Sep 27, 2024
Kendall Payton, social media manager
Artificial intelligence is an ever-evolving tool, integrating itself into society. AI can be used for simple and complex tasks—whether for creating grocery lists and meal plans or as complicated as coming up with a workout plan.
Why it matters: AI is a nuanced topic, making it nearly impossible to put into a single category. Credit professionals handle a variety of responsibilities daily, and it can take more time to complete manual tasks. Many departments have already switched to automation such as ERP systems or electronic payments.
If you take a look back at traditional and manual lending models, they have worked well for several years. Some financial institutions even develop new models based on old ones.
Although relying on existing internal expertise has its benefits there are setbacks as well, such as:
- Slow reaction times. Building a credit risk model can take several months—but if there are unforeseen circumstances such as COVID, or an economic shift, these models can start to underperform.
- Less effective performance. Applying generative AI or machine learning models to your credit processes can increase effectiveness leading to overall better credit decisions.
- Limited data sources. Traditional credit scoring models analyze a limited amount of data. However, credit risk scores and models can be tailored by including data from AI-driven models. These models can analyze large amounts of information at once while simultaneously catching data that points to high-risk.
The most common uses of AI in credit management include: - Payment trend analysis
- Credit limit recommendations
- Cash application process
- Customer emails
Some credit professionals will use AI for credit scoring as well. Unlike traditional data models that rely on credit history, AI systems can analyze a wide range of data sources including bank transactions, bill payments and usage patterns.
While it is thought that AI will replace human jobs, that is not necessarily the case. AI can be used as a tool that handles routine or analytical tasks, while humans provide the gutfeeling and creativity in decision-making.
By the numbers: Artificial intelligence is expected to create 97 million jobs, according to the World Economic Forum.
- 72% of companies across the U.S. use AI.
- Larger enterprise companies are 2 times more likely to use AI than smaller businesses.
- 82% of global companies are either using or exploring the use of AI in their organization.
What they’re saying: One of the most popular forms of artificial intelligence is generative AI such as ChatGPT. Credit processes that take weeks or months can be shortened to days or sometimes hours. Chantal Rousseau, CCP, corporate credit director at MPG Canada said she uses ChatGPT to research customer information. “Depending on the case, I will ask variations such as, ‘Tell me what the relationship is between X and Y,’ or, ‘How are X and Y related?’” she said. “It helps with finding those relationships between two entities or the owner of a corporation.”
Yes, but: Though generative AI such as ChatGPT can offer suggestions, identify mistakes and provide alternative wording, it is just as important to keep ethics first and foremost when using or training any type of AI system to assist with credit processes. “What science can’t put into AI is values and ethics,” said Martin Zorn, managing director of risk research and quantitative solutions at SAS Institute Inc. (Honolulu, HI). “We tend to already have bias on what we think the outcome should be, and that’s the biggest danger. When you set up those models, you are putting your biases into the model or the parametrization of the model.”
The bottom line: Investing time in understanding the right uses for AI implementation is key to its successful integration across various industries, ensuring efficiency and cost-effectiveness in leveraging this transformative technology.