Business process automation (BPA) is on the rise. According to a McKinsey Global Survey in 2020, two-thirds of the respondents said that their business was using, or at least piloting, BPA in one or more functions or business units.
Artificial intelligence and machine learning enable solutions to automate a growing number of processes in business. Examples of BPA are identity verification checks and payment initiations. Both examples can be carried out by automated credit card scanning.
In this blog, you’ll find out what the benefits are, which fields can be extracted from credit cards and what use cases of an automated credit card scanning solution via a text recognition OCR API are.
Which fields can be extracted from the credit card?
Before we get to the benefits and use cases of automated credit card scanning, we’ll explain which fields can be extracted from the credit card:
- Credit card issuer
- Credit card processing network
- Card type
- Card number or PAN
- Card holder name
- Expiration date
- CVC (optional)
- Signature (beta)
Benefits of automated credit card scanning
Automated credit card scanning has a couple of advantages to help your business move forward:
- Cost reduction
- Fraud prevention
- Speed increasement
- Error reduction
Since manually checking and processing credit cards can be quite labor-intensive, a card scanning solution can save you a lot of time, and thus money. Intelligent automation usually results in 40 to 75% in cost savings, with the payback period ranging from several months to several years.
Automated credit card scanning solutions are able to detect forged images within seconds. This makes it easier to filter out fraud, before this can cause you any trouble. Strange changes in lighting, sudden color changes in text and incoherent pixel structures might be indications of fraud.
Humans aren’t anywhere close to being as fast as computers. It only makes sense that the process of automated credit card scanning is much faster. This will save you a lot of time and money.
Manual data entry inevitably leads to data-entry errors. Error rates of manual data entry range from 0.55 to 3.6%, but higher error rates such as 26.9% have also been found. This is where automated credit card processing comes into play, since it minimizes the risk of human error.
Use cases of automated credit card scanning
There are many use cases when it comes to automated credit card scanning. The most common ones are:
- Simplifying credit card payments
- Automated age verification through credit card scanning
- Automatic anonymization of bank card data
- Digital customer onboarding with credit cards
Simplifying credit card payments
Having to type in the credit card number is something users have to do on many applications and websites. This is an annoying task, which can easily be replaced by scanning the credit card with the user’s phone camera.
The only thing users have to do is simply take a picture of the card, or upload a PDF file of the card to the API. Fields are identified, localized and automatically entered into the payment forms.
This results in a dramatically improved UX of the application or the checkout. Users no longer have to enter their own card information.
Automated age verification through credit card scanning
Many companies carry out identity checks as part of their customer onboarding process. Examples of companies that do this are tobacco, alcohol and video gaming websites.
Age verification through credit cards requires the card holder to prove that they are the rightful owner of the card. They can do so by methods such as CVV, 3D Secure and AVS (Address Verification System). To be able to apply for and own a credit card, you have to be over 18 years of age. This is a much more sophisticated age verification system, than asking users to validate their date of birth on a form.
Automatic anonymization of bank card data
Over the years, some companies might have collected thousands of documents, containing bank card data and other privacy-sensitive information. Some of these companies are not fully aware of what is in there or are unable to navigate through them effectively.
With an OCR API, companies can detect specific combinations of numbers in those documents and have them anonymized. This can be done by removing the numbers or document completely, or by blacklinking specific lines in the document.
Besides that, anonymizing card data right from the start by automatically blacklining certain elements is also a possibility.
Digital customer onboarding with credit cards
Many trading- and online payment platforms require a person’s credit card information to sign up. They have to do so in order to ensure a stable and compliant service. The concern is verifying that the person on the platform actually is who he says he is, done by using an authorized, valid credit card.
BPA with credit card scanning
In conclusion, there are many use cases in which BPA comes in helpful. It helps with simplifying credit card payments, automating age verification and anonymization of card data and simplifying digital customer onboarding. It reduces fraud, increases speed, reduces cost and errors.
About the Author
Yeelen Knegtering, CEO & Co-founder of Klippa, is passionate about developing digital products that help people to save time on administrative hassle and spend time on the things they love.
With a degree in Information Technology at the University of Groningen, he started Klippa with the idea that there had to be a better way to organize and manage receipts. Now, Klippa is a document digitization company with a focus on digitizing and automating document streams for companies.