Connecting LLMs to Bank Accounts: Authorization, Data Minimization, and Privacy Partitioning

1. Why Personal Finance Scenarios Need Account Data

The main reason for connecting an LLM to bank accounts is to upgrade general conversation into personalized financial analysis. When users ask questions such as “Did I overspend this month?”, “Which spending categories are taking up too large a share?”, or “How much credit card limit is left?”, the model can only give generic advice if it lacks real account context.

To produce truly personalized financial conclusions, the system needs structured account information, including account balances, transaction histories, and credit limits.

This creates a core tension: stronger model capabilities depend on richer personal financial data, but this type of data is highly sensitive. If leaked or misused, the consequences can be far greater than in ordinary scenarios. Therefore, account integration should not only answer “Can the data be read?” It must also answer a more important engineering question: “How can it be read safely, in a controlled manner, and in compliance with requirements?”

It is also important to note that the financial analysis discussed here is for reference only and does not constitute investment advice.

2. Authorization: Two Implementation Models and Credential Boundaries

Letting an LLM service directly connect to every bank is neither practical nor safe. Bank interfaces vary by institution, and directly holding bank credentials would create a major risk. There are two common implementation approaches in the industry, and engineering teams must distinguish between them:

• Bank Open API / Direct Authorization: Credentials remain on the bank side. The bank’s authorization server issues tokens. This model has a relatively smaller attack surface, but its coverage is limited to banks that already expose APIs.
• Credential-Based Aggregation: Users provide their bank passwords to an aggregation platform, which logs in on their behalf and then exposes standardized data to the business side. This approach offers broad coverage and fast integration, but the aggregation platform itself becomes a high-value attack target, and users’ raw credentials are effectively handed to a third party.

Regardless of the approach, the core design principle is the same: the LLM service should never touch the user’s raw bank password. It should only use access tokens with defined permission scopes.

Before integration, the team should also verify common implementations in the service provider’s target market and confirm terms related to licensing, encryption, data retention, training use, and cross-border data transfer. These details are often buried in the service terms, and readers may mistakenly assume that “credentials do not pass through a third party.”

Example flow:

User → Logs in on the aggregation platform / bank side and completes identity verification
      ↓
Authorization server completes identity verification and issues an access token with scopes
      ↓
The LLM service calls the standardized API using the token and retrieves limited data
      ↓
The user's raw bank password never passes through the LLM service

The value of this design is that even if a leak occurs on the business side, the attacker only obtains a scoped token rather than full login capability. The attack surface is significantly reduced.

However, token leakage is still a risk. Within its validity period, a leaked token may still allow an attacker to pull transaction data within the authorized scope. Therefore, tokens should have short lifetimes and support immediate revocation rather than being treated as permanent credentials.

3. Authorization Sequence and Token Contract

To make authorization practical, the process needs a complete sequence and a clear token contract. Tokens are usually issued by an aggregation service or the bank’s authorization server. Scopes are declared by the business side and enforced on the aggregation side, so the business side cannot grant itself additional privileges.

Standard authorization sequence:

User grants scope consent on the authorization page → issue authorization code
      → business side exchanges the authorization code for a token (token request)
      → business side calls the API using the token
      → token expires or refreshes (refresh / expire)
      → user revokes (revoke)

Revocation should map to a specific interface, such as DELETE /item or a dedicated revocation endpoint.

The business side should also handle common error codes correctly:

• 400: Invalid request parameters or scope. Check fields and permission declarations.
• 401: Token is missing, expired, or revoked. Trigger re-authorization.
• 403: Token is valid but lacks the required scope. This is unauthorized access.
• 429: Rate limit exceeded. Use backoff and retry instead of blindly resending requests.

4. Data Access Scope: Read-Only Authorization and Data Minimization

Once an account is connected, the system must strictly define what can be accessed. The data minimization principle requires the system to request only the minimum data needed to fulfill the defined function. It should not collect more than necessary or exceed the authorized scope.

For accounts actively connected by the user, the model should usually receive only:

• Bank name, account name, and account type;
• The last four digits of the account identifier, rather than the full account number;
• Current balance and available credit limit;
• Transaction records within a defined time window, for example up to two years.

Two details are especially important:

1. Account number masking: Only the last four digits are exposed. This is enough to identify the account while avoiding exposure of the full account number.
2. Time-window limitation: Transaction history should have a maximum lookback period. This satisfies the history needed for analysis while avoiding indefinite storage of sensitive data.

The API layer should explicitly declare permission scopes and reject “full access by default”:

{
  "account_scope": {
    "fields": ["institution_name", "account_name", "account_type",
               "account_masked_tail", "balance", "credit_limit"],
    "transaction_lookback_months": 24,
    "access_mode": "read_only"
  }
}

In addition, the model side should not directly consume full raw transaction records. A sanitized data contract can be used instead, exposing only category-level summaries or retrieved snippets:

{
  "model_view": {
    "institution": "Example Bank",
    "masked_tail": "1234",
    "balance": 8600.00,
    "credit_limit": 20000.00,
    "category_summary": {"dining": 1200, "transport": 400, "shopping": 2300},
    "denied_fields": ["full_account_no", "card_cvv", "password", "raw_merchant_detail"],
    "cache_ttl_seconds": 300
  }
}

Supporting rules include: perform aggregation and summarization locally, manage context-length budgets, and set short TTLs for caches.

The read_only constraint must apply across the entire chain. The model should only issue query-style requests. Any transfer, debit, or modification operation should be prohibited at the protocol layer, limiting fund-related risk to what can be controlled at the protocol level.

Residual risks should also be acknowledged. Aggregator services may be compromised, and tokens may be abused or leaked. These risks should be reduced through minimal scopes, token expiration, anomaly alerts, and audit-based response.

If bank card information is displayed, it should follow PCI and regulatory masking requirements for full card numbers and be shown only in a form such as **** **** **** 1234.

5. Privacy Partitioning: Training Data Isolation

Once financial data is connected, a frequent concern is whether this content will be used to train models. The partitioning strategy should clearly define who sets it and what the default is.

A reasonable approach is to create a dedicated privacy zone: by default, conversations in the personal finance area do not enter training corpora as part of the product’s default policy. Ordinary conversations outside this zone can continue to follow the user’s existing privacy settings.

This partitioning gives users stronger privacy expectations in sensitive scenarios and makes training policies clearer and easier to explain.

Implementation can cover several stages:

1. Request tagging: Conversations entering the dedicated zone carry a privacy-level tag. Downstream data pipelines can use this tag to determine whether data may be retained or fed back into training.
2. Storage isolation: Sessions from the sensitive zone are stored separately from ordinary sessions, with shorter lifecycles and stricter access controls.
3. Auditability: Data-flow records are retained so users and compliance teams can verify what data was used and for what purpose.

A special note: if a commercial model API is used, whether data is used for training depends on the service terms and data residency configuration. Teams should check whether enterprise-grade or zero-retention options are available and should not make implicit guarantees to users.

The core principle is to give users a clear and revocable choice, rather than collecting data by default and using it implicitly.

6. Unbinding and Revocation: Returning Control to Users

Even a well-designed authorization flow needs an exit path. Users must be able to disconnect an account at any time, which is a direct reflection of data ownership.

After unbinding, the system should immediately invalidate the corresponding access token. After that, the LLM service can no longer read data from that account.

Engineering considerations include:

• Immediate token revocation: Revoke the token on the aggregation side synchronously to avoid a window where the UI shows the account as disconnected while the backend can still pull data.
• Cache cleanup: Determine deletion scope according to applicable laws and retention obligations. Data that must be retained for legal reasons should be encrypted, isolated, and protected by strict access controls. Other data should be deleted or irreversibly anonymized.
• Visible state: Users should be able to see the current status of each connection clearly, avoiding long-lived “zombie connections.”

For long-term operation, token expiration and periodic re-authorization should be introduced so that access requires user confirmation again after a period of time.

The system should also handle failure and rebuild flows. If the bank password changes, MFA settings change, or the provider-side token becomes invalid, the system should mark the connection as abnormal and prompt the user to re-authorize.

Tokens should be encrypted at rest, for example using KMS or envelope encryption, used by least-privilege service accounts, and covered by access audits.

7. Aggregator Selection and Common Failures

When selecting an integration approach, teams need to balance coverage, real-time capability, credential exposure, and compliance:

• Bank Open APIs offer the best compliance and credential security, but coverage is limited.
• Aggregation providers offer broader coverage and faster integration, but credentials are concentrated, so their compliance terms require careful evaluation.
• Manual statement export by users offers the strongest privacy control, but real-time capability is weak and the user experience is fragmented.

After integration, common failures and troubleshooting directions include:

• No data after authorization: Check whether the scope is sufficient, whether the token is active, and whether a 403 error is occurring.
• Empty balance field: Confirm whether the account type supports the balance interface and whether a secondary call is required.
• Duplicate transactions: Deduplicate by transaction ID and handle pagination and incremental synchronization from the aggregation platform.
• Inconsistent merchant names: Build a local merchant-name normalization dictionary and use multi-source reconciliation as a fallback.

8. Engineering Implementation Checklist

Check Item	Acceptance Criteria
Least privilege	Only read_only; field-level authorization; the API layer rejects full data collection
Credential isolation	The business side never touches raw passwords and uses only scoped tokens
Masking	Account numbers are stored or logged only in masked form, such as t****1234; full card numbers are masked according to industry standards
Data partitioning	Personal finance conversations are excluded from training by default, and the policy is transparent and explainable
Encryption and key management	TLS in transit, encryption at rest, KMS-managed keys, and logs that never record full account numbers or passwords
Multi-tenant isolation	Storage and retrieval are isolated by user and tenant
Revocable at any time	After revocation, refresh tokens return 401, and caches are cleaned according to retention obligations
Full-chain auditing	Data access and usage are traceable throughout the process and support compliance verification

9. Conclusion

Connecting an LLM to bank accounts is fundamentally an engineering problem of balancing capability with security. A mature design does not aim to “read more.” Instead, it builds a controllable trust chain around authorization isolation, data minimization, privacy partitioning, and revocation.

Treating read-only authorization, token expiration, masked fields, and auditability as default constraints rather than afterthoughts is key to making personal finance AI features both useful and trustworthy.