The Reason Artificial Intelligence is the Future of Lending

September 14, 2018

Lending is built on trust

Lending has always been highly driven by personal relationships and the face-to-face meetings – where trust is key to understand if and how a consumer will pay back his/her debt. The trust is traditionally based on a personal relationship between the applicant and the bank clerk – they have gotten to know each other through several meetings at the local bank office. The digitalisation has truly changed how and when consumers are able to apply for a loan and thereby the requirements on the lenders – the consumers do not have the patience to fill paper forms or to wait days for approval. The consumers want a smooth application process and an answer within a matter of seconds. The lender needs to calculate and understand if the debt will be paid back – and thereby understanding if they can trust the borrower in a matter of a second. This blog post we discuss the transformation that lenders will need to go through in order to keep the pace with the consumers and still being confident that they lend to the right people.

Transforming the banking experience

Lenders who haven’t managed to transform into a fully digital and automated application process will have another problem – they will lose customers to the players who have. Based on a survey by Signicat this was obvious – more than one-third of the respondents (consumers) said that they abandoned their loan application as a result of the length and effort required to complete it. Where the requirement of physical papers and waiting-time was the major reason why they abandoned. When consumers come across a frustrating on-boarding will the low-risk and high-risk consumers behaviour differ. Low-risk consumers with good credit who are likely to repay their loans can easily go to another lender. While the high-risk consumers are more willing to jump through challenging on-boarding because they have no other options. Hence, a smooth application process is key for many reasons.

The digitalisation has resulted in several obvious changes when looking to lenders’ way of doing business. During the recent years has the way of marketing changed – going from traditional mails to email campaigns, affiliate networks, agents and social media. Applying for a loan has moved from face-to-face meetings at the bank to become online and mobile. Being able to follow the consumers and keep them satisfied will require the backend of the bank to equally transform.

Figure 1: The transformation of marketing, application processes and credit underwriting in banking.

The financial companies won’t able to understand all possible customers using the traditional methods. By being able to use more sophisticated credit and risk models applied to more data – the data which you have thought about using but never have – is it possible to find more desirable customers. This can help to reach and understand the ones who were traditionally difficult to score, such as college students and millennials. And since model developed with Artificial Intelligence (AI) come with automated updates is it possible for lenders to meet changes in the market conditions, new channels (such as mobile) or when the customer populations are reshaping.

Leveraging all data

Today are we acquiring data at a faster rate than ever. A deeper embrace of the AI and machine learning being used elsewhere at the bank, combining with the rich data being harvested, can meaningfully change a financial firm’s profits for good.

Yet the widespread use of AI to more sophisticated predict credit risk has eluded the industry until today. The early adopters o AI in credit have measured financial performance benefits – through higher conversion rates or lower credit losses, or a blend of both.

Down below is an illustration of some of the effects going from traditional credit underwriting methods to more sophisticated ones.

Figure 2: The effects and benefits of going from traditional credit models to ones based on machine learning and artificial intelligence.

Fewer resources and more relevance

It can take up to several months to develop, validate, and deploy a new scorecard model. That’s an eternity in the fast-paced digital industry we are living in today. Customer populations, economic conditions, and markets can change dramatically. This could potentially cause introducing errors or human bias into the original model – decreasing its effectiveness. Using AI allows lenders to put powerful models into production quickly and responsibly. Then it is possible to quickly refit new models to adapt to these changing conditions.

Full Explainability

AI algorithms are considered a “black box” where models are unable to explain the reasons for its outputs. We at Evispot has recently developed a software which allows us to fully understand the reasoning and decisions of the AI models. This enables lenders to understand the specifics. Explainable AI models help lenders know exactly why applicants are approved or denied, in alignment with their existing business processes and procedures. (A further introduction of explainable AI-models in credits will be featured in an upcoming blog post).

If you’re ready to capture the benefits AI can give your credit risk models, let’s have a chat! Contact to learn more.

Intresserad att höra mer?

Följ oss och för att ständigt få uppdateringar med dem senaste nyheterna inom AI och kreditbeslut

Image Description