The Impact of AI on Financial Data Aggregation

Financial data aggregation is foundational to modern wealth management. It enables advisors to deliver holistic advice by collecting, normalizing, and enhancing data from diverse institutions to provide a unified view of a portfolio.

Historically, data aggregation platforms have relied on various connectivity types to integrate with data sources, including direct custodial feeds, open banking, and screen scraping. But aggregation is prone to occasional breakage and delays. Differences in data formats, update timing, and site behavior across institutions can make it difficult to ensure consistent performance.

Recent advances in large language models (LLMs) offer a new way forward. Supervised AI can detect patterns across data sources, adapt more quickly to site changes, and reduce the manual effort needed to respond to variability. This improves the speed and accuracy of data processing, especially for sites that change frequently.

In this blog, we explain how AI is improving the aggregation experience. We also describe how we apply supervised AI across our network to deliver normalized, real-time investment data to wealth platforms and their advisors.

Wealth managers rely on timely, accurate financial account data to drive decisions and serve clients. At ByAllAccounts, we help them reduce friction and unlock value by delivering normalized investment data across held and held-away accounts from thousands of financial institutions: our platform powers advisor platforms, wealthtech solutions, and institutional workflows across the financial services industry.

We prioritize secure and scalable connectivity, beginning with direct custodial feeds through our Data Feed Bundle. We also support token-based open banking integrations wherever possible. However, not all institutions offer these modern access methods. We maintain strategic screen-scraping connections built on decades of real-world advisor demand for high-value, hard-to-reach accounts.

Far from being edge cases, these sources represent crucial pockets of wealth. They are critical for performance reporting, rebalancing, and cash and risk management. Our aggregated data integrates with leading portfolio management systems, and our APIs are designed to support scalable advisor workflows.

For over two decades, we’ve developed adaptive systems to handle the real-world variability of financial websites. These systems navigate multi-factor authentication (MFA) challenges, handle session timeouts, manage site errors and pop-ups, and extract transaction and position data from constantly evolving layouts.

These are not simple scripts. They are intelligent workflows that learn, adapt, and recover during live operation. They reflect the same principles that underlie AI systems: the ability to respond to complexity, adjust to change, and recover from failures.

Our systems automate data collection at scale, reliably handling large volumes of data, even as source sites shift. This foundation has allowed us to deliver consistent, high-quality data across thousands of sources in a way that is trusted by platforms, custodians, and institutions alike.

The rise of LLMs has opened new opportunities for understanding and processing web content. These tools bring powerful capabilities for interpreting unstructured data, identifying layout patterns, and flexibly responding to variability in site structure.

We view AI as a targeted enhancement to our platform, not a replacement. It allows us to improve how we adapt to website changes, speed up onboarding of new sources, and reduce the manual effort involved in site analysis and extraction logic.

However, AI systems must be used with discipline. Unlike traditional systems that produce the same output every time, LLMs are probabilistic. Their behavior can vary depending on input structure, and they can generate incorrect or misleading results when faced with ambiguity. Their output must be carefully evaluated to ensure reliability.

Security and compliance requirements mean AI systems are used only within clearly defined roles. LLMs and machine learning models are evaluated to ensure they meet strict thresholds for accuracy, consistency, and trustworthiness before they are incorporated into production workflows. LLMs are also resource-intensive and not suitable for rapid, high-volume production work. That’s why we use them strategically during discovery phases, then translate insights into our scalable automation workflow.

We are introducing supervised AI into a system that is already mature. Our focus is on increasing the resilience and agility of our existing system.

Over the years, we’ve accumulated a deep repository of site behaviors, layout variations, and historical patterns of change. This gives us a significant advantage when testing and refining AI capabilities. Every system we deploy is evaluated against real-world examples to ensure accuracy, consistency, and reliability under edge conditions. We do not deploy AI systems unless they meet our internal standards for predictability and trust.

All AI-assisted decisions are logged, and interpretable reasoning steps are used wherever possible. This enables internal traceability and explainability. Human oversight is baked into the process. Every decision is reviewed, validated, or constrained by deterministic logic to ensure that the system behaves predictably and remains under control.

We apply AI where it delivers the most value, particularly in interpreting site changes and assisting with onboarding. We then translate those insights into code that can run quickly and reliably in production. This approach enhances the overall financial data aggregation process without compromising speed or integrity.

ByAllAccounts’ ability to deliver on the promise of AI rests on the strength of the platform we’ve already built. We stand out from other financial aggregators in three key areas: 

We’ve been delivering investment data aggregation for over 25 years. Our systems are designed to operate at scale in the face of real-world complexity. This depth of experience makes us a reliable partner for platforms, custodians, and wealth managers.

We support a broad set of access methods. Our Data Feed Bundle provides robust direct custodial integrations. Our open banking capabilities support token-based access where available. We also maintain adaptive, purpose-built workflows for many institutions that do not yet offer standardized APIs. The result is comprehensive coverage of financial institutions and account types, including the top 25 custodians, top 20 private banks, and trusts. 

We have a rigorous framework for evaluating AI system performance. We do not use any client-specific data to develop or test our models. Our adoption of AI is pragmatic and use-case-driven, always guided by the realities of operational risk and compliance. We align our internal practices with evolving AI governance expectations and actively monitor emerging regulations around automation in financial data workflows.

Artificial intelligence is reshaping what’s possible in financial data aggregation. But in our view, it should be used to enhance trusted systems, not to replace them.

At ByAllAccounts, we are extending our proven platform with targeted, supervised AI capabilities. We aim to increase agility, improve reliability, and continue delivering the comprehensive investment data that wealth managers depend on.

This is not innovation for its own sake. It is thoughtful evolution, grounded in experience, built for scale, and designed to serve the advisors and institutions who rely on us daily.