7 Exciting Use Cases of Machine Learning in Finance

The finance industry is one of the industries with the best machine learning applications. One of the reasons for this is that this industry collects a much higher volume of customer data than most. But being a naturally conservative industry, the financial space has not always been at the forefront of the machine learning revolution.

Fast forward to the present day, machine learning algorithms offer a new level of opportunities to transform the sector. Let us look at seven of the most exciting use cases of machine learning in finance:

7. Fraud Detection and Prevention

The combination of increased access to the internet, vast amounts of computing power and valuable data available online sets the stage for massive technological progress. But when you really give it some time though, it is the perfect storm for untold security risks.

In the past, fraud detection systems were programmed according to a set of rules. Though this was to some extent effective, it left loopholes open when attacks did not conform to the pre-set checklist.

Unlike traditional systems, machine learning frameworks keep on learning from available data and recalibrating to handle novel situations. Consequently, machine learning-based systems are better able to detect abnormal behaviors and block or flag them as potential security risks.

For instance, when a particular user makes unusual financial transactions, such a system can block all activities until the user confirms them.

6. Algorithmic Trading

In financial markets trading, every second counts and that is where algorithmic or automated trading comes in. It involves the use of machine learning applications to make split-second trading decisions that are not humanly possible.

High Frequency Trading (HFT) systems take advantage of the smallest windows of opportunity to make profits. They are known to execute millions of transactions daily that last a few milliseconds and at times are only worth cents. But the cumulative effect of such a model after a day’s work is remarkable.

The ability to compete effectively in automated trading is dependent on having the fastest systems for analyzing available data. In the past, mathematicians would use historical data to create algorithms for such trading. With the rise of machine learning, it is much easier and a lot more effective as they keep learning and constantly improve performance.

5. Portfolio Management

For millennials and other tech-savvy investors, the emergence of robo-advisors for portfolio management is one of the most exciting machine learning use cases. Robo-advisors are a new class of online software that can help users manage their investments.

What makes this irresistible to millennials, apart from their love for technology, is the fact that they may not always have the resources to afford in-person financial advisory services. In other cases, the amount of assets does not justify hiring an advisor.

A robo-advisor automatically picks investments for the user and creates a diversified portfolio. More importantly, after investing funds, the software will constantly adjust the investments so as to align the portfolio based on a set target.

Such systems take the emotions and so-called gut feelings out of investing which, in turn, can reduce investment risks.

4. Loan and Insurance Underwriting

Underwriting refers to assessing the potential risks that an individual or company applying for a loan or insurance might face in the future. It is an especially sensitive area of machine learning application. This is because some applicants intentionally omit important information about themselves. In other cases, getting useful information manually is not so easy.

Machine learning systems automate the process, digging information online, for example, on social media. Furthermore, large financial institutions could already have lots of useful consumer data. They use this to train machine learning models and assess applicants.

For example, how much does one’s age, type of job or marital status contribute to the likelihood of paying back a loan or defaulting? Automated systems can easily identify patterns from such information and reduce the risks involved by setting appropriate premiums.

3. Prevention of Money Laundering

Financial institutions are yet to win the war against age-old practices in money laundering. Notably though, such activities usually involve complex interactions between a number of players and accounts.

Traditional models often use a rule-based system with a focus on the transaction level, an outdated approach that results in many false positives. They also require constant re-tuning to keep up with fraudsters or risk failure.

The idea of using machine learning to prevent money laundering is based on the ability of such systems to uncover hidden connections and networks. They analyze vast amounts of data across customers, time and accounts to identify suspicious activity.

For example, they can detect mule accounts opened using synthetic or stolen identities to transfer funds. They can also identify launderers deposit funds in small denominations so as to avoid required reporting. The anti-money laundering machine learning system does all these by looking beyond individual transactions and analyzing networks of transactions instead.

2. Cybersecurity

The amount of sensitive data that financial institutions have to handle is staggering and far more than humans can effectively monitor manually. At the same time, attackers are constantly improving their approaches to stay a step ahead of security systems. Even when they use known approaches, traditional systems could fail to identify them if they happen to be swamped.

Machine learning applications for threat detection use three main approaches: risk scoring, anomaly detection and classification. Risk scoring identifies risks in the systems and determines which ones should get top priority.

For anomaly detection, the model gets training on behaviors that are typical of any given network. With this information, it can now identify anything that seems unusual or suspicious. Classification, on the other hand, is exposing a model to known behavior, good and bad with the purpose of training it to sort different behaviors into two categories.

In all three approaches, machine learning algorithms identify potential threats and flag them for the security personnel to assess.

Some machine learning systems go a step further and automate responses to reduce the amount of damage through faster mitigation. This also frees up the security personnel to focus on other more complex problems.

1. Predicting Market Insights

Due to the illogical, unpredictable and chaotic nature of financial markets, traditional investment analysis and prediction methods often fell short of requirements. Finding sustainable patterns was rather difficult and much of it seemed like guesswork.

In the present day, machine learning models have streamlined things and enhanced the prediction of fund trends. Fund managers are better able to identify market changes much earlier than they would with traditional approaches.

These models are designed on the premise that past events have a significant impact on both the present and the future. As such, they use historical data to predict future investment instruments’ pricing.

To enhance accuracy, some use a combination of multiple algorithms, often leading to higher efficiency and better performance.

A Tip of the Iceberg

In view of the high volume of data, the accuracy of records and its quantitative nature, the financial industry is indeed ripe for a machine learning revolution. Machine learning plays a key role in many facets of the sector’s ecosystem. Numerous processes that were in the past cumbersome and time-consuming have become a lot more streamlined. The above list is only a tip of the iceberg as the list of machine learning applications in finance is constantly growing.

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