It’s not the technology but how you use it: four ways AI will transform the credit industry

With so much talk of how generative AI is set to make an impact across industries, it is easy to understand why some will take these promises with a pinch of salt. The noise surrounding AI and its potential can be deafening, as firms adopt the badge to bolster their marketing efforts, making it difficult to separate hype from reality. However, when applied thoughtfully and to a well-defined problem, AI has the potential to transform the credit and loans industry by revolutionising how it reads, comprehends and summarizes vast swathes of data.

Credit generates a constant stream of data and requires close management at every point in its lifecycle. This applies to banks and credit issuers looking to lend, borrowers requiring tools and information to keep on top of their payments, managers ensuring loan books are stable, and investors mitigating portfolio risks. Digitisation and the rise of online banking have been vital in improving the availability of data. However, without an army of people extracting, processing, and analysing this data, much of it has historically gone to waste.

Despite its potential, according to US mortgage lender Fannie Mae’s Mortgage Lender Sentiment Survey 2023, only 7% of businesses in the mortgage space deployed AI last year, which leaves a huge amount of room for improvement. However, while transformative, on its own, AI is a blunt instrument. Credit market participants must think intelligently about how to apply the technology. The question all firms should be asking is: how can the technology add concrete value to our stakeholders?

Areas for improvement

Due to the complexity of the market and variety of stakeholders, it is impossible to single out an individual transformative impact of AI. The technology’s potential lies in applying it alongside large language models to the universe of mortgage industry data in ways that serve specific groups. By taking a step back to understand these groups’ fundamental needs and challenges, we can identify four key areas in which AI can change the industry for the better.

1. Help customer-facing and operational teams by automating data analysis:
   Managing multiple books of sometimes thousands of loans and mortgages is a significant undertaking. Managers need to ensure that repayments are being made, customers are supported, and risks are identified and dealt with early. While robotic process automation can be deployed to streamline these processes, an ability to derive predictive insights on how to improve and manage books and cut operational costs using historical context can provide an unprecedented window into firms’ operations. These AI models can also help companies provide customer-facing staff with faster data retrieval using natural language processing and summarize a client’s entire account history at a glance without requiring sifting through rows of data. Pepper’s AI Assistant today helps our operations team query any process specific concerns for a given customer using an “Ops chat” function at the click of a button.
   
   
2. Enhanced risk management for alternative investment portfolios: Due diligence and risk management are essential for institutional investors. Investors with diversified portfolios will be used to having a clear view of their public market investments. Alternative investments, however, have historically been opaquer. AI has the potential to act as an assistant analyst that helps provide deeper insights into the performance and make-up of credit books, giving greater oversight of investments outside traditional equity and fixed income markets. Coupled with AI-driven data visualisation, improved transparency into alternative assets can be a game-changing benefit for investors and managers.
   
   
3. Effective personalisation for customers: Generative AI is paving the way for highly personalised services for individuals managing their debts. Advanced chatbots can analyse and relay individuals' information, giving enhanced call assistance, tailored insights, and advice on demand that helps customers better control their debts. This is a crucial step in increasing credit managers’ capacity to respond quickly and compliantly to routine customer enquiries while freeing human resource to deal with more complex requests. Pepper’s own internal general chat, which is confined to its our inhouse team, serves as a ready reference for quick analysis and summarisation for business, operations, and tech staff.
   
   
4. Greater transparency for regulatory reporting: Greater transparency is a significant boost to regulators. Advanced fraud detection and the ability to marshal large datasets is an important step in painting a more accurate picture of the state of the credit market. Graphs that map entities and their relationships in such large datasets and the ability to query against them will lead to identifying associations that were not obvious before. Such connections can be used to detect early warning signals that can help inform decisive actions. Meanwhile, for firms operating in the space, greater transparency allows for increased accountability, more effective communications between companies and regulators, and helps to improve the overall quality of credit books.

No time like the present

The drive to leverage AI is only going to grow alongside the hype. The McKinsey Global Institute recently estimated that across the global banking sector, generative AI could add between $240 and $340 billion in value annually, driven primarily by increased productivity. The credit space is set to reap the same benefits, not just for managers but also for customers and investors.

These sentiments are backed by more than just words. The median size for AI-driven investments has more than tripled since Q1 2023 according to data from Pitchbook, with the average deal in Q1 2024 reaching $8 million as more companies invest in the technology. Knowledge is power in any industry, and attention to detail essential. This is especially true in credit, where individuals’ debts are highly consequential, and risks need to be carefully managed against a challenging economic backdrop.

Carefully implementing AI in specific areas has the potential to meaningfully change the sector by improving the scale and accuracy with which data is analyzed, automating cumbersome processes, and enhancing how stakeholders access information.  For example, Pepper Advantage’s operations team has embedded GenAI into its LoanGuard product workflow, which allows them to assess policies specific to various customer groups at speed, helping reduce the turnaround time for loan applications. 

This is just one small use case among many, but it demonstrates the value the industry can derive from intelligent and thoughtful use of GenAI technology. This technology is clearly going to evolve further, and it is incumbent on the industry to proactively look for, identify, evaluate how this evolution can be used to advance better outcomes for borrowers. Those who fail to do so risk falling short of their stakeholders’ expectations and, ultimately, falling behind.