Artificial intelligence (AI) is causing a stir in the world of finance. The financial services industry, which is heavily data-driven, yet plagued by outdated processes, is increasingly adopting AI-based solutions and leveraging its powerful capabilities. In this article, we will look at some important aspects of the use of AI in the financial sector, namely in lending.
AI and Scoring
AI helps banks create more accurate and objective scoring models. Credit scoring is a system for assessing a borrower’s creditworthiness based on their personal credit history. Conclusions about this are made on the basis of a large amount of data: total income, credit history, analysis of transactions, and even seniority.
In essence, scoring is a mathematical model based on statistical methods and takes a large amount of information into account. AI and big data help to cope with this task quickly and efficiently.
Algorithms not only check the client but also make predictions about their future behavior based on data on other borrowers with similar characteristics. Previously, at the dawn of digital consumer lending, banks issued loans to everyone, but after the crises and default of 2007-08, they began to build scoring models based on the accumulated history.
As a result, the bounce rate has been steadily rising for several years, and the number of problem customers has been decreasing accordingly. Today, this process has begun to be automated. There are banks where 100% of such decisions are already made with the help of AI. Human intervention is required in only 5% of cases.
Thus, the loan processing takes from 5 to 10 minutes and does not even require personal presence. It is enough to go to your favorite messenger from any device and fill out the questionnaire. If the client has already applied to the bank, the chatbot algorithms will automatically analyze all possible information and make a decision on a loan decision based on the client’s data and the criteria of the credit institution.
Experts believe that, with the development of AI for finance in the near future, an AI-based chatbot or voice assistant will be able to conduct a dialogue with a potential client in real time and accompany them at all stages of the application process, including obtaining a loan and offering individual solutions.
What are the Benefits of Issuing Loans with AI?
Experts from the consulting company Capgemini believe that automated systems could save banks 50-90% over hiring traditional employees. In addition, AI improves the quality and speed of bank service, in particular when analyzing the creditworthiness of a client.
Using AI for credit scoring reduces losses for the largest players in the US auto loan market by 23-25% by more accurately assessing the risk of borrowers.
According to McKinsey analysts, lenders that adopt AI across the organization have a cost-to-income ratio that is 12% points lower than the market average.
The use of AI provides several more advantages when issuing loans:
The ability to accurately determine credit limits. Leading banks are using advanced analytics and machine learning to automate the process of determining the maximum amount a borrower can receive. OCR (Optical Character Recognition) technology in these banks is used to extract data from common sources such as bank statements, tax returns, and utility bills. Therefore, banks can quickly assess a client’s income and their ability to make regular loan payments.
Fair pricing. As a rule, banks offer standardized loan rates, while bank employees have a certain freedom of action to adjust rates within the specified range. However, fierce competition for quality borrowers puts banks using traditional approaches at a disadvantage compared to the leaders in AI and analytics. By relying on highly accurate machine learning models for risk assessment and loan pricing, AI-focused banks have been able to offer competitive rates while keeping risk costs low.
Anti-fraud. Serving customers in digital channels opens up new opportunities, including for fraudsters. The most common cases are identity theft, fraud by employees, partners, and customers, as well as money laundering and violation of sanctions. Banks should continually upgrade their fraud detection and prevention models. For example, the Chinese financial holding Ping An uses an image analysis model to recognize 54 involuntary facial expressions of customers. In general, more accurate identification of suspicious customers will allow banks to increase loan approval rates without increasing credit risk.
What is the benefit for the borrower of an automated AI-based scoring model?
First, a quicker response to the application — not a few days, but a few minutes. Secondly, there is no human factor in the assessment. If objective facts speak for the client’s trustworthiness, they will receive a loan. For example, thanks to the introduction of technology into the loan approval process, some banks began issuing 18% more retail loans.
Uplift Models for Banks
These technologies help assess debt and conduct customer credit analytics. For example, many banks are already willing to implement machine learning technology to analyze delinquency in retail.
Experts build self-learning uplift models that predict customer responses to reprimands. This allows you to streamline the process and think about the best way to communicate with individual borrowers. In particular, uplift models can identify customers who need reminders about payments from a list of those who pay themselves and those who definitely need a reminder. To analyze the data, the project uses Python programming language.
The accuracy of forecasts is assessed using the Gini coefficient—this is a universal scoring metric. Even at the stage of testing self-learning AI models, the main coefficient increases from 65% to 88%. Previously, financial institutions used logistic regression models.
AI in credit scoring can save time and overall bank costs. AI helps banks improve the accuracy of the Gini coefficient, which leads to faster loan decision-making, while banks do not lose money on unscrupulous borrowers.
According to many experts, AI technology for lending to both individuals and businesses will become popular in large financial institutions in the coming years. The active development of this area is taking place, among other things, within the framework of the digital economy program of many countries.
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