In today's technologically advanced world, Artificial Intelligence (AI) continues to transform various industries, making inroads into even traditionally manual and human-centric processes. Allied Market Research indicates that AI in the banking market was valued at $3.88 billion in 2020 and is projected to reach a staggering $64.03 billion by 2030. Some forecasts even offer higher estimations. For instance, Statista suggests that by 2030, the business value from AI in banking will reach $99 billion in the Asia Pacific region alone.
One specific area within the banking industry experiencing a profound change due to AI is credit risk management and reporting. This shift has been gaining momentum, attracting attention from both professionals in the field and regulatory bodies. Following an industry survey on the application of AI conducted by the Hong Kong Monetary Authority (HKMA), the HKMA issued the High-level Principles on Artificial Intelligence and the Consumer Protection in respect of Use of Big Data Analytics and Artificial Intelligence by Authorised Institutions. These principles represent the first clear directives from the Hong Kong SAR regulators about the use of AI by Authorised Institutions.
This burgeoning interest and growing regulatory recognition set a clear precedent for financial institutions worldwide that are exploring AI in credit risk reporting, highlighting the growing importance and revolutionary potential of AI in this field. With such a surge in the global application of AI in banking, it is imperative for industry professionals to understand the dynamics of AI and its capacity to redefine credit risk reporting.
Defining AI: A Look at Machine Learning
AI embodies the imitation of human intelligence processes by machines, covering a wide spectrum of problem-solving procedures. A significant subset within AI is Machine Learning (ML), which allows computers to learn through data exposure independently. With technological advancements, ML algorithms like deep learning, random forests, Gradient-Boosting Machines (e.g., XGBoost, LightGBM), and cluster analysis (e.g., k-means, DBSCAN) have found application in various tasks, including regression, classification, network formation, and discriminant analysis (clustering). These tasks have significant utility in credit risk management, particularly for predictive applications like default predictions. For instance, ML algorithms have been demonstrated to surpass traditional models in predictive power. Moreover, ML's ability to analyse unstructured data opens new avenues in credit risk management, like developing early warning models based on media reports.
How AI is Shaping Credit Risk Management
AI's impact on credit risk management, though still in its early stages, is undeniably transformative. With an explosion of data and refined ML algorithms, AI holds significant potential to revolutionise the sector. Two key applications emerge as noteworthy:
1. Probability of Default
Traditional probability of default (PD) models have long relied on logistic regression. Despite their simplicity and interpretability, these models fail to capture the complex relationships potentially inherent in the data. This is where ML outshines traditional models. As observed in a case study by Deloitte France, models built using random forest, gradient boosting, and stacking methods all outperform logistic regression models in multiple performance measures. These observations hint at a future where ML-based models, despite their 'black box' reputation, could offer enhanced accuracy in credit risk management.
2. Early Warning Signals
AI algorithms excel at uncovering patterns in high-velocity, large-volume data, which can be employed to generate credit default signals. With sufficient computational power, these algorithms can draw from diverse sources to enhance the accuracy of these signals. One such example is Natural Language Processing (NLP), a technology increasingly used to extract valuable insights from textual data. NLP can facilitate the capture and use of information from various written media in credit analysis.
The Benefits of AI in Credit Risk Reporting
AI offers several compelling advantages to financial institutions, the key among them being the ability to process real-time transactional data and generate models that can work with a wide range of data points. Let's delve into the benefits AI brings to credit risk reporting.
1. Automated and Personalised Decisions Across a Customer's Lifecycle
One of the most compelling benefits of AI is the capacity for automation. Financial institutions can use AI models across various use cases, which adds value by automating numerous decisions associated with different customer journeys. For instance, in the early stages of the customer lifecycle, banks can utilise AI analytics to enhance aspects such as customer acquisitions, deepening relationships, and smart servicing. AI thereby ensures a more seamless and appealing journey for customers, providing personalised experiences tailored to individual needs.
2. Enhanced Credit Decisioning
The improved customer lifecycle journey directly contributes to better credit decisioning. McKinsey suggests that AI has a positive impact on credit-approval turnaround times and the percentage of applications approved. Essentially, AI enables technology-driven credit decisioning that helps scale the business while concurrently lowering costs through the accurate identification of riskier customers. This results in improved approval rates and reduced credit risk.
AI-driven credit decisioning demonstrates its benefits in three main areas
AI helps analyse a broad segment of consumers and accurately determines whether a particular client qualifies for a loan. AI algorithms can process numerous variables, going beyond traditional metrics, to offer more nuanced risk assessments.
With AI-based algorithms, institutions can automate the process of determining the maximum borrowing threshold based on a multitude of processed factors. This leads to more precise credit limits that match the customer's ability to repay, further minimising credit risk.
AI empowers banks to offer highly competitive rates and allows for the adjustment of pricing in response to market shifts. This level of dynamic and personalised pricing wouldn't be possible without the computational power and flexibility provided by AI.
In summary, these benefits ensure accurate credit decisioning, lower costs, and help financial institutions acquire clients who can repay their loans and are eligible to receive new ones. AI in credit risk reporting not only enhances efficiency but also optimises the allocation of credit, which is fundamental to the functioning of the economy.
It is worth mentioning that in addition to enhancing current credit risk reporting processes, AI can also provide predictive insights that are invaluable in managing credit risk. AI models can analyse past trends and patterns to predict future credit behaviour, offering early warnings about potential defaults and allowing institutions to mitigate risks proactively. This use of AI in predictive modelling represents a significant step forward in credit risk reporting, setting the stage for more proactive and efficient credit risk management.
Despite the promise of AI, several challenges obstruct its adoption in credit risk management. These include regulatory compliance, model governance, data quality, and ethical considerations surrounding AI use. A black-box design, for example, could lead to bias or unexplainable decision-making. Additionally, AI models' sensitivity to data quality means that banks must ensure standardisation, accuracy, validity, and integrity in processing vast datasets.
While challenges exist, the drive towards AI is clear. HKMA’s survey revealed that 89% of the banks in Hong Kong SAR have adopted or plan to adopt AI applications. As this momentum continues, AI's integration is expected to expand into areas like fraud detection, model validation, stress testing, and credit scoring. However, this expansion must be tempered with a robust governance framework, which is currently immature due to the opaque nature of most AI algorithms.
To take advantage of AI solutions while ensuring regulatory compliance, credit risk practitioners should stay informed about AI implementation within their organisations and maintain open communication with regulatory authorities. As technology continues to mature and understanding of AI models improves, the next three to five years are likely to witness significant growth in AI's role in financial services.
The AI Revolution at Cedar Rose
The transformations brought about by AI in the realm of credit risk reporting aren't merely theoretical – organizations are already harnessing these innovations. Cedar Rose's approach to data utilization serves as a testament to the capabilities of AI.
Our Data Licensing uses advanced AI modelling to ensure comprehensive data handling. Cedar Rose’s CR Score summary tool, built on AI, offers insights that are both profound and actionable. The CR Score model doesn't stand alone; it's complemented by the Auto Size Indicator (ASI), an AI-powered tool that categorizes a company's size based on multifaceted parameters. Likewise, the Auto Risk Rating factors in both the CR Score and the company size give a more holistic risk evaluation. To further streamline operations and aid decision-making, Cedar Rose introduced the Automated Credit Limit (ACL) algorithm in 2019. This innovative system calculates a business's Maximum Credit Limit for short-term periods, leveraging both the expertise of Cedar Rose’s credit analysts and an advanced statistical model.
AI-powered tools at Cedar Rose enhance operational efficiency, reduce costs, and foster informed decisions. For firms keen on leveraging AI in credit risk, partnering with innovators like Cedar Rose is crucial.
Sources: Cedar Rose