Michael Shustek on AI in Finance

From retail to education, automated programs are being integrated into various industries. Finance is not immune to this development. Already, we have seen mobile banking and money transfer apps, cryptocurrency, crowdfunding, and more. Now, it seems the next step is implementing artificial intelligence programs to provide security, balance, and efficiency.

Data-Driven
Artificial Intelligence (AI) was developed to assess and organize massive quantities of information in the form of data. Because the finance industry is composed largely of such data, AI is well-suited for this field. Integrating AI programs primarily to comb through, analyze, and sort data allows for optimization, increasing productivity while reducing labor costs.

Predictive Methods
When accounting for future movement of stocks and profit, the task can be daunting. With AI programs, the process is made simpler and, oftentimes, more accurate. Rather than hire a team of analysts, some companies are choosing to implement AI programs that can assess more data more accurately in a shorter span of time. Of course, analysts are still needed to proof the analyses made by AI, but eliminating wasted time can improve efficiency and overall accuracy.

Risk Alleviation
While mistakes are practically inevitable, implementing AI programs to manage important data can help limit the number of catastrophic mistakes that occur such as irreparable data deletion or inaccurate dividend distribution. With immense amounts of money and private information at risk, it is necessary to implement the most effective method of securing, protecting, and accessing such data; automated programs, at this point, seem to be the best option.
In a similar fashion, AI will likely be integrated for the purpose of improving cybersecurity measures. Increasing the security power through automation can help minimize threats and data breaches, thus improving data security and client trust.

Time Management
For finance professionals, there is simply not enough time in the day to complete necessary work and address client concerns. This is true in a number of fields including e-commerce and investment. In order to best prioritize and structure their time, finance professionals are integrating AI into client-facing programs and sites; because such programs have advanced significantly, implementing them on websites and interfaces to aid in assisting troubled clients can help alleviate the added responsibility of managing client concerns and inquiries on a day-to-day basis.
Limiting the amount of time spent parsing and sorting data frees up availability for more important operations like meetings, problem solving, and idea implementation.

Automation in the financial industry has already begun, but further integration may provide more accurate data analysis, minimized risk, and more efficiency in all fields.