A No-Code Environment Brings AI To The Business User
Last year, a financial services company we worked with was able to scale its credit risk analyst team from covering 130 companies to over 2,600 companies, while maintaining their team size of 25. How did they do it? They automated the manual credit research workflow to enable analysts to flag risks in real time with artificial intelligence (AI). Now the analysts focus on making decisions about risk, rather than researching.
This type of transformational change has emerged in the finance industry in recent years, with a variety of fintech startups providing solutions, but it's not widespread among business end users yet. AI-powered efficiency gains mean businesses are becoming nimbler, making faster and better decisions and, importantly, saving time and money. Yet many financial services companies are still working with highly manual processes that require significant time. Risk managers, underwriters, lenders and other business analysts are heavily reliant on their data scientist and IT teams to model automated processes for them. Creating and implementing a single automated solution may take months or years, so IT and data science teams at financial service organizations routinely face backlogs of requests. It is typical for a financial analyst to wait 12-18 months for a process to be automated by IT.
It's not that IT and data scientists are taking too many coffee breaks. Unfortunately, writing code, cleaning, categorizing and structuring data all take time. To make matters worse, employers cannot hire data scientists fast enough. There is a massive shortage of qualified data scientists across industries. LinkedIn's 2018 jobs report found there were more than 150,000 unfilled data science jobs.