By: Eric Emans, CFO Nintex
The financial services industry is at the forefront of AI implementation. This is not surprising as the financial sector is amongst the most exposed to the impacts of AI.
According to the recent PwC AI Jobs Barometer, the demand for AI skills in financial services is 2.8 times higher than in other sectors. Financial institutions and finance departments handle vast amounts of data that AI technologies can rapidly analyse to deliver actionable insights for decision-making.
This is particularly important in risk management as finance teams are facing increasingly stringent regulations. For example, EMIR Refit and revisions to MiFID II are some of the regulatory changes impacting UK banks. At the same time, EU-wide regulatory frameworks such as NIS2 will require UK financial institutions to prove their cybersecurity standards.
In compliance, AI can streamline processes by automating tasks such as real-time transaction monitoring and predictive modelling of future breaches. AI can also enhance regulatory reporting by automating large parts of the reporting process and serving as an independent validation tool throughout the process, improving efficiency and accuracy.
The potential benefits of AI can be enticing, prompting CFOs to consider large-scale implementation. However, scaling AI prematurely may be counterproductive, potentially undermining efforts to achieve long-term ROI. Instead, CFOs should stress-test their business processes to identify manual processes and implement automation before any AI deployment.
AI risks scaling inefficiencies
Skipping automation and applying AI to manual tasks or rushing deployments risks undermining organisation-wide productivity gains because AI cannot effectively operate in silos. Without automated processes that enhance cross-team collaboration, the deployment of AI projects may be stunted as resources are misallocated and opportunities for synergy are missed.
Attempting to scale AI without prior automation can backfire and exacerbate operational inefficiencies. Take for example a loan approval process. Using AI to automate specific tasks within an inefficient loan approval process, such as evaluating an applicant’s credit score, may shorten the time taken for a specific task. However, without improving the entire workflow – from loan application to post-closing – transitions between steps may still take longer.
Another potential pitfall is in financial modelling and compliance. Manual data collection leaves room for human errors, such as typos or missed entries, where automation can help mitigate such errors. Implementing AI that learns and adapts from flawed data will result in faulty analysis.
So, in what ways can CFOs lead AI implementation while limiting the risks of premature deployment?
- Using automation as a precursor to AI
Layering AI on top of end-to-end automated processes is essential for holistic improvement and enhanced workflow integrations. This approach identifies inefficiencies, ensures seamless coordination, reduces errors, and improves scalability.
To illustrate the point, let’s revisit the loan approval process example. With automated processes, the entire journey from start to finish will become seamless, connecting the dots between each step. Similarly, automating the data collection process minimises risks associated with human errors, because any changes in data inputs can be immediately captured. This ensures that data fed into AI algorithms are accurate and consistent.
- Starting small, then scale
By starting small and applying AI to already automated workflows, organisations create a necessary foundation for future AI scaling. Being able to prove success and improvement in productivity on a smaller scale will help secure the stakeholder buy-in required to successfully scale AI implementations.
An IDC Whitepaper found that organisations succeeding in their automation projects are more than twice as likely to experience an increased automation budget in 2024 compared to those facing challenges. With this solid foundation, AI-enabled capabilities can further build and expand automation programs, enhancing team productivity.
- Maintaining data integrity
One of the key advantages of AI algorithms is their capacity to learn and adapt. AI tools become increasingly proficient through the assimilation of extensive data.
To ensure data quality, CFOs should collaborate with their accounting teams to audit data collection processes and ensure they are fully optimised before AI implementation. In the long run, this is an investment in safeguarding financial integrity and business reputation.
For finance leaders, ensuring AI initiatives deliver long-term ROI requires a strategic approach, with automation preceding any AI deployment. With this strategy, financial leaders can harness the full potential of AI, achieve sustainable growth, enhance productivity, and maintain a competitive edge.
