Debunking Myths: Why Machine Learning for Finance is Applicable, Accessible (Part 2 of 2)

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Editor’s Note: Last week, the authors discussed the first two misperceptions about machine learning that lead many finance teams to avoid investing in related technologies. This week, let's delve into the other two misperceptions and why machine learning has already become an essential part of the modern finance skillset.

To read part 1 of this series, please click here.

Misperception: Prohibitive cost

Most assume that implementing machine learning will be complicated and expensive.

Combine that with unknown benefits and it is easy to see why only 35% of Finance departments have invested in it.

Our view is that these techniques will be commonplace in Finance functions such as FP&A and Treasury within the next few years.

That growth will increase competition among vendors and reduce costs. The primary perceived cost to running algorithms is processing power.

However, the cost of processing power is already low and decreasing. Moreover, for “finance functioned sized” datasets the processing power required isn’t nearly as great as originally imagined.

We built our Alvarez & Marsal Machine Learning Platform (AMMP) using Python and open source libraries with processing and data storage powered by AWS.

In developing SCRE, our increased monthly costs on AWS over the development period was in the hundreds of dollars.

Processing and storage on demand is inexpensive due to the competition between cloud service vendors and technological advances in material science and hardware development.

Once you jump the cost hurdle the bigger obstacle to move forward is the feeling that the benefit is unknown and potentially intangible.

Two key takeaways that we learned from building out SCRE that might highlight the benefits are: the machine corrected for our bias and outperformed any of our prior models, and the end product created tangible and intangible benefits beyond our stated purpose.

Imagine running a scenario analysis to determine what your performance would be under certain market conditions.

To do this, even the best analysts will have to make certain general assumptions, take shortcuts and potentially introduce certain biases in the processes.

This isn’t necessarily a fault of the analyst, it’s just that reexamining every single assumption in light of updated information is difficult and time consuming.

In this example, machine learning techniques could be applied and continuously updated over time to ensure that the predictions are both more accurate and reflective of the most current possible information.

Not only will this produce better results, it will also lead your analysts to doing what they do best: asking questions and searching for insight.

Misperception: Science fiction

Many finance professionals feel that artificial intelligence, deep learning, or machine learning is still science fiction.

Or if it isn’t science fiction it is only for marketing, robots, tech companies, or tech companies marketing robots. The truth is that tech companies are developing and using these types of tools and have been for over a decade.

The trickle down to the finance department is happening now and it is science no different than statistics or regression analysis. The difference is that this science is now accessible to everyone.

The science fiction or magical part of the process is that we can combine technology and financial know-how to collate disparate, unstructured data sources into a useful dataset.

It is no longer science fiction that we can capture data at the SKU (stock keeping unit) level and combine that with purchase data for the ingredients in that product.

Combining those means that we can have a machine predict when the next sale will be and therefore the next ingredient purchase.

In our opinion, the biggest misperception that keeps machine learning investment at bay is the idea that the current process is working.

Machine learning is here now, and it is accessible and applicable to all sizes of data without the need for an army of data scientists. The benefits are tangible and intangible and ultimately lead to a competitive advantage.

The costs are negligible, providing you have the right partner to guide you through organizing your data and making the platform user friendly for your current data analysts.

Most importantly, the finance department is responsible for providing the informed and accurate analysis to drive decisions and growth using the best tools available.

As part of this mission, machine learning is an essential part of the modern finance skillset.


About the Author

Chandu Chilakapati, Managing Director, and Devin Rochford, Director, are with Alvarez & Marsal, Valuation Services.

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