The COVID-19 pandemic has had a pronounced effect on the lending industry. Creditworthy borrowers, wary of uncertainty, borrowed less, while those higher-risk customers had to increase their balances to cope with reduced incomes. In the UK, after a considerable drop during repeated lockdowns, mortgage approvals boomed as pent-up demand was released with people looked to move out of the cities in search of more space and tempted by cuts in stamp duty. In all areas, consumer lending moved almost entirely online with banks, building societies and credit unions having to rapidly raise their game in the digital arena, while consumers once uncomfortable with digital lending learnt to accept and then value it. As we slowly inch our way out of the pandemic, what are the biggest trends in the digital transformation of the lending industry?
The rise and rise of digital lending…
Digital lending accelerated during the pandemic as its advantages for both consumers and lenders became clear. For consumers the ability to self-serve in an end-to-end digital lending journey is quick and convenient, leading to faster lending decisions with less documentation needed. They can enjoy an omnichannel experience, able to switch between website, app and social media easily and seamlessly. For the lenders, automating processes not only reduces the workload for back-end staff, it also increases efficiency and reduces errors and delays in decision-making. In addition, it arms lenders with huge amounts of customer data that can be analysed to further improve service and create attractive new products. In terms of mortgages, some lenders, for example Canada’s Scotiabank, were already pioneering end-to-end digital mortgages well before the pandemic, but now they’ve gone mainstream. According to The Mortgage Reports, 43% of 2020 homebuyers completed their entire mortgage application online.
As open banking becomes more common, customers can choose to share their banking data with potential lenders outside the traditional banks with their narrow qualifying criteria. Millennials and Gen Zedders are often lacking the credit profiles needed by traditional banks. Together with freelance and gig economy workers and new immigrants they’ve gravitated towards the FinTechs who use digital technology to widen their criteria and make more nuanced assessments of lending capacity. This means that millions of people who might have been refused lending by traditional banks can now gain access to lending products, making for a more inclusive financial ecosystem,
Given that Gen Z makes up 40% of the market, or 65 million people in the US, the traditional banks need to fully embrace digital lending with the same panache as the FinTechs if they’re not to be edged out by this digital native generation who expect nothing less than effortless, hassle-free online experiences and would be amazed by the paper-based physical-world processes that have so quickly become outmoded.
…enabled by AI and machine learning…
All of this has been made possible because of the capabilities offered by artificial intelligence and machine learning. It means thousands of data points can be analysed to provide much more accurate credit assessments, reducing loss and speeding up decision making, looking not just at ability to pay, but also at behaviour that suggests whether they are minded – or not – to settle their debts. US-based company Personetics combines AI with nudge theory to help students with loans to pay off get out of debt quicker, by predicting and analysing their spending and notifying them when they can afford to up their loan payments, saving them potentially thousands of dollars in the long run.
Though artificial intelligence can suffer from the same biases as humans, now it’s also being used to reduce bias that might otherwise be perpetuated in lending decisions. Business Review reports that one lender conducting an analysis of historical data found that women had to earn 30% more than men to be accepted for the same-size loan. Using AI, the lender could ‘level up’ the data before it was fed into a new algorithm, improving equality in decision-making without increasing risk.
Meanwhile, machine learning can not only help lenders give fairer rates to applicants who are in reality low risk but lacking in a strong credit history, it’s also being used to determine whether an applicant is being truthful about their income and detect potential fraud.
…which allows for hyper-personalisation
AI and machine learning in turn allows for hyper-personalisation. From Netflix’ recommendations to Starbucks’ tailored offers, people can’t get enough of unique experiences made possible through sophisticated and wide-ranging data analysis.
According to Accenture, 83% of consumers are willing to share their data to create a more a personalised experience. In retail banking 90% of customers who receive personalised services say they are highly satisfied and will definitely use their bank again for another product. Hyper-personalisation may be powered by highly sophisticated technology, but it creates a satisfyingly intuitive and human experience, one in which you are treated as an individual and your needs are anticipated.
New business models, new collaborations
As open banking takes hold, non-banks such as the tech giants are not only entering the lending space, they are also collaborating with long-standing financial institutions. By working together each can leverage their expert skills, brand and customer base in their respective areas to launch innovative new loan products. Both Apple, with the Apple Credit Card, and Amazon, with hyper-personalised working capital loans for favoured sellers, have partnered with Goldman Sachs to realise their products. Meanwhile traditional banks are also partnering with FinTechs using their expertise in data analysis and processing to help transform their systems.
These new entrants were once seen as a threat by traditional banks, and no doubt still are to some degree. But what the banks recognise is that collaboration could be the key to staying afloat in the modern banking ecosystem. Some banks could even become marketplaces of personalised products offered to their customers by other suppliers, sharing valuable data in return for a cut of the profits and retaining their own relationships with customers who might otherwise melt away.
There’s also a realisation that loan products aren’t interesting per se, it’s what people can do with them that counts. By being able to offer products in the context of purchases, they can be there at the right moment, rather than waiting, perhaps in vain, for a customer to turn up at their website because they need to, for example, raise the finance to buy a car.
It’s also worth noting that one UK company is turning this idea on its head. Oodle is aiming to change the used car market by merging search and finance into one seamless digital process. Customers first apply for the loan they need and are then matched with vehicles from car dealerships. In the five years since launch it’s already found considerable success, receiving more than £1.18 billion-worth of finance applications and backed by Citibank and global investment firm KKR.
Blockchain technology speeds up the lending process
Blockchain can do away with the need for banks altogether. Its decentralised ledger technology can create a platform that brings two parties together and indelibly records all documents and transactions so there’s no need for a gatekeeper. This up loan approvals to driving down costs, allowing for lower interest rates, as well as providing security and transparency. At Salt Lending users can use cryptocurrency as collateral against a loan, something traditional banks wouldn’t even dream about. Blockchain can also enable peer-to-peer lending platforms, with Lendoitcalling itself ‘the world’s first’. Though it’s in the early stages, this Tel Aviv-based start-up and others like it could be the future of the lending experience for some consumers.
The pandemic has speeded up digital transformation in the lending industry. If traditional banks want to tap into younger generations of lenders – and let’s face it, if they want to survive as lenders they will have to – they need to beat the FinTechs at their own game. This means using AI and machine learning to broaden their lending criteria or bringing them on board to supply technology and even products. Younger borrowers will be looking to access products that build credit ratings without charging unreasonable fees and rates.
New collaborations and business models will see a wave of innovation in the next few years, as banks seek to provide products where their customers are browsing and blockchain perhaps begins to find its place in the lending industry. As different trends in transformation combine and gather pace, the only thing that never changes in this industry is change itself.
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