Our Founders

I come to banking from a different direction than most.

My professional foundation was built in global macro hedge funds, which are environments where data is incomplete, systems are dynamic, and static models fail quickly. I managed capital for Julian Robertson, George Soros, Moore Capital, Fortress, and Discovery Capital Management. Trained in computer science, I developed data-driven systems to identify complex patterns, manage risk, and deploy capital in highly volatile global macro markets—producing consistently high Sharpe ratio portfolios.

Macro investing teaches you one lesson above all else: cycles change, and those who rely on yesterday’s framework get punished.

After leaving hedge funds, I invested in and advised data-centric technology companies, serving on boards including SoFi and Cardlytics. I saw a pattern where industries transform when intelligence replaces process and when raw data converts into insight, and action, at scale.

Across roles as an operator, investor, and builder, I’ve been drawn to systems that matter, systems that scale, and systems that fail when their assumptions stop holding. Financial services sit squarely in that category.

By the mid-2010s, I saw banking approaching a structural inflection point.

Every day, banks generate an enormous volume of behavioral data in their transaction ledgers—paychecks, bills, and spending decisions that reflect how customers actually live and manage money. This data is the ground truth of financial behavior, yet it remains fragmented, unstructured, and largely unused. Aliya converts this transaction “gibberish” into AI-driven operational intelligence, enabling banks to unlock an advantage that fintechs cannot access and mega banks struggle to realize due to legacy constraints and talent gap.

For more than a decade, fintechs have skimmed banks’ most attractive customers with sleek digital experiences. Yet static credit models and deteriorating efficiency economics have left these business models fragile. The implication was unavoidable: banks would have to unlock materially more intelligence from their existing data and balance sheets.

Aliya emerged to do exactly that.

Aliya came from this realization, but we underestimated how hard it would be to rectify these problems correctly.

Banks do not lack data or discipline—they have both in abundance—but many of their most critical decisions are still made through abstractions instead of reality. Credit scores, static income snapshots, and rigid segmentation schemes compress dynamic human behavior into simplified proxies. Those proxies worked reasonably well in a more stable economic era. They work far less well now.

I watched wave after wave of fintech attempt to “disrupt” banking by skirting regulation, absorbing balance-sheet risk, or optimizing for short-term growth at the expense of durability. Some scaled quickly. Very few scaled well.

The next generation of financial innovation won’t replace banks. It will strengthen them by improving the data driving their decisions. Cash flow is the most honest signal we have of financial behavior. When analyzed longitudinally and contextually, it reveals far more about resilience, stress, and trajectory than traditional metrics alone ever could.

Aliya operationalizes these insights by translating raw transactional data into decision-grade intelligence that banks can use across the full lifecycle—origination, pricing, line management, and portfolio monitoring—without compromising risk standards or regulatory expectations. Better, faster decisions that hold up across cycles.

I favor businesses that compound through process and infrastructure rather than leverage or narrative. I like platforms that sit close to the core of an ecosystem, align incentives rather than distort them, and become more valuable as complexity increases. Aliya reflects those principles—software-driven, capital-light, and embedded where marginal insight has outsized impact.

Aliya does not rely on benign macro conditions to succeed. Its value increases as volatility rises and dispersion widens, which is precisely when institutions need clearer signals and tighter controls. That counter-cyclical relevance is not accidental; it is foundational to how the platform was designed.

I started Aliya because the data already exists, the inefficiency is persistent, and the stakes are high. Better decision systems in banking don’t just improve returns, they reduce fragility in an essential part of the economy and allow banks to fulfill their social mandate – which is to be the stewards of trust, charged with allocating capital in ways that sustain economic stability and shared prosperity. Unfortunately, a combination of post 2008 regulation and over a decade of zero interest rates resulted in the dilution of this mandate and a subsequent lack of investment in infrastructure and capabilities to fulfil the mandate in the current macro environment.

Building that kind of infrastructure takes patience, rigor, and respect for the institutions involved. That’s the work Aliya is committed to doing, and it’s why I believe this platform can create enduring value over the long term.

Instead of building in a sandbox, we spent eight years operating inside a top-five, OCC-regulated U.S. bank. We solved the hardest problems first: turning chaotic transaction data into structured, auditable intelligence; building machine-learning models that outperform legacy approaches without sacrificing explainability; and designing an AI operating system that can run continuously with governance, controls, and regulatory alignment.

The result is an intelligence layer designed to sit at the center of a bank’s decision-making. It is learning continuously, enforcing consistency, and allowing human judgment to focus on oversight rather than manual processing.

Aliya was not built to disrupt banks, but to elevate them—by embedding AI-driven intelligence in a governed, compliant, and durable way. This is not technology infrastructure that gets deployed and abandoned, nor a tool whose value decays over time. Aliya compounds. As the system learns, the institution gets smarter. AI will redefine the middleware layer across every industry, and banking will not be exempt. The signal is clear. What remains uncertain is who will act early—and who will be forced to react later.

Wije

I’ve spent my entire career in environments where mistakes are expensive and volatility is unforgiving.

I began at Fidelity, where I built and led the Emerging Markets Fixed Income and Currency Group, managing more than $7 billion in client capital. I then joined Julian Robertson at Tiger Management before founding Discovery Capital Management, a global macro hedge fund I ran for more than two decades with a singular obsession: protect capital first, understand risk deeply, and never confuse short-term performance with durable advantage.

That discipline led me to co-found Aliya.

Studying and investing in companies like Palantir and SoFi revealed a pattern: the winners weren’t automating workflows, they were replacing fragmented, human-dependent decision making with intelligence that operates continuously, consistently, and under stress.

As I looked across the banking landscape, a clear disconnect became apparent—especially within the regional and community bank sector that underpins economic growth.

Banks hold the most complete behavioral dataset in the economy: the transaction ledger. Yet most institutions still rely on static credit scores, manual processes, and disconnected systems to make their most important decisions. For years, a zero-rate environment masked the cost of this inefficiency. That era is over.

Aliya addresses this gap, but not in the way most fintechs do.

We didn’t start with a product pitch. We spent eight years building Aliya inside a top five, OCC-regulated U.S. bank under live conditions, audits, regulators, and consequences. That environment forced discipline and required explainable models. Automation had to reduce risk, not introduce it. Compliance and governance wasn’t a slide—it was operational reality.

Aliya emerged as an operating system for intelligence, leveraging data banks already had. It transforms raw customer bank account transaction data into intelligent, auditable insight using AI-powered systems and embeds machine learning decisioning models in a way regulators can understand; and shifts humans from manual processing to oversight.

Consumer credit still relies on backward-looking and slow-to-adapt frameworks. Credit scores, static income verification, and broad segmentation worked well in an era of stable employment and predictable cash flows. Today, income volatility, fragmented work, and rapid shifts in household balance sheets have made these approaches increasingly ineffective. As AI begins to reshape employment and income patterns, static and linear risk frameworks will become even more problematic.

Most banks allocate capital using tools that compress highly dynamic behavior into a small number of lagging indicators. That creates mispriced risk and underutilized balance sheets. Both are tolerable in benign environments. Neither is durable across cycles.

I emphasize resilience over optimization. I am skeptical of systems that perform only on continued liquidity, low volatility, or regulatory arbitrage. The most attractive opportunities come from improving decision quality at the point where capital is deployed.

Aliya accomplishes this by allowing banks to translate real-time cash-flow behavior into actionable intelligence happening at origination, pricing, line management, portfolio construction and post-origination monitoring. This produces a rare combination: tighter risk controls alongside broader, more accurate credit access. Better information does not increase risk – it reallocates it more intelligently.

This is not a consumer-facing fintech built to grow quickly and offload risk. Nor is it a model designed to work only under favorable macro conditions. Aliya is operational intelligence that strengthens incumbent institutions by improving how they understand and manage their existing customers, manage cost-to-serve, allocate capital and target risk-adjusted-returns.

That distinction matters.

Across cycles, capital flows to institutions that see deterioration earlier, price risk more accurately, and avoid forced behavior under stress. Aliya reflects that principle: transparency over leverage, process over prediction, systems that perform when assumptions break and agentic workflows to drive efficiency.

I started Aliya because the inefficiency is real, the data already exists, and the cost of not acting is growing. As credit conditions normalize and dispersion increases, the gap between institutions with true customer insight and those relying on legacy proxies will widen materially.

Durable systems matter. Whether managing capital through multiple crises or helping coordinate a citywide organ donation initiative in Pittsburgh, success depends on trust, coordination, and disciplined execution.

If you are thinking seriously about how to protect returns, manage risk, and remain relevant in a structurally changing banking landscape, our experience—and our system—will resonate. At Aliya, we want to be the foundation for banking’s agentic future.

Rob