Central banks are rapidly deploying AI driven by the promise of efficiency and cost reductions. AI engines are already serving as central bankers. But with most AI applications today at a low level and with the conservative nature of central banks, AI adoption is slower than in private sector financial institutions. Still, the direction of travel seems inevitable, with AI set to take on increasingly important roles in central banking. 

Artificial intelligence and machine learning applications in banking are constantly increasing. A similar trend can be seen in other areas of the economy. Banking risk management is one of the finance fields with the strongest development during past decades, but the need for further development in this area is constantly increasing. 

Key areas used 

AI, ML and DL are already intensively used in banking risk management, and we can see a further increase in their usage. In credit risk, stated fields of AI have been strongly present during past decades. Many sophisticated classification algorithms are applied in modern credit scoring. Some solutions used for credit scoring are logistic regression, discriminant analysis, Bayes classifier, nearest neighbour, classification trees, Lasso logistic regression, DL, i.e., artificial neural networks, etc. 

ML is used in the different areas of market risk management, such as forecast volatility, interest rate curves, foreign currency risk estimation and other areas. For liquidity risk management, there has been some research in the recent period regarding the application of Artificial Neural Networks and Bayesian Networks. 

Artificial intelligence and machine learning have the potential to support the mitigation measures for the contemporary global economic and financial challenges, including those caused by the latest coronavirus disease.

Successful applications- India 

The Reserve Bank of India (RBI) recently announced its use of AI to gain deeper insights into the operations of supervised entities. The tools that RBI uses include, early warning system, stress testing models, vulnerability assessments, cyber key risk indicators, phishing, and cyber reconnaissance exercises, targeted evaluations of compliance with Know Your Customer (KYC) and Anti-Money Laundering (AML) norms, and data analytics. 

Supervisors play a key role in ensuring the alignment of banks’ business models with their risk appetite. Such evaluations delve into critical factors, including projected business growth, sustainability of earnings potential, extent of diversification and many more. According to Deputy Governor MK Jain evaluating the quality of assurance functions enables supervisors to identify potential vulnerabilities, assess the effectiveness of internal controls, and mitigate risks proactively.

Successful applications- across the globe 

One notable example of successful AI-powered initiatives in the UAE banking sector is the Abu Dhabi Commercial Bank, which uses an AI-powered risk management platform called Falcon. Falcon leverages ML algorithms to analyze large volumes of data and provide real-time risk assessments to the bank's risk management team, enabling them to identify and mitigate potential risks more effectively. 

Similarly, Emirates NBD has implemented several successful AI-powered initiatives, including an AI-powered voice banking service and an AI-powered chatbot named 'EVA.' HSBC has also implemented AI-powered solutions such as the chatbot 'Amy' for wealth management clients and an AI-powered anti-money laundering (AML) solution to improve the bank's compliance processes.

Managing banking risk 

It can be stated that all banks, regardless of their size, length of business history, profile, strategy, development degree of the market, local or international orientation and level of risk management complexity, can benefit from the AI and ML application in risk management. 

Some of the crucial elements for a successful and comprehensive implementation of AI and ML in bank risk management are: 

• Definition of the strategy, operative plan and project for implementing AI and ML in bank risk management. 
• Analysis of which bank risks could be part of the AI and ML enforcement scope, such as credit risk, liquidity risk, market risk, operational risk etc. 
• Analysis of the technical and human resources necessary for enforcement. 
• Are additional external resources necessary (recruiting, outsourcing, etc.)? 
• What are the legal and regulatory requirements and restrictions? 
• Budget and cost-benefit analysis. 
• Involvement of all necessary organizational units 

A bank can conduct testing of the various AI and ML techniques for this segment, such as logistic regression, discriminant analysis, Bayes classifier, nearest neighbour, classification trees, Lasso logistic regression, DL, i.e., artificial neural networks, SVM, etc. 

Positive results in the AI and ML credit scoring and internal rating implementation can bust the credit process and receivables disbursement. These processes are wider than the core credit risk management functions but are closely related to credit risk management. 

Using the already stated AI and ML credit risk management techniques, a bank can apply additional stress tests or improve the existing ones. Positive results of AI and ML applications, especially on stress testing, can be used for simulation and planning improvement. 

In market risk management, ML is used in different areas like forecast volatility, interest rate curves, foreign currency risk estimation, etc. Most of the stated recommendations, phases and steps relevant to credit risk management are also valid for market risk management. 

Applying AI and ML in managing operational and related risks, significant results have been achieved worldwide in areas such as cyber security, fraud prevention, and anti-money laundering.

Conclusion 

Artificial intelligence and machine learning have an increasing influence on the financial sector and the economy. The impact of artificial intelligence and machine learning on banking risk management has become particularly interesting after the global financial crisis. A measured and well-prepared further application of artificial intelligence, machine learning, deep learning and big data analytics can have a further positive impact, especially on risk management.