There seem to be some commitment issues in the relationship between financial institutions (FIs) and AI-driven risk monitoring. While AI offers significant benefits to the risk-monitoring sector, banks have been slow to take the plunge. A recent survey found that only 27% of risk personnel think leveraging AI and machine learning is vital to improving credit risk management at their organization. The fault for this hesitation doesn’t all lie on the side of the banks though. Security concerns, the threat of AI regulations, and associated costs all contribute to the general tentativeness towards this tech. To make matters worse, the failure rate for AI projects stands at a whopping 80%. So, what can be done? Do FIs continue with manual-driven risk monitoring systems or find a way to harness this transformative tech? This blog discusses why implementing AI-driven risk monitoring is important for banks and 4 guidelines that ensure the process is successful.
Setting the record straight – Why FIs need to harness AI
For anyone sitting on the fence, here’s some data that shows the potential impact that AI can have on risk-monitoring functions in a bank:
Accuracy – According to FICO, machine learning (ML)-based credit scoring models provide a 10-15% improvement in predictive accuracy over traditional statistical models. This improvement can translate into a 20-40% reduction in credit loss rates.
Efficiency – AI-based automation and data collection can improve a bank’s operational efficiency by 30% and reduce the time needed to gather data by up to 70%.
Access - AI’s ability to analyze non-traditional data broadens the reach of financial services and gives lenders access to current data that produces an up-to-date view of borrower risk.
Speed – Machine learning and deep learning models help risk management professionals improve the speed and precision of portfolio anomaly detection by about 30%.
Savings – According to McKinsey, AI can deliver a potential annual value of over $350 billion to a bank’s risk functions alone through more efficient data processing and insight generation.
The data is speaking and it's pretty clear that AI can transform a bank’s risk management by streamlining data processing, generating current and alternate insights, enhancing early and future risk predictions, and speeding up the entire process from start to finish. The challenge now is for FIs to find a way to harness it successfully.
4 guidelines for FIs to successfully implement AI-driven risk monitoring
Secure stakeholder and leadership engagement
According to Extreme Ownership, a popular leadership book written by U.S. Navy seals, projects fail mainly due to leadership shortcomings (There are no bad teams, only bad leaders). Similarly, a successful AI project requires proper leadership and stakeholder buy-in. Leadership involvement ensures adequate funding, organizational goal alignment, tech adoption, and implementation.
And how can risk teams secure this kind of support? Here are a few pointers:
Show how AI-driven risk monitoring can reduce risks, cut costs, and increase profits. Plenty of studies showcase this, some of which we have shared above.
Implement a pilot project. This is a low-risk, low-cost way of demonstrating how AI can solve lending problems and achieve company goals.
Choose platforms that meet ethical and compliance requirements. This can influence stakeholder buy-in.
Look beyond the traditional
Here’s an open secret – it’s all about the data and the problems it solves. This is particularly true for AI technology. While traditional tech projects rely heavily on coding knowledge, AI-led ones are driven by data, the quality and quantity of which can influence the outcome and effectiveness of the solution. As such, it requires a different skill set.
Concurrently, today’s risk monitoring teams face very specific problems that require specific solutions that traditional linear-based systems cannot provide. So, it is prudent to involve AI-savvy AND domain-savvy folk. This way, you get a solution that is both technically sound and laser-focused on solving the problems lenders face today.
Keep a human in the loop
Despite their immense capacity, AI systems are not infallible and need some form of human oversight. Take the example of an AI-based meal planner app launched by a New Zealand supermarket last year. The app generated recipes that were nonsensical and, in some cases, downright dangerous – such as insecticide-infused roast potatoes, human meat stews, and chlorine-gas-producing drinks. Given the high-stakes nature of financial decision-making, such mistakes can lead to costly, biased, or unfair determinations. Having human-in-the-loop systems can prevent this while improving transparency and building trust among stakeholders and regulators..
Augment and integrate, don’t replace
Entirely replacing old systems with new ones is a costly affair. Not only is this a challenge for most lenders, it is also an unnecessary undertaking. Doing that would be like throwing the baby out with the bath water. Traditional risk monitoring systems work on historical financial data. Notwithstanding its limits, such data forms the cornerstone of corporate portfolio risk monitoring. Obviously, it cannot be replaced. However, there is so much room for improvement. This is where AI can step in to supercharge the process by bringing real-time and alternate data into the equation so lenders get a more current and holistic view of risks. By bringing in systems that augment financial monitoring and seamlessly integrate into existing systems, lenders can reduce the complexity and cost of an AI initiative.
How TRaiCE can help
TRaiCE is an AI-based risk monitoring system designed with commercial lenders in mind. Our team consists of AI and financial domain experts who know what problems FIs face with risk monitoring, all the data needed to solve these issues, where to ethically source the information, and how to process it to give FIs crucial data-backed risk insights. Importantly, we have the tech skillsets needed to create, scale, and seamlessly integrate bespoke systems that meet the complex needs of diverse lenders. We’ve also kept all the processes explainable and auditable to ensure regulatory compliance (to see how we do this, check out our blog on the topic).
If you’re a commercial lender looking to augment your risk monitoring with AI successfully but don't know how to go about it, why not give us a try? Contact us at info@traice.io or schedule a demo with us today! Our team would be happy to talk to you about implementing a pilot project at your organization or give you a free walkthrough of our platform.
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