As our relationship with Artificial intelligence (AI) evolves, it presents both exciting opportunities and complex challenges for the lending landscape. While lenders begin to leverage AI’s capabilities for tasks like credit decisioning, fraud detection, and portfolio monitoring, they also grapple with critical ethical considerations and changing regulatory frameworks.
At the precipice of significant AI-driven shifts in the industry, our Director of Product, Sam Goodacre recently sat down with Credit Strategy to offer his valuable insights into what’s coming next and the areas that have the potential to drive real, tangible positive change for both lenders and borrowers alike. We summarise his conversation here.
Credit decisioning with AI
Talking about AI in this context, we need to consider whether we mean AI, generative AI, or machine learning, because there are different applications of those different technologies in different parts of the credit decisioning. The use of some early stages of AI, and some applications of machine learning provide an opportunity for significant change in credit decisioning. I think the major benefits are still on the cusp of taking effect.
Potential benefits for lenders include increased approval rates, increased revenue, reduction in credit loss rates, and increased efficiency through automation. For borrowers, benefits include potentially opening up access to credit for those with thin credit files by utilising alternative data sources enabled by AI and machine learning. It allows for a more holistic, personalised view of the borrower to provide more tailored loan pricing and terms.
Challenges include the need for large datasets to train AI models, the potential for algorithmic bias if the training data has inherent biases, and the need for explainable outcomes to meet regulatory expectations around transparency.
AI provides the opportunity for huge efficiencies but also removes that human touch, which I think is very important within lending. There needs to be some element of human interaction in there to make sure the decision is the right decision.
AI's role in fraud detection
AI provides advanced capabilities like pattern analysis, trend verification, advanced data analytics, real-time monitoring, increased accuracy, and predictive analytics. Traditional rules-based fraud detection systems are limited to identifing known patterns. Generative AI can learn and detect new, emerging fraud patterns autonomously, then flag them for review by human experts.
AI and portfolio monitoring
AI and machine learning can more accurately predict borrowers’ propensity to default by analysing large datasets in ways humans cannot. This allows lenders to efficiently monitor ongoing creditworthiness and affordability as required.
It wasn’t too long ago that people would have been doing that sort of analysis against a spreadsheet of data. That obviously has flaws, and just can’t react as quickly as we would want. AI also enables more personalised customer experiences, like providing customised repricing offers to low-risk borrowers based on their full profile and behaviour.
The ethics of AI in lending
In terms of open banking, people are consenting to their financial transactions and financial history being shared with the lender through an open banking connection. But are people really aware they are consenting to an artificial intelligence algorithm making some decisions on their behalf?
There are valid concerns that if the training data for AI models reflects human biases, algorithmic decisions could amplify those biases and lead to unfair outcomes for certain groups like minorities. The FCA’s view on it, which I share, is that it has the real potential to make the situation worse for certain groups of consumers. We need to be careful to ensure unbiased decision making with the use of AI and that we’re doing so in a way that isn’t actually causing consumer harm.
Regulation and compliance
The key challenge is how regulators can keep pace with the rapid technological changes brought by AI. The UK government has principles-based AI regulation that existing regulators like the FCA will need to apply on top of their existing frameworks.
Applying blanket AI governance while allowing the technology to evolve at its own pace will be an ongoing discussion to prevent causing consumer harm. Lenders will need to proactively ensure they adopt AI responsibly within regulatory expectations.
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AI and alternative data sources
This is an interesting area that ties directly into ethics and bias concerns. The reliability and trustworthiness of data from sources like social media is highly questionable given how much misinformation exists on those platforms. There are also questions around whether consumers knowingly consent to that data being used for lending decisions.
In the UK, we are much more risk-averse compared to the US about using those kinds of alternative data sources. We are moving towards using some alternative datasets like open banking information that can’t be as easily manipulated. Other reliable sources like payroll data from Revenue & Customs can also better inform decisions than social media profiles.
We know that there’s over a million people in the UK who are unbanked, and they just won’t appear on a traditional credit bureau report. So, if a lender is just relying on that data set, whether they’re using AI or working manually, they are highly unlikely to lend to that customer. There are lenders now who are specialising in using open banking data and they are pulling together financial transaction information into their bureau data set to try and provide a more holistic picture.
Lenders exploring alternative data need to tread very carefully and be upfront with consumers about what information is being used to make decisions that impact them. I would be uncomfortable lending money based solely on social media profiles.
Current and future applications of AI in lending
The current impact of AI in lending has largely been behind the scenes, with many applications still in their early stages. AI and machine learning are being applied in areas such as credit decision-making, but these advancements are not always visible to consumers. Promising use cases have emerged, such as combining AI with open banking data and traditional credit bureau information to provide predictive behavioural insights. At Lenvi, for example, AI is being used for document classification, ID verification, and email processing automation.
However, the truly disruptive potential of AI in lending is yet to be fully realised. The industry is expected to undergo significant shifts over the next 6 to 18 months, as companies increase their AI research and development efforts. The key to unlocking this potential lies in AI’s ability to deliver real, positive changes for consumers—moving beyond the hype to provide practical benefits.
One of the most anticipated advancements is the rise of explainable AI. In the lending sector, regulators such as the FCA emphasise the importance of transparency, fairness, and unbiased decision-making. Explainable AI will play a pivotal role in this, allowing lenders to offer clear, understandable reasons for AI-based decisions, whether it’s an approval, denial, or referral.
This capability will give lenders a competitive advantage. Rather than leaving borrowers confused by opaque decisions, explainable AI ensures that both lenders and borrowers can trust the outcomes. Involving a human element to explain AI-driven decisions will further enhance trust and lead to better results for all involved.
With the rapid democratisation of AI technologies through platforms like Microsoft and OpenAI, the industry is on the verge of seeing AI move from niche applications to standard practice. However, the most exciting developments are still on the horizon, with explainable AI set to transform lending by building transparency, accountability, and trust into the decision-making process.
Concluding thoughts
The lending industry is at an exciting juncture with AI at the forefront of innovation, and it is still in the early stages of its hype cycle. While current applications like generative AI, chatbots, and enhanced Machine Learning models are emerging, the true potential is yet to be realised. The growth curve for AI in this space promises to be steep and rapid.
At Lenvi, we are at the cutting edge of this transformation. I am thrilled about Lenvi’s direction and, more importantly, what it means for our clients and their customers. By leveraging AI, we can drive industry efficiency and deliver superior end-user experience, service, and value.
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