In this special guest feature, Kumesh Aroomoogan, Co-founder and CEO of Accern, believes that data is increasingly being used and desired and financial professionals need faster ways to take advantage of it. Founded in 2014, Accern accelerates AI workflows for financial enterprises with a no-code development platform and has raised $16m to date. In 2018 Kumesh was named to the Forbes 30 Under 30 Enterprise Technology list. Previously, he was the co-founder and CEO of BrandingScholars, an advertising agency, a General Accountant at the Ford Foundation, an Executive Board Member, Chairman of Public Relations at ALPFA, Equity Researcher at Citigroup, and a Financial Analyst at SIFMA.
Companies are relying on data scientists to access the benefits of AI – which has only gotten more difficult as talent has become harder and harder to come by. Many enterprises face significant challenges due to a shortage of data science experts, which impacts their ability to strategically incorporate AI into their organizations.
An increasingly limited number of people have the experience to build AI models from scratch and tap into AI’s advantages. Instead of training employees to be fluent in coding or waiting for the right subject matter experts, financial leaders need to make AI simpler and more accessible so that non-technical executives can use AI as well. If AI were something that nearly anyone in the company could use to their advantage, the benefits would be endless.
Utilizing no-code technology is a way to accomplish this, and when you simplify AI, the benefits and opportunities become profound and exponentially more available.
The No-Code Effect
Billions of structured and unstructured data sets are generated daily, and companies look to data scientists who can code AI models to tap into the valuable insights data can provide. But the number of open data science roles surpass the amount of talented data scientists. As a result, new technologies, like no-code, are emerging, encouraging enterprise organizations to seek quick, easy, and cost-efficient solutions to stay present within their competitive landscape.
It’s not only become difficult to find talent, but the process of building out AI models can be time-consuming. Building out an AI model, such as NLP (natural language processing) requires extensive time and technical expertise. For example, research has shown that creating a single AI model takes eight to 90 days on average for an IT team. Data scientists spend as much as 80 percent of their time finding, cleaning, and reorganizing vast amounts of data, and only 20 percent on actual data analysis.
Instead of focusing on hiring and training employees to be fluent in coding, financial leaders can access the advantages of new tools that make AI simpler and more accessible so that technical and non-technical users alike can deploy AI models. No-code technology makes simple AI a reality, becoming more accessible to business leaders, analysts, and software developers, offering a pre-developed backend and highly customizable user experience for teams to build without knowing how to code.
What Greater Access To AI Can Lead To
AI can help resolve issues that non-technical executives deal with, like customer experience and retention, underwriting contracts, claims management, and even credit risk, in industries ranging from tech and finance to healthcare and e-commerce. More specifically, the financial services industry and banking industry can help to grow and modernize their business through competitive banking analytics, gaining insight into their competitors.
It can also help investment advisors understand how current events are changing market outlook, assisting investors better. With a no-code AI application, non-technical finance professionals can run AI and NLP models to analyze large amounts of unstructured data and understand the sentiment behind the information. Users can specifically look at things like mergers and acquisitions, the macroeconomy, trade wars, and legal actions to see how these events impact different industries and specific companies. Financial institutions can use the analytical and automation benefits of AI and NLP to eliminate manual research and analysis processes and make better-informed investment decisions in real-time.
More broadly, enterprises can also implement automatic responses via AI chatbot, filter and sort content and documents (internally and externally) to determine the next best steps for customer services.
It’s important to implement these ideas efficiently, so industries – particularly financial services – can more quickly and efficiently act on new ideas. Financial professionals no longer need to wait for data scientists to make their ideas a reality.
In the same way that Apple grew an entirely new category by simplifying the personal computing experience, the same is possible for AI. Data is increasingly being used, and professionals need faster and simpler ways to take advantage of it.
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