The financial landscape has undergone significant change over the past few years, this is hardly breaking news. Large banks are readily partnering with agile fintech startups, and cash is predicted to make up merely 21% of payments by 2026. Despite this progress, up to 90% of loan applications are still rejected by banks and alternative lenders worldwide.
Many banks and lenders still rely on traditional data and inaccurate risk assessment processes. However, within the past few years, we have seen the rise of Open Banking, the development of new behavioural analytical solutions, and a huge increase in available data. The financial landscape is finally undergoing a much-needed process of evolution. Not only do we now have the resources, but we also have the tools to utilise them effectively.
Treading water
The fundamental problem, that is often overlooked, is that roughly three billion adults worldwide do not have credit records and struggle to open a bank account, secure a loan or purchase a home. Consider the catch 22 many immigrants face when arriving in a new country; one needs a registered home address to open a bank account, but cannot sign a tenancy agreement without a bank account. For those lucky enough to have credit, their data is currently processed by traditional credit bureaus that can provide suboptimal recommendations on their financial stability.
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This also means that banks are missing out on huge swathes of viable customers in numerous markets. Accenture recently revealed that bringing this unbanked population into the formal banking sector could generate a staggering $380 billion in additional revenue for banks.
With an increasingly fragmented market and challenger banks snapping at their heels, traditional retail banks, in particular, need to re-orientate their assessment processes or risk being left behind.
Diversifying the pool
Partly due to the ever decreasing cost and size of processors, we are in an unparalleled age of information availability. This trend is only set to continue, with McKinsey finding that 1.7 megabytes of new information will be created every second for every person by 2020. Unfortunately, whilst information is available, banks and lenders still often only rely on traditional data sources from credit bureaus that leave many financially excluded.
Traditional methods for assessing financial stability often use paychecks, tax returns and transaction data. However, the former two can be ineffective. Paychecks, historically, have no standardised forms and are incredibly time-consuming when it comes to processing. As well as this, their paper and pdf formats are easily forged and pose a higher risk. Similarly, tax returns forms are difficult to acquire, are often coupled with a time lag and present negative bias towards individuals who are not required to report particular income sources.
Unsurprisingly the latter method, transaction data, has proven most effective as it eliminates time lags and reduces both human error and negative bias towards individuals. However, whilst most lenders do produce monthly summaries using transaction data, they often do not utilise this resource to its full potential.
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This is not so surprising – effectively categorising these complex data sets is an incredibly difficult task for an engineering team to tackle; the data is rich, but it is also unstructured. However, this decade has seen the rise of behavioural analytical tools that can produce robust assessments using this once unwieldy data source. This is already happening, with the likes of Solaris Bank and Zopa as early adopters of behavioural analysis.
This is not to say that traditional verification procedures should be scrapped altogether, far from it. Credit checks are a fundamental pillar of the loan application process. Rather, it is to say lenders must take a more holistic approach to risk assessment if they want to help the unbanked and grow their potential customer pool.
A growing dataset
We have further cause to be optimistic. With the rise of new and improving aggregation services and Open Banking APIs, account data is becoming increasingly accessible. It is now possible to factor in additional revenue sources such as rent, the ‘gig economy’ (e.g. Uber drivers), pension and income from investors. Not only does this give the most complete overview of someone’s income, it means those currently without a credit history or those moving to a new country, for example, can be quickly and adequately assessed.
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Unlocking the three billion unbanked adults means gaining access to this alternative data, such as transactional, telecom, utility and rental data. These individuals represent a huge gap in the market, both those existing in established credit infrastructures but without credit, as well as people in developing markets.
Banks now have the chance to improve the lives of the financially excluded by taking on a whole new set of customers and assess them, and all their other customers, accurately. To put it simply, we now finally have a reason to get excited about data again.
Written by Rolands Mesters, founder and Managing Director of Nordigen
