Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used by Fintech platform developers to make more intelligent or informed decisions regarding key processes. This may include using AI to identify potentially fraudulent transactions, determining the creditworthiness of a borrower applying for a loan, and many other use cases.
Research conducted by Accenture found that 87% of business owners in the United Kingdom claim that they’re struggling with finding the best ways to adopt AI or ML technologies. Three out of four or 75% of C-Suite executives responding to Accenture’s survey said they really need to effectively adopt AI solutions within 5 years, so that they don’t lose business to competitors.
As reported by IT Pro Portal, there’s currently a gap between what may be considered just “hype” and actual or “practical implementation” of AI technologies and platforms.
Less than 5% of firms have actually managed to effectively apply Ai, meanwhile, more than 80% are currently just exploring basic proof of concepts for applying AL or ML algorithms. Many firms are also not familiar or don’t have the expertise to figure out how to best apply these technologies to specific business use cases.
Yann Stadnicki, an experienced technologist and research engineer, argues that these technologies can play a key role in streamlining business operations. For example, they can help Fintech firms with lowering their operational costs while boosting their overall efficiency. They can also make it easier for a company’s CFO to do their job and become a key player when it comes to supporting the growth of their firm.
Stadnicki points out that a research study suggests that company executives weren’t struggling to adopt AI solutions due to budgetary constraints or limitations. He adds that the study shows there may be certain operational challenges when it comes to effectively integrating AI and ML technologies.
He also mentions:
“The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.”
He adds:
“For businesses to harness the benefits of AI and machine learning, there needs to be a move away from an overhyped theoretical narrative towards practical implementation….It is important to formulate a plan and integration strategy of how your business will use AI and ML, to both mitigate the risks of cybercrime and fraud, while embracing the opportunity of tangible business impact.”
Fintech firms and organizations across the globe are now leveraging AI and ML technologies to improve their products and services. In a recent interview with Crowdfund Insider, Michael Rennie, a U.K.-based product manager for Mendix, a Siemens business and the global leader in enterprise low-code, explained how emerging tech can be used to enhance business processes.
He noted:
“Prior to low-code, the application and use of cutting-edge technologies within the banking sector have been more academic than actual. But low-code now enables you to apply emerging technologies like AI in a practical way so that they actually make an impact. For example, you could pair a customer-focused banking application built with low-code with a machine learning (ML) engine to identify user behaviors. Then you could make more informed decisions about where to invest in customer experience and most benefit your business.”
He added:
“It’s easy to see the value in this. The problem is that without the correct technology, it’s too difficult to integrate traditional customer-facing applications with new technology systems. Such integrations typically require millions of dollars in investment and years of work. By the time an organization finishes that intensive work, the market may have moved on. Low-code eliminates that problem, makes integration easy and your business more agile.”
