In credit decision-making, transparency is essential between the lender and the applicant. However, the lender’s reasons for a specific offer or a rejection can often be unclear to a potential customer.
For a lender, there are considerable benefits available in sharing the reasons for denying a credit application. A lender can advise the applicant on how to improve their chances of success when reapplying or suggest other products that may be a better fit for the individual through Alternative Deal Structures.
On the flip side, there can be damaging consequences for the lender in not communicating effectively with the customer. Banks may lose out on future business from a potential customer who may not require major changes in circumstances. In addition, insufficient communication can land banks in trouble with regulators, taking into account the UK Consumer Duty and the European AI Act.
How improved transparency can unlock new business for banks
Increasing the quality of communications with customers can open new business opportunities that may have otherwise been out of reach.
There are advantages in explaining to customers why they have not met certain criteria for a loan because of factors such as their monthly income threshold being too low to sustain repayments or a history of bad credit score events. This creates opportunities to suggest other products that may be available.
“It goes back to the idea that a banker should be an advisor first and foremost – not just a salesperson trying to sell a financial product,” explains Giovanni Oppenheim, Director of Banking Solutions at Earnix – a provider of data analytics solutions for the financial services industry.
“Transparency is a big part of this. Lenders need to be able to explain in simple terms and educate the consumer to some degree,” he adds.
How technology has increased complexity in banking decisions
As credit decision-making increasingly involves algorithms, artificial intelligence (AI), and machine learning, this often creates further complexity and can reduce clarity for credit applicants.
A lender must find a way to cut through these often-opaque layers and ensure sufficient information behind a decision is relayed to the applicant, along with constructive feedback on potential future steps.
“Most platforms in the past ten years have evolved to become complex black boxes because of the use of machine learning,” says Oppenheim.
“In the past couple of years, there’s been a lot of talk about how you can take these complex rules and simplify them,” he adds. “From an analytical perspective, there’s been much investment in finding statistical tools or solutions that explain complex systems in an easily readable way to the users.”
However, there is a fine line for banks to tread.
The role of regulations in improving financial transparency
While lenders must be more transparent and improve communications with customers, they also need to protect sensitive information and comply with data protection laws.
The need for increased transparency between banks and customers is being driven by regulations introduced around the world as authorities seek to catch up with technological advances.
“Transparency is not only lacking in the decision-making. In many cases, it’s increasingly required from a regulatory point of view,” says Oppenheim.
In the UK, the Financial Conduct Authority (FCA) has recently introduced regulations dedicated to communicating information to banking customers to allow them to make better-informed decisions. In the US, the Federal Consumer Protection Bureau has introduced similar rules for lenders.
This can help to better protect customers and put the onus on banks to communicate the reasons behind decisions.
Decision-making powered by technology
Despite the return of more traditional banking advisory services, the way they are delivered has changed completely. What was human interaction for decision-making on the bank side is largely handled by intelligent platforms. Chatbots, for example, are becoming more commonly deployed by banks to explain lending decisions and advise on alternative options.
“With an automated system, on one hand, people expect the bank to become an advisor – but on the other hand, they want to have a decision in a microsecond,” says Oppenheim. “Sometimes these factors are not really going in the same direction.”
Yet tools such as pricing analytics driven by AI are examples of how technology can increase transparency in lender decision-making. Furthermore, connecting decision engines with pricing engines can improve processes even more.
“This is a very interesting aspect for every decision engine now,” adds Oppenheim. “At Earnix, we strongly believe that decision engines should be working in conjunction with pricing engines.”
Movements in markets
Most U.S. lenders experienced increases in portfolio delinquency In Q2 2024 when compared to the same quarter in 2023 and 2022, according to the TransUnion Q2 2024 Quarterly Credit Industry Insights Report (CIIR)*.
The UK lenders reported that default rates on majority of consumer loans increased in Q3 of 2024 and were expected to increase again in Q4 of this year**.
Additionally, Credit Benchmark predicts that UK financial default risks will increase by 6% in the second half of 2024 and the first half of 2025.***
This trend has influenced some lenders to reconsider strategies for loan pricing and credit risk, along with increased competition, narrowing margins, and growing profitability pressures.
Some lenders are choosing to withdraw from certain business areas– such as auto lending or unsecured consumer loans – in favor of more profitable options with less risk. Meanwhile, other lenders are focusing on revising their underwriting standards and leveraging technology to support an increase in financing volume.
In this challenging environment, it makes sense for lenders to invest in pricing and decision-making efficiency and accuracy, as well as enhancing lending technology.
In a single solution, the combination of advanced price optimization and simulation capabilities with AI-driven credit risk decision-making not only simplifies the lending process but also boosts profitability for the lender and transparency for the borrower.
This is particularly important in today’s landscape. Lenders can easily conduct joint simulations and evaluate the combined impact of pricing or credit policy changes for a more intelligent, automated lending operation.
For a more in-depth understanding of pricing analytics in consumer lending, download a free eBook below, compliments of Earnix – a global provider of real-time, AI-driven dynamic pricing, product personalization, and digital decisioning solutions for financial services industry including consumer lending, auto finance, mortgages, and more.
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*TransUnion Q2 2024 Quarterly Credit Industry Insights Report (CIIR) https://newsroom.transunion.com/q2-2024-ciir/
** Bank of England Credit Conditions Survey – 2024 Q3 https://www.bankofengland.co.uk/-/media/boe/files/credit-conditions-survey/2024/2024-q3
*** 2024/25 Default Risk Outlook: UK Industries https://www.creditbenchmark.com/2024-25-default-risk-outlook-uk-industries/
