How is Open Banking changing the alternative lending industry? A deep dive


For alternative lenders, what Open Banking brings to the lending experience is a data revolution.

Sure, lenders can use Open Banking powered payments to disburse loans, or integrate it into their apps so customers can make payments at lower transaction costs. However, the true value for alternative lenders is in the ability to bring in data from multiple sources under one roof and layering in machine learning algorithms for categorisation and credit scoring.

Lending pain points and why Open Banking is needed

Despite growing technology adoption, alternative lenders still experience problems such as data manipulation by loan applicants, inability to verify income, serial loan applicants juggling loans from different financial institutions, a paper heavy and tedious application process/customer journey that increases drop-off and decreases conversion rates.

Additionally, operational costs such as manpower that go towards manual credit decisions decrease lending volume and affect profitability.

This also hurts vulnerable loan applicants who get locked out of getting loans or suffer through long inefficient application processes, even when they need emergency loans.
How Open Banking works in lending
Data, data and more data.

Open Banking, through account information access, allows third parties to access customers’ current account data—with the consent of users of course—through APIs.

This includes any online account used to make payments; from business/current accounts, to credit card accounts and online e-money accounts.

Here’s how Open Banking creates value for lenders.

Imagine a customer applying for a loan with a fintech company, they give consent and access their bank accounts without leaving the lender’s platform. This happens through intermediaries who’ve built API connections to multiple banks. Lenders can then aggregate transaction data from their customer’s account and pull it into their own credit decisioning processes.

As a result, lenders get a richer picture of their customers’ financial health: from outstanding bills, how promptly they’re paid (or not), expenses, sources of income and their account balance. That way, lending decisions are data-backed without lenders having to lift technological mountains to make it possible.

From the real-life use cases I have seen in Open Banking, some of the Open Banking intermediaries have layered machine learning models on top of their bank API connections to help categorize transactions and enrich the data and get lending insights. They then sell access to tech to alternative lenders and fintechs.

Behavioural lending vs historical lending

If you are still scratching your chin, yet to see what all the fuss is about. And thinking about how traditionally, lenders have been getting on all right without all these fancy “Open Banking shenanigans”. But here’s the big change in Open Banking powered lending:

Imagine a retailer that discovered they could sell their transparent, sea-moss socks online instead of door-to-door when Covid19 first hit. The second month after going online, they tripled the previous month’s sales and suddenly can’t keep up with demand.

Under traditional credit decisioning models, if this retailer applied for a loan, the data available to their lender would probably not be up-to-date (their recent spike in online sales would not be used to determine if they deserved a loan).

With Open Banking, transaction data is real-time, and this allows for alternative lenders to use behavioural mapping, where—with artificial intelligence thrown in—they can identify revenue inflows, spending patterns, etc. and if there are other loans, lenders can analyse previous repayment behaviour to determine whether the retailer can afford a loan.

Even after disbursing the loan, thanks to the flow of real-time Open Banking data, lenders can analyse the applicant’s expenditure (ahem! did you use $5 to gamble this month…). This allows them to tailor future offerings to customers’ needs.

PS: though it hasn’t happened yet, (if you know anyone doing it, leave a comment…), this opens the door to custom loan terms tailored to individuals: hyper-personalisation. The challenge of hyper-personalisation in financial services has been data access, but Open Banking data might be a step in the right direction.

Financial exclusion, loans and Open Banking

Lenders who cater to individuals with low credit scores (or those who’d not get approved for a loan even if half of their family, and their childhood pastor worked at the bank) often rely on alternative credit scoring models with non-traditional sources of data.

Incorporating Open Banking data enables them to improve these lending processes.

For example, Wollit, a fintech company serving irregular income workers, used Open Banking data to analyse their customers’ income sources to determine their average monthly income, then, in the months it fell short, topped up the difference to be paid back interest-free. Wollit earned revenue by charging a subscription fee.

They’ve since pivoted to helping people build up their credit score, but their earlier business model stood out to me because it felt like a fusion of income insurance and lending—a truly interesting application of Open Banking.

Where exactly does Open Banking fit in my lending flow?
Credit risk assessment

Through analysing customer transaction data, lenders can identify triggers, which—for corporates—could include direct debits that have not been paid, large transfers by company directors, and expenditures over overdraft limits. For individuals, the triggers include gambling transactions, financial liquidity, fraud applicants detection, etc.
Income verification

Companies are using Open Banking to automate income verification through categorisation models that can identify regular and irregular income.

Tedious customer onboarding requirements contribute to user drop-offs during loan application. Open Banking helps with this.

For instance, peer-to-peer lender, Lending Works, automates form filling during onboarding using Open Banking data; with the aim to increase conversion rates due to a less stressful customer journey and experience.

This onboarding use case is also gaining ground within Buy Now Pay Later (BNPL) offerings, with Nelo and Aplazo both using it to reduce friction for their new BNPL customers.


Both alternative and traditional lenders have a chance at leveraging Open Banking data and payment capabilities to improve their offerings’ appeal. They can make the loan origination and application process smoother, or layer machine learning models on top of the data to improve credit decisioning. With these capabilities, alternative lenders can reach new groups of customers. For more examples of Open Banking use cases in lending check them out here.


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