On the road to building better financial services experiences for business customers, all will encounter the banking vs. general ledger data crossroad. Should we integrate one, the other, or both?
The answer, as with all things is, “it depends” – especially on what problem you’re trying to solve. In any case, understanding the value of both datasets is a key requirement for building better fintech products.
What is the difference between banking and general ledger data?
The bank feed data is typically made available by banks and financial institutions via API. These APIs are growing increasingly commonplace with the advent of
Open Banking in the market and among regulators. The bank feed dataset contains:
• Real-time transactions
• Real-time account balances
• Recurring or scheduled payments
The general ledger (accounting) data is sourced from accounting software and made available to enterprising fintech companies to build with via API. The general ledger is a nexus for financial data in a business. In the past, manual and/or bulk uploads of data were required to get the underlying transactional data in. But today, it is a seamless experience being able to connect to payment software, ecommerce programs, inventory management tools and more. The general ledger dataset typically contains the following:
• Categories for transactions
• Supplier information
• Customer information
• Invoice and billing information
• Tax data
• Future payments schedules
The value of bank feed data
Bank feed data can unlock clarity and transparency of money balances. A painful problem that many small businesses share is the friction the of managing their money balances. While bigger companies can invest in a dedicated treasury function within the finance organization, small businesses are left time-starved and stress-induced in trying to ensure they have enough money to continue day-to-day operations while investing in the future. In many cases, operators must deal with multiple bank accounts across multiple banks which is a headache at the best of times.
An popular use case is using the transactions to
build anti-money laundering or other risk management controls. Direct access to a business’ bank feeds presents a golden opportunity to create and innovate on antimoney laundering controls and practices. Whether it’s through experimenting with machine learning techniques or combining the dataset with other non-obvious datasets, the appetite for such solutions will only continue to grow.
The value of general ledger (accounting) data
General ledger data carries with it a lot of depth surrounding key items of interest like transactions, customers, and suppliers. If a bank feed can supply a record of transactions coming in and out of a business account, the general ledger will take each record and tie it to additional information like the category of expenditure and supplier or customer details. The rich layer of insights associated with key items make it possible to build intelligent financial service experiences for business customers.
Potential use cases include (but are not limited to):
Automated analysis
Since all the transactions in the general ledger are categorized, companies can build automated and recurring analysis tools to solve answer a range of business questions such as:
• What is the biggest source of spend?
• How does the ROI of our various investments fare?
• How well are we managing our inventory?
• How are we getting paid?
• Are we too reliant on too few suppliers?
Data synchronizations for risk-monitoring
The economy is moving towards a service-first and partner-centric model. The value of
risk-monitoring solutions will only grow whether that is between a service-provider and customer or between partners. A business’ financial health will almost always a major factor during negotiations, and as such, solutions that allow for an easy yet secure way to share data around business performance and risk factors will be in-demand.
The combined value of bank feed and general ledger data
The bank feed dataset and general ledger (accounting) dataset are popular pillars on which to build next-generation financial services experiences. In many cases, they are best paired together: the bank feed to track real-time transactions and the general ledger to overlay insights on spending, customers, suppliers, and more.
An apt analogy is to think of the bank feed as offering transactions layer to a financial services solution and the general ledger as a source for the insights layer.
You can build a more complete picture of a business’ financial health by integrating both datasets into the financial product you’re building. Having both unlocks the ability to compute measures of liquidity, solvency, or profitability.
