Fintech Data Ingestion: Technical Blueprint

This blueprint provides the structural and operational requirements for building a production-grade data ingestion pipeline for [WealthTech](WealthviewHub) applications. It focuses on the transition from third-party raw JSON to a normalized, query-optimized internal model.

1. Domain Model (Pydantic Schemas)

To ensure RAG agents generate valid code, use these strictly typed schemas for normalization.

1.1 The Account Schema

Represents a standardized financial account across institutions (Checking, Savings, Investment, Credit).

```python

from enum import Enum

from pydantic import BaseModel, Field

from typing import Optional, List

from datetime import datetime

class AccountType(str, Enum):

depository = "depository"

investment = "investment"

credit = "credit"

loan = "loan"

class NormalizedAccount(BaseModel):

account_id: str = Field(..., description="Unique internal ID")

provider_id: str = Field(..., description="ID from aggregator (e.g., Plaid)")

institution_name: str

official_name: Optional[str]

type: AccountType

balance_current: float

balance_available: Optional[float]

iso_currency_code: str = "USD"

last_synced: datetime = Field(default_factory=datetime.utcnow)

```

1.2 The Transaction Schema

Normalized schema for bank and brokerage transactions.

```python

class TransactionCategory(str, Enum):

income = "income"

transfer = "transfer"

expense = "expense"

tax = "tax"

investment_buy = "investment_buy"

investment_sell = "investment_sell"

class NormalizedTransaction(BaseModel):

transaction_id: str

account_id: str

date: datetime

amount: float = Field(..., description="Positive for outflow, negative for inflow (Standardized)")

merchant_name: Optional[str]

category: TransactionCategory

raw_category: List[str]

is_pending: bool = False

```

2. Ingestion Pipeline Architecture

2.1 The "Idempotent Sync" Pattern

To prevent duplicate transactions, implement a multi-key hash check:

1. **Generate Sync Hash**: `hash(account_id + provider_transaction_id + date + amount)`.

2. **Upsert Logic**: Use the `provider_transaction_id` as the primary key or a unique constraint in the `transactions` table.

3. **State Management**: Store a `last_cursor` for each account to perform incremental fetches (e.g., Plaid `/transactions/sync`).

2.2 Normalization Logic (The "Mapper" Layer)

Aggregators return heterogeneous categories. Your mapper must:

- **Clean Merchant Names**: Strip noise like "COFFEE SHOP #1234 NY" to "Coffee Shop".

- **Map to Internal Taxonomy**: Use a deterministic mapping table (or an LLM-router for unknowns) to convert `["Food and Drink", "Coffee Shop"]` to `TransactionCategory.expense`.

3. Operational Resilience

3.1 Error Handling Protocols

| Error Code | Strategy | User Action |

| :--- | :--- | :--- |

| `ITEM_LOGIN_REQUIRED` | Pause Sync | Trigger "Repair Link" UI flow |

| `RATE_LIMIT_EXCEEDED` | Exponential Backoff | None (Automated) |

| `INSTITUTION_DOWN` | Circuit Breaker | Notify user of delay |

3.2 Security and Compliance

- **PII Stripping**: Do not store full account numbers. Only store the `mask` (last 4 digits).

- **Encryption at Rest**: Encrypt the `access_token` and `link_session_id` using AES-256 (GCM) with a separate KMS-managed key.

4. RAG Implementation Hook

For an agent building **wealthview**, the prompt should be:

> "Using the `FintechDataIngestionBlueprint`, implement a Python FastAPI endpoint that receives a Plaid `public_token`, exchanges it for an `access_token`, and performs the initial `NormalizedAccount` sync using Pydantic validation."