Why do fintech organizations implement machine learning technology in their processes?
In simple words, there are core benefits like:
- Automation of processes – it leads to operational cost reduction
- Finer user experience – it leads to the revenue increase
- Security and compliance – it leads to no vulnerability
The goal of automating the habitual processes is to get rid of manual work, replace people with the software for repetitive tasks and increase efficiency. Here are some use cases that we have today:
- Automated call-centers
- Automated paperwork
How does it look in real life? Natural Language Processing technology can be used for dealing with legal documents and extracting the required piece of data from them. Manual document processing can take a few days while an automated product will spend a few minutes on the same task.
Machine learning algorithms can supervise, control and detect various types of operations with thousands of different parameters. All the checkups are held in real-time. Automated algorithms detect fraud attempts and make decisions based on predefined rules. One of them may be sending a message to the customer for further verification or canceling the payment.
You can also enhance network security. Algorithms will detect cyber threats within a second or even less time. All happens in real-time. If you still have doubts about that, you can find information on how Skrill, PayPal, Payoneer, and Adyen use AI for preventing and solving their security concerns.
This machine learning component can be used for portfolio management. What does it mean? You have an automated component that collects and deals with information for managing and optimizing the assets of the customers. If a customer decides to save $4 mln until 50 years old, the robotic advisor will analyze the current assets, risk preferences and goals. As a result of this procedure, a customer will receive a personalized insurance plan. It takes little time and effort to provide such advisory service for a big number of customers.
How can we implement machine learning?
There are a few basic rules and approaches that we follow to make each new project successful. And here are some of them.
- Precise goals
- Robust architecture
- Big data ecosystem
- Improved ETL processes
- New algorithms
- Insights with their visualization
The bottom line
If you need samples of successful implementation of artificial intelligence (AI) functionality, have a look at products from Google, Amazon or IBM. We are inspired by these samples and we aim at getting the most from the achievements of other companies and our own products in the fintech industry.
OporaSolutions team is experienced and qualified to deliver robust and reliable software products that can solve current and future business needs of fintech and banking organizations. Let us know about your ideas or problems, and we’ll find a good solution to it.