AI is currently having as much of an impact on the finance sector as it is on the rest of the world. Here, as everywhere, intelligent AI solutions have the power to simplify workflows, streamline in-depth research, and generally provide ease across customer journeys. Unfortunately, tools like these are also bringing some constraints that can’t be avoided.
This is perhaps unsurprising considering the strict regulatory landscape of the financial world, but the situation is more worrying than a lot of professionals realize. This was highlighted in a recent global study, which found that, while almost a third of professionals rate AI as potentially transformative, 80% are concerned about priorities like data protection.
Luckily, there are ways to overcome these issues and successfully integrate AI into your financial bottom line, and we’re going to consider them here.
# 1 – Choose Industry-Specific Solutions
There’s arguably no place for one-size AI solutions in an industry that’s so heavily reliant on regulatory compliance. Instead, financial institutions must utilize industry-specific AI software like Perplexity or Claude for financial services, both of which are uniquely designed with financial compliance in mind. These tools gain answers from finance-specific resources like Databricks and Snowflake, making it far easier to ensure the information and compliance that you need to thrive. That said, even these solutions should be implemented with care. Create clear guidelines for compliant use, which should include accountability and monitoring that leaves no room for error.
# 2 – Stress Test Before Deployment
63% of respondents to a recent IBM survey consider stress-testing of AI-powered systems to be a priority in the financial sector. This process should ideally happen before deployment, and will involve running hypothetical scenarios to see both how AI systems function and how easy data protection and other risk factors are to manage. Based on this, you’ll be able to develop a far clearer idea of notable weaknesses and begin implementing a plan to overcome them before they leave potentially damaging holes in your processes.
# 3 – Ensure Ongoing Risk Assessments
Even after stress testing, it’s always a mistake to simply let AI loose. After all, the financial world is open to an ever-changing regulatory landscape that has altered significantly in the last year alone. Changes look to happen with particular regularity as AI becomes baked into back-house processes, meaning that ongoing risk assessments are key to ensuring compliance. This may include vetting vendors, monitoring AI decision-making, and classifying all AI systems based on their risks.
# 4 – Develop Professional Skill Sets
Many institutions make the mistake of implementing AI software and expecting it to function independently. In finances, that approach can prove incredibly damaging, making way for AI hallucinations or inefficiencies that leave your data sets wide open. Talent and skill development are essential to help you avoid this, and they should be implemented both within your own team and with the help of industry-specific AI consultancies where relevant.
AI in the financial sector looks like it’s here to stay. Ensure you’re setting yourself up for success using these top tips for risk avoidance.
