The importance of machine learning in the finance market

April 2, 2019

The head of finance at Anaplan has recently highlighted what actions should be taken by finance managers when implementing machine learning into their business.

Tackling Fraud in Finance

Technology has improved dramatically in recent years, with machine learning viewed as a strong market within technological change. Machine Learning has entered nearly every market worldwide and for some is a vital tool used to transform business activities by enabling significant changes in managing large volumes of data.

Although there may be more focus on the growth of Artificial Intelligence, Machine Learning is growing rapidly, with a number of industry examples of businesses incorporating Machine Learning into their business activity.

From social media to voice assistants, many of our systems we use are continuously implementing machine learning to understand our behaviour and personalise our online experience. Machine Learning can go even deeper, however, with revenue management systems using ML to create an algorithm to generate pricing and inventory suggestions.

More businesses are incorporating the benefit of ML, with other companies prioritising plans to incorporate ML into their business activity, particularly within the finance market.

Many finance experts believe ML can enhance financial planning and analysis, wealth management, and how finance managers can control advanced analytics within their company.

What are the opportunities in Machine Learning?

Machine Learning is highly flexible and can potentially be integrated into a range of technologies. Looking specifically at the finance market, data is by far the most critical part of the industry. ML can supply finance teams with more capability to reveal and measure business potential and associated data that allows financial managers to make more informed and insightful decisions.

A good example of ML in finance is with the wealth management technology platform Forward line. The platform is utilising both AI and ML systems to support wealth managers in organising and measuring their data, providing real-time information to make informed choices for their customers.

What to consider with Machine Learning?

Implementing new systems does involve significant investment planning. With this in mind, it is essentially a business ensures they gain the full benefits of ML.

What is the quality of your data? – Data is essential for financial businesses, but as numbers increase, data can get diluted and a little chaotic. Prior to implementing any ML system, businesses need to prioritise the process of cleaning their data and ensuring it is reliable and accurate.

Do you have data governance?

During the ‘data cleansing’ process, there will undoubtedly be inaccurate forms of data and a number of errors. Finance teams must ensure this data is repaired prior to implementing any new technology.

Ensure you understand external and unstructured data

Ensure your team understand your data and what insights it could offer for your business. ML technologies can then support this process and take it to the next level.

Prepare for connected planning in your business

As data grows and diverges across a number of business areas, it is critical to ensure teams can communicate and collaborate on the information provided. This generally involves implementing dynamic planning systems and collaborative business processes, connecting teams with each other in real time. With this in place, ML technology can create insights and communicate between multiple areas of a business.

Ensure you allow time for effective business planning

Developing a business plan is definitely something that shouldn’t be rushed. It is critical that companies establish a realistic time-frame to achieve their end goals.

There is undoubtedly an emerging trend of innovative technology and the rise of more sophisticated machines. Despite the concerns, there is unlikely any threat of machines replacing the human workforce. In reality, most systems will always require some form of input from humans. As finance organisations expand their interests in machine learning, a key factor is ensuring they understand where human input is necessary. Finance leaders need to deliver plans in how machine-learning and human activity will interact in the future.

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