Machine Learning (ML) is a subset of Artificial Intelligence (AI). Instead of relying on rules-based programming, ML is based on algorithms that can learn from data. It is not only confined to tech companies like Google, Amazon, and Facebook but is becoming increasingly important in Finance industry as well. Increase in Machine Learning in Finance jobs is a perfect indication of this trend. Because of the highly disrupting potential of ML, now is the right time for the finance executives to explore its potential for their business. The following is what you need to know, as a finance executive, to maximize the opportunity of ML for your business.

For machine learning in business, a lot of uncomplicated use cases are present. Improving the target and acquisition of new clients in sales and marketing provides a suitable example. Also, prediction of customer churn offers another tried and tested use. Finance executives, however, are in a position of strategic influence. Designing and implementing a strategic vision should be the light through which machine learning is seen.

Seeking the help of experts in this field is a necessity if finance executives are to design a strategic vision. A crucial role is played by experts in this field because they have the multi-disciplinary knowledge across departments such as ML, data analysis, decision design and execution. There needs to be a manager on your team who can understand what the experts are offering and can enable them to reframe complex data patterns into actionable insights which can be executed by your team.

In the field of machine learning, finance executives would be well advised to follow the lead of marketing executives. They can learn about A/B testing and follow the techniques of controlled experimentation, which would offer important learning opportunities for future.

We would advise finance executives to follow these phases of Machine Learning for its incorporation in their business.

At first stage, getting the data in order is a foundation. A common mistake made by finance executives at this stage is to get paralyzed because of their misconception that data has to be perfect. It needs not be perfect. Great insights can be obtained even from messy, incomplete data. Also, the quality of data does not need to be perfect either. Moreover, additional data has marginal positive effect on the performance of the business as opposed to the common belief.

Once this foundation has been built well, the next phase is prediction and it has extremely high potential. It can have tremendous impact on the performance of your business. This is where ML uses algorithms and the sorted data to make predictions about the future. Then comes the third stage where Man and Machine work together. This is the perspective stage where you have to make sense of the data and predictions and decide which actionable steps you would take that would have the most leveraged impact on your business.

After a starter guide, wouldn’t the executives love to see the applications of Machine Learning?

There are numerous applications of Machine Learning in Finance but we have selected two of the most important for Finance Executives to ponder.

Using Machine Learning since as early as the 1980s, investment banks were pioneers of machine learning. Traders and fund managers are heavily dependent on Machine Leaning driven market analysis to make investment decisions which are leading fintech companies to create new digital solutions for financial trading. Some of the machine learning based innovations available on the market today, which finance executives must know, include stock-ranking based on pattern matching and deep learning for designing investment strategie

Two of the biggest challenges facing financial organizations in today’s world are compliance and security due to the need to manage gigantic amounts of data. Exponential growth of data and legitimate access to that data has increased the probability of a breach on the inside. Moreover, storage of large amounts of data by banks across hybrid and multi-cloud environments increase the chances of a cyber-attack.

Finance executives need to know that, in helping firms overpower these hurdles, the use of machine learning in data analytics has been massive. To detect suspicious activity and minimize the risk of fraud, money laundering, or a breach, machine learning picks up on unusual user behavior.

Whereas Machine Learning is rapidly transforming Finance industry and hence, must be kept a close eye on; however, finance executives need to keep in mind that there are still some considerations in this field. As an example, we still need humans to look after the predictions and insight produced by machine learning in finance. Also, there can be some anomalies in the predictions as well which need to be taken care of by the team under finance executives.

To check out our services in Machine Learning, follow this link.

To breathe in some confidence, check out our profiles on Clutch, GoodFirms, & DesignRush.