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4 Expert Ways to Effectively Utilise Machine Learning In Finance
3 months ago by Angelique Assaf

4 Expert Ways to Effectively Utilise Machine Learning In Finance

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Over the last decade, machine learning (ML) technology has become an invaluable tool in many industries, but it has become especially crucial in the finance industry. 

But why does the finance industry have a particular interest in machine learning?

Just like in other fields when financial companies employ machine learning in their operations, they improve their efficiency and as a result, increase their profits. More specifically, machine learning allows companies to replace outdated and inefficient processes and helps them to develop new and more effective solutions. 

This article will delve into some crucial ways that machine learning is changing the financial sector with examples of how companies are using ML applications within their businesses. 

Security & Fraud Prevention

Security is one of the most critical concerns for financial companies. With more transactions, more users, and third-party app integrations, security threats are becoming more and more common. 

As a result of this increasing threat, the finance industry has turned to machine learning as this technology is highly effective at detecting possible threats and fraudulent behaviour.

Banks that utilize machine learning technology can simultaneously monitor and review thousands of transactions from every client’s bank account. For every activity that occurs on a client's account, the algorithm can determine if that action is characteristic of the account owner.

Because ML systems learn the patterns of behaviour of each client, they can very quickly and with extreme accuracy identify fraudulent activity, i.e., when specific actions don’t follow the client's previous patterns. When this occurs, the system responds by requesting further information to verify the identity of the user, or blocks the transaction from being completed. Within seconds, machine learning software can assess all bank account activity and prevent fraud from occurring in real-time before it is too late.

Some well-known fintech companies that heavily invest in security machine learning include Adyen, Payoneer, Paypal, Stripe, and Skrill.

Automation of Multiple Procedures

One of the most common applications for machine learning is process automation. Because ML programs have extensive learning capabilities, they can be virtually taught anything which allows them to replace and automate many manual processes.

By using ML algorithms to automate processes, companies can boost their productivity, create a better customer experience and optimize their costs.

Below are the most common examples of how machine learning is used for automation in the finance industry:

·         Call-centres.

·         Chatbots.

·         Bookkeeping and processing paperwork.

·         Gamification of employee training.

A current example of machine learning automation currently used in banking is COiN by JPMorgan Chase.

COiN, a contract intelligence platform, uses Natural Language Processing (a subset of ML) to read legal documents and extract their most valuable data. COiN can process 12,000 records in just a few hours instead of the 360,000 hours it would take for employees to do it manually.

Insurance Underwriting and Credit Scoring

The finance and banking industries have long histories of collecting accurate records. This collection of data has led to the creation of large datasets which scientists now use to teach ML programs a variety of tasks such as matching data records, calculating credit scores, and looking for cases where exceptions are possible. This is possible because these databases include thousands of profiles that contain a record of each client’s entire transaction history. Therefore, ML models have plenty of data to learn from, which allows them to identify trends that may affect an applicant's qualification for a loan or insurance.

Here are two companies that use machine learning to calculate credit scores and underwrite insurance:

·         ZestFinance uses their platform Zest Automated Machine Learning (ZAML) which has thousands of data points to help financial institutions re-assess loan applicants who would typically be classified as high risk due to very little or non-existing credit history.

·         The insurance company Lemonade employs machine learning as well as chatbots to perform various tasks like processing insurance claims. By downloading their app to their mobiles, customers can file a complaint, pay their insurance and modify their policy. According to Lemonade, their ML algorithm allows the app to take only 90 seconds to insure someone and only 3 minutes to pay out a claim.

Algorithmic trading

Another popular application of machine learning is the algorithmic trading that is used by forex companies to help them make better trading decisions. 

In algorithmic trading, the AI model will simultaneously monitor all news sources that relate to forex as well as thousands of real-time trades so that it can learn the patterns that influence the value of stocks. Once the AI learns these patterns, it can automatically make trades based on a predetermined set of instructions that include quantity, timing and price. These instructions will allow it to proactively trade, i.e. buy or hold stocks based on its predictions of how the market will move next.

In addition to proactively trading, machine learning allows forex software to react in real-time - meaning it will change its course of action according to what is happening in a particular market.

Through the automation of trading processes, traders not only gain a competitive advantage, but they also increase their chances of profits because the system allows them to trade in multiple markets at once.

Final thoughts

Despite the many businesses adopting machine learning applications, there are plenty of companies that still have not.

That is because:

·         They have unrealistic expectations of machine learning and how it can contribute to their organization.

·         The research and development stage that is required to implement ML is costly.

·         The demand for DS/ML engineers far exceeds the supply.

Other than the reasons stated above, many are simply unsure of how to begin implementing machine learning into their business. That is why we have written this article which gives some advice.

Additionally, if you are interested in learning more about where machine learning will be heading in the future, you can read our article discussing machine learning trends.

When you need data or language processing capabilities to underpin or enhance your machine learning capabilities, that’s where we come in. Contact our team today.

  • Data
  • Machine Learning
  • Finance
  • Fraud
  • AI
  • Automation