Case Study

Banking Industry

Empowering companies big and small

Automate Bank Statement Scanning through OCR and NLP

The financial industry and the banks in particular are burdened by excessive documentation. Typically, a bank has to process heaps of customer-related documents annually. 

To keep up with the workload, a bank has to employ more and more people to perform redundant tasks such as manual bank statements processing or bank statements data extraction, etc. Doing this manually is costly in terms of finances and doesn’t contribute much in terms of productivity.

Use case of UBIAI in Banking Industry

The world is currently undergoing a Data Revolution. Digitization has been overhauling every aspect of human life and the banking sector is no exception too. 

Time is money and it couldn’t be any truer than it is in present circumstances.

Owing to the abundance of customers’ data and bank statements, it has become crucial for banks to automate their bank statements for quick and error-free storage and retrieval.
The solution for the bank statements automation is bank statement scanning through OCR and NLP-based products. Let’s look at how and why should banks employ bank statement scanning through OCR and NLP products.

Optical Character Recognition and Bank Statement Scanning through OCR

In recent times, OCR has become a buzzword and you as a decision-maker may have heard of it. So, what exactly is OCR?

OCR is Optical Character Recognition which is a technology that is utilized for image scanning and conversion to readable formats. OCR is a software solution which extracts data and characters from documents or images, recognizes the information, and then converts it to an electronic, readable copy an electronic readable copy.


Natural Language Processing and Bank Statement Automation NLP:

Another popular terminology is NLP which stands for Natural Language Processing. NLP is actually a broader term that encapsulates two other – NLU (Natural Language Understanding) and NLG (Natural Language Generation).

The NLU part is responsible for extracting meaning behind speech or text. NLU takes unstructured text and transforms it into meaningful and structured formats that computers can understand.

NLG takes it a step ahead and converts the structured data into speech or text that humans can understand. The bank sector can apply Natural Language Processing techniques for bank statement scanning and bank statement image to text conversion.


The Future of Banking Belongs to Automated Bank Statement Scanning:

It is justified to say that future of banking belongs to automated bank statement data extraction and bank statement scanning through OCR and NLP. The top benefits a bank gains by leveraging automated bank statement extraction and scanning includes strategic advantage over its competitors through:
Enhanced customer satisfaction
Enhanced customer satisfaction
Improved service delivery
Elimination of redundant tasks
Streamlined operations and Improved fraud detection, anomalies and tighter compliance

According to a McKinsey report, a new wave of automation in the banking sector will surge in coming years that will result in the automation of 10-15% of banking functions. As a result, this will reduce financial burden and allow employees to focus on high-impact tasks. 


To take full advantage of this upcoming wave of automation, the banking sector should be pro-active and automate bank statement data extraction, bank statement image to text conversion and bank statement scanning through OCR and NLP.


Recent statistics reveal that approximately 35% of banks who have automated their processes – particularly bank statement scanning through OCR – have reported 2-5% surge in profits just by incorporating automated solutions.


What’s more? The extracted meaningful information from bank statements can be utilized for identifying meaningful patterns like purchasing habits or the frequency of transactions from similar category locations which can be utilized for fraud detection and anomalies in transactions.


All these factors contribute to higher profits and improved customer services delivery.



Manual Processing of Bank Statements

Manual Bank Statements Processing​

  • An extremely time-consuming process prone to human errors and requires extra-time for corrections.
  • Owing to the need of human intervention, this process is limited to just 6-7 hours per day.
  • Manual data corrections are needed due to the mundane nature of task.
  • More workforce is required with increasing account holders.
  • Limited number of bank statements processing each day due to manual process.
  • Higher chances of fraudulent activities.
  • No identification of meaningful patterns in manual bank statement data extraction.

Automated Bank Statements Processing

  • A very fast automated process with very little need of human supervision. Data is automatically scanned and extracted with the help of intelligent OCR systems like UBIAI OCR tools.
  • Bank statement OCR tools can process bank statements 24/7 without any break.
  • No data correction is needed because bank statement OCR tools are free from human errors.
  • No long term employee hiring costs with increasing account holders.
  • Unlimited processing of automated bank statements scanning through OCR and NLP tools. 
  • No document fraud.
  • Automated bank statement data extraction enables identification of customers’ spending habits and it can then be utilized for relevant promotional campaigns.

Top 5 Reasons to Use UBIAI for Bank Statement Scanning Through OCR and NLP:

UBIAI is a US based company that offers the most economical, comprehensive and latest OCR and NLP solutions for a wide array of industries including bank statement OCR. 


UBIAI takes pride in helping the developers’ community by providing easy solutions and helping companies to immediately adopt Machine learning and NLP ideas without spending excessive man-hours on coding. Here’s what makes UBIAI tools efficient and a smart choice for the banking industry:


Optical Character Recognition to annoate from digital or hand-written text images to machine-readable text ( invoices, reciepts, contracts) in a perfect layout

UBIAI provides fully automated file uploading with auto-annotation, auto-labelling of documents and model training. The following file formats ae supported by API:

  • Native PDF (OCR)

Schedule a consultation based on your project type, such as OCR, Span Categorizer, or Character-based Annotation. Schedule a demo with UBIAI to learn more about our tools. .Schedule a consultation based on your project type, such as OCR, Span Categorizer, or Character-based Annotation. Schedule a demo with UBIAI to learn more about our tools. .

UBIAI supports span-based, character-based, OCR annotation, and Image Classification, as well as multiple languages and entities, relations, and classes.

UBIAI offers free trials, team collaboration, and enterprise packages that are both cloud-based and installable license-based.

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If you’re looking for a tagging platform on NLP projects, UBIAI probably has all the features you need. lt supports many document types and multiple OCR engines. It’s super simple and powerful.

Ihsan S.

Machine Learning Engineer

It is very easy to work with. A couple of things that draw me to UBIAI are that it could accept data in a LOT of formats (even including PDFs!).

Jetson W.

Data Architect

I love this product as this provides amazing support for document classification and named entity recognition. The NLP tasks UI is very intuitively embedded into this product and it is really easy to use.

Jasmeet K.

Data Scientist