AI is rapidly spreading across the business world, and the introduction of ChatGPT is causing seismic waves across a wide range of industries. This widespread adoption has put AI in the spotlight, with RBC noting that AI has “moved from a long-term bet to a near-term reality with the potential to transform a wide range of industries.”[1] While predictive AI for finance and accounting functions such as cash management is already somewhat mainstream, we are still in the early stages of how AI can improve these operating models. This article details the impact of AI on money management, how it is leading a paradigm shift in banking, and the importance of balancing adoption and security.
How AI and machine learning are impacting money management
AI is poised to transform cash management, including but not limited to:
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Cash flow forecast:
One of the biggest use cases for AI in treasury management is around cash flow forecasting, a feature that treasurers frequently cite as one of their biggest challenges. Finance teams rely on accurate cash scenario modeling to drive strategic decision-making and drive profitability and efficiency. For example, machine learning can be applied to rich historical data sets to determine trends and patterns and predict future cash flows within a defined confidence level or time period. Inaccurate forecasts, if not executed correctly, can lead to problems such as liquidity risk and inadequate working capital management.
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settlement:
A large number of adjustments can quickly become complex and time consuming. AI for Cash Management can reduce both errors and time spent by automating steps like finding duplicates and enabling anomaly detection using pattern recognition. This feature helps finance professionals focus on tasks that can actually make a difference, rather than getting bogged down in daily payments that can and should be automated.
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Fraud and Cyber Prevention:
AI can be a powerful tool to protect your team from cyber-attacks by harnessing the power of data to prevent fraud. This technology identifies in detail whether you scroll or click on a page and aggregates this data to create a biometric profile of your online behavior. While a scammer may be able to access your ID and password and impersonate you, it’s much more difficult for a scammer to imitate your keyboarding habits. This provides an additional layer of protection against social engineering fraud by allowing you to identify anomalous activity before a transaction begins.
How banks can balance AI adoption with security
Introducing AI technology into banking means balancing innovation and security. That includes embracing technology that supports finance teams while keeping regulatory requirements, privacy, and fairness as top priorities. There are security details, such as how AI relies on collecting customer data for training models, how this data can be compromised by fraudsters or competitors, and the impact of a potential security breach on banks from both a reputational and financial perspective. This is a major concern in this space, as malicious parties can connect to cloud providers and exploit their data models or use them for their own training. Threat actors are also becoming more sophisticated with AI-generated phishing email scams. In the past, phishing emails were easy to spot due to a number of misspellings and other relevant indicators.
However, with the introduction of AI, phishing calls, emails, and text messages have become much more complex. Threat actors are also beginning to create separate AI-generated codes that can be combined to trigger malware attacks. To help combat these AI risks, the National Institute of Standards and Technology (NIST) created the first NIST AI Risk Management Framework (RMF). This enables organizations to develop AI systems in a privacy-enhanced, safe, secure, resilient, accountable, and transparent system.
How to tell if your bank offers the right AI tools
Banking AI tools must be able to transform raw data into actionable next steps and provide predictive scenarios based on historical information and accurate future scenarios. Banks burdened by legacy infrastructure may not be able to provide the above capabilities if data cannot be accessed and interpreted for the benefit of the finance team. Money management can greatly benefit from AI. Finance teams can benefit from implementing this technology into their operating models, from saving valuable time to increasing security.
[1] RBC Insight (rbcinsightresearch.com)
