Banking the unbanked

Banks have spent the last 70 years applying breakthrough technology to customer service: from credit cards in the 1950s and ATMs in the 1960s, through to 24-hour banking, biometric mobile payments and the arrival of cryptocurrencies.  Today, individuals anywhere in the world can make contact – and complete a transaction – within seconds.  The implications for everything money-related, including financial inclusion, are vast.  And when it comes to reducing systematic financial invisibility, AI has a big role to play.

Even pre-COVID, figures for the global unbanked population were staggering and today, the sector is thought to number 1.7 billion people.  In Sub-Saharan Africa alone, more than half the 1.1 billion population is excluded from the financial system.  In the US, the world’s biggest economy, around 22% of citizens are unbanked or underbanked, putting them at the mercy of short-term payday loans or check-cashing services.  In short, the contactless revolution, which millions of us take for granted, has bypassed these individuals, leaving them to rely on cash, prepaid cards or a combination to make their everyday purchases.

To get credit, you need a personal credit score.  To get a credit score, you need a financial ‘paper trail’.  Yet many citizens lack a formal bank account, payslip or digital financial track record, and this lack of historic data often throws up a red flag to lenders, setting these citizens on a path towards predatory loans and lifelong debt.   And financial exclusion is not an isolated issue: it seeps into other areas of people’s lives, such as homelessness, mental illness and isolation.  

Quite simply, access to financial products and services means the ability to make and receive payments, pay basic utility bills, get credit and insurance, start a savings account and get the best money-saving financial offers.  It means accessing healthcare or education, coping with a sudden job loss or reduced hours and ensuring you don’t become a target for opportunist cash theft.  

For entrepreneurs it means launching a new business; for SMEs (90% of global businesses) it means keeping the company afloat; for farmers it means absorbing shocks like crop failure.  For vulnerable sections of the population, particularly women, it can make the difference between life and death.  

And if we look at financial inclusion through a global lens, it can go a long way towards rescuing some of the 700 million people currently facing extreme poverty.

With extraordinary speed, the recent pandemic has digitized banking for millions of global customers. Simultaneously, AI-powered analytics has become an essential part of the digital toolkit for innovative banks looking to measure credit risk and meet lending criteria for previously un-lendable applicants.

Technology, like that offered by SparkBeyond, applies AI-automated credit risk scoring by taking vast amounts of internal bank data, however scarce or fragmented, and augmenting it with external datasets to find unexpected correlations.  This external data ranges from mobility, geospatial, demographics, healthcare, telematics and IoT, through to census, politics, trade and economic indicators, and climate change.  

By leveraging ‘world knowledge’ data and testing millions of hypotheses per minute, AI reveals previously hidden root causes behind customer default and identifies the macro-economic pressures likely to affect future behaviour – giving lenders a holistic picture.  New insights are then used to generate fast, accurate credit risk assessments of non-traditional loan applicants.  

Revolutionary credit scoring like this eliminates human decision-making bias and makes loans available to millions of people with patchy or unverifiable credit history, ranging from sole traders and smallholders to gig economy workers and students.  

For the team behind SparkBeyond’s AI platforms, two recent use cases underlined the remarkable potential of data-driven analytics when it comes to reducing financial exclusion.

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Australian social enterprise Speckle launched in 2018 – when an estimated 11% of the country’s adult population (more than two million people aged 18+ were experiencing real financial stress.  This not-for-profit lender offers digital loans between $200 and $2,000 and its mission is to help struggling Australians escape the cycle of debt and manage their money better.  Two years after launch, the Speckle team noticed a big leap COVID-driven loans to cover essential living expenses or unexpected expenses (bills or car repair), plus a higher rate of gambling, online retail and food delivery services.  They also noticed that borrowers were getting younger – and really struggling to meet repayments.

Working alongside Speckle, SparkBeyond utilised its AI platform to analyze customer datasets in granular detail – loan term and amount, income and repayment frequency, employment type and income, disposable income and loan amount, age and disposable income.  Within hours, AI-powered analytics had generated hundreds of novel insights previously hidden deep within Speckle’s vast troves of internal data.   

These insights – explainable and clearly visualised, dashboard-style – enabled Speckle to determine exactly who defaults and why, to automate its credit decision-making, and ultimately help unbanked Australians avoid exploitative cash loans.  Speckle also came up with an easy-to-use money management app with income, savings and loan tools, clear balance information plus nudges to get customers back on track.  Not only did Speckle address an important social issue, but it improved its business strategy, raised brand awareness in Australia, and got its team engaged with a renewed sense of purpose.

Partnering with another bank – leading Nordic player SEB and its new digital banking initiative called SEBx – SparkBeyond was tasked with helping to expand banking services to a 500,000-strong ‘high-risk’ customer segment – solo business owners. Using its AI-powered platform, SparkBeyond automatically combined six of the bank’s previously siloed internal and external datasets, and revealed 50 million patterns driving risk. SEBx used these insights to successfully launch its first venture – UNQUO – a unique card+app service to help solo entrepreneurs manage their private and corporate finances in a single place. UNQUO is now serving a growing microsegment – solopreneurs who build and run their business alone, and have traditionally been underserved by banks.

Clearly, technology will continue to drive progress.  After all, it’s not long since major lenders would have baulked at the idea of offering a loan to someone based purely on the data available on their mobile phone.  But today, AI can identify spending patterns, prepaid cards, digital wallets, social media information and behavioural attitudes from an applicant’s phone.  By mining data like this, AI can easily generate an accurate, mobile digital credit score – vastly expanding the possibilities for drawing previously unbankable citizens into the financial system.  Think about a smallholder farmer who needs credit to buy seeds.  AI-driven analytics can enable a lender to interpret the applicant’s personal data, combine it with external data on farm yields or market prices, generate a risk-scoring model and define lending criteria – within minutes.

As the world evolves, financial services companies will surely view inclusion as a business imperative.  New customer trends mean new revenue streams and innovative players who look beyond traditionally served sectors have the most to gain from scaling real-world impact.  And this, of course, is one of the keys to unlocking sustainable global recovery and making sure economic benefits are shared more widely.

The AI-powered revolution continues.