Beyond verification: Data extraction for credit assessment

Assessing creditworthiness both accurately yet quickly is tricky. There are rules to follow to remain compliant, yet any incremental improvement in speed is a clear competitive advantage. 

Lenders often relied on static data points and manual document submissions to make their decisions around approval for certain financial products. But these traditional methods have proven to be insufficient for capturing the true reality of borrowers. A new standard has formed to solve this problem, called Instant Bank Account Verification (IBAV).

The primary function of IBAV, as you may have guessed, was to simply confirm the existence of a bank account. But, its potential goes way beyond this simple check and is now use as a gateway to a deeper, data-driven understanding of a customer. 

Account verification

Let’s at least start with verification, seeing as that is at the crux of IBAV. Verifying a bank account was always a very cumbersome process - it was often micro-deposits of $1 sent to an account which the user would then have to verify days later - perhaps giving the reference code as proof. It was certainly effective for confirming that the account exists and is accessible, but was clearly time consuming, especially in the US where these transfers were not instant.

API-based verification has now changed this workflow entirely. Modern verification tools confirm the validity of a bank account in moments. It’s an immediate feedback loop that can help reduce abandonment rates, but it also has the data rails which are established during this verification process. Once a secure connection is made to a financial institution, the dialogue between lender and bank can move on from a simple query of existence to a bit more insight around ownership and operational status.

Name matching

The ability to confirm account ownership opens a lot of doors. A valid account number does not guarantee that the person applying for a loan is the legitimate owner, so this makes it much more secure against identity theft and synthetic fraud.

"Name Match" compares the name provided by the applicant during the onboarding process with the official records held by the bank. You get a match status, like "Full Match," "Partial Match," or maybe "No Match"). Cutting down on fraud prevention can reduce insurance premiums but also merchant account chargebacks, which in turn can reduce merchant account fees. For credit products, fraud is even more serious.

Financial data to help risk assessment

IBAV can eventually lead you to the world of Open Banking and data extraction. When a user consents to connect their bank account for verification, they enable a channel for broader data access. This is where the synergy between verification and credit assessment becomes most useful.

Access to raw financial data like transaction history and income patterns is a game changer for lenders. It provides a much clearer picture of the person and their financial health. Rather than a shallow credit score, there’s instead much more context. And, more context can lead to better accuracy over judging their risk.

It’s possible to verify customers income very easily - not just the amount but the regularity, along with automated classification of spending. Debt-to-income raito, among other metrics, can all paint a very clear picture.

Personalized loans based on this extensive understanding of customers can lead to fairer terms for both parties. Removing asymmetric information can in and of itself reduce a risk premium that lenders charge - a charge that is for the risk of not being exactly sure about their risk level. Some people have tarnished pasts, often unfairly, by the way credit scores work. This is a way to achieve more meritocracy so that customers can redeem and prove themselves with the fact - their habits today.

Reducing friction in the lending lifecycle

Then there is speed. Speed is very important in the lending market, with many customers citing KYC and checks as one of their biggest problems with financial products. The manual collection of bank statements and proof of ownership documents added days to the approval process. In the end, borrowers may seek funds elsewhere, especially with fast loans becoming more common.

To stay competitive, automated data extraction is clearly needed to be a bridge between risk management and user experience. Verification and data retrieval become bundled together, so the time-to-decision can be reduced from days to seconds. AML and KYC compliance is met, but in a fraction of the time.

This efficiency does not come at the cost of risk either. Instead, it only reduces risk. The data is sourced from the financial institution itself and is therefore tamper-proof and up-to-date unlike manually attaching statements. 

Intelligent assessment

The separation between verifying a user and understanding a user is quickly disappearing. Information is becoming more symmetrical, and this is always a benefit in the world of credit and risk. 

Technology that started as a way to prevent failed transfers has now matured into the furniture of modern credit infrastructure.