Fintech turned banking into software. Wallets, instant transfers, and card-linked apps brought everyday payments onto digital infrastructure, scaling even in places where branch banking never could. Crypto added a second shift: open networks that let value move globally, with decentralized venues increasingly sharing execution flow with centralized exchanges.
A third wave is now forming, one that treats intelligence as infrastructure. This convergence sets the stage for “AI finance,” a category with a feel closer to crypto’s 2019 era than its current scale. The premise is straightforward: adaptive systems turn digital access into consistent execution. That shift matters most where the stakes are highest, and the margins for error are thin.
Emerging Markets Want Execution, Automation, and Risk Controls
The world still has over 1.4 billion adults outside the traditional banking net, reaching digital finance primarily through phones and agent networks. In developing markets, people want more than just owning a bank account. Households have to deal with currency fluctuations and inflation that erodes purchasing power. Political instability adds urgency, pushing users toward automated tools that adapt rapidly to local conditions.
Market data validates this urgency. Chainalysis has pointed to APAC as a hub for grassroots crypto adoption, with on-chain value received up 69% year over year in 2025. Separately, retail interest in artificial intelligence is gaining momentum: a recent survey found that retail investors’ use of AI tools for portfolio management jumped 46% in just one year.
In these economies, remittances and mobile wallets already function as everyday financial infrastructure. The next leap is automation that preserves value and manages risk inside those same apps. This is where AI finance creates “retail power investors”: everyday users equipped with agentic systems and institutional-style execution discipline. Automation handles scanning, sizing, and rebalancing, reducing the need for constant screen time.
A Focus on the Infrastructure Gap
Bryan Benson, CEO of Aurum and a former Managing Director at Binance, has spent years tracking how digital channels, crypto markets, and automation are converging. Aurum builds an AI-powered crypto finance ecosystem for capital management and payments.
“Fintech built the rails, and crypto opened the network,” Benson said. “AI finance adds the intelligence layer that turns access into continuous execution.” While earlier waves digitized services and broadened access, he notes that AI finance automates decision-making and adapts to changing market conditions in real time.
AI finance can connect across the places people already use, according to the Aurum CEO, such as wallets, exchanges, and payment apps, and rebalance or pause trading when individual preset limits are hit.
Benson argues that retail participation scaled faster than retail-grade execution tooling, pointing to the gap between consumer apps and institutional market infrastructure. “Institutional desks rely on venue-aware routing and continuous risk checks. AI helps bring those capabilities into products that everyday users can actually operate,” he said. “Aurum’s Zeus AI Bot focuses on automated spot trading and real-time portfolio tracking, delivered through interfaces people already use, including Telegram.”
That critique lands in a broader financial context. UK regulators found that 75% of surveyed financial services firms already use AI, with additional firms planning adoption in the next three years, a sign that automation has become the baseline across large institutions.
What “Retail Power Investors” Look Like in Practice
AI finance delivers three practical upgrades: scale, speed, and behavioral discipline. Benson’s description centers on throughput. “AI wins on throughput,” he noted. “It can track cross-venue liquidity and volatility continuously, then execute inside predefined risk limits without falling behind the market.”
The technical foundation blends market history with order-book dynamics and on-chain activity, pushing outputs into execution engines with embedded risk constraints.
“In practice, it looks like automated rebalancing, volatility-aware sizing, and risk limits that keep a portfolio within defined drawdowns, running 24/7 without emotional overrides,” Benson said. “For example, our Aurum Flash tool uses AI to scan decentralized exchanges for arbitrage opportunities, executing flash loans to capture value without requiring the user to hold massive capital upfront.”
This is the core of the “power investor” concept. The user gains institutional-style reflexes through automation, while retaining human control over constraints and goals.
The Race To Automate the Financial Stack
AI finance fits cleanly into existing market roles. Banks provide regulated custody, local compliance, and credit lines. Exchanges provide liquidity and price discovery across fragmented venues. Fintech apps provide distribution, onboarding, and consumer-grade interfaces. AI systems connect these layers, running continuous decision loops across the stack.
Benson stresses that AI-driven execution supports resilient liquidity during off-hours. “That’s why transparency and stress tests are no longer nice to have,” Benson said. “They’re the whole point if you’re letting automation run.”
Effective guardrails are critical because crowded signals can trigger synchronized moves and amplify volatility, the Aurum CEO argues. This dynamic increases the premium on transparency and stress testing. The winners in this next phase will pair automation with robust controls and distribution that reaches emerging market users where they already live, inside wallets, cards, and everyday financial apps.
A Five-Year Shift Still in Its Early Innings
AI finance is moving quickly from feature to infrastructure. The rails already exist through wallets, mobile money, stablecoins, and exchange connectivity. The competitive frontier now centers on intelligence that runs continuously and safely for mass-market users, especially in economies where volatility turns risk management into a daily necessity.
Benson’s public thesis points toward autonomous “digital teammates” that can scan opportunities, manage exposure, and handle execution workflows that once required specialized desks.
That future will be measured in adoption and resilience, and emerging markets are positioned to lead because they supply the strongest demand signal: a need for execution, automation, and risk mitigation at scale. Over the next five years, wealth creation increasingly looks like disciplined compounding powered by automated execution and risk controls.
Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.
