How Banks are Upping their Game with Technology

Reading Time: 3 minutesWherever you were on your technology journey at the start of 2020, COVID-19 has likely caused dramatic changes in your business. Many organizations were pleased by the ROI they’ve experienced from COVID’s “forced” technology business transformation, and now they have an appetite for more.   

Are you using technology to its full advantage? 

We have seen a noticeable increase in the use of artificial intelligence and data analytics and expect to see banks continue to incorporate new technologies into their business strategies.  Some examples… Right now, compliance software is booming in the banking industry as a means to enter customer activity and produce alerts for suspicious/unusual activity or when regulatory reports (i.e., CTRs – currency transaction reports) must be submitted.  The automation of technology is changing the game for generating suspicious activity reports (SARs) as well.  In addition, the uptick in the examiner request for risk analysis evaluations cannot go unnoticed, as technology is making it easier to assess all areas of the bank to see what needs to be reviewed and examined.  These are just a few examples where we’ve witnessed technology really taking hold all over the industry – and paying off big time with efficiencies and ROI. There’s no denying that technology is a major factor in future success for banks, and there are multiple avenues of technology for you to use.  Below, we have outlined the advantages that we’ve seen, as well as some underlying concerns for three technology areas: data analytics, artificial intelligence, and data strategy.    

Know Your Technology Options

Data Analytics is defined as “the science of analyzing raw data in order to make conclusions about that information.”  Basically, it helps a business to optimize its performance and allows banks to create a more complete picture of what each of their customers is like.  Data analytics can be used to track customers’ actual online banking behaviors and make adjustments based on their preferences “like a friendly teller at their local branch would do.”

• The following articles provide some excellent examples of how Data Analytics is used in Finance:
  • https://opendatascience.com/how-big-data-analytics-are-used-in-the-banking-industry/
  • https://www.perle.com/articles/the-advantages-of-data-analytics-for-modern-banking-40190846.shtml
  • https://www.wipro.com/banking/banking-on-analytics/

Artificial Intelligence (AI) is defined as “the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.”  AI has the unique ability to rationalize and take actions to meet a specific goal.  As contrary as this may sound, AI is actually helping banks spend more quality time with their customers.  It is a tool that has allowed fast access to pricing models for specific deals using complicated algorithms that result in greater efficiency and better customer service.

• The following articles demonstrate the ways AI is being used in Finance:
  • https://learn.g2.com/ai-in-banking
  • https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies

Data Strategy is defined as the “tools, processes, and rules that define how to manage, analyze, and act upon business data.”  By using the combination of Data Analytics and AI together, banks are able to make well-informed decisions and keep their data more secure. 

• An effective data strategy will include:
  • An outline of company needs
  • Company roles
  • Data structure
  • Data management
  • Read more here to ensure your data strategy incorporates all the elements for success: https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/5-essential-components-of-data-strategy-108109.pdf

Use Some Caution with Technology

While technology can be amazing, and open up new possibilities and ease processes, it is wise to remember these things when employing data analytics and artificial intelligence within your business:

• Know what information is being used.
• Identify first the information you want to obtain – the end goal of the analysis. The correct inputs/parameters are required to get the analysis you are looking for.
  • Confirmatory – looking at the evidence to verify the hypotheses
  • Exploratory – obtaining evidence to understand the data and patterns presented
  • Remember AI is basically a form of data analytics and will only perform as well as the data it is given.
• Address how you should present the analysis once it has been completed. The presentation of the data can make the difference on the decisions made and the understanding of the data presented, and here are some nice examples to consider:
  • https://towardsdatascience.com/data-visualization-101-how-to-choose-a-chart-type-9b8830e558d6
  • https://blog.hubspot.com/marketing/types-of-graphs-for-data-visualization
• Understand the risks/costs involved with relying on the results of the data analytics and the use of AI. Address how your organization handle these issues:
  • False Positive – the system/analysis is agreeing or saying yes, when the real condition is not favorable.
  • False Negative – the system/analysis is not agreeing or saying no, when the real condition is favorable.

The evolution of technology and the use of it within the banking industry is astonishing.  As we forge forward and embrace more and greater technologies in our organization, remember that the key to getting advantages from it relies upon how you set the parameters, and then how you interpret the results.