AI is revolutionising the way we do business. AI can automate processes, enhance decision-making and enable new levels of efficiency and innovation. According to GlobalData, the AI market is now expected to grow to US$909 billion by 2030[i] – a fact echoed by GlobalData’s Patent Analytics database, which shows that the total patent publication count for AI in financial services increased from 1,136 in 2016 to 5,625 in 2021. Additionally, the high number of mentions of AI in company filings shows that companies are aware of AI’s importance.[ii]
Use cases in finance range from customer support chatbots, diagnostics for money management and financial planning, personalised customer offers, computer code generation and more effective pricing and risk modelling. This last point enables lenders to offer more competitive rates to consumers while keeping loan profitability in check. For example, AI can analyse historical price movements to predict how changes in interest rates will affect demand and volume, ensuring that pricing is optimised to maximise both demand and profitability.
One of the most significant benefits of AI in consumer finance is its ability to streamline the credit decisioning process. Traditional methods, such as manual reviews and static credit scores, are both time-consuming and inaccurate.
AI can look beyond traditional risk profiling, examining a wider range of alternative data sources, quickly, to provide a more comprehensive view of a borrower’s risk profile. These alternative data sources can include historical price movements, market elasticity, customer segments and clusters, open banking data, competitive moves, regulatory developments, green finance and environmental rates.
Additionally, AI can speed up decision-making and improve operational efficiency, enhancing the customer experience by providing faster and more accurate loan approvals.
Adoption
However, implementing AI-driven platforms for pricing and credit risk decisions is not without its challenges. Cultural resistance to change is a common issue, with many institutions hesitant to change processes if traditional methods seem to work. Additionally, legacy IT infrastructure can also pose a significant problem, as updating outdated systems can be both expensive and difficult, and lack of data can also make creating models more challenging. Finally, the need for explainability in AI models is crucial. Lenders must be able to justify their decisions to both regulators and customers.
However, the opportunities are substantial. AI can help in optimising pricing by predicting the impact of rate changes on demand and volume and it can also automate underwriting processes, speeding up decision-making.
Additionally, by incorporating more diverse data sets into their loan decisioning processes, banks and lenders can enhance their credit risk assessments and improve affordability checks by offering more personalised loans. A better service for overlooked or underserved customers with open banking data provides insights into a borrower’s financial behaviour that traditional credit scoring can miss.
What’s next in consumer finance?
This is where Earnix Lending Plus comes in. Pricing and risk teams often struggle to accurately forecast loan profitability or volume based on changes in pricing strategy or adjustments in underwriting rules. Accessing a lending portfolio performance is frequently hindered by complex, disconnected pricing and credit risk decisioning systems requiring multiple handoffs.
Earnix’s new Lending Plus combines advanced pricing analytics, price optimisation, and simulation capabilities with automated credit risk decisioning in a single solution, so lenders can avoid lengthy and costly software implementation cycles. This results in an improved portfolio performance and ensures that each lending decision is optimised for risk, profitability, volume, or any other combination of KPIs set by the lender.
AI-based pricing and credit risk decisioning offer powerful tools for growing loan volume and loan size in consumer finance. By utilising advanced analytics and data sources, financial institutions can make more accurate decisions, leading to improved profitability and customer satisfaction. While there are challenges, the potential benefits make the investment in AI worthwhile, and embracing such technology can help high street, smaller banks and other lenders stay competitive and thrive in a data-driven world.
To learn more about increasing the profitability of your consumer loan portfolios, download the free paper below.
[i] GlobalData: Generative AI in Financial Services, Executive Briefing, Understand the business impact of technological advances in AI, July 2023.
[ii] GlobalData: Artificial Intelligence in Financial Services, July 2023
