Risk management in banks are data dependent and data driven.The standard of integrity of data can define the precision with which the measurement of a risk can be undertaken.
Data pooling, data mining, data analytics, and data interpretation by risk management professionals is an ongoing process where the algorithms are built and assessed. The guidance of central bank and risk management policies of individual bank continue to be the reckoning factors in working out mitigation strategies.With the advent of more sophisticated technology tools, measurement of risk is getting progressively fine-tuned and mitigation technologies are evolving into more effective tools. The formation of data centers have redefined the measurement of risk management. Now new tools are getting developed with the help of artificial intelligence and robotics.
Artificial Intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. In the banking parlance, AI is frequently applied to the project of developing risk management systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalise, or learn from past experience. Since the development of the digital computer and its interconnected digital space, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or statistical models with great proficiency.
They do not suffer with fatigue or aggression. They can be tuned to the preset prescriptions. It will execute what is programmed. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programmes that can match human flexibility over wider domains or in tasks of data analysis that drives risk management. Hence AI cannot substitute the human discretion and intellectual ability in management space. It can aid in execution of programmes.
A game changer
AI is evolving as a game changer in improving the skills of risk management in banks. The developments in the field of risk management is the recent emergence of AI concepts—specifically cognitive computing. These concepts involve advanced technology platforms that can address complex situations that are characterised by ambiguity and uncertainty.Due to dynamic operating and market environment, there are more uncertainties in predicting or assessing the impact of economic events. Therefore, the cognitive computing has begun to augment business decisions and power performance right alongside human thought process and traditional analytics. In fact, the domain of risk management lends itself particularly well to cognitive computing capabilities, as typical risk issues often include unlikely and/or ambiguous events.
When developed and integrated well AI can be a game changer to supplement the strategies of risk management. The use of AI to manage risk is particularly helpful when handling and evaluating unstructured data—the kind of information that doesn’t fit neatly into structured rows and columns. Cognitive technologies, such as natural language processing (NLP), use advanced algorithms to analyse text in order to derive insights and sentiment from unstructured data. Therefore globally AI is gaining prominence to be a game changer in the banking industry.
Surge in data volumes
Globally banks have begun using massive amounts of internal and external data to take a more preventative risk stance.However, in many large sized banks where the transaction data is huge, traditional methods of analysis have become increasingly incapable of handling this data volume. Instead, cognitive capabilities—including data mining, machine learning, and natural language processing—are supplementing traditional analytics and being applied against these massive data sets to help find indicators of known and unknown risks.
Wherever such usage of AI in data mining and data analysis is not in vogue, they can very well explore the nuances to fine tune risk management methodology. Given the increases in computational processing power and corresponding decreases in the costs of data storage, AI in the business world of banking and financial sector too is fast becoming a reality. These AI or cognitive-based technologies help computers interact, reason, and learn like human beings.
A fraud risk mitigation tool
Look at fraud risk mitigation by early detection as an example in the domain of risk management. The old method of detecting fraud was to use computers to analyse a lot of structured data against rule sets. For example, fraud specialists would create a threshold for wire transfers say at US$10,000 or so. The threshold for flagging can be different for different economies but it is right kind of flagging that will enable detection at a later stage but at the earliest possible time span. Then any transaction over that amount would be flagged by the computer for additional investigation. The problem is that this type of structured data analysis often creates too many false positives which require hours of close scrutiny.
With cognitive analytics, fraud detection models can become more robust and accurate and can be customised to situations that the domestic banking system needs. If a cognitive system kicks out something that it determines as potential fraud and a human determines it’s not fraud because of interpretational differences, the computer can be programmed based on those learning when seen together with those human insights. Next time, the cognitive ability will be fine-tuned to detect in the institutionalized methods. Thereby data system can be made to get smarter with AI. Thus banks which can leverage cognitive technologies to anticipate and proactively manage risk to gain competitive advantage and use it to power their organisations’ performance.
Better decision support system
These new AI capabilities are not limited to detecting risk. Cognitive analytics allow businesses to quickly tap unstructured information, personalise services, and reduce subjectivity in decision making. It can help in shaping a better Decision Support System (DSS) that can pave way for better risk management. Among the areas where these tools can be applied spans across the formulation and design of products, features, pricing and creating competitive differentiators. As banks prepare to operate in uncertain and unstructured environment, AI can help in modulating the decision support system that ultimately decides the extent of risk inherent in a decision and its outcome.
Even in smaller banks, AI and cognitive abilities could be improved to navigate better in unstructured business format. Since banks in Asian Region are accustomed to operate in predictable markets or where the balance sheet of banks have predominance of loan book and low composition of trading book, it may be easy to manage risk. But the preparedness of banks should be conditioned to use the latest technology and AI so as to develop better capabilities. Since the banking system is on integrated technology platform, it will be possible to integrate AI and even use robots in future to handle the data for better and accurate risk forecasting to improve the risk management systems. Hence the future of risk management will center on the capabilities to use the AI.
It is also interesting that way back in 2000, Sri Lanka Association of Artificial Intelligence (SLAAI) was formed which can provide interesting and implementable guidance to the banking system. Though AI is predominantly used in academic research, it could be augmented in financial sector too. Robotics enabled AI is already in use in Sri Lanka and it can be replicated in risk management areas to improve efficiency. Currently, the humanoid teller is used to dispense routine banking services which can be extended to other areas as a competitive differentiator in DSS.
(The author is a Director at the National Institute of Banking studies and Corporate Management – NIBSCOM, NOIDA, National Capital Region, Delhi, India. The views are his own)
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