The Role of Machine Learning in Supporting the Financial Industry
20 September 2022
Author by Finantier

The Role of Machine Learning in Supporting the Financial Industry

Machine Learning (ML) is part of Artificial Intelligence (AI) technology which aims to enable computer systems to learn continuously by recognizing behavioral patterns from data. Its use is for various things, from making predictions, identifying objects, automating processes, etc. Behind the technology lies complex algorithms and statistical modeling so that computers can carry out their tasks without explicit instructions.

This article will dig deeper into how ML technology can provide many benefits in developing financial technology-based services, including the use of ML in developing the Open Finance infrastructure, resulting in reliable application performance.

Machine learning in the financial industry

In financial technology, Machine Learning also handles various tasks to make it easier for companies to provide a better user experience for their consumers. All processing will be faster without having to be checked and handled manually. On the other hand, investment in ML development also has the potential to provide savings and efficiency in the long term.

There are many use cases of using ML in the financial world. One of the most popular examples is fraud detection. The system will study, analyze, compare, select, and recognize existing data through a special algorithm developed to make a conclusion. In the process, each data entered will go through the stages of the testing process to ensure that the data is valid. The whole process happens automatically and quickly.

It doesn't just stop at fraud detection. Machine Learning can play a more significant role in the financial industry. This technology works in the background at every stage of the existing process, including:

  • e-KYC (applied in computer vision systems such as to perform face detection)
  • Onboarding processes (applied to produce personalized services by studying supporting data)
  • The process of using the services (providing recommendations for actions or products according to their needs).

One thing that needs to be underlined is that the output of ML processing will depend on the owned input data. In this case, ML technology cannot work alone. It is necessary to have a supporting platform that ensures the incoming data can be relevant and valid.

The urgency of using Machine Learning

Several things make the use of Machine Learning quite crucial in the digital finance business, including:

  • Reduce human error for data accuracy. If done manually, it can sometimes be missed during the checking process, especially if there are a large number of data stacks. Machine Learning Algorithms are designed to understand trends and data patterns in detail, even to very specific data details.
  • Efficiency to avoid data manipulation. Machine Learning works according to standard operating procedures that have been embedded into the system. As performance and data patterns are learned, the system will automatically improve its performance and sensitivity to incoming data. This automated process will eliminate friction and silos resulting from manipulative things. So that all existing processes will be more transparent and accountable.
  • Scaling up businesses. Like humans who learn, ML will become smarter. Likewise Machine Learning, the more data it processes, the more it will have a broader understanding of the types and patterns of existing data. Machine Learning can also work with Big Data, a database system that manages and processes large amounts of data. The automation generated by ML can make businesses more efficient, increasing the amount of processing without worrying about excessive operational burdens.

Machine Learning in Open Finance

As a technology infrastructure service for the financial industry, the development of Open Finance also embeds Machine Learning capabilities into its features. This includes being done by Finantier, one of which is for its Credit Scoring products. As is known, the Credit Scoring platform is tasked with conducting a feasibility assessment of prospective customers for a financing product.

Finantier provides Alternative Credit Scoring services by utilizing various types of data—including data on telecommunications consumption, PPOB, e-wallet, e-commerce, and so on—to complement data sources owned by financial institutions, such as SLIK or current accounts. Finantier will process data from various sources to be generated into valuable insight for credit assessment.

In the process of working, the system will retrieve data (under user authorization) and process it into a centralized dashboard. Behind the scenes (backend), the system works automatically and quickly to analyze and assess related data, to conclude a decent score for the prospective customer. The variety of data sources—which does not only focus on banking data—allows potential customers among the unbanked to get a fairer assessment.

By applying leading technologies such as Machine Learning, Finantier can provide Credit Scoring services that are reliable (99.9% uptime), comprehensive, and can work in seconds. It gives value to business owners and more convenience to prospective customers when carrying out the onboarding process in financial services.

Learn more about Finantier's Open Finance products here: