Finance professionals cannot grasp the opportunities offered by AI, unless they are able to understand, identify and manage the associated risks. Here’s our handy guide to what you should and shouldn’t be doing with AI…
There are numerous use cases for artificial intelligence within the finance function, from the simple speeding up of administrative tasks or supporting due diligence activities. However, practitioners shouldn’t start exploring AI until they have considered the potential risks and ethical considerations, such as combatting bias and ensuring accuracy.
Here we outline some simple tips to help those in finance take their first steps to using AI effectively.
Do
- Be clear on what you want to achieve – set out AI-related goals and objectives.
- Understand the capabilities and limitations of different AI models and implementations.
- Ensure you have appropriate policies and guidelines in place on how AI should be used.
- Ensure you, and those using AI in your organisation, have an introductory understanding of how the technology works and are adequately trained.
- Start small – focus on small projects or proof-of-concepts that are not business critical. Learn about potential impacts and feasibility with these real-life projects, before scaling up.
- Prepare your data – focusing on accuracy, hygiene, quality and diversity
- Continuously monitor and evaluate AI outputs against your objectives and standards and against expected outputs.
- Include humans in the loop as necessary. Review and challenge AI outputs with professional scepticism, avoiding automation bias.
- Be transparent – make it clear when AI has been used in processes and in creating outputs.
- Consider a scientific approach to using AI, where rigour and accuracy are important.
- Be curious and experiment but be responsible too.
Don't
- Ignore ethical aspects – develop guidelines for responsible use of AI.
- Abdicate responsibility – AI models and outputs require human oversight.
- Overestimate generative AI models – they are tools to complement human expertise.
- Underestimate the hardware or infrastructure requirements – AI requires significant volumes of good quality data.
- Disregard user feedback – use feedback to identify and address issues, as well as improve models.
- Fall foul of data protection, privacy and intellectual property (IP) considerations. Keep client and confidential internal data off public AI tools, and make sure you have permission to train a model using someone else’s IP or data.
- Ignore potential inconsistencies and inaccuracies in outputs.
- Forget that the implementation of new technology requires cultural change. Supporting staff and stakeholders with legitimate concerns about AI is crucial to its successful adoption.
There is no single AI implementation approach that is fail safe for every situation. Finance professionals using AI must use their knowledge of their organisations and sectors to adapt their approach to reflect those specific needs. Professional judgment will also be a key skill to evaluate use cases, and identify and manage any risks.
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