Machine Learning (ML) has been the silent force behind technological innovation for the past 20 years. Before its introduction, only expert human agents could do complex tasks. Today, various businesses opt to use computerized systems to streamline and optimize their processes. According to recent studies, AI and ML are the transformational forces to optimize internal operations and enhance customer experiences.

One study revealed that 65% of customers are comfortable handling issues without human agents. Another study disclosed that 74% of customers prefer using chatbots when looking for answers to simple questions. According to a similar report, businesses will have invested approximately $5 billion in chatbots alone by the end of 2021. This is because of two main reasons. First, over 60% of their customers use them every year. Second, chatbots reduce $1.3 trillion in operational costs eCommerce businesses spend every year on customer requests.

What Is Machine Learning?

The term machine learning has existed for more than 60 years. Arthur Samuel, who built one of the first self-learning programs – the Samuel Checkers-playing program, coined it.

According to Samuel’s definition, machine learning is the science of getting computers to grow or evolve by themselves. Put differently; machine learning allows computers to exceed the parameters they are explicitly programmed to act within.

ML deviates from standard practice, where programs are first written as explicit instructions laid out in code. The programs are then put into a computer, where they manipulate and manage data to produce the desired outcome.

Industry leaders like Goldman Sachs’s managing director Charles Elkan recognize machine learning as a new concept in finance compared to other industries. In his book Machine Learning Engineering, Andriy Burkov considers that “machine learning” does not refer to one discipline.

According to Burkov, most businesses fail at implementing ML because they don’t distinguish between its two disciplines: machine learning research and applied machine learning. The first one is the study of what ML could do for industries such as finance. Nothing mentioned under this category can be applied in the real world today and is at early experimental stages. It is, therefore, of no concern or benefit to businesses in finance.

Alternatively, applied machine learning denotes the practical and implementable solutions that ML can offer to address the problems finance businesses usually face. Such problems include fraud and risk management.

In his book Machine Learning, Ethem Alpaydin stated that before the advent of machine learning, programmers were in charge of solving the above-mentioned problems. They created programs in the form of algorithms that performed specific pre-assigned functions like detecting fraudulent activities.

However, such programs were only effective to a certain extent since they were only made to address the problems that the programmers had envisioned before making them. Moreover, the programs were not made with a self-learning feature, making it hard to adapt to new challenges and threats. This characteristic often rendered such programs ineffective over time.

With ML, data drives operations, not humans. Algorithms are set up in such a way that the data itself defines what action will be taken next. ML revolves around this simple idea: a system that is in a changing environment should have the ability to learn.

Types of Machine Learning

There are three types of machine learning:

  • Supervised learning denotes the usage of labeled/sample datasets that are fed into the model to yield the desired output. If the results aren’t satisfactory, data adjusts its weights until the output is accurate. Supervised ML can simply be defined as allowing a model to learn over time, measuring its accuracy.
  • Unsupervised learning uses raw/unlabeled data allowing the algorithm to act on it without any human guidance. This is the reason why unsupervised ML tends to produce inaccurate/messy results.
  • Semi-supervised learning is a combination of the two above-mentioned learnings. It aims at increasing the size of your training data if you do not have enough labeled data to generate an accurate model.

How the Finance Industry Can Benefit from ML: Application

Typically, financial institutions and BaaS providers use large volumes of data. To deal with massive data sets and efficiently manage the processes without errors, finance businesses leverage machine learning algorithms that can produce immense benefits:

1. Automation of Business Processes

Businesses need to hire employees and teach them to handle the processes efficiently. This practice became unsustainable over time since functions are growing and demand more staffing. Acquiring more human resources means more operational costs, which affects the total profit.

ML helps automate most of these functions, thus eliminating the need for extra human resources. This, in turn, reduces operational costs and maximizes revenues. ML also helps eliminate human error by offering results that are transparent and free of bias. Human error has been one of the main reasons financial institutions suffer losses.

Most of the business processes that machine learning helps to automate involve customers. It enhances customer experience, which benefits a business in three ways. First, it manages customer data to provide customers with a good onboarding experience by studying, anticipating, and accommodating their needs. Second, it keeps existing clients satisfied, ensuring a high retention rate. Finally, it allows a business to better distribute its resources to promising leads and return customers.

2. Asset, Portfolio, Risk Assessment and Management

ML allows businesses to insulate themselves from liabilities. It uses data to inform businesses of their transactions and prepares them to accommodate changes both over time and in real time. Wealth and asset management companies have benefited from this as they can stay up-to-date on their client’s assets.

With this treasure trove of information, companies can help clients achieve their financial goals by optimizing and maximizing the performance of their assets. ML also helps businesses keep their clients informed of any risks so that they can make the best financial decisions.

3. Credit Scoring and Underwriting

Financial institutions such as banks make a good percentage of their profits from providing credit. The difference between profit and loss can be estimated by how well and fast banks can perform their credit risk assessments. To assess the risk and make the proper decision on underwriting and credit scoring, banks have to analyze millions of consumers’ data, their attributes, and borrowing behaviors.

Undertaking such activities consumes a lot of time and resources, which can be streamlined by ML algorithms empowering banks and insurance companies with huge possibilities. Data scientists can direct employee efforts and facilitate fast and accurate decision-making through supervised training of ML algorithms based on millions of consumer profiles.

4. Fraud Detection

The most common application of ML in finance is detecting fraud. Banks used to have in-built fraud detection systems that were effective at discovering security breaches and anomalies. Unfortunately, such contingencies became redundant over time, exposing them to fraudsters. ML emerged as a realistic solution since it can evolve to anticipate and neutralize fraudulent activities.

Recent research conducted by Deloitte revealed that ML increases the chances of detecting suspicious transactions and the likelihood of flagging fraudulent cases and reduces the possibility of new ones.

Banks store huge amounts of data sets online, with thousands of transactions handled every minute. With ML, banks can monitor and identify suspicious behaviors by examining the user’s actions (transaction history, IP address, location).

The algorithms then evolve to become adept at noting any deviation based on the typical characteristics. Such a deviation is then categorized as suspicious account behavior. Consequently, ML algorithms are leveraged to prevent fraud by requesting extra authentication or completely blocking a transaction.

Challenges Finance Companies Face when Deploying Machine Learning

In terms of risk management and financial management business decisions, machine learning models provide fast and accurate predictions, compared to traditional models. However, ML models are complex and less transparent, leading to a set of challenges in risk management and model validation. Let’s take a look at the main challenges for validation teams:

1. Incompetency of Model Users Validators and Data Scientists.

ML models are black-box in nature due to the complex calculations that are hard to comprehend. Thus, there is a limited understanding of the output. Data scientists and ML models do not have appropriate training, which is a significant shortcoming for financial institutions that can lose their customers due to the absence of model explainability.

For instance, if a customer requires a loan and a bank declines the application based on the artificial intelligence model, there is a risk of the claim from the customer requiring an explanation of the denial. Therefore, in credit risk management, negative decisions should be explained to advocate the suitability of ML-based models.

2. Poor and Siloed Data

Data integrity is the key driver to achieving intended results and preventing bad trades and financial loss. Poor data quality and inaccuracy can lead to poor business outcomes since machine learning algorithms are trained on this data.

Therefore, if ML models are learned from siloed datasets and lack integrity, there is a high risk of generating poor business insights. That’s why 84% of CEOs are concerned about data quality.

3. The Imperfection of Scalability Support

There is a multitude of processes involved in ML workflow, including data analysis, manipulation, model training, validation, testing monitoring, etc. When data grows and processes scale up, there is a necessity to rearrange ML algorithms, which means that permanent monitoring and maintenance are of paramount importance. This, in turn, may be an overwhelming task.

Harnessing the Power of Machine Learning: Real-Life Examples

Let’s take a look at some examples of finance companies that have succeeded in optimizing business processes by integrating machine learning models.

JP Morgan Chase and US Bank

A global leader in the banking industry, JP Morgan Chase, has developed a proprietary Contract Intelligence (COiN) system that uses unsupervised ML algorithms to automatically review and process documents for credit scoring and loan underwriting. With this innovative method, the extraction of 150 attributes from the yearly business credit can be performed in seconds, compared to the traditional approach where lawyers and loan officers spend approximately 360,000 hours per year.

US Bank has also developed an ML solution, dubbed Expense Wizard. The travel expense management app facilitates cost-control and risk-reduction benefits for financial companies. It helps streamline employees traveling within company policies by applying AI-based chatbots that eliminate the time needed for payment reporting processes.

S&P Global $ Kensho

In cooperation with Kensho, S&P Global integrates ML capabilities allowing government corporations and investment agencies to unlock actionable insights to make solid decisions. With Kensho integrated with the S&P platform, users may get simplified answers to complex financial questions in a Google search format in minutes, regarding monetary policy changes, economic reports, political events, etc.

One more use case relates to the data mining process: with the help of bots, the data analyst can easily automate their processes, drive data-based desition making, and ensure faster, more accurate, and efficient outcomes.

Cerebellum Capital

According to the BarclayHedge survey, 56 percent of hedge fund respondents apply artificial intelligence and machine learning algorithms to streamline alternative investment processes. One of the examples is Cerebellum, a hedge management company that uses AI and ML platforms to automate investing decisions, covering such processes as data discovery, assessment, model creation, testing, and learning trading strategies.

Finding the Right Machine Learning Partner

Now, when you are equipped with basic machine learning concepts, benefits, and challenges, you can start developing your strategy for applying artificial intelligence and ML models to financial and investing processes.

Selecting a machine learning vendor for your finance business requires a solid understanding of your business goals and awareness of how the outcomes of ML models can be explained to your customers. Mind that the company you select needs to be expertized in the finance industry since security concerns are highly important. Contact us, and we will help you find a perfect solution and your business stay ahead of the curve.