Making wholesale banking profitable requires the intervention and smart use of artificial intelligence for predictive credit risk management.
Wholesale banking involves giving loans, credit lines, and financial products to large corporations, governments, and clients. Lending money to borrowers who cannot keep up with their financial commitments is bad for business. Using AI can make all the difference.
Read this blog to learn why traditional credit risk models are redundant and explore how banks can stay financially stable and profitable by managing credit risk using AI.
Traditional Credit Risk Models and Their Limitations:
For many years, banks and financial institutions have relied on traditional credit risk models to determine their borrower’s ability to repay loans.
While these models have worked in the past, they come with many limitations, such as:
- Credit Scoring Models: These models analyze a borrower’s past financial data to create a score that predicts whether they might default on a loan. They rely on historical data, which doesn’t always predict future behavior accurately. This makes it tough to assess new borrowers who don’t have any credit history.
- Expert Judgment/Manual Assessments: This method involves people looking over financial statements, credit reports, and other information to evaluate a borrower’s risk. The downside is that this process can be affected by personal biases and can vary from one person to another. It’s also slow and labor-intensive, making it hard to manage a large number of borrowers efficiently.
- Probability of Default (PD) Models: These models estimate the chances that a borrower will default based on their financial ratios and past behaviors. They often depend on historical trends, which may not hold up in unpredictable market situations or during financial crises when new risks emerge.
- Loss Given Default (LGD) Models: These models predict how much money a lender could lose if a borrower defaults on a loan. They usually assume that the recovery rates from past defaults will stay the same, but that’s not always the case as the market keeps changing.
- Exposure at Default (EAD) Models: These models estimate how much exposure a lender has at the moment a borrower defaults. They can underestimate risks, particularly when borrowers use revolving credit or if there are unexpected increases in exposure.
What is a Predictive Credit Risk Model? Why do Banks need it?
Predictive credit risk models are tools that use statistics, machine learning, and data to help banks and lenders identify a borrower’s risk appetite and make smarter lending decisions.
Predictive models offer a smart solution that provides smarter, quicker, and more accurate ways to assess risk. Here are some ways in which predictive credit risk models can help:
- Data-Driven Analysis: These models look at a huge amount of information. They consider things like a borrower’s financial history, spending habits, economic trends, and even unique sources like social media activity or payment history for bills.
- Advanced Algorithms: Predictive models use smart algorithms to find patterns in the data. This helps them guess how borrowers might act in the future.
- Real-Time Processing: Lenders can make faster decisions based on the most up-to-date information as these models can analyze data right away in real time.
- Dynamic Risk Assessment: Predictive models adapt to fluctuating market conditions, adjusting risk assessments with a borrower’s changing financial situation.
- Customizable Inputs: Lenders can tweak these models to focus on specific factors that matter to them, making the assessments even more relevant and useful.
How AI is Revolutionizing Credit Risk Management?
AI is shaking up how lenders look at credit risk, making it easier for banks to make smart, quick decisions.
Here’s how AI is simplifying credit risk management in a simple, human way:
- Machine Learning: AI sorts through huge amounts of data and finds patterns that people might miss. It’s like teaching a system to get better over time by learning from all the data it sees, so lenders can make more accurate decisions about who’s likely to repay a loan.
- Neural Networks: These systems work a bit like our brains—they recognize patterns that aren’t obvious at first glance. This helps lenders spot hidden risks and predict if someone might struggle to pay back a loan.
- Natural Language Processing (NLP): NLP is like having a tool that reads emails, social media, or reviews to understand what’s going on with a borrower’s emotions or financial situation—things that don’t show up in normal financial reports.
- Decision Trees: This is like a choose-your-own-adventure map for AI. It helps lenders see different possible outcomes for each borrower, so they can make more informed, accurate loan decisions.
- Support Vector Machines (SVMs): SVMs help sort borrowers into different risk categories. They give lenders a clear idea of who’s a safer bet and who might be riskier, based on many different factors.
How Using AI For Credit Risk Management Can Benefit Banks
AI is changing how credit risk management works, making it faster, smarter, and more effective. Here are some benefits of using AI for credit risk management:
- Tracking borrower’s behavior: AI constantly monitors what’s happening with a borrower’s finances and flags any changes that might mean there’s a higher risk of them defaulting.
- Quick insights: AI gives lenders instant updates on potential risks, helping them react fast and make better decisions in real time.
- Spotting fraud: AI can catch suspicious activity right away, reducing the chances of fraud and helping lenders avoid big losses.
- More accurate assessments: AI learns and adapts quickly to new information, so it gives a more accurate read on a borrower’s risk compared to older methods.
- Cutting out bias: Since AI works with data and algorithms, it helps reduce human bias and errors when evaluating credit risk.
Credit Risk Management with Aurion Pro
Aurionpro’s credit risk management solutions are an ideal choice for banks that are serious about reducing lending risks and making money. The tool enhances credit risk management for banks through digital transformation, advanced analytics, and AI-driven systems.
Auriopro is used by Asia’s leading banks and supports assets exceeding 1 Trillion dollars.
Here are some unique features that make Aurionpro an exciting choice for predictive credit risk management:
- Smartlender financial analysis: Capture financial information consistently with predefined industry financial templates and associated key ratios.
- Smartlender collateral management: Use robust processes in compliance with Basel II principles to control the legal, operation, liquidity, and market risk of collaterals.
- Smartlender limit management: Set limits that keep exposure under tight control and handle lending risk according to different hierarchies of risk dimensions.
- Smartlender retail origination: Reduce application turnaround time, improve time to market, and proactively manage risk with smartlender retail origination.
Conclusion
Traditional models have limitations that can lead to inaccuracies and inefficiencies. And as the financial landscape evolves, predictive credit risk management becomes increasingly important.
The future of credit risk management lies in leveraging AI to create a more efficient and fair lending environment. Using Aurionpro’s AI-backed tech, you can access data-driven insights and real-time processing, and improve decision-making for your business.
