3 little-known use cases of financial data aggregation

Financial data aggregation has been on the rise. Despite its popularity, few know about these high-upside use cases that provide substantial value for banks and users alike.

Many already know about the most widespread use cases of financial data aggregation. You have probably heard of bank account aggregation services. They allow customers to get an overview of all their accounts from different banks in one place. Personal finance management with its budget categories might also sound familiar. This service aggregates customers’ finances from their various accounts, substituting that famous budgeting Excel sheet. Despite these highly exposed use cases, not many have heard about other financial data aggregation solutions, even though they can be even more game-changing than the prominent ones. But, before diving into these use-cases, here’s a quick recap of what financial data aggregation means.

What is financial data aggregation?

The term refers to collecting financial data from multiple bank accounts, aggregating it, and displaying the information in a way that provides value. As mentioned before, a well-known example is compiling data from various bank accounts from either one bank or multiple banks and utilising it in one place. This allows the user to see all of their accounts in one place without navigating between various interfaces.

In financial data aggregation, data is usually exchanged via Application Programming Interfaces (APIs), which are sets of computer programmes making it easier to communicate. This type of data exchange got widespread in Europe after the EU’s PSD2 regulation. It requires all financial institutions to grant easy access to customers’ financial data. This regulation provided a new playing field, and many players within the finance sector such as banks and financial aggregation companies began developing new services. Here are 3 of the little-known ones.

Loan switching

Loan switching aggregates data about customers’ loans that can either be in the same bank or different banks. This means that such a solution can also show who your competitors are selling to. The loans can be anything from a mortgage and a consumer loan to a car loan or similar. They can then be switched to the bank offering the loan switching service. Here is how it works.

Banks and other lenders can aggregate data from PSD2 and open banking to even if a customer has a bank account or multiple bank accounts with other banks. Aggregating this financial data allows banks to detect if one of their customers has a loan from a competing bank or a new player like a Buy Now Pay Later (BNPL) provider. Moreover, this type of aggregation service can also uncover actionable insights. It shows critical data points such as

• which competitor provided the loan
• the loan’s start date
• the size of monthly instalments

This is important as traditional banks can discover a hidden revenue stream in loan switching. Without loan switching, they will continue to lose consumer loan revenue to new players in the industry, such as BNPL providers. However, a data aggregation service like this can help lenders offer personalised loans that are more likely to convert.

Creditworthiness assessment

This is probably the newest use case. The EU recently enacted a new regulation requiring a stricter creditworthiness assessment process than earlier. Before granting a loan, EU law now mandates that lenders document customers’ income and expenses. Additionally, lenders also have to categorise these recurring transactions with several levels of granularity. Financial data aggregation can help banks comply with the regulation by establishing a creditworthiness assessment process that is fully automated and documented.

The European Banking Authority (EBA) recently published guidelines for each EU country to follow. One of the first countries to implement the regulation locally was Denmark. According to the Danish Financial Supervisory Authority, lenders must identify, categorise, and document recurring income and expenses. In Denmark, complying with the regulation presents a significant challenge for lenders. Some lenders took loan products off their shelves, resulting in a substantial revenue decline. Others hired extra personnel to assess borrowers’ recurring expenses and income manually. One major Scandinavian bank hired 15 people for the task.

To avoid these types of inconveniences, Subaio provides an automated solution that complies with the new regulation. The service can use any type of payment data, from PSD2 aggregated data via open banking to transactional data shared from a data warehouse. Its foundation is a financial data model based on identifying recurring income and expenses with 98.7% accuracy. Before aggregating the identified recurring transactions, Subaio labels them with the service behind the payment. The labels range from a salary and a child benefit transaction to a mortgage or a car loan, covering all transactions relevant for the creditworthiness assessment. After labelling, Subaio aggregates the transactions and presents them in a report as described in the EBA guidelines. Then, the lender can use the document to comply with the regulation in its creditworthiness assessment process.