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Of all the enterprise functions influenced by AI these days, perhaps none is more consequential than AI and financial processes. People don’t like when other people fiddle with their money, let alone an emotionless robot.

But as it usually goes with first impressions, AI is winning converts in monetary circles, in no small part due to its ability to drive out inefficiencies and capitalize on hidden opportunities – basically creating more wealth out of existing wealth.

Attention to detail

One of the ways it does this is to reduce the cost of accuracy, says Sanjay Vyas, CTO of Planful, a developer of cloud-based financial planning platforms. His take is that while finance has lagged in the adoption of AI, it is starting to catch up as more tech-savvy professionals enter the field. A key challenge in finance is to push data accuracy as far as you can without it costing more than you are either saving or earning.

To date, this effort has been limited largely by the number of man-hours you are willing to devote to achieving accuracy, but AI turns this equation on its head since it can work all day and all night focusing on the most minute of discrepancies.

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This will likely be a particular boon for smaller organizations that lack the resources and the scale to make this kind of data analysis worthwhile. And as we’ve seen elsewhere, it also frees up time for human finance specialists to concentrate on higher-level, strategic initiatives.

Finding the bad actors

AI is also contributing to the financial sector in other novel ways — fraud detection, for example. GoodData senior content writer Harry Dix recently highlighted the multiple ways in which careful analysis of data trails can quickly lead to fraud discovery and take-down of perpetrators. Most frauds require careful coordination between multiple players in order to disguise their crimes as normal transactions, but a properly trained AI model can drill down into finite data sets to detect suspicious patterns. And it can do this much faster than a human examiner, often detecting the fraud before it has been fully implemented and assets have gone missing.

Implementing AI in financial processes is not just a way to get ahead, social media entrepreneur Annie Brown says on Forbes — it is necessary to remain afloat in an increasingly challenging economy. With fintech and digital currencies now mainstream, organizations that cannot keep up with the pace of business will find themselves on the road to obsolescence in short order.

New breeds of financial services — everything from simple banking and transaction processing to sophisticated trading and capital management — are cropping up every day, virtually all of which are using AI in one form or another to streamline processes, improve customer service, and produce greater returns.

Keeping AI and financial processes honest

Still, the overriding question regarding AI in financial processes is how to ensure the AI behaves honestly and ethically. While honesty and ethics haven’t exactly been hallmarks of the financial industry throughout its human-driven history, steps can be taken to ensure AI will not knowingly deliver poor outcomes to users. The European Commission, for one, is developing a legal framework to govern the use of AI in areas like credit checks and chatbots.

At the same time, the IEEE has compiled a guidebook with input from more than 50 leading financial institutions from the U.S., U.K., and Canada on the proper way to instill trust and ethical behavior in AI models. The guide offers multiple tips on how to train AI with fairness, transparency and privacy across multiple domains, such as cybersecurity, loan and deposit pricing and hiring.

It seems that finance is feeling the push and pull of AI more than other disciplines. On the one hand is the lure of greater profits and higher returns; on the other is the fear that something could go wrong, terribly wrong.

The solution: Avoid the temptation to push AI into finance-related functions until the enterprise is ready.  Just like any employee, AI must be trained and seasoned before it can be entrusted with higher levels of responsibility. After all, you wouldn’t promote someone fresh out of college to CFO on their first day. By starting AI out with low-level financial responsibilities, it must then prove itself worthy of promotion — just like any other employee.

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