lot of competition from local investment apps and banking apps, and huge discrepancies in regulation?
Our approach has always been regulation-first. In Indonesia, we hold licences across multiple regulators: OJK for crypto, capital markets and derivatives, Bappebti for commodities— and we operate through dedicated legal entities for each asset class. That infrastructure took years to build, but it’s what allows us to offer everything from local stocks and US equities to crypto futures and options on a single platform.
When it comes to competition, we don’t try to out-spend anyone. We’ve built Pluang with a 200-person team while some competitors have scaled to multiples of that. 75% of our users come to us organically, with a payback period of under six months. So it’s not a marketing strategy, but a product strategy. If the product is right, users come and they stay.
For regional expansion, we follow the same principle: go through the front door. In the Philippines, we applied for and were admitted to the SEC’s regulatory sandbox. It’s slower than launching without regulation, but it builds the kind of institutional trust that matters when you’re asking people to invest their money with you. We’d rather be in two markets with proper licensing than in five markets with regulatory risk.
Concerns regarding incorrect advice and data security remain the primary barriers preventing non-users from adopting AI. What can be done to eliminate hallucinations and deliver the verifiable “proof of accuracy” that investors demand?
I think the industry needs to be honest about where AI is genuinely useful and where it introduces risk.
At Pluang, we use AI as a tool to help investors access and interpret information, not to give financial advice. Our Aura AI feature provides market analysis, fundamental data, and trend insights that help users make more informed decisions. But the keyword is “help.” The user makes the decision. We’re not positioning AI as an oracle.
On the hallucination problem specifically, the practical answer is constraining what the AI is allowed to do. If you’re serving financial data, the AI should be pulling from verified, structured data sources and not generating answers from scratch. The moment you let a model improvise with financial figures, you’ve created a liability. So the architecture matters: what data feeds into the model, what guardrails exist around its outputs, and whether the user can trace any claim back to a verifiable source.
Data security is non-negotiable in our sector. We’re a regulated financial institution. User funds are held in segregated accounts, and we’re subject to the same data governance standards as any licensed broker or asset manager. AI doesn’t change those obligations. If anything, it raises the bar.
Markets like Singapore are tightening compliance frameworks around algorithmic accountability and data governance. How can regional wealth platforms balance rapid tech deployment with absolute compliance?
You don’t balance them; compliance comes first. The temptation in fintech is to ship fast and figure out the regulatory piece later. That’s a dangerous approach when you’re handling people’s savings and investments.
What we’ve learned at Pluang is that building compliance into your product architecture from day one actually makes you faster in the long run. We’ve spent years securing the right licences across different asset classes and jurisdictions. That upfront investment means when we want to launch a new product — like Indonesian equities this year — we can move quickly because the regulatory infrastructure already exists.
The markets that are tightening their frameworks around algorithmic accountability are doing the right thing. As platforms integrate more AI into their investment tools, there needs to be a clear audit trail — what data went in, what recommendation came out, and why. For us, it’s an opportunity: if you can demonstrate that your AI tools are transparent, well-governed, and built on verified data, that becomes a competitive advantage, not a cost centre.
Scaling fintech platforms across fragmented emerging markets presents major localised challenges. How can you train your AI engines to accurately parse unique regional narratives, local data feeds, and localised investor sentiments?
You can’t build one AI model that works perfectly across every Southeast Asian market. The data environments, languages, market structures, and investor behaviours are too different.
In Indonesia, we have the advantage of operating at scale — over 13 million users, years of transaction data, and deep familiarity with how Indonesian retail investors behave across different asset classes and market conditions. That gives us a strong foundation for any AI feature we build domestically.
But when we enter a new market like the Philippines, we start from scratch on the local data side. The approach has to be humble. Partner with local data providers, listen to how local investors talk about money and risk, and build tools that reflect those realities rather than projecting Indonesian assumptions onto a Filipino user base.
The biggest risk with AI in emerging markets is training models on data that doesn’t represent your actual users. If your training data is predominantly from developed markets, your outputs will reflect developed-market assumptions about risk tolerance, portfolio construction, and financial literacy. For Southeast Asia, that’s not good enough. You need local data, local context, and local validation.
As wealth AI shifts from a basic utility to a proactive tool for opportunity discovery, how will the core definition of “market literacy” change for the average everyday investor over the next few years?
I think we’re already seeing the shift. Market literacy used to mean understanding financial statements, reading charts, and following macroeconomic indicators. For most retail investors, that was an impossibly high bar, and it kept a lot of people out of the market entirely.
What AI is doing is lowering the entry point for informed participation. You don’t need to read an annual report from cover to cover if an AI tool can surface the key metrics, compare them to industry benchmarks, and flag what’s changed since last quarter. It doesn’t replace understanding, but it accelerates it.
I’d caution against the idea that AI will make financial literacy obsolete. The investors who do best over time are the ones who understand what they own and why they own it. AI can help you find opportunities faster and process information more efficiently, but the discipline of knowing your own risk tolerance, diversifying properly, and not panic-selling during a downturn is still human.
At Pluang, we’ve always invested in financial education alongside our products. The two have to go together. Making investing accessible doesn’t mean making it mindless; it means giving people the tools and the knowledge to participate confidently. AI is one of those tools, but it’s not the only one.