Errors in arrears: automating the future of finance

Errors in arrears: automating the future of finance

Errors in arrears: automating the future of finance

Zahi Yaari, VP of EMEA at SnapLogic


In times of economic uncertainty, businesses simply can’t afford to be unproductive. With the harsh prospect of a recession comes the pressure to evaluate every aspect of a company for inefficiencies, and identify an Achilles’ heel that could really do with some armour. AI-powered automation might just be the solution many are looking for.

Despite a focus on improving performance, IT leaders within companies are facing dwindling resources and are often forced to deprioritise work projects to make ends meet. This problem affects the whole business; and as resources are unlikely to increase significantly in the near future, other options need to be explored to avoid stagnation and maintain a competitive edge.

A recent survey from Gartner showed that 44% of insurance CIOs and 49% of banking and investment CIOs are planning on increasing investment into intelligent automation, making it clear that automation is the way forward. And, with new AI tools also on the rise, businesses cannot afford to let themselves fall behind.

Is AI the answer?

Generative AI has business leaders asking how it can be utilised to benefit their industry, and the finance sector is no exception. Starting from humble beginnings, this technology has evolved dramatically from using pre-selected responses to specific inputs. We have now witnessed the exciting arrival of AI’s ability to participate in meaningful conversation, fulfill tasks and give advice.

So, what are the possibilities?

The finance sector has always been a numbers game, but with the advent of generative AI, finance professionals can now leave the mundane tasks to the machines and focus on more strategic initiatives that require human insights.

AI-based automation can empower firms to devote more employee time to what the Chief Executive of Britannia Financial Group calls the “high touch” tasks. With Large Language Models, such as ChatGPT, which uses natural language processing, business users are able to increasingly automate common tasks without an extensive knowledge of code.

Another benefit of generative AI is increased accuracy. By automating manual tasks, AI reduces the likelihood of human error through a reduction in human intervention. This not only leads to more accurate results, but it also helps to ensure that regulations are being met and that errors are caught before they become costly mistakes. Compliance also becomes infinitely easier when you have an AI picking up on any potential flaws. No more having to blame the dog for eating your paperwork!

The use cases do not stop there, either. We’ve already seen JPMorgan using bots to respond to internal IT requests, such as resetting employee passwords, and Barclays introducing robotic process automation across a range of processes, such as accounts receivable and fraudulent account closure.

A majority of employees are on board with the introduction of AI to the workplace too. Our recent research revealed that almost two-thirds of workers like the idea of using AI in their role, over half (54%) said they think AI will save them time, 46% said it would improve productivity and 37% said it would reduce risk and errors in their work.

Security doesn’t come second

Nothing is more valuable to a business than their own data. When faced with the prospect of feeding their data through a third-party application built on processing and incorporating data, many will feel concerned about the risks of data misuse or leaks, and particularly so in heavily-regulated sectors such as banking and finance. So how do we mitigate or avoid the security risks, ethical concerns and trust issues typically associated with generative AI?

The truth is that these concerns are valid, but businesses can tackle them successfully by prioritising governance, transparency, ethics, and security in their AI strategy. Building a culture of trust and collaboration between humans and machines is key to unlocking the full potential of enterprise AI.

With the right platform, these concerns will already be accounted for. This can start with the training of AI – ensuring it can only access relevant, accurate data that can influence the output. Data bias is a real issue, but one that can be monitored and mitigated by using the right data sets. AI systems are only as good as the data they are trained on, and if that data is biased, the results will be too.

From a data security perspective, by limiting the access vendors have to metadata and not their customers’ data, privacy can be almost guaranteed. It’s incredibly important to work with a vendor that applies strict security and compliance standards, and doesn’t monitor or hold the data they’re processing on your behalf.

One finance customer of ours has already been able to use automation to improve payment transfers, speeding up the process and removing the need for manual input. Sensitive payments data is processed in the cloud but never actually leaves the customer’s premises, providing all they security reassurance they need. The premise is similar to the way the post office works – it sorts and delivers mail without opening it, ensuring that the letter inside the envelope remain private.

Looking to the future

Today, generative AI can automate repetitive tasks, such as data entry and analysis, but as AI technologies continue to advance, we can expect to see even more sophisticated applications. For example, AI can be used for fraud detection, risk management, and investment analysis. AI can also help identify patterns and trends that humans may not be able to see, leading to more informed decision-making.

In summary, businesses that embrace automation and AI now will be well-positioned to reap both short and longer-term benefits. By automating manual tasks, finance professionals can focus on strategic initiatives. However, it’s important to address concerns around AI, such as bias and security, with a proper AI strategy. With the right approach, businesses can unlock the full potential of enterprise AI and drive innovation in the finance sector, while remaining mindful of how they’re spending their time and money.

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