Understanding the advantages and challenges of implementing machine learning
The use of machine learning in finance can do wonders, even though there is no magic involved. Successful machine learning projects often depend on choosing the right datasets and applying the right algorithms. Let’s take a closer look at why this technology is a great fit for finance, what implementations it has in that domain, and how financial services companies can utilise it.
Machine learning is a subset of Data Science. While Data Science covers the whole data processing pipeline, Machine Learning is about using specific algorithms and chosen datasets to train mathematical models to find patterns, make predictions, segmentation, and more.
As soon as the model is trained, it can process data and produce results automatically, without interference from data scientists. You can retrain models to keep them up to date and efficient.
In simple words, machine learning solutions can learn from both experience and new data, thus improving results without being explicitly programmed.
This powerful tool has numerous applications in finance, as it fits perfectly with the quantitative nature of the financial services industry.
Four reasons why financial services mustn’t ignore machine learning
Despite the challenges, many financial companies already use machine learning in process automation solutions. This way, they increased productivity, cut costs, and increase revenues thanks to enhanced user experiences. Additionally, machine learning also helps to reinforce compliance and financial security.
That said, most financial services companies are not ready to extract the value from this technology for four reasons:
First of all, it’s costly. Second, the majority of financial incumbents are very slow at updating their software infrastructure to accommodate machine learning. Third, they often have completely unrealistic expectations towards machine learning and its value for their organisations. Fourth, there is a major shortage of data scientists and machine learning engineers globally.
The key machine learning cases in finance
Given the quantitative nature of the financial domain, easy access to vast computing power, huge historical records, and a wide range of open source tools means machine learning is easily applicable to this area. Moreover, this technology already enhances many aspects of the financial services industry.
Let’s take a look at some of the most common and effective machine learning cases in finance.
Robotic Process Automation (RPA)
Currently, this is one of the most common applications of ML in finance. The technology allows to replace manual work, automate repetitive tasks, and increase productivity and accuracy. As a result, it enables companies to cut costs, grow operations, and scale up.
Here’s how machine learning helps to automate tasks in finance: chatbots, call-centre automation, legal work automation, gamification of employee trainings, and more.
For instance, Wells Fargo uses AI-driven chatbot through the Facebook Messenger to communicate with its users. The chatbot helps users get the information they need regarding their passwords and accounts.
Another American bank, BNY Mello, integrated RPA into their system and the company estimates that this innovation is responsible for $300,000 in annual savings. Additionally, it has brought about a wide range of further operational improvements.
The number of security vulnerabilities and attacks in finance is growing along with the increasing number of transactions. By leveraging a large amount of data and comparing each transaction against an account history, machine learning can reinforce fraud detection, cybersecurity, and financial monitoring.
The algorithm learns from each action an account user takes and can assess if an attempted activity is characteristic of their behavior or has the signs of fraud. For instance, the detection of a large number of micropayments thus enabling a bank to prevent money laundering.
Underwriting is one of the most suitable tasks for machine learning algorithms, which can be trained on big datasets from real consumer accounts of banks and insurance companies.
Big banks and publicly traded insurance firms have a huge pool of customers and can rely on their own data to train and retrain machine learning models. Companies with smaller customer bases can leverage data from the telecom providers and utility companies.
Current machine learning algorithms usually don’t trade themselves but rather help humans make better trading decisions. Some models can also detect unusual conditions and trigger the system to stop trading, predict the potential profitability of a specific trade, and more.
Furthermore, real-time news and sentiment analysis can be a game changing solution in trading. Processing stock prices and the news that affect the stock market is a significant competitive advantage.
Machine learning models still can’t beat the human competition by a large margin, but thanks to enormous data sets, you can squeeze a tiny advantage. Given huge volumes of trading operations, that small advantage translates into significant profits.
Robo-advisors are now commonplace in the financial domain. There are two major applications of robo-advisors.
Portfolio management is an online wealth management service that uses algorithms and statistics to allocate, manage and optimise clients’ assets. A user enters their present financial assets, and goals (for instance, saving $300,000 by the age of 60), and the robo-advisor allocates the current assets across the investment channels.
Solutions that recommend financial products. Many insurance recommendation sites use robo-advisors to suggest insurance plans to a particular user. These services are gaining steam, as customers are beginning to prefer robo-advisors to personal financial advisors due to lower costs and great personalisation features.
Making use of machine learning in finance
While developing machine learning solutions, financial companies encounter common challenges.
First and foremost, they lack real business KPIs. Financial incumbents want to exploit this new opportunity but, realistically, so far, they often only have a vague idea of how data science works, and why they need it.
Second, financial services companies often have fragmented bits of data stored at numerous locations like CRMs, reporting software, and so on. This data is not ready for data science. ETL and cleaning take up 80% of the project’s time, so getting the data ready is the most time-consuming task, which is often neglected
The combination of these factors results in unrealistic estimates and drains the project’s budget.
So, first of all, there is a soaring need to set viable KPIs and make realistic estimates for every machine learning development project. After that, financial companies that want to adopt machine learning may go four ways depending on their core business objectives.
Going without machine learning and focusing on big data engineering
Most financial companies need to start with proper data engineering, not data science/ machine learning. Applying statistics to the collected and well-structured data would be enough to isolate bottlenecks and inefficiencies.
What’s more, the biggest part of any data science project is dedicated to building a well-orchestrated ecosystem of platforms that will collect all the siloed data from hundreds of sources (CRMs, reporting software, spreadsheets, and more) and enable further processing it.
Before validating your idea and applying any algorithms, you need to have the data properly structured and clean. Only then, you can turn that data into business insight.
Third-party machine learning solutions
If your use case does call for machine learning, you don’t necessarily need to develop a brand new solution. Most machine learning development projects revolve around issues that have already been addressed.
Large tech companies like Google, Microsoft, and IBM create machine learning software-as-a-service that can solve numerous specific business tasks. If your project covers such use cases, you can’t really expect to outperform algorithms from these tech giants.
That is especially applicable to recommendation services that utilise machine learning technology. As an example, Google offers multiple plug-and-play recommendation solutions targeted at various business domains.
All you need is a machine learning engineer who can implement the system focusing on your specific data and business domain. The specialist needs to understand what kind of data is going to be extracted from different sources, transform it, receive the results, and visualise it in a lucid form.
However, there is a tradeoff when using third-party services, as you do not have full control over the system. What’s more, no universal algorithm can be applied to different business cases across different domains.
So if there is a solution targeted at solving your specific task in a particular domain, you should use it. If not, go for the in-house development.
Innovation vs integration
Developing an AI and machine learning solution from scratch is one of the riskiest and most time-and-money-consuming ways to go. Still, it may well be the only way.
Machine learning R&D usually targets unique data science need in a particular niche. For such problems, SaaS machine learning solutions, which are intended for other use cases, produce highly inaccurate results.
Some considerations in machine learning R&D:
- You need to have a clear understanding of what data you are going to use, and how Data Science can help you. It is preferable to have viable KPIs and realistic estimates.
- Before validating an idea, ML developers need to make an investigation, which takes up additional time and costs.
- To validate an idea, you need to have the data collected. Otherwise, you would have to involve a data engineer to collect the data first.
Buying a machine learning startup?
This strategy is pretty straightforward, though it has two significant disadvantages. First, machine learning startups are costly. There is a lot of venture capital being poured into AI and ML startups that’s why they often have a high price tag. Second, like with any other acquisition, integrating this new entity can be a challenge.
Machine learning is here to stay. Sooner or later, all financial services companies will have to incorporate this technology into their business models.
Hopefully, this article gives you a rough overview of how you can approach this technology and eventually adopt it
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