Fintech Startups & Machine Learning
Fintech innovation is sweeping through the financial services sector. With data analytics and process automation gaining momentum, many banks and credit unions are scrambling to keep pace with fintech startups that can disrupt the sector free from the yoke of legacy technologies and operational methods. The disruption isn’t finished, however, as machine learning and similar forms of artificial intelligence are beginning to gain momentum in the sector.
Frost & Sullivan found that artificial intelligence, analytics and machine language are coming together to make big data a default part of the financial services industry, disrupting many longstanding processes and operational models. The transition to AI is only in the early stages. At this point, approximately 34 percent of organizations in banking and capital markets said they believe machine algorithms will be used for their upcoming big decisions, PricewaterhouseCoopers reported.
While machine learning is starting to get momentum in the financial services sector, many of the discussions happening in the industry are focused on the forward-thinking potential of the technology. But some forms of machine learning are ready for real-world use.
3 operational segments where financial services firms can already use machine learning:
“Machine learning is starting to get momentum in the financial services sector.”
1. Fraud prevention
In theory, machine learning works in a relatively simple way. The software analyzes large quantities of data in order to identify trends in the information. From there, a machine learning platform will gain an understanding of the underlying data patterns that signal an action they are looking for. This process requires two key measures – the ability feed machine learning systems a huge sampling of relevant information and human input to establish the parameters that the system is supposed to operate by.
Machine Learning Financial Services
In the case of fraud prevention, the financial services industry has large repositories of data regarding fraud cases. It also has experts across the sector who can identify signs of fraud and inform software parameters. As such, fraud prevention solutions employing advanced forms of analytics and machine learning principles are beginning to emerge in the sector, setting a foundation for automation in fraud prevention without sacrificing quality.
2. Credit scorecards
Access to alternative data sources is empowering banks and credit unions to create more flexible, robust scorecard models. Machine learning can take this core functionality to another level by allowing for automated scorecard updates. Furthermore, a machine learning system can gain an understanding of precisely which types of information are most useful relative to different borrower profiles, adding a greater degree of responsiveness and intelligence in scorecard processes.
3. Decision engine
A decision engine platform can gather anonymous data from a variety of lending scenarios across the industry, drawing in rich, varied data sets. Machine learning systems can constantly analyze this information to take on processes such as risk-based pricing, loan origination and setting credit limits.
Ultimately, machine learning is an extension of modern analytics platforms. There are many settings where existing analytics platforms lack the data or programming to employ machine learning effectively. However, the advanced, mature state of analytics in some financial services segments is driving innovation. At GDS Link, we have laid the foundation for analytics success across the financial services sector, and our technology is evolving to help firms take full advantage of what machine learning has to offer.
