Artificial Intelligence-powered document search capabilities have become a key focus in the banking industry. With the increasing demand for more automated solutions, banks are exploring ways to improve how they carry out their business processes. One of these is through implementing document search in Banking using the capabilities of AI. 


AI-Powered Document Search Solutions

“There are two main types of AI-based search solutions that are being utilized by the banking community, which are natural language processing and computer vision.”


1. Natural Language Processing Search Solution

Natural language processing has been particularly important because it helps enhance quality services to consumers within a field by providing an easy-to-use user interface. One of the most common uses for NLP Use cases in Banking is mainly beneficial to provide intuitive input methods.


For example, in order to make a withdrawal, a customer will no longer need to use an ATM machine that only provides them with several options as to how to complete the process as opposed to just asking them to say “Withdraw $100 from my account now”. This is only one of several common examples of how this is already being used by banks around the world. However, this has been limited due to difficulties with natural language patterns and computer recognition accuracy among other things. This ultimately motivated the scientific and engineering communities to create Computer Vision as a solution.


2. Computer Vision

With computer vision, banks are able to provide a complete solution by identifying document types, documents within documents, the image of the document, and where certain document types are located on a page.


Implementing computer vision-enabled search has allowed for more accurate and precise ways to analyze data and has also enabled banks to provide precise and accurate workflows, by recognizing text based on its depiction within images. One of the hallmarks of computer vision-based search is that it can process all three dimensions. Processing images can be done using techniques such as texture analysis, edge detection, regularization, video-based object detection, audio analysis, and specialized algorithms all of which can help with identifying and separating different types of documents.


Banks commonly use computers with specialized chips known as vision processors that enable computers in Document Analysis in Banking to recognize documents and recognize images with enough accuracy and perform monitoring and data analysis.


Like natural language processing, this also has some use cases that both consumers and the banks themselves would appreciate. One that has been able to stand out as a particularly common use is automatic check processing. Although some say that this is credit card processing more than anything else, any transaction that involves a bank checking account requires a check, which is eventually processed by a bank employee who is eventually going to wear out more and more over time. “This is a known problem, and the solution lies in implementing ‘AI-powered image processing’ because this process can allow a machine to perform the level of tasks that a human being would do.”


Similar to how natural language processing can be used to create intuitive input methods for people to use to interact with the system, Computer Vision can also be used to create algorithms that can allow a machine to do the same thing in order to process images.


Data Types Processed in  Banking

There are various types of data that can be processed as part of an AI initiative, however, most of the  Solutions in the Banking industry have mostly focused on the following three types of data: Internal data, regulatory data, and customer data which includes both online and offline data.


  • In the field of banking it is important for one to have all of their internal data be processed, can be accessed, secure, private, and intelligible.
  • With regulatory data, in the banking industry, ensuring that all data is in a readable and structured format is a priority. Additionally, optimal security is also another.
  • For customer data, banks work hard to ensure that customers feel confident with the level of privacy and security their data is being held and processed.


Benefits of AI-Powered Solutions

The AI opportunity holds immense potential is becoming more and more prevalent in the banking industry. AI-based document search is already being implemented on a global scale to improve overall customer service quality.

  • One of the main benefits of employing this technology is that it can process, analyze and extract information from all types of documents.
  • It can also help provide a 360 degree customer view to the banks to help plan better strategies for marketing campaigns. For example, If for a particular client the GDPR has certain issues with respect to the number of data types, it may mean that for another bank the same GDPR requires a different set of data types. Many such roadblocks and more can be overcome by the use of AI
  • It can also help to create new jobs by creating new jobs as opposed to just replacing them all. AI has already been able to be fully integrated into some technologies used in banking while others have received less functionality due to limitations based on industrial protocols.


Banking by default is customer-centric and thus, the industry works hard to implement solutions that improve the client experience through AI. Implementing machine learning can provide for a more intelligent and less human-dependent service delivery, which is an opportunity for banks to embrace this technology as opposed to fearing it.



AI in banking can be considered a double-edged sword, which is a fact that has been proven by the banking industry. It can increase efficiency by lowering unnecessary work processes, thereby decreasing workforce requirements.


The positive outcomes from utilizing AI in banking are numerous, and they range from increased efficiency, customer satisfaction, to enhanced human-machine collaboration. Banks should use the positive approach in the potential technology to use in their industry.



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