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AI-Based OCR Technology Revolutionizing the Banking Sector

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The advent of technology has brought convenience to life. Believe it or not, survival without technology is one of the darkest thoughts that can cross your mind in the digital era. The world has become a global village thanks to rapid digitization, but it has also opened doors for many fraudsters to step in and terrify people.

Organizations in every sector are unsafe due to increasing ransomware and data breaches. Considering the increasing number of frauds, companies opt for robust verification systems with OCR technology to only onboard legitimate customers. These systems allow enterprises to filter fraudsters before becoming a problem for the customers and the company.

The banking sector works like a goldmine for fraudsters and faces huge losses due to money laundering, identity theft, and several other frauds. The governments of different states have also enforced stringent know your customer and anti-money laundering regulations.

Complying with these regulations is a challenge without a sound verification system. For the same reason, organizations are adding OCR technology for efficient data extraction. Complying with the ever-growing regulatory burden and seamless customer onboarding is becoming simpler for the banks now.

The question is, what is OCR, and how does it work? How are banks benefitting from it? Keep reading to find out the answers to your questions.

What is Optical Character Recognition (OCR)?

Optical Character Recognition (OCR) is an advanced technology that helps businesses and individuals extract document information more accurately and in a matter of seconds. An OCR software helps individuals extract data from documents and convert it into a machine-readable format that can be further used.

OCR has been in action for a while now, but organizations have started to recognize its need for the past few years. It has become an essential part of organizational operations now for the sake of convenience. According to some reports, the global OCR market will reach $70 million by the end of 2030.

Undoubtedly, this is the result of technological advancements. Enterprises are significantly benefitting from OCR technology and providing a remarkable experience to their customers. Furthermore, companies are using this technology for verification purposes and fraud prevention.

How Does AI-Based OCR Technology Work?

Previously, extracting data and converting it into machine-friendly language was a challenging job. It used to take hours to verify one document and gave the workers a hard time. Guaranteeing the accuracy of the job was impossible due to human errors.

With optical character recognition, the scenarios have changed with the time-efficient process and accurate results. An artificial intelligence-based OCR technology works for fetching data and converting it into a machine-friendly language.

Pre Processing

Preprocessing aims to ease the character distinguishing process for the OCR. OCR comprehends images in a multidimensional array; therefore, the document image is optimized with techniques like deskewing, normalization, binarization, etc., in preprocessing.

Once techniques are applied for differentiating the text from the background, the OCR extracts texts from the fed image. It fills the form using AI algorithms that identify the document template.

Data Extraction

Afterimage optimization, data from the document is extracted in two parts – segmentation and feature extraction. For segmentation, the deep learning neural networks are used for detecting templates and defined segments from the document. After identification, the software extracts features from the document.

For example, in an online bank registration form, segments for the name, date of birth, etc., are identified and filled with respective data.

Post Processing

Once data extraction is complete, organizations have to comprehend whether the extracted data is correct or not. The extraction of wrong data can be lethal for companies dealing with sensitive information. For instance, incorrect extraction of account number can result in a loss for the bank. Hence, the post-processing stage deals with data verification through NLP techniques.

Advantages of OCR for the Banking Sector

The banking sector is prone to financial crimes, like money laundering and account takeover fraud. OCR technology can significantly help the banking sector identify fraudsters because the software checks for any unauthorized documents. Here are the most compelling ones with numerous benefits of optical character recognition software in the banking sector.

Brings Convenience to the Verification Process

The world of technology is bringing convenience to every industry, and AI-based OCR simplifies the verification process, especially in banks. Employees do not have to invest the effort to extract data from every document and verify it for legitimacy. Everything relies on the software, and employees oversee any discrepancies in the technology. While manual processes consume days for one customer’s verification, OCR takes a few minutes to do the job.

Optical Character Recognition Optimizes Time

In the manual data extraction and processing method, verification requires at least a week. An employee has to cater to the queries of the customer first. Then, it checks the documents carefully and extracts relevant data. Once extracted, it is converted into machine-readable language.

However, an AI-based optical character recognition fetches data, converts it into machine-friendly form, and completes the process in seconds. Hence, employees can optimize time for verification.

Reduces Cost of Verification

Previously, firms used to hire a team for verifying every customer for onboarding. Furthermore, the equipment required for verification purposes was uncountable. With the introduction of artificially intelligent OCR, banks have saved many costs for hiring and acquiring equipment. Hence, OCR not only saves time, but it is also cost-efficient, especially for the banking sector.

Legitimate Customer Onboarding

Getting in touch with fraudsters is easier since they are also using sophisticated methods for their evil desires. Forged documents are common these days, and manual verification cannot identify tampered documents. Hence, banks need something more stringent than simple document analysis. This where optical character recognition steps in and saves the day for the banking industry.

The technology can detect forged documents in seconds, making it simpler for banks to filter fraudsters before they cause any trouble. Moreover, onboarding legitimate customers is not a problem anymore, and the customer experience is not compromised.

OCR Technology Makes Fraud Prevention Easier

Money laundering, account takeover fraud, open banking, virtual currencies, and tax evasion are some of the frauds that the banking sector faces every year. According to Statista, the UK has closed two-thirds of bank branches in the last 30 years, the US has closed nearly 9000 branches, and Europe has shutdown 6000 branches due to banking frauds, especially in digital banking.

Preventing fraud is becoming a challenge for all banks globally. Combating these crimes is now possible with OCR. Banks can now onboard legitimate customers, and verifying everyone means there are no fraudsters to increase hassles for the banks.

Complying with KYC/AML Compliance

States worldwide are enforcing stringent KYC/AML regulations, and complying with these regulations is nearly impossible without a robust verification system. An optical character recognition technology is enabling banks to comply with these laws in a better way.

Wrapping It Up

Frauds in the digital world are rapidly increasing, and fraudsters’ primary target is the financial institutions. Protecting banks from account takeover frauds, identity theft, and data breaches is becoming complicated since criminals enhance their strategies.

Moreover, regulatory authorities across the globe have enforced strict laws for KYC and AML, and complying with these regulations is another big hassle. The traditional verification method is time-consuming and costly too. The introduction of optical character recognition technology has made it convenient for banks to prevent fraud, comply with the regulations, save time and cost, and onboard legitimate clients only.

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Working Remotely or “In Officeâ€� — What You Need to Know About the Duality of Files

Think about your files. You probably picture the Microsoft Word document you were just editing as it appears in your Microsoft Word application. Or you may think about a PDF as it appears in a viewer like Adobe Reader, a presentation in PowerPoint, a spreadsheet in Excel, or as an email as it appears in Outlook.

What you see in Microsoft Word, Adobe Reader, etc. is not the full nature of these files. These files all have “a dual nature.”

In fact, these native applications views are more like the tip of the iceberg when it comes to a file’s alternate binary format existence. A file’s binary format is the relevant mode when it is just sitting on your hard drive, network or online portal.

The binary format typically looks nothing like what you see inside an associated application.

For example, inside of Microsoft Word, a document is typically easy to read in terms of complete sentences and paragraphs. In binary format, it may be hard to pick out even a single word. You may just see random letters floating in a sea of gibberish-looking codes.

While a binary format may look like a sea of gibberish to the naked eye, to a search engine, a binary format is more like a crystal ball. Inside the crystal ball is not just what you can see in an associated application view, but so much more.

How does a search engine parse a binary format?

The first step to parse a binary format is to identify the correct binary format specification to apply. The binary specification for “interpreting� a OneNote document is very different from the binary specification for “interpreting� a PDF.

The PDF is very different from the binary specification for “interpretingâ€� an email. And these specifications can be beyond complex — approaching hundreds of pages of technical documentation.

One way to identify the correct binary specification to apply would be to look at the filename extension.

If a filename ends in .DOCX the Microsoft Word specification would apply and if it ends in .PDF — the PDF file specification would apply. But what if someone saves their PDF files with a .DOCX filename extension and their OneNote files with a .PDF filename extension?

The more accurate way to identify the relevant specification to apply to a binary file is to look inside the binary file itself. Looking inside the binary file itself — you can determine the format type, rather than looking at the filename extension.

With the correct format type — no matter what extension someone tacks onto a Microsoft Word document — the correct parsing mechanism can still apply.

First: When you use a search engine like dtSearch: the filename extension does not affect the ability to find a file.

A lot of times, you can have metadata relatively hidden in an associated application view. This means that the data will not pop up by default; you’d have to do some considerable clicking around to find the information.

However, to a search engine, all text and data are on the same footing.

Second: The second practical tip relating to the dual nature of files and a search engine then is that there is no metadata too obscure for the search engine to easily find.

Third: The third practical tip relates to “black on black� or “white on white� or “red on red� text. These types of text will typically be completely invisible in an associated application view. However, it is just as apparent as any other text to a search engine. Therefore, the third tip relating to the dual nature of files and a search engine is that the visual contrast between words and background inside of an application does not matter to a search engine.

The final tip:  The last suggestion here is “file specific,” and relates to a subset of files that I will call “image onlyâ€� PDFs.”

Sometimes you’ll run across a PDF where you try to cut and paste the text from it, but you can’t, because it is a picture of text only, and does not actually include a digital version of the text.

By the same token, as an image only, a search engine is not going to see the text there either — the search engine only “sees” the image (along with any metadata).

Keep in mind that a search engine can identify “image only� PDFs specifically. The search engine then flags the image to indicate that the file requires optical character recognition or (OCR).

Remember that OCR is a separate application — such as an app like Adobe Acrobat can perform.

Once optical character recognition (OCR) happens — you can then cut and paste the text at will and the text will be “all thereâ€� for a search engine to find.

Image Credit: Ketut Subiyanto; Pexels

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