Over the past few years, we’ve heard a lot about how Open Banking will transform the financial industry and help improve products for consumers. How can open APIs enable third-party developers to build services and apps around financial institutions that help users?
The Open Banking space consists of three main parts: access to transaction data, analysis of such data, and follow-up actions based on the insights. Latvian fintech start-up Nordigen focuses on the second part of this triumvirate. They offer financial institutions an instrument for data analytics, account-based income verification, and transaction categorization to improve bank’s lending processes and products.
Most banks base their decisions regarding creditworthiness of clients on credit history and income data. Nordigen’s account analytics engine leverages Open Banking to create transaction-based, behavioural predictions to improve credit-scoring. They go beyond credit history by recognizing the purpose of a transaction based on its description, verifying various sources of income (including part-time job compensation). Essentially, theypredict defaults based on recurring patterns in transaction data. The engine is able to identify 1,000+ risk-critical behaviours within every bank statement. One bank stated that implementing Nordigen’s categorization engine enabled them to significantly decrease loan assessment time and increased lending efficiency by 30%.
As we prepared to meet Roberts Bernāns, the co-founder of what is probably the most visible fintech start-up in Latvia, we asked ourselves a few questions: “Why don’t banks develop and execute these analyses internally? How does a start-up from Latvia, with a team of just 30 people, operate across 16 countries, including Spain, Germany, the United States and Australia, working with 80+ global financial institutions?”
“The main reason why banks are buying fintech solutions is time-to-market. They are not scared of fintechs, on the contrary – banking industry competition drives demand for constant innovative advantage angles.”- Roberts Bernāns
Traditional banks have trouble developing such internal solutions not because they lack the capabilities, but because bureaucracy, budget restrictions, time, and risk-aversion gets in the way. These obstacles free up space for agile fintech companies.
Initially, Nordigen was not an analytics company, but rather a provider of descriptive bank summary statements with a focus on automation. Quite soon, the founders realized that banks “are sitting on all this data” not knowing how to leverage it. It became clear that Nordigen’s focus should be on adding value by using transaction data to improve lending decisions, detect fraud, or even just help banks better understand their customers.
Their first clients were alternative lenders, who were more flexible and eager to adopt new technology. For these clients, Nordigen provided access to the cloud platform, where they could upload client data, and the built-in models would generate the analysis. Later, Nordigen began working with Tier-1 banks, to whom they provided on-site solutions.
“A lot of startups overengineer. There are many problems where you don’t need rocket-science solutions but practical ones which add business value… The hardest part is not the model but to prepare the input data for the model. We built a smart tool to do that!”
Roberts doesn’t reveal the secret recipe. He also admits that, as with any other tool, there are limitations, however, the engine supplies banks with valuable insights and a more holistic view on the creditworthiness of their clients. Their software also has potential in other sectors, such as insurance and telecoms. Nordigen’s API accepts various file formats and can support transactions from any country in the world, allowing its global clients to implement it across all geographies of their operation.
How do products like the one offered by Nordigen benefit regular banking customers? The question is actually broader – how does Open Banking benefit consumers? What do we receive by sharing our data? Analysis of transactions and financial data will certainly allow some underbanked categories of people, like those with part-time jobs, to verify their income and get access to debt. Moreover, it helps both individuals and SMEs to receive better loan terms. Finally, it enables improved default predictions, which helps secure the financial system, and therefore the economy and everyone in it.
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