How AI is making lending easier (and safer!) than ever before

3 min read

Today, lending plays a role in nearly every part of the economy. Data from September 2017 places US consumer debt at over USD 3.5 trillion—with credit card and automobile debt at nearly USD 1 trillion each and student loans close to USD 1.5 trillion. At the same time, the value of mortgage debt in the same dataset is more than USD 14.5 trillion.

AI in Fintech Lending

Millions of debtors hold loans worth trillions of dollars across the world, and Artificial Intelligence (AI) can be leveraged to improve financial institutions’ recovery of these loans, improve their market share, and maximize profits through healthy lending and minimum defaults. Established financial institutions and Fintech startups are scouting for new and improved ways to innovate using AI-powered solutions.

How can AI help?

With the massive amount of customer information present in today’s digital economy, big data and machine learning (ML) can play a central role in lending activities. Financial institutions can leverage these solutions to analyze data about individual borrowers and how similar borrowers have repaid past debts, leading to better assessments of clients’ creditworthiness.

Using AI, loan values can be linked with value assessments of the collateral (home, business, car, etc.), the predicted level of inflation in the future, and likely economic growth overall. AI is capable of analyzing all the above mentioned data sources at the same time in order to generate deep and highly useful insights. AI will also help make the overall administration of the financial industry smoother—an accurate determination of creditworthiness will lead to more streamlined internal processes, which in turn leads to less hassle and a better experience for clients overall.

Financial institutions derive the value of credit based on the probability of an individual or business repaying it. Therefore, determining this probability is critical for the banking and finance sector. However, generating this value is difficult for humans and traditional computers, even if all the needed information is present. This is one of the main reasons for the expanding influence of AI in the direct lending vertical.

Normally, lenders examine only a few metrics—like credit score and annual income—before extending credit. With AI, banks are able to examine the entire life of a borrower through their digital footprint in order to determine their ability to repay their debt. Fintech vendors refer to such information as ‘alternative data.’ Such data generates reliable insights about potential borrowers and helps determine creditworthiness regardless of the presence of traditional credit history.

For financial institutions, new and improved methods to determine creditworthiness are few of the many ways to expand business avenues and increase the size of their customer base. Eliminating administrative delays is another opportunity to use AI for maximizing profits. Lenders have relied on computer systems to automate major parts of the credit operation, but now some pioneering companies are aiming to automate the entire process.

The players in this space

Upstart is a renowned startup that uses AI to streamline the credit process and determine the creditworthiness of borrowers. It began operations by focusing on young adults with sparse credit histories, where it analyzed SAT scores, education, field of study, GPA, and job history along with traditional credit scores through machine learning in order to predict creditworthiness.

Today, Upstart leverages modern data science technology and techniques to automate the credit process and successfully extend automated loans—as of September 2017, the company had achieved 40 percent automation. As a hybrid lender, Upstart extends credit directly as well as processes loans for third-party lenders. Recently, the company began offering its Fintech solutions to other enterprises through a Software as a Service (SaaS) model.

Other companies in this space include Equifax, Lenddo, and ZestFinance. These companies, along with others in this segment, have reached different stages of automation. However, the majority of financial institutions have only achieved partial automation in paperwork, data entry, and verification. Loan applications are still being reviewed by human underwriters before approval.

On the borrowing side of things, Personetic—a cognitive banking institution—leverages AI through its Personetics Act solutions to help individual customers save more money. Personetics makes use of ML to analyze individuals’ financial habits and if possible, help them pay back their loans more swiftly by automatically suggesting how much they should contribute at regular intervals. Personetic’s clients include major banks such as Ally Bank and the Royal Bank of Canada.

Amazon is a pioneering stalwart in this space. In the lending space, it relies on its vast cache of proprietary information on the number, type, and specifications of products sold, customer reviews of those products, the financial status of the suppliers of those products, and even the predicted demand for these products. By processing this information using AI and ML models, Amazon is finding enterprises to offer business credit to.

The concerns with AI in lending

As effective and appealing as these AI in Fintech solutions are, using AI to analyze alternative data is bound to raise a few ethical and legal concerns. Not all clients might feel comfortable with granting access to sensitive personal information. Assuming that the companies use all the stored data ethically, the information can still be stolen by cybercriminals. 

Big data solutions may also discriminate against certain groups by accident. For instance, while AI programs are not designed to deny applications from clients with a criminal history, they might consistently deny these applications either way because of other data markers such as unproductive online activity, low levels of education or income, and patchy transactional history.

However, these concerns should not deter financial institutions from leveraging AI and ML solutions to process alternative data in order to determine creditworthiness. Oversights and errors like those mentioned above can be corrected through human intervention and advanced technology, but millions of potential clients without credit histories are being denied payment plans, mortgages, credit cards, and other forms of credit due to existing credit scoring models. Through the use of AI, this sizeable group of ‘unbanked’ people will finally have access to standard credit; allowing them to increase their contribution to the formal economy and increase their quality of life.

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