Machine learning is allowing leaps and bounds in the financial industry. Here we will tell you what are some of the advantages and uses of machine learning in financial services.
What is machine learning?
Machine learning is a subset of data science that uses statistical models to obtain information and make predictions.
The advantage of machine learning, as its name suggests, is that it learns from experience without being explicitly programmed. It is responsible for selecting the models and feeding them with data. The model automatically adjusts its parameters to improve results.
Data analytics specialists train machine learning models with existing data sets, and then apply them to real-life situations.
The model is launched as a background process and returns results automatically, depending on its configuration. Models can be trained frequently according to business needs to keep them up to date. Some companies update their models every day, although it also depends on the amount of data obtained.
Generally, the more data available and added to the model, the more accurate the results will be. Fortunately, in the financial industry, there are large amounts of data about different types of transactions, clients, and invoices, among others. So, without a doubt, data is an indispensable component of machine learning.
Machine Learning Adoption Barriers
Technology advances very quickly, in addition, the volume of information increases more and more, so in the future, it will be impossible to see the uses of machine learning in financial services.
However, most financial institutions are not yet ready to extract the real value from this technology. Because?
- Companies are unaware of the real benefits of machine learning.
- Machine learning research and development is often expensive.
- There is a shortage of machine learning and artificial intelligence engineers.
- Managers of financial institutions are not risk-takers and take a long time to update data infrastructure.
Few companies have implemented machine learning; However, those that have implemented it report great benefits. For example: reduced operating costs thanks to process automation. There is also an increase in revenue thanks to better productivity and better user experience. In addition, reinforced security increases.
Uses of machine learning in financial services
Some of the machine learning applications that companies around the world have made are as follows.
Process automation:
Process automation allows you to streamline manual work, automate repetitive tasks, and increase productivity. Here are some uses: chatbots, call center automation, paperwork automation, employee training, and more. One of the clearest examples is the case of JPMorgan.
JPMorgan Chase developed software called COiN to automate the document review process. This automation, once error-proof, can produce accurate results, consuming only a few seconds. It is an alternative to the conventional documentation carried out by lawyers, which usually takes days or weeks.
The first stage of testing the COiN software included reviewing JP Morgan credit contracts. The key technique the software uses is known as image recognition. By using image recognition, the software can compare and distinguish between different agreements.
The company has also indicated that COiN is unsupervised learning software, meaning there is minimal human involvement once it has been implemented. This is an important step that reduces the time, resources, and efforts required to review documents. The company has also stated that COiN can identify more than a hundred attributes of contracts and then classify them into different groups.
Security:
One of the security threats that has increased the most is financial fraud. In Mexico, in the first half of 2018 alone, for an amount of 9,231 million pesos, 3.5 million claims were registered. But between 2011 and 2018, more than 30.8 million fraud claims have been registered. But algorithms are a wonderful tool for detecting fraud.
For example, banks can use this technology to monitor thousands of transaction parameters for each account in real-time. The algorithm examines every action a cardholder performs and evaluates whether an attempted activity is characteristic of that particular user. This model detects fraudulent behavior with high precision.
If the system identifies suspicious account behavior, it may request additional user identification to validate the transaction. Or even block the transaction completely, if there is at least a 95% probability that it is fraud. Which can be crossed with information from previous fraud complaints. Machine learning algorithms need only a few seconds (or even seconds) to evaluate a transaction. Speed helps prevent fraud in real-time, not just detect it after the crime has already been committed.
Economic monitoring is another case of the use of machine learning in financial services in the security aspect. Data scientists can train the system to detect a large number of micropayments and flag money laundering techniques.
Machine learning algorithms can also significantly improve network security. Data scientists train a system to detect and isolate cyber threats, as machine learning is second to none in analyzing thousands of parameters in real-time. This technology is likely to power the most advanced cybersecurity networks in the near future.
Adyen, Payoneer, Paypal, Stripe, and Skrill are some notable fintech companies investing heavily in security machine learning.
Credit rating:
Machine learning algorithms are a perfect fit for the risk-scoring tasks that are so common in finance and insurance.
Data scientists train models on thousands of customer profiles with hundreds of data entries for each customer. A well-trained system can perform the same credit-scoring tasks in real-life environments. Such scoring engines help human employees work much faster and more accurately.
Banks and insurance companies have a wealth of historical consumer data, so they can use these inputs to train machine learning models. Alternatively, they can leverage data sets generated by large telecommunications or utility companies.
For example, BBVA Bancomer is collaborating with an alternative credit rating platform Destacame. The bank aims to increase access to credit for customers with poor credit histories in Latin America. Destacame accesses bill payment information from utility companies through open APIs. Using bill payment behavior, Destacame produces a credit score for a customer and sends the result to the bank.
Algorithmic trading:
Another use of machine learning in financial services is in algorithmic trading. Machine learning helps in making better business decisions. A mathematical model monitors news and trading results in real-time and detects patterns that can force stock prices up or down. You can then act proactively to sell, hold, or buy shares according to your predictions.
Machine learning algorithms can analyze thousands of data sources simultaneously, something that human traders cannot achieve.
Machine learning algorithms help human traders gain a small edge over the market average. And, given the large trading volumes, that small advantage often translates into significant profits.
Advice on wealth management:
Robo advisors are now common in the financial domain. Currently, there are two main applications of machine learning in the counseling domain.
Portfolio management is an online wealth management service that uses algorithms and statistics to allocate, manage, and optimize clients’ assets. Users enter their current financial assets and goals, for example, saving a million dollars by age 50. A robo-advisor then allocates current assets to investment opportunities based on risk preferences and desired objectives.
Recommendation of financial products. Many online insurance services use robo-advisors to recommend personalized insurance plans to a particular user. Clients choose robo-advisors over personal financial advisors because of lower fees as well as personalized and calibrated recommendations.
As we can see, there are many advantages for financial services in the use of it. Although it may seem expensive, there are some alternatives such as financial service providers that offer this technology, but we will talk about this in another article.
