The last 20 years have transformed the face of banking and lending. Previously, the traditional high street bank was where customers did everything from withdrawing and paying in money to applying for a loan or mortgage. You can now do everything at the click of a button. In the future, artificial intelligence (AI) and open banking will drive credit.
The Role of AI in Modern Lending
AI is the key to unlocking the potential of lending. By more accurately assessing borrowers’ risks through real-time analysis, it enables lenders to make quicker and more informed decisions. This also enables them to improve their portfolio management and cut out human error.
The technology can be even more powerful when combined with other capabilities. When you pair it with speech recognition technology, you can record and analyze verbal customer contact. In addition, it can provide more efficient sales prompts, enhance fraud detection and improve feedback. Going a step further, explainable AI allows for full scrutiny of every decision-making process.
The importance of data
Data is key to improving AI-driven credit decision-making. By gathering vast amounts of relevant data, lenders can help developers to build the right AI and machine learning (ML) tools needed to drive this decision-making process.
No doubt, early adopters like retail have made big strides with AI. The business lending sector hasn’t yet utilized AI to its full potential. That said, its slower uptake should come as no surprise, given the billions of pounds at stake.
Despite all this, organisations are beginning to embrace this new and innovative technology. To bring more people on board, however, first, all stakeholders need to get up to speed with AI’s full capabilities. That requires bringing everyone together – not just the lenders and technology firms, but also the banks providing the capital for the loans.
Lenders also need to be careful with AI’s adoption. There’s the danger that they try to go too fast and fully automate the lending process which will result in mistakes that are then hard to undo. Rather, first lenders need to get comfortable with the technology at their disposal and that requires using human intelligence to develop it.
Open banking as an enabler
Open banking technology is enabling the successful implementation of AI. It has essentially allowed loan providers to access businesses’ accounts. Through greater transparency and secure data sharing, it helps lenders to better assess applicants’ creditworthiness. This, in turn, enables them to make informed credit decisions, as well as reduce default rates. Added to that, it allows lenders to reach new borrowers they would never have considered or known about before.
The use of open banking data is particularly beneficial to startups that struggle to access more funding because of a lack of sufficient data. But as increasingly more data is gathered on multiple businesses that have applied for finance, lenders are able to make better predictions. This means they can widen the pool of businesses they can work with, above all, to newer businesses that require financing the most.
Risk Management and AI Adoption
By providing regular, data-driven insights into their financial performance, companies can improve their chances of getting approved for a loan. Using a monthly overview of the business and based on predictive technology, they can quickly identify any issues that may occur, the most common of which are caused by cashflow gaps, allowing them to take appropriate steps.
When used in conjunction with open banking, AI enables lenders to quickly assess a borrower’s likelihood of default. It also helps them to identify low-risk customers, allowing lenders to build a more sustainable customer base and, thus, improve profitability. Together, they will bring proactive customer advice and process innovation, as suggested in the FLA Future of Credit Report, as well as support the development of financial regulations and products that pursue a green agenda.
Successfully securing funding
Businesses can also improve their chances of success by sourcing funding when they don’t need it. This is because it’s easier to access better funding solutions from a position of strength. Unfortunately, most only seek funding when they desperately need it, however, by that stage, it’s extremely difficult to secure.
A lender’s ultimate goal is to accelerate loan approval times and develop instant, one-click lending through the use of embedded finance, thanks to AI and ML. But closing that gap has been notoriously slow as a result of data segregation on the business lending side, with the data having to be collected from various different sources.
However, there are reasons to be optimistic about the future. As firms increasingly make better decisions around capital investment and planning future growth, demand from small-to-medium-sized businesses has grown, and that trend only looks likely to continue.
There has been a large amount of government financial support over the last two-and-a-half years, which has resulted in a slight drop-off in demand. While still not at the pre-Covid levels, it is, however, starting to return.
About the author
Chirag Shah, founder and CEO of Nucleus Commercial Finance. Shah launched Nucleus over 11 years ago with a mission to provide flexible funding solutions to the UK’s economic backbone after realising that businesses had very limited options beyond the much-known incumbent banks. He vowed to be at the helm of a fintech lender that doesn’t outright reject SME applications with little to no explanation, instead focusing on a more transparent approach. Through our AI-powered automated underwriting machine, we can tell customers why their loan applications were rejected and work closely with our introducer network to support clients in getting that all-important yes.