Artificial Intelligence in Banking | Banking Technology | AI in Financial Industry

AI-based Risk Management Process

Developed countries with AI and cyber capabilities have a clear head start in establishing the control mechanisms. This provides security to their citizens and eases the risk management process. Unfortunately, maintaining that comparative advantage requires a significant ongoing commitment from a plethora of resources. Installing an enterprise risk management system and maintaining it is expensive. But the growing number of financial technology companies have made credit risk management systems accessible. Even Small and Medium Enterprises are now able to implement a financial risk management system. The risk management solutions have gained importance today and it is on a constant rise.

Types of risks

The first of the 3 specific risks include hidden biases, not necessarily derived from the part of the designer but from the data provided to train the system. For example, if a system learns to select job applicants by using a data set of decisions made by human recruiters in the past, it may unknowingly learn to perpetuate racial, gender, ethnic or other biases. These biases may not appear as an explicit rule but embedded in interactions among the thousands of factors considered.

The next risk is that unlike, traditional systems, neural networks deal with statistical truths rather than literal truths. Thus, it makes it difficult to prove with complete certainty that a system works in all cases. It becomes tough to prove its capability particularly in situations where it is not represented in training data. Limitations to verifiability can be a concern in mission-critical applications. Hence, risk identification technology has emerged as a proven method in fraud risk management.

The third risk is errors made by the learning systems when diagnosing and correcting the precise nature of the problem. There can be a complicated way towards solution, and the solution may be far from optimal. The system development happens in trained conditions. If these conditions change, the appropriate benchmark is not the pursuit of perfection. 

Role of Risk managers in Compliance Risk Management

Risk managers are more prone to integrate unknown unknowns into their risk calculations. But this presumes that they do have a firm grounding in the subject matter. For instance, as cyber risk evolved, many risk managers had the opportunity to become more familiar with what the risk is. The insurers have had time to develop new insurance products to address these risks which helped to develop insurance risk management strategies.

Risk managers need to become more knowledgeable about the threats from developing AI. Some organizations devote resources to develop systems such as operational risk management internally. But there are only a few who develop a comprehensive risk analysis framework. Quite a few recognize the need to anticipate the threats and allocate resources specifically designed to address such threats.

Risk managers have a vital role to play in the risk analysis framework. They ensure that management is well aware of the potential threats while proposing solutions to neutralize the threats. They must be also trained in risk mitigation technology which avoids huge damages caused by various risks. The systems developed by financial technology companies are correct steps in developing a wholesome credit risk management system.

AI in Banking – Risk Management Tool

The AI world that gets created is kinder to organisations that excel at embracing the technology and anticipating its impacts. The organisations that attempt to maintain a wall between humans and machines are going to be at an ever-greater competitive disadvantage. In comparison with their rivals, the use of Artificial Intelligence in banking and other financial sectors can uncover system vulnerabilities. Those who prefer to break the barrier will make use of AI in every possible way by integrating its capabilities with those of humans effectively.

The organizations that can quickly sense and respond to opportunities will acquire the opportunities in the AI landscape. In the near term, AI isn’t replacing risk managers, but risk managers who use AI are going to replace the rest. They will be in a position to better handle the enterprise risk management system. This technology will ease the risk management process of organizations and bring great efficiency in their operations.

Conclusion

There are several risk management strategies to address credit, financial, enterprise and operational risks. The capabilities displayed by AI in risk control strategies are making them the most favoured option of financial institutions.  Financial technology is setting a newbench mark for risk management in the banking industry by opening new prospects of innovation.

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