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The Critical Components of Loan Personalisation in Unsecured Lending

Giovanni Oppenheim

February 28, 2024

  • Personalization
People Sitting at the Table

A quick guide for consumer lending leaders on key personalisation capabilities

For many, the term "personalisation" is a buzzword with strong ties to digital experiences in online retail and consumer marketing.

Personalisation applies just as much to the banking world.

Personalisation is enabled by a set of specific core capabilities grounded in a modern tech stack, data analytics, and Machine Learning (ML) that deliver the right loan (amount), with the right offer (terms), to the right customer (the one most likely to accept), at the right time (the moment of decision).

So, why is loan personalisation so important today? And, most importantly, what it's in it for you, the lender?

In this blog post, we'll review the impact of loan personalisation on conversion rates, revenue and volume growth, and profitability.

After a rapid growth in interest rates for personal loans in 2023 despite a lagging increase in deposit rates, UK banks will face mounting margin pressures in 2024. The mortgage slowdown, regulatory scrutiny, and deposit shifting — where customers move excess funds out of current and instant access savings accounts to take advantage of higher interest rates — will all work in the same direction: a tight squeeze in access to credit to contain costs.

On top of tighter margins, lenders will have to navigate a complex matrix of regulatory changes, consumer behavior shifts and technological advancements to secure a competitive advantage and foster sustainable growth in personal lending. High-interest rates will continue to strain affordability, lenders will need to compete to attract and retain responsible borrowers looking for easy credit access, and intensifying regulatory initiatives such as the Consumer Duty Act will necessitate a strategic overhaul of lending offers to ensure compliance. And like digital, the Age of AI and the presence of Account Information Services (AIS) is likely to have a transformative impact on the industry, affecting roles in virtually every part of the bank.

"A quarter of a century ago, banking stood on the verge of the Digital Age. The Internet was starting to reveal its potential and most bankers had a strong premonition that far-reaching change was coming. Today we feel a similar sense of awe as we contemplate the potential of gen AI, especially when powered by the cloud and rapidly expanding data capabilities. Banks have always known optimized pricing can hugely impact their top and bottom lines. Now they're starting to combine intuition with gen AI and more comprehensive data to turbocharge scenario planning and move closer to personalized pricing."

Accenture Research Report, "Top 10 Trends in Banking in 2024", January 2024

Why is Loan Personalisation Critical Today?

The potential customers that are out there,  are increasingly selective when it comes to who they'll borrow from and whether they will remain loyal over the long term. Driven by the "Amazon Effect", borrowers have become ever more demanding.

When customers are ready to commit to a loan, they are ready to move decisively. They expect the process to be timely, painless, predictable, and efficient. Unless these conditions are met, customers will describe the loan application process as too slow, too cumbersome, too long, too "one size fits all" and unnecessarily opaque, and they will walk away.

It's All About Personalisation

As a lender, how do you cut through the noise to reach those buyers looking for the loan that's just right for them, and steal that business from the competition?

The answer is loan personalisation — and that's exactly why personalisation has been gaining popularity in the past several years across all consumer lending lines.

As stated above, loan personalisation is about delivering the right loan (amount), with the right offer (terms), to the right customer (the one most likely to accept), at the right time (the moment of decision).

This creates a win-win scenario for the customer and the lender. The customer wins by getting a market-competitive rate and terms that are acceptable to them, and the lender wins by increasing conversion at an optimized margin. Offer-influencing factors can include personal experiences, market trends, cost of funds, historical customer data, the purpose of the loan, and many more.

The Four Pillars of Loan Personalisation

When a consumer shows interest in a loan, lenders must get down to real business in real-time, and with the best possible offers. Here are the four pillars of loan personalisation.

1. Digital Delivery

As mentioned earlier, loan applicants, particularly younger ones, are primed to engage directly with lenders who digitally deliver personalized loan offers. Having "grown up digital" and in an instant gratification world, these consumers expect to research, compare, apply for, and close loans online.

Loan terms, conditions, and alternatives must all be made available for their consideration in real-time, with the underwriting process automated and instantaneous as well.

2. Presentation of Alternatives

Developing terms for unsecured loans is a multivariate process, requiring sophisticated analytics to optimize the key parameters of the loan, tailored to each customer.

Alternative deal structures (ADS) can also play a role here, allowing lenders to offer up several financing options in response to a single credit request. ADS are quickly becoming a must-have way for lenders to keep potential borrowers from walking away, by giving them a range of pre-approved options at the moment of decision.

3. Smart Decisioning

Smart decisioning revolves around machine learning (ML) models and their constant tuning using artificial intelligence (AI). This allows a lender to understand which offers are most relevant to each applicant, leading to higher conversion rates and increased customer satisfaction.

Price optimization — the lynchpin

Price optimization is a major part of the personalized loan.

A customer who is more concerned about their monthly payment might be willing to take a longer-term loan with a higher APR, while a customer who is prioritizing the total cost of the loan over its life will be inclined to overlook all other aspects of the loan in favor of the lowest possible APR.

These variations are numerous, creating a favorable environment for AI-driven analytical tools where data about a specific consumer, market trends, behavioral models, and profitability models all come together for a real-time decision on what loan variations can be offered to a consumer.

In the end, the consumer receives a highly personalized loan offer that fully aligns with their individual willingness to pay.

Data — a lender's "ace in the hole"

Data is also key to analytical prowess. Some data, of course, is gathered during the application process itself. For existing or return customers, the bank already has access to additional data that aid in personalisation and relieve borrowers of having to find and input that data when applying for a loan.

By tracking the "whole customer" during the life of the relationship and analyzing consumer behaviors (on-time payments, late payments, interactions with other products and accounts, transfers to and from other institutions, etc.), lenders can offer updated prices, more favorable terms and conditions, additional offers, complementary products and services, and improved customer service.

The more products and services each customer accepts, the deeper the relationship and the more intense their long-term loyalty.

4. Impact

As mentioned earlier, loan personalisation is a "win-win" approach — lenders who can meet customers' needs stand to deepen banking relationships and build loyalty.

In the realm of customer acquisition and lifetime revenue, some banks have already reported transformative results:

  • At TSB in the UK, real-time personalisation contributed to a 300% increase in loan sales among mobile users

  • A European bank with nearly €1 trillion in assets launched a personalisation effort that increased its unsecured booked loan volume by 9% in just the first year

  • That same bank experienced a 17% increase in "all-in" revenue (interest and fees) from those loans, adding what is projected to be €180 million in top-line revenue over the life of the loans closed in that first year

Personalisation has other business benefits as well. Operational costs are reduced, as human intervention is not required in most lending scenarios, and smart routing to call center personnel can also take place to keep costs under control and provide continuity in the customer experience.

Risk exposure can also be monitored and adjusted continuously, and the system can also aid in regulatory oversight and compliance.

Key technology components of personalized lending

Lenders who master personalisation will reap significant rewards. The technology is there and takes several forms. Here's the checklist of what to look for:

  • Predictive modeling — using data available about the customer, we have the context with which to make individualized lending offers. The personalized banking solution must include a wide set of optimization algorithms geared to handle various pricing structures, regulatory and business requirements, and market scenarios.

  • Advanced analytics — analytics are at the core of every personalized lending solution. Analytics gives lenders the critical ability to craft an offer (or a set of offers with ADS) in real-time, with a predictable outcome. By employing world-class data science, artificial intelligence (AI), pricing optimization algorithms, and machine learning (ML), lenders can deliver the best-priced and most personalized product to every customer, every time.

  • Real-time capabilities — markets are highly dynamic. Prices must change regularly. Developing and implementing the best pricing strategy for personalized lending can no longer take weeks or months. The lender must be able to adjust pricing in short order and present the offer to the customer in real time, at the point of decision.

  • LOS/lending platform integration — once an offer is delivered in real time and accepted by the customer, that loan information must be passed seamlessly to the lender's loan origination system (LOS), so that the completed loan application can enter the workflow, document management and compliance tools already in place.

  • API-based architecture — Application programming interfaces (APIs) allow seamless connections to complementary technology. In the case of legacy applications and infrastructure, APIs allow transitions to take place in steps, so that software can be replaced or augmented over time. APIs also open new possibilities for connecting with business partners, increasing reach and allowing lenders to potentially offer a broader array of products and services.

  • Governance and regulatory compliance — The personalized lending solution must not be a "black box". It must be open and transparent, with an audit trail for all transactions. This ensures that lenders can comply with fair lending rules and regulations, can back up their decisions and offers with an easily understood set of algorithms, and have immediate access to a transaction history that can be reviewed internally and by external auditors.

Summing up

Earnix ticks all these boxes.

The Earnix platform offers product owners, data scientists, and pricing professionals a robust suite of tools to define, implement, and operationalize loan personalisation from a single vendor.

Personalisation projects are adaptable to different customer journeys and can be directly integrated via APIs to the lender's customer experience platforms, whether on an app, on the website, in a chat, or on the workbench of a virtual branch assistant.

To learn more, please download our eBook which presents loan personalisation and the Earnix solution in more detail. You'll be on your way to "getting personal" with the most demanding and most profitable consumers out there.

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Giovanni Oppenheim

Director of Banking Solutions, Earnix

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