How to Use Your Bank Feed to Track Software Spend

Why Bank Feeds Beat Expense Reports for SaaS Tracking

Expense reports are a notoriously unreliable source of SaaS data. They capture charges that employees have manually submitted - but many SaaS subscriptions are billed directly to company cards, bypassing the expense report process entirely. An employee signs up for a tool, puts it on their corporate card, and it bills automatically every month. No one ever submits an expense for it.

Bank feeds do not have this blind spot. Every transaction that hits your bank accounts or credit cards appears in the feed, regardless of whether anyone submitted an expense report. The bank feed is the ground truth of what you are actually paying. Expense reports are filtered, delayed, and dependent on employee behavior. Bank transaction data is automatic and complete.

The Second Advantage: Historical Coverage

Expense management systems often only retain 90 days of easily searchable history. Bank feeds and bank statements typically go back 12 to 18 months. This longer window matters enormously for SaaS audits - annual subscriptions renew once a year, and you need at least 13 months of data to catch every annual renewal in a single pull.

The Third Advantage: Consistent Format

Expense reports have inconsistent categorization - one person books Figma as "design software," another as "SaaS tools," another as "miscellaneous." Bank transaction data is more consistent because it uses the merchant's actual billing descriptor. "FIGMA.COM" is the same merchant name every time, making pattern matching straightforward.

How to Set Up Bank Feed Monitoring for SaaS

Step 1: Export 13 Months of Transaction Data

Download all transactions from every company bank account and credit card for the past 13 months. Export to CSV. If your company uses an expense management platform like Ramp, Brex, or Expensify, export from there as well - these will catch charges that flowed through expense reimbursement rather than direct card charges.

Step 2: Filter for Recurring Patterns

In your spreadsheet, filter for transactions where the same merchant name appears in multiple months. A charge from "NOTION.SO" appearing on the 15th of every month for the past 13 months is clearly a monthly subscription. Sort by merchant name alphabetically to group repeat charges together.

Look specifically for:

• Monthly charges from the same merchant (same name, same or similar amount)
• Annual charges - single large amounts from software vendors that only appeared once in 13 months
• Charges with ".COM", ".IO", or ".APP" in the merchant name - these are almost always SaaS tools
• Charges with billing descriptors that include words like "SUBSCRIPTION", "PLAN", "MONTHLY", or "ANNUAL"

Step 3: Categorize and De-duplicate

Create a clean list of unique software vendors. For each, record: the monthly equivalent cost (divide annual charges by 12), the billing cadence, and when you first saw the charge (to estimate renewal timing). This becomes your master SaaS inventory.

Connecting Plaid to Your Analysis Workflow

Manual CSV exports work for a one-time audit, but for ongoing tracking you want an automated data feed. Plaid is the most widely used financial data API and connects to over 12,000 financial institutions. It allows read-only access to your transaction data and can push new transactions to your analysis workflow automatically.

How Plaid-Based SaaS Tracking Works

The basic architecture is: Plaid connects to your bank accounts and credit cards with read-only credentials. New transactions are pulled nightly. A categorization layer uses merchant name and transaction pattern to identify SaaS charges. The classified charges are then aggregated into your software inventory, with running totals, renewal date estimates, and anomaly flags.

Setting this up yourself requires developer resources and ongoing maintenance. Plaid's API is well-documented but non-trivial to implement correctly - particularly the categorization layer, which requires a continuously updated database of SaaS merchant names and their products.

The Ready-Built Alternative

This is precisely what SubScrub provides out of the box - a Plaid-based bank feed connection, a continuously updated SaaS merchant database for accurate categorization, and an analysis layer that identifies waste without requiring you to build or maintain the infrastructure yourself.

What SubScrub Does with Bank Feed Data

When SubScrub connects to your bank feed, it does several things that go beyond a raw transaction export:

Merchant Resolution

Bank descriptors are frequently cryptic - "ADOBE SYSTEMS INC" could be Creative Cloud, Acrobat, Sign, or multiple Adobe products. SubScrub resolves merchant descriptors to actual product names using a proprietary database built from thousands of real SaaS transactions. This means you see "Adobe Creative Cloud (25 seats)" rather than "ADOBE SYSTEMS INC $749.00."

Renewal Date Estimation

By analyzing the billing pattern of each subscription - the day of month charges hit, the cadence, and the amount - SubScrub estimates the next renewal date. For annual subscriptions seen only once in the feed, it estimates the renewal based on the charge date. These estimates trigger advance alerts so you are never caught by an unexpected auto-renewal.

Anomaly Detection

SubScrub flags unusual patterns in your SaaS spend: charges that increased by more than 15% month-over-month (a seat expansion you may not have authorized), new vendors that appeared in the past 30 days (potential shadow IT), and vendors that billed twice in a single month (a possible billing error). These anomalies often represent real money - billing errors and unauthorized seat expansions are more common than most finance teams realize.

The bank feed approach catches what every other method misses - the tools nobody remembers authorizing, the annual charges that fly under the radar, and the creeping seat expansions that inflate your bill without anyone noticing. Join the waitlist to see this in action with your own data.