4 ways your finance department can survive automation
To keep any company’s finance function current, CFOs and other senior executives must understand how to use machine learning and adaptive intelligence responsibly — with minimal risk to fiduciary integrity. It’s important to understand how these algorithms are operating, what decisions they’re making, and what the ramifications might be for shareholders. With much of the time-consuming data crunching becoming automated, here are some ways CFOs can restructure the finance team to adapt.
Source: World Economic Forum
1. Create a data lab for experimentation
A data lab is a creative space and platform for innovation where developers can bring together discrete data sets and use machine learning to identify hidden patterns and predictions, which can then be commercialized to add value to the business. However, new automation techniques must be tested before implementation so a well-run lab can allow companies to test new kinds of machine learning algorithms and assess the potential risks before the technique is applied.
2. Form a data ethics committee
A data ethics committee can audit algorithms for unintended consequences, which can reduce the risks associated with machine learning. Algorithms need auditors just as much as — if not more than — any other area of the business.
Before you put any of your data lab findings into practice, examine all the potential pitfalls, including any legal, financial, and brand implications. According to technology research firm Gartner, by 2018, 50% of business ethics violations will occur through improper use of big data analytics.
3. Build an economic model for information assets
Even small to midsize companies collect far more information than they might ever commercialize — and as more and more devices are connected to the Internet of Things, the amount of data transmitted will grow. Not all data needs to be stored; some will be just noise and only a fraction of it might be useful. A good economic model should help finance teams build a corporate culture that accurately assesses the value of stored data and is not afraid to reassess it as the game changes. There’s a lot of power in this relatively young science, so don’t underestimate the importance of prioritizing which information assets deserve the most attention.
4. Prepare staff and invest in advanced analytics skills
The World Economic Forum predicts that by 2020, 5 million jobs will be lost to artificial intelligence. Already, finance organizations are increasingly automating traditional accounting tasks of performing transactions, reconciling accounts, and compiling reports. With these tasks automated, CFOs and their teams can refocus on analytic skills. This won’t just be an advisory role; understanding machine learning and AI will free up finance to focus on identifying new business opportunities and providing strategic guidance to the company.
As machine learning continues to evolve, finance leaders should familiarize themselves as much as possible with the business opportunities it creates. The winners will be neither the machines or humans alone, but the two working together effectively to create new products, services, and value.