When it comes to the introduction of Artificial Intelligence (AI) into the banking industry and Flexcube implementation, it has been referred to as “game-changing,” “transformative,” and even “disruptive.” To be sure, one could be forgiven for believing that financial services organizations should already be utilizing artificial intelligence to great advantage in order to become more efficient, improve the use of data, and actively detect and control the wide range of hazards associated with technology.
In spite of evidence to the contrary, we think that this narrative is not an accurate representation of the facts of the situation. Instead, even if enterprises are investigating these solutions, they are often at the proof-of-concept stage, with their efforts focused on implementing the foundations of non-financial risk management into their current technology portfolio as a top priority at this time. There are inherent issues in relocating existing systems and integrating other systems with their Governance, Risk, and Control (GRC) platform, which needs short- to medium-term investments in current technologies and the quality of data contained within them. By future-proofing non-financial risk management in this way, it will be possible to take advantage of emerging technologies and their long-term benefits.
What role does artificial intelligence play in risk management?
While artificial intelligence (AI) may not be as integral to non-financial risk management as some may imagine, it is becoming more popular in areas where the advantages exceed the costs, such as enabling large amounts of data analysis, working with Oracle Flexcube 14.x, and customer-facing applications. The following are examples of common AI applications in the financial services industry:
- Credit risk decisions: Artificial intelligence (AI) analyzes a customer’s data points in order to reduce the decision-making cycle.
- The management of financial crime: machine learning is employed in transaction monitoring to identify differences in client behavior patterns, which you may then utilize to identify instances of suspected fraudulent or illegal conduct.
- Security: machine learning is used to analyze previous risk occurrences and use the findings to detect future cyber risks.
- Chatbots: Artificial intelligence (AI) powered chatbots automate internal assistance ‘helpdesks,’ analyzing policy and framework papers to deliver responses to staff concerns. They are becoming more popular.
- RCS: The use of machine learning to analyze risk assessments to determine if they are of adequate quality and include the anticipated controls, problems, and actions is known as risk assessment classification systems (RCS).
Through the implementation and use of these use cases, organizations are able to minimize pressure on their employees by re-prioritizing efforts on fewer administrative activities, and they are able to utilize more data to make choices that are based on deeper and more accurate insights. In spite of the expenses, the use cases for artificial intelligence in NFR management are not without their dangers and obstacles. For example:
- Regulatory risk — as technology advances and becomes ‘smarter,’ the transparency of outputs created by machine learning decreases, reducing organizations’ capacity to justify decision-making to regulators.
- Conduct risk — machine learning bias may be introduced accidentally by the data used to train the technology, resulting in a systemic bias that can be difficult to recognize and correct once it has been introduced.
- Data — Many organizations may not have the vast amounts of high-quality data necessary to train and test machine learning technologies, which can result in erroneous results.
With regard to the possible dangers connected with the use of artificial intelligence in NFR management, it would be smart for organizations to carefully consider the use cases and verify that appropriate controls are in place before implementing them more broadly.
In the short-to-medium term, banks will use NFR technology.
As mentioned in the introduction, organizations must continue to work on strengthening their present technology suite in the short to medium term in order to be prepared for artificial intelligence in the near future. While this is especially true for those, who do not have the resources of larger financial institutions, the fine that Citibank received last year for failings in their enterprise risk management and data governance clearly demonstrates the level of attention that this issue deserves across the financial services sector.
Instead, organizations should concentrate on the three areas outlined below:
- First and foremost, there is the user interface. Organizations prioritize ‘usability’ while building customer-facing products, making sure that the system is aesthetically attractive and straightforward to use, for example, by reducing the number of click-throughs. While internal systems have not always received the same attention as external systems, this is a critical area of improvement that will help to increase buy-in across all three lines of defense.
- Flexcube implementation along with building high-quality MI and analytics features into a GRC system improves both the solution’s usability and the value that can be extracted from it. It is essential for reporting dashboards to give clear thematic insights across the three lines of defense, allowing managers to identify areas of concern early and put mitigation measures in place, hence minimizing the possibility of risk events occurring in the first place.
- Put your attention on one GRC solution that is a one-stop-shop and increases your organization’s capacity to give thematic insights. Suppose your organization is using several GRC systems. In that case, you should consider consolidating them into a single platform to simplify risk management while also providing a single platform on which to incorporate other technologies such as analytics functions and, in time, future technologies.
In many ways, artificial intelligence and machine learning may be ‘the last innovation that mankind will ever need to produce,’ and there is no question that they have enormous promise in driving improvements and efficiency in non-financial risk management and throughout the whole sector.
Despite this, the path to nirvana is lengthy, and AI should be seen as the sports vehicle you will purchase in 5 to 10 years. It’s gleaming and thrilling, and the sales pitch is out of this world. However, the configurable extras will always be an additional expense. However, you are unlikely to purchase it until you have secured the bare necessities – a place to live and food on the table. When it comes to non-financial risk management, the fundamentals — a sound structure, reporting, and governance – must be in place before adding extra complexity. Meanwhile, organizations should work with regulators and suppliers to better understand future technology and resolve any concerns before implementing it extensively.
JMR Infotech’s non-financial risk experience enables us to provide industry-leading framework solutions (such as Oracle Flexcube universal banking) that are adapted to our customers’ requirements. Non-financial risk specialists can help you design frameworks, implement risk governance, and set monitoring standards. To learn more about our non-financial risk expertise, contact us.
