There was a time when successful people in the finance industry primarily needed strong math skills and well-developed business sense. However, financial jobs have evolved, and many roles are now data-centric.
Automation Will Become Progressively Important
One of the significant advantages of automation is it allows people to speed up their workflows and use tools to find information that might otherwise stay hidden. For example, automation could make data mining more efficient, helping entities such as banks learn how to serve their customers better.
Because it’s possible to examine large quantities of data quickly, financial representatives can spot patterns by using automated platforms. Picking up on suspicious activity could minimize instances of banking fraud or lead to more accurate risk models for extending loans or credit card offers or tracking stock market activity.
Statistics show jobs requiring automation skills ” such as those related to artificial intelligence, and machine learning in particular ” are limited in number, but growing more than six times faster than the demand for other capabilities in finance.
There’s a Lack of Crucial Data Analysis Skills
Research highlights how the financial industry needs data analysts, but there aren’t enough qualified individuals to fill the skills gap. More specifically, accountants recognize knowing data analysis fundamentals could boost their careers.
Plus, financial executives from companies that have data analysis talent spend less time trying to analyze data and have more time for other tasks. A poll revealed 68 percent of leaders at companies with a deficit resolve the shortage through internal skill development. That finding could be encouraging for workers in the financial sector who are interested in continual learning.
Having Knowledge of Certain Programming Languages Is Particularly Valuable
An overview of programming languages and the frequency with which they get mentioned in job postings emphasizes some programming languages are in higher demand than others for finance professionals. According to statistics published in July 2018, there were only 15.5 candidates who knew C++ for every available finance job.
So, the implication is that competency in that programming language could make people exceptionally competitive during their job searches.
In contrast, that same study’s findings regarding Java programming knowledge showed 28.9 people knew that language per available financial sector job. Moreover, since 2017, the number of job ads requiring Java skills went down from 460 to 346.
The Rising Prominence of Predictive Analytics
Analysts believe within a few years, all financial software will have predictive analytics components in it. That means now is the time for the finance sector to start scaling up to get ready for what’s ahead.
A survey of professionals in the banking sector found 76 percent believed personalization had a primary or substantial impact on building relationships. Predictive analytics could help pinpoint the factors that make people happiest with their financial brand experiences. Similarly, it could gauge the likelihood of customers switching brands in a competitive market.
Additionally, such statistics could indicate the probable success of potential marketing campaigns or new features. Many analytics platforms sort through huge amounts of data in minutes, demonstrating a speed impossible for humans to achieve without weeks of work. Then, the details provided aid well-informed decision making.
Various Applications for Artificial Intelligence and Machine Learning
Artificial intelligence (AI) is impacting the world at large, and the finance industry did not overlook adopting the technology, or at least considering doing so. Multiple sources of research indicate AI will transform banking. And, brands are already researching ways to use it. People who know AI technology will set themselves apart in the job market.
Some banks offer chatbots to address customer queries and eliminate the need to interact with human customer service agents. Others have AI that streamlines processes, such as that which scans documents, looks for key characteristics, then automatically sends the content to the proper departments.
In many cases, the uses for AI in finance relate to the topics brought up in the automation section above. However, one potential barrier to widespread adoption is the fact that financial brands vary widely in the ways they choose to experiment with AI.
Machine learning, which is a subset of AI, also stands to have tremendous benefits for finance. Some applications can give decisions about creditworthiness in minutes. Others track customers’ spending habits and suggest how they could increase their financial literacy.
Wells Fargo launched a new feature in its mobile app at the beginning of 2018 that gives customers alerts based on their banking activities, thereby making things more convenient for end users. The technology can also remind people to pay bills or contact their banks to give travel notifications before going abroad.
Data Skills Are Essential for Today’s Finance Professionals
The financial professionals enjoying significant levels of career success are typically those with diverse skill sets, and that reality is still present. However, when people are particularly interested in increasing their salaries and making themselves as competitive as possible in the industry, data skills are crucial.
Additionally, individuals must stay abreast of the technologies, methods and use cases associated with data in the financial area. Continual education that keeps pace with the industry’s tech evolutions could make professionals exceptionally valuable to the companies that hire them.
