Lenders weigh up the pros and cons of automation

The rise of artificial intelligence (AI), machine learning, and automated systems is transforming how loans are processed. Tasks that once took days now take only a few hours, although sometimes they are not as fast as customers demand.

VASS and Salesforce organized a roundtable to delve into the pros and cons that this technology offers. While it is clear that automation speeds up operations, reduces costs, and improves accuracy in decision-making, it is also evident that there is still a long way to go.

In the words of Lee Goodenough, managing director of VASS for the UK and Ireland, "There’s a lot more thirst and drive to get that instant decision back, and people don’t understand why it sometimes takes so long."

For Goodenough, not only does the general public demand greater automation, but this growth is also driving demand in the B2B sector. While many are concerned about possible biases and the high cost of automation, the trend continues to grow unstoppably.

Customers, in all consumer sectors, are becoming accustomed to immediacy. Waiting days for a result is no longer acceptable. As new generations enter the financial market, this demand becomes even more essential.

In that regard, Senior Director of Customer Transformation at Salesforce, highlighted that 'the next generation has never known life without the internet.' These future customers “don’t use ATMs, they’ve never been to a bank branch and wouldn’t even know why you would go to one. Everything for them is digital, so even if we’re not ready today, it is coming, so we have to start preparing for that future.”

Thus, technology must adapt to that demand by being fast but also accurate in such a complex environment as finance.

Current State of Automation in Lending

Automation in the lending industry is already widespread. Many lenders are using AI and machine learning to improve their processes. These technologies help analyze credit risks, automate loan approvals, and even predict borrower behaviour. 

Automated systems reduce the need for manual tasks, saving time and money.

Head of new applications at Iwoca, explained that while the credit decisions for over half of iwoca’s funded SME customers are now fully automated, even the non-automated decisions still harness the technology when a lending decision is being reviewed by a credit analyst.

“What we have built is more like a hybrid approach. Rather than using a human decision, we feed information gathered by the human to go along with the outcome of the automated credit decision into a system decision. By doing so we can reduce bias in our lending.”

By relying on data-driven algorithms, lenders can make faster and more accurate decisions. This allows them to handle more loan applications with fewer staff members.

Despite these advancements, not all lenders have fully embraced automation. For Andy Smith, who was formely appointed Chief Technology Officer at Interbridge Group, the complexity of the real estate lending market means that automation will always be a tool to increase speed and efficiency, but it will never be able to replace human decision-making.

In general, the lower the loan amount, the more automated the process is. However, for higher amounts, human intervention is greater.

For example, Director of Credit at Cashplus Bank, highlights the process for obtaining the bank’s credit card: “it uses automation at every stage, from the start of the application to the dispatch of the card.”

The challenges of a complex sector

The lending sector is quite different from others, with many players involved in a process that becomes longer as the complexity increases. 

Smith points out that the average onboarding time for a customer in the second-charge mortgage market is 30 days. As he notes, "there’s a lot of legal documentation, you have lawyers involved, we need to obtain consent from the first-charge lender. There are many external processes.”

Therefore, the use of technology can help significantly reduce that average, but it is still far from the immediacy that new generations seek.

Lenkie’s CEO and co-founder, emphasizes the importance of the human factor in decisions involving large sums of money. His company provides loans to SMEs ranging from £50,000 to £500,000, with plans to grow up to £1 million in the future.

Human involvement is significant in their decision-making and assessment process. In that regard, he points out that it is “a matter of efficiency and accuracy (...) where there are many nuances and a great deal of important details. So it makes sense to have a human underwriter to approve £250,000, £500,000, or £1 million decisions.”

This is also reflected in the general trend, where as loan amounts increase, the level of automation decreases. The goal is to gradually reduce the amount of time human analysts dedicate to higher-value loans.

Artificial intelligence already aids in pattern discovery, and AI-driven risk analysis serves as a starting point for human experts. The objective is not only to speed up the process but also to uncover insights that might be missed by the human eye. It's not just about moving faster, but also being more efficient and discovering behavioural patterns that would go unnoticed without the power of AI.

Thanks to machine learning and big data technologies, these analyses are becoming increasingly accurate, but they are always overseen by humans for a double-check.

In any case, as Sales Director for the UK and Ireland (Banking) at Tink, points out, the difficulty in accessing customer data in commercial banking is an increasingly growing challenge. In this regard, startups like Teal are looking to unlock payroll data for lenders through partnerships with data providers.

For its CEO and founder, Michael Hart, it is not feasible to wait for regulators to act, and alternative solutions must be sought.

Customer experience, key to automation adoption

A fundamental key to automation is that it must become an ally of the customer experience, not the other way around. The increasingly automated systems must also be more personalized for the end customer.

As Chris Rae, Regional Vice President of Financial Services for the UK and Ireland at Salesforce, points out, what is always emphasized is "that customers have little time. They want to be informed if there are any delays, and if there aren’t, there should be some kind of feedback or instant gratification about what they have done and where they are in the process.”

The secret lies in using automation and AI empathetically. It’s not just about being faster, but also about ensuring that customers feel more informed and supported at every stage of the process, without necessarily requiring human involvement at every point.

In this regard, Lenkie is working to “convey empathy through technology,” ensuring that the design of its technological processes also takes the customer into account.

For its CEO, "if you design your processes with empathy as a principle, you can actually use technology to recreate some of the empathy that a human provides. We try to combine both, which benefits both the customer and us."

In conclusion, automation is transforming the lending sector by speeding up processes, reducing costs, and improving decision-making accuracy. However, there is still progress to be made to achieve the agility that new generations demand.

Automation can help financial companies adapt to growing expectations of immediacy and personalization, while artificial intelligence enables faster and more reliable processes.

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