25 September 2024
Organizations can uncover great value in harnessing AI for a variety of use cases in finance and risk. A discussion with Varrlyn experts Stephan van der Windt and Brian Mudhara on what value can be extracted and what is needed to make it happen.
Artificial Intelligence (AI) is rapidly reshaping the landscape of financial services and risk management, providing innovative solutions that drive efficiency, enhance decision-making, and uncover new business opportunities.
“AI has the potential to save a lot of manual efforts by digitizing processes that once required human knowledge,” kicked off Stephan van der Windt, Director at Varrlyn. “In marketing and sales, AI can leverage market and client data to better predict and propose solutions, leading to increased sales targets as observed in some financial services.”
This capability to predict patterns and optimize processes is particularly valuable in risk management, where AI is being used to detect risks previously undetectable by traditional methods. Moreover, AI can enhance financial forecasting, providing more informed steering decisions through predictive analytics.
With these and other benefits rapidly emerging, “it’s not a question of whether, but how AI will be used across the organization,” said Brian Mudhara, Senior Business Analyst at Varrlyn and an expert in asset liability management.
Innovative AI use cases in Finance and Risk
Within the domains of finance and risk, there a number of use cases that already can bring much value:
Integrated Cash and Liquidity Management
“AI-powered models can continuously analyse market data, interest rate trends, and economic indicators to manage the balance sheet dynamically,” explained Brian. This allows financial institutions to minimize funding costs, maintain adequate liquidity buffers, and optimize the risk-return profile of their balance sheet.
Interest Rate Risk Management
AI models can forecast interest rate movements and analyse their impact on asset and liability cash flows. Brian: “By implementing dynamic hedging strategies and adjusting the duration of assets and liabilities, banks can mitigate interest rate risks and optimize net interest income.”
Predictive Cashflow Forecasting
AI-based models can analyse historical data, economic indicators, and external factors to forecast cash flows accurately. This allows banks to optimize liquidity reserves, manage funding needs effectively, and enhance liquidity risk management.
Regulatory Compliance Reporting
Leveraging AI to automate regulatory compliance processes such as Basel Liquidity requirements and stress testing frameworks. “Enhancing compliance reporting can help financial institutions avoid fines and penalties,” noted Brian.
Key considerations and pitfalls during implementation
While AI offers numerous opportunities, there are also several pitfalls to consider. It starts with a strategy that sets the contours.
“AI must be a centrepiece in the strategy of banks,” emphasized Brian. “And then a comprehensive approach is needed to integrate AI into an organization’s strategy, including defining clear objectives, identifying key use cases, building organizational capabilities, ensuring regulatory compliance, fostering a culture of innovation, collaborating with partners, and continuously measuring and monitoring performance.”
In doing so, leaders should not consider AI technology as the silver bullet, warned Stephan. Similarly, all kinds of requirements and pre-requisites need to be in place for an effective roll-out.
“One of the key challenges is implementing a data strategy to improve data quality across multiple systems. Financial institutions should define a clear data strategy, including data collection, cleansing, integration, and governance.”
Stephan also highlighted the importance of a robust technology infrastructure and the need to invest in scalable and flexible solutions if current capabilities fall short. Additionally, he stressed the need for talent acquisition, particularly data scientists, machine learning experts, and domain specialists, to drive AI initiatives forward and nurture a culture of understanding and effective use.
Another critical area is ethical and responsible AI. “Financial institutions must consider the ethical implications and societal impacts of AI applications,” Stephan emphasized. “Developing guidelines and governance frameworks is essential to ensure responsible AI development and deployment.”
Navigating the future of AI
As financial institutions continue on their path to embracing an AI-driven future, Stephan said that careful thinking needs to be put into the road ahead. “Every area of the bank can benefit from AI. So the future of AI in financial services is bright, but institutions must carefully navigate the complexities of technology, talent, and governance to fully realize its potential.”
And as AI increasingly takes the stage, “keep in mind that the technology is not here to take away our jobs. Instead, AI is a companion for a more enriching and value-adding job, while providing new opportunities for growth and innovation.”
Source: https://www.consultancy.eu/news/10683/how-can-ai-streamline-finance-and-risk-in-financial-services