How AI and Machine Learning Revolutionise Credit Risk Assessments

Many companies in the BFSI market (banking, financial services, and insurance sector) are turning to AI and machine learning for credit risk assessment and management. In fact, a recent report by Allied Market Research estimates that global AI in the BFSI market will reach $368.6 billion by 2032, with fraud detection and prevention expected to witness the highest CAGR of 36.8% from 2023 to 2032. As for banks, global adoption of AI is estimated to reach $64.03 billion by 2030, with a CAGR of 32.6% from 2021 to 2030.  

Consequently, with the ever-growing amount of complex corporate data, the heightened economic turbulence and geopolitical uncertainty, traditional credit risk assessment models are falling short—particularly in capturing the complexity of our current financial markets. 

Therefore, this article explores: 

•  The limitations of traditional credit risk assessment methods 
•  How AI and machine learning address these limitations  
•  How Cedar Rose leverages AI and machine learning for credit risk assessments 

The Limitations of Traditional Credit Risk Assessment Methods

While traditional credit risk assessment methods are long-standing, they rely on subjective human judgement which can introduce biases and inconsistencies. Furthermore, they are restricted by data availability and quality. Additionally, their over-reliance on outdated and incomplete structured historical data limits their predictive power.  Hence, they fail to reflect how complex modern financial market are and fall short when it comes to providing a comprehensive picture of a borrower’s financial status.  
 
They also heavily rely on manual data gathering, analysis and evaluation. This makes them slow, inflexible, unable to capture real-time data and adapt to changing market shifts or adjust to new information. Moreover, their focus on quantitative factors like credit history and financial ratios often neglects crucial qualitative information such as market conditions, borrower behaviour, and industry trends. This narrow scope might result in inaccurate risk evaluations, particularly for niche customer categories with specific risk profiles. They also fail to adhere to evolving regulatory standards such as IFRS 9 and CECL, which pose issues like data availability, model validation, and auditability. Eventually, this combination of factors limits their ability to produce accurate, fast, and complete credit risk assessments.  

How AI and Machine Learning Address These Limitations 

Though AI and machine learning are used interchangeably, it’s crucial to understand their key differences before exploring how they address traditional credit risk assessment limitations.  

AI is broader than machine learning and its overarching goal is to create intelligent systems that imitate human thought and behaviour. Machine learning, on the other hand, is a subset of AI. It focuses on training algorithms with a goal of having machines learn from data to improve the accuracy of their output. Compared to AI, machine learning’s scope is limited, for it uses self-learning algorithms to perform specific tasks. It also only works on structured and semi-structured data, as opposed to AI which works on all types of data.  

AI by itself can create expert systems that use the knowledge and experience of credit risk analysts to analyse complex credit applications, flag potential risks, and provide recommendations based on pre-defined rule- based logic. This reduces inconsistencies and helps address subjective human judgement. Moreover, its use of natural language processing (NLP) allows it to analyse unstructured data from news, customer reviews, and alternative data sources. This broadens the scope of borrower evaluation and addresses the operational limitations of traditional methods. By streamlining tasks like data extraction, document verification, and initial credit scoring, AI reduces manual errors and improves efficiency.   

Machine learning compliments AI by uncovering complex relationships between credit risk factors that human analysts may overlook. It also allows for easy identification of patterns between borrower characteristics, credit history, financial transactions, and macroeconomic factors. Its predictive modelling capabilities help improve risk differentiation leading to higher accuracy. Moreover, its ability to detect anomalies, unusual patterns and outliers in credit applications helps address fraud risks and identify changes in a borrower’s financial behaviour.  

Similarly, it makes personalised credit scoring possible since individual’s circumstances and alternative data can be taken into consideration. On top of that, it also improves credit risk assessment by boosting automation. This saves labour costs, enhances efficiency, and allows for faster loan processing applications. Hence, its ability to handle large datasets and adapt to varying market conditions and regulations makes it scalable and suitable for dynamic environments traditional credit risk assessment methods struggle in. Additionally, it delivers precise, timely insights for better portfolio optimisation, and risk management, identifying emerging risks, detecting fraud and customising lending practices for individual borrowers. This ultimately reduces credit losses and improves risk-adjusted return.  When employed responsibly, and strategically, ML can further contribute to regulatory compliance by offering transparent auditable processes. Regular audits of ML models can correct any biases that emerge. Furthermore, differential privacy and homomorphic encryption, a form of encryption that allows computations to be performed on encrypted data without decrypting it, allow its models to learn from data without accessing the raw data or violating its privacy.  

In short, the most effective credit risk assessment systems combine both AI and ML. 
AI helps refine ML models with feature engineering and model selection, while ML enhances the predictive power of AI- driven fraud detection, and automated credit decisioning. AI offers a more comprehensive human-like approach to decision-making, while ML provides significant data analysis and pattern recognition capabilities. This synergy creates adaptable, efficient credit risk systems that overcome traditional constraints and unlock new opportunities in the credit sector.  

How Cedar Rose Leverages AI and ML for Credit Risk Assessments 

Cedar Rose has been a pioneer in using AI and machine learning since 2004.  

We have developed proprietary algorithms and techniques that transform raw data into meaningful, actionable intelligence even when the data is scarce. Additionally, our expertise in data integration allows us to combine diverse data sources—financial, behavioural, and market-based—into cohesive AI models, enhancing credit risk assessments through tools like the Auto Size Indicator (ASI) and the Auto Risk Rating.  
 
We have also pioneered automated transliteration systems, accommodating diverse languages and scripts. In 2018, we became the first Middle Eastern company to introduce fully autonomous AI-generated credit scores, setting a new standard in credit risk assessment. Thus, our long-standing commitment to innovation, coupled with our advanced suite of AI-powered tools, positions it as a trusted partner for companies seeking to leverage AI and ML in their credit risk processes. Our CR Score, ASI, Auto Risk Rating, and Automated Credit Limit (ACL) algorithm exemplify our dedication to cutting-edge solutions, transforming credit risk assessment and management for businesses worldwide. 

Want to unlock the power of ML and AI in your credit risk assessment?  
Reach out to learn how we can help you make faster more informed decisions.  

Sources 

1. https://fastercapital.com/topics/the-limitations-of-traditional-credit-risk-assessment-methods.html  
2. https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning  
3. https://eajournals.org/ijmt/wp-content/uploads/sites/69/2024/06/Machine-Learning-Algorithms.pdf  
4. https://www.highradius.com/resources/Blog/ai-in-credit-risk-management/#chapter_7 
5. https://www.hyperstack.cloud/blog/case-study/exploring-risk-assessment-with-machine-learning-in-finance#toc-fraud-detection-and-prevention-in-online-banking