Paul Randall, Executive Director, Creditinfo
Historic institutions, outdated technology and consumer-unfriendly approaches have ripened the financial industry for digital transformation. There have been some changes; today, cryptocurrencies are continuing to prove their resilience, mobile payment take-up is showing no signs of slowing, and traditional lenders are investing in young, digital-native start-ups – including, most recently, RBS taking a gamble on Loot.
A number of fintech innovations have taken place in emerging markets – think of MPESA in Kenya in the noughties – all of which comes together to make it easy to forget that, despite significant progress, there are still huge swathes of the global population with limited or no access to formal finance. According to a recent report by the World Bank, there are currently about 1.7 billion adults worldwide that remain unbanked; nearly one third of the world’s population. In sub-Saharan Africa, an estimated 80% of the population is unbanked. Conversely, the maturity of the financial industries in developed and Western regions masks a lesser, but persistent, problem: 3% and 6.5% of the population of the UK and US, respectively, are unbanked. Forty-six million Russian citizens are either unbanked or underbanked, with 37 million across Europe similarly lacking access to crucial financial services.
Without access to formal, regulated financial services, many individuals rely on unregulated credit, often with staggeringly high interest rates. Rather than delivering benefits for the long-term, this approach can result in individuals rapidly accumulating debt.
Banking on the unbanked
Far from presenting a risk to lenders and the broader economy, widening financial inclusion and reducing the under banked population offers a significant opportunity, and could add more than $600 billion per year to the global economy. Effort from traditional and fintech players to increase financial access must continue, in order to help alleviate poverty, support entrepreneurs and start-ups.
Improvements in connectivity infrastructure and increases in smartphone penetration globally have gone some way to addressing this problem. Many individuals who have struggled to obtain access to formal finance are already taking advantage of mobile lending solutions, particularly in those areas where the cost of deploying banking infrastructure is high. Mobile money transactions in sub-Saharan Africa, for instance, reached $19.9 billion in 2017, 63% of the global figure.
The same goes for start-ups and SMEs, with mobile banking helping to streamline operations (many of which were traditionally manual and laborious), reduce overheads, and widen their customer base via online services. Developments in mobile-based financial services and the growth of the industry means that more (and more tailored) products and services are now available, providing SMEs with bespoke solutions to best support the needs of their individual business.
This is a step in the right direction, and great news for those who are able to take advantage of lending and start/sustain a business. Yet in order to get on this ladder, individuals will be required to demonstrate their credit history to potential lenders and service providers. For those with no previous relationship with formal financial services – and therefore no data accumulated on their financial activity – this is problematic.
Assessing credit risk: a bulwark to progression
Many of the traditional banking infrastructures designed to manage corporate loans and consumer savings have proven to be unprepared for the challenges presented by both substantial increases in the volume of credit and specific requirements of an unsecured lender. In traditional banking, much of the initial risk assessment and data storage processes are manual.
For example, traditional lending methods typically screen out ‘thin file’ customers. These are customers with a limited credit history, and are often rejected due to the lack of information on how risk-tolerant or risk-averse they are when it comes to money. The absence of available background information for these customers on record creates an uphill struggle to receive the financial support they need. With a limited credit rating, some lenders may demand a high level of security before loaning any money.
One option may be to take out loans with high repayment rates, but in not being able to pay back the interest, these individuals jeopardise their ability to receive formal finance in the future, as any loan payments that are defaulted upon will make up the little information credit bureaus have to make decisions next time around.
There’s been much investment in and development of new fintech products which support payments, lending, transfers, budgeting, and so on. Yet there’s been less attention paid to the crucial first step in the financial inclusion roadmap: credit risk assessment. Providing (and adopting) new, digital methods of assessment is now needed.
A new approach to an old problem
Government officials are increasingly issuing licences to third party credit bureaus, allowing them to work alongside central banks to plan, deploy an infrastructure which will support financial inclusion. Some of these credit bureaus have pioneered modern fintech techniques, offering financial institutions a single source of data, whether that’s through a traditional credit file, or a digital file of aggregated data. As a result, lenders can provide access to financial services at a lower cost, to more people, while also reducing risk.
Solutions such as psychometric analysis allow lenders to combine new credit scoring methods with traditional models. Psychometric credit testing, based on questioning a person on their perceptions of the world, allows customers who lack relationships and historical financial data to build up a credit file and access funds.
In such a scenario, individuals are asked to answer a set of numerical and linguistic questions, and their responses are used to gain insight into their thought processes, behaviour patterns and spending habits — which are then used to predict their credit risk. The set of questions is based on the five-factor model of personality variation, also referred to as the OCEAN model, considered one of the most trusted and accepted models of personality to date. By mapping psychological traits to other data, psychometric credit score tools can be used to build propensity models across the consumer risk lifecycle and help lenders and insurers acquire new customers. These include those individuals who may have previously been rejected, not due to their risk level but to the lack of information related to their financial activity.
The approaches and solutions to reducing the number of under banked individuals are there; what’s needed now is a concerted, industry-wide commitment to adopting and leveraging them, with stakeholders working alongside providers of these tools to benefit their own business and wider society.
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