How artificial intelligence is disrupting the lending industry

By Jim Garlock | 23 August 2019

The banking industry has experienced massive changes over the past several decades. Gone are the days of completing all financial transactions in-person at a bank or maintaining all personal financial records as paper documents. Electronic document management systems and third-party credit rating systems allow banks to quickly process loan applications and other consumer inquiries with only minimal human effort required.

Even though the banking industry has changed drastically, there are still leaps and bounds of improvements to be implemented across the board. The simultaneous processing of different algorithms to establish patterns in consumer behaviours and the ability of computers to extract data from documents to answer questions will further minimise the input required from humans to complete a range of banking transactions. Instead of focusing on form completion and transferring data, mortgage underwriters will be available to spend more time examining loan applications and credit assessments at a higher level. Ultimately, this means that decisions to extend credit will involve less risk and be available to more worthy borrowers.

Missed opportunities from relying on credit scores

The missed opportunities created by banks' misguided reliance on credit scores alone to evaluate an applicant's creditworthiness restrict the economic activities of consumers and reduce the banks' profit potential. For instance, many loan applicants are denied loans by banks because they do not have a lengthy credit history due to age, spending additional years in school, family circumstances as a stay-at-home parent or other situations. However, a number of these applicants may not default on their loans. Thus, other sectors of the economy, such as real estate, automotive sales, and construction, are likewise unable to capitalize on new business.

Machine learning provides credit insights

Algorithms used in machine learning help assess non-numerical factors in an applicant's creditworthiness evaluation, such as their consumer behaviour in other industries and social media activity. These cutting-edge credit scoring tools offer greater insight into an applicant's willingness to pay their debt and result in the extension of credit to deserving applicants who might otherwise have been denied a loan.

Digital footprint analysis using AI

AI compiles information about an applicant's digital behaviour, including the profiles of people they most frequently associate with on social media, any inconsistencies in their employment information, their spending habits and major organisations they belong to. Previously, it would have been too time-consuming and labour-intensive for a loan underwriter to manually compile and assess all of these separate data points. This information is supplementing and sometimes even replacing a credit score as the ultimate factor in whether to extend credit to an applicant.

Improving risk-adjustment margins with AI

Figuring out the appropriate interest rate to charge on a consumer loan is simpler and more accurate with AI. Banks can more effectively leverage their assets to increase profits with the help of AI software in extending credit to more worthy borrowers and charging them an appropriate interest rate that will allow them to continue making timely payments on their loans.

AI and risk reduction

The use of AI in a bank's loan origination system helps to reduce the likelihood of human error in processing a loan application or overlooking critical factors in whether a borrower will default on a loan. AI will also play a vital role in the bank's loan management system to identify patterns of behavior that indicate a consumer may be close to declaring bankruptcy or completely defaulting on the debt. The ability to reduce those risks will stem costly losses and preserve credit availability for worthy borrowers who will become or continue to be full-fledged economic participants.

Final thoughts

The transformations in the lending industry that have been ushered in with the rise of AI are resulting in more efficient transactions, more accuracy in risk mitigation and fewer errors on the lender side. This has already resulted in better profit margins for financial institutions from lower administrative costs and fewer unexpected losses. Loan origination and management processes are less prone to human error and reflect more proactivity on the part of the bank.

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