PortfolioPilot 3.0 Introduces Financial AGI, Outperforming Human Benchmarks

PortfolioPilot is proud to announce that it is introducing Financial AGI to the world.

This comes after years of hard work and product evolution.

However, we realize that AGI is a poorly defined term, and financial AGI is even more so. So, to avoid any confusion, we choose to define financial AGI in a narrow fashion: 

Briefly, a platform that has achieved financial AGI is one that can perform the majority of day-to-day analytical and advisory tasks performed by competent finance professionals. 

Moreover, we want to set a truly high bar for any platform to qualify for said term, so we have selected 7 criteria that we feel would give any platform the right to claim having achieved financial AGI:

1. Breadth & Depth
2. Context processing and personalization 
3. Learning
4. Explainability
5. Compliance and auditability
6. Price
7. Speed

That being said, we will demonstrate throughout this article that we meet each criterion. For instance, to demonstrate breadth and depth in our internal study, we had our platform tackle multiple standardized tests taken by financial advisors. Here are the results:

And this was possible thanks to the technical sophistication of our platform. (We explore that further down below)

The definition of financial AGI

Because "AGI" (Artificial General Intelligence) has no universally accepted definition, the term "Financial AGI" is defined here as a domain-specific standard that is measurable, auditable, and intentionally narrow so it does not collapse into a generic "financial chatbot" or common fintech feature set.

Accordingly, we define a platform that has achieved financial AGI as an integrated, tool-augmented AI system that can perform the majority of day-to-day analytical and advisory tasks performed by competent finance professionals (e.g., financial research analysts, investment adviser representatives, wealth managers) across a broad set of personal finance and investment domains, at or above a professionally competent threshold, while providing:

• Transparent, user-facing explanations and assumptions;
• Verifiable grounding in current financial data;
• Personalization to an individual user’s full financial context;
• Controls sufficient for operation within an SEC-registered investment adviser’s compliance and cybersecurity program.

What financial AGI is not

It is also just as important to delineate what financial AGI is not:

• It is not general AGI: the system is not designed to perform arbitrary human tasks outside finance.
• It is not superintelligence: the claim is benchmarked to competent professional performance, not perfection.

A deep dive into the criteria for Financial AGI and how PortfolioPilot meets each one of them

Let’s dive deeper into each criterion necessary for financial AGI. We will look at:

• What it is
• What is needed to show its existence
• How PortfolioPilot meets it

1. Breadth and depth

Breadth is defined as the platform’s ability to cover a wide scope of financial topics that cut across the entire spectrum of financial advisory services. Depth is defined as the quality of the answers and services provided by the system to any given financial topic.

We believe the best way to assess this criterion is through standardized tests. 

To meet the breadth component, we believe that a platform asserting that it has achieved financial AGI should be able to solve and pass the following tests:

• CFA (All three levels)
• Series 7
• Series 65
• CFP
• CPA FAR
• CPA Reg
• CPA BAR
• CPA TCP

Alternatively, to meet the depth criterion, a platform must achieve a score that places it at least in the top 75% of all human test takers.

How PortfolioPilot meets this criterion

[performance chart]

Regarding breadth and depth, the table below highlights how PortfolioPilot performed across different tests and how its scores compare to those of the average human test taker. 

(To further understand how we calculated these numbers and came to them, please refer to our internal study)

Seeing as the platform comfortably outperforms 75% of human test takers across all of the above tests, it passes this criterion.

2. Context processing and personalization

For a platform to achieve financial AGI, it must ensure its advice and recommendations account for users’ unique contexts and preferences.

As a result, a platform needs to be able to show how it factors a user’s unique context and preferences into the recommendations it offers.

How PortfolioPilot meets this criterion

All modules in the portfolio management system are dedicated to providing users with personalized advice tailored to their portfolios. 

For instance, the top recommendations offered to the user are based on a host of elements, including:

• Which suggestions provide the largest improvements to the user’s portfolio score?
• Which securities are held by similar investors and would make sense to be recommended here?
• Which securities match the investor’s stated preferences?

Of the several user variables used to direct the platform’s responses and recommendations, here are some of the explicitly stated ones by the user:

• What the user wants the platform to help with (e.g. increasing risk-adjusted returns vs. boosting downside protection)
• Which sector the user works in
• Their tax filing status
• Their stated risk preference
• Their primary investment objective and main financial goals
• The kind of securities for which they would like to see recommendations
• The kind of asset classes for which they would like to see recommendations
• Any restrictions the user might have with regard to fund fees or fund dividends
• The level of fund eccentricity preferred by the user

On top of all of this, the platform leverages its understanding of the user’s full net worth, of the different asset classes held by them, and of the chat history between the user and the AI assistant.

All of these variables factor into the recommendations offered.

3. Learning

A platform must also adjust its recommendations based on user feedback.

Accordingly, to meet this criterion, the platform needs to show:

1. How it collects feedback from its users
2. How this feedback influences future recommendations
3. How collective feedback impacts the platform as a whole (i.e. if a large swathe of users provides the same feedback, how is that factored into the platform?)

How PortfolioPilot meets this criterion

PortfolioPilot improves using feedback, but only under controlled governance. We believe that feedback loops must be privacy-preserving and supervised. 

A case in point regarding how feedback updates our system is the Portfolio Management System. It leverages both global and personal preferences to tweak its recommendations:

• Global preferences: The engine will select potential securities based on what other people are holding as well as based on the other recommendations that different users have widely accepted or rejected. For instance, if the Engine finds that a certain recommendation has been rejected widely by PortfolioPilot’s user base, then it will adjust accordingly and suggest it less in the future.
• Personal preferences: The engine adjusts its recommendations based on the user’s preferences, both stated and unstated. 
  
  • Throughout their everyday use of our platform, users indirectly express certain preferences. For instance, if they reject, downvote, or just refuse to act on a certain recommendation, then this gets factored into the recommendation engine and alters future recommendations.
  • The recommendation engine also factors in the user’s similarity to other users of the platform, and accordingly, it will offer suggestions based on the recommendations that have proven popular with other similar users.

4. Explainability

The outputs of a platform must be comprehensible and understandable. In other words, it should be clear why the platform offered a particular recommendation rather than another.

Here, the threshold for meeting the criteria needs to be twofold:

1. The platform must explain the logic behind every recommendation or suggestion
2. The ramifications of every financial decision, both good and bad,must be fully highlighted so that the user makes the best-informed decision possible. 

How PortfolioPilot meets this criterion

Here are different ways that PortfolioPilot meets the explainability argument:

• When the platform offers a recommendation, it also explains why the recommendation makes sense for the user and their portfolio.

• When chatting with the AI assistant, it will highlight the different pros and cons of a decision. 

5. Compliance and auditability

A platform claiming financial AGI must operate controls consistent with those of an SEC-registered investment adviser.

To meet this criterion, there are 4 elements that we feel are worth mentioning:

• Account connectivity and credential handling
• Encryption and data protection
• Audit logs and recordkeeping
• Marketing claim controls

A platform must show how it handles all the above elements.

How PortfolioPilot meets this criterion

To demonstrate that PortfolioPilot meets this criterion, we will quickly go through the aforementioned elements:

Account connectivity and credential handling

Connected accounts are accessed via secure, read-only connections through trusted partners. PortfolioPilot does not store user banking credentials; the user maintains control of access. User data is processed in an anonymized and encrypted form within the modeling infrastructure.

Encryption and data protection

Encryption and data protection are met through the following elements:

• 256-bit encryption at rest and in transit for sensitive user data.
• Access controls following least-privilege, with environment separation (dev/test/prod).
• Vendor risk management for account aggregation and market data providers.

Audit logs and record keeping

Audit readiness requires retaining, subject to applicable privacy policies and recordkeeping rules: user context (holdings/goals assumptions), tool/model outputs used in recommendations, model/data version identifiers, and the final delivered output with disclosures. Logs support reproduction, supervision, and incident investigation.

Marketing and claim controls

Public statements about AI capabilities (including "Financial AGI") must be reviewed and substantiated with objective evidence. Claims should be limited to what the system does in production and must avoid implying guaranteed outcomes.

6. Price

A platform must be accessible at a cost-efficient rate, especially compared to human financial advisors. This stems from our belief that for financial AGI to be useful to all, it must be accessible to all.

As a result, we believe that a platform needs to be, at least, cheaper than 75% of all human financial advisors.

How PortfolioPilot meets this criterion

From research available in the internal paper, the typical range for flat annual fee financial advisors ranges from $2,500 to $9,200. So, the average is $5,850, and anyone charging less than $4,744 is in the bottom 25%.

As of this writing, PortfolioPilot has 3 paid tiers:

• The Gold tier costs $240 per year
• The Platinum costs $600 per year
• The Pro plan costs $1,200 per year

All of these plans cost less than $4,744, meeting the cost criterion.

7. Speed

A platform claiming financial AGI must rival, if not exceed, human financial advisors in the speed of response and service delivery.

Consequently, using the benchmark tests mentioned in the breadth and depth criterion, we can compare the speed of our platform with that of human test takers.

How PortfolioPilot meets this criterion

[speed chart]

With regards to speed, the metric here is the number of questions the platform solved per minute compared to the average human test taker. 

Taken from our internal study, the table below summarizes these results: 

The breakdown of PortfolioPilot’s technical system:

PortfolioPilot is built on a stack of models and data that combine economics, finance, and ML/AI.

In a very abstracted sense, the system can be broken down into 4 main components:

1. Data core
2. The Economic Insight Engine
3. The Portfolio Management System
4. The AI assistant

The Data core enables the platform to connect financial data, news, macro insights, user data, and historical trends through a global economic map. To that end, the platform aggregates macroeconomic, financial, and market data through 16+ APIs and proprietary scrapers.

The Economic Insights Engine connects recommendations with macro context: trends, relationships between indicators, and continuous monitoring of changing scenarios.
It provides portfolio-specific interpretation influenced by economic models.

The Portfolio Management System incorporates quantitative portfolio construction frameworks inspired by institutional asset management. Broadly speaking, the portfolio management system can be broken down into the following 5 modules:

1. Recommendation Engine
2. Portfolio Optimizer
3. Tax Calculator
4. Retirement planner
5. Stock Screenr

Finally, the AI Assistant is not a standalone chatbot. It sits on top of all of the aforementioned modules - from data infrastructure and forecasting to portfolio optimization and recommendations. The AI Assistant can access portfolio insights, answer specific security questions as part of the user’s research, answer investing questions, and teach the user more about the platform.

If you want to learn more about how our platform works, check out our technology page.

Conclusion

Given the definition we have set for financial AGI, as well as the clear criteria required to meet it, our platform comfortably meets and even exceeds the required thresholds.

That said, it is critical to reiterate some of the caveats highlighted at the beginning of the paper:

• Financial AGI is no guarantee of performance; analyses are hypothetical and educational/analytical.
• User retains control; platform does not execute trades.
• Assumptions and data sources are disclosed; outcomes depend on future conditions.
• We always encourage consulting qualified professionals for legal/tax matters when 

Disclosure:

The term "Financial AGI" is descriptive only and does not imply human-level intelligence, professional licensure, certification, or equivalence. The system has been evaluated using financial industry examination materials for internal benchmarking purposes only. Such testing measures conceptual knowledge only and does not represent investment performance or professional judgment.

Outputs generated by the system are based exclusively on information provided by the user, together with programmed methodologies, data inputs, and model assumptions. The accuracy, completeness, and relevance of results depend on the accuracy and completeness of user-provided information. The system does not independently verify user inputs and does not provide tax, legal, or individualized advice beyond the scope of the information supplied.