Digits May Upgrade
After two years of heavy development, we’ve announced Digits for Expenses! We couldn’t be more humbled by the support and excitement we’ve seen from countless business owners and accountants across the country and around the globe, and we’re working tirelessly to get them into our Early Access program.
Since our launch, we’ve also been solidifying Digits’ underlying financial engine. Businesses come in all shapes and sizes, so we need to be absolutely certain that Digits supports them all.
This month, our biggest upgrades focused on new technology to help ensure that our understanding and your understanding of your business’ financials are the same.
Automated P&L and Balance Sheet Validation
Data quality is at the heart of our mission to provide real-time visibility and actionable insights into how your business is spending money, and we’re constantly iterating on approaches to guarantee both precision and accuracy, while keeping your data private and secure.
Over the past month, we’ve built fully-automated validation using two of the three most common financial reports. Here’s how it works:
We first generate a full Profit & Loss Statement and a Balance Sheet report using Digits’ internal, proprietary representation of your company’s financials. We then export the same P&L and Balance Sheet from your ledger’s API, for each month of your company’s history. Our validation pipeline then automatically compares them—line by line—guaranteeing that Digits’ representation of your data is accurate and that you can trust the numbers we display.
Automated Regression Detection
While validation is critical for all historical data, it’s meaningless for real-time transactions that have not yet hit your ledger. That’s where Digits’ online classification engine comes in.
We are constantly iterating and improving our algorithms to give you even better real-time insights into spend (with correct categorization and vendor identification), and it’s critical that those improvements don’t unintentionally regress other data points.
Over the past month, we’ve architected and implemented an automated classification regression monitor, which continuously watches a large and growing set of known-correct transactions from our sample data, and then automatically alerts on any unexpected changes.
This is just the start, and we continue to invest heavily in data validation, and data quality assurance.
There’s lots more we’ve been working on, though…
Digits for Expenses
- Improved time-series navigation with “Jump to today” feature.
- Improved support for yearly period views.
- Stabilized transaction deep-link URLs.
- Polished commenting UX.
- Enhanced activity feed with additional event types.
- New UI for identifying unknown/novel transactions.
- Automated P&L and Balance Sheet validation.
- Automated regression detection.
- Improved anomalous transaction detection.
- Improved vendor identification for journal entry line items.
- Productionized automatic vendor deduplication.
- Implemented depreciation and amortization exclusions for expense analysis.
- Improved auto-reconciliation of source transactions across larger time windows.
- Productionized new recurrence detection algorithm.
- Improved status monitoring of 3rd-party data providers.
- Tuned importer pipeline for performance and scale.
- Deployed API upgrades to improve imported data consistency.
- Improved handling of popular expense management software transactions.