Since the last few decades, the explosion in processing volumes of information has led to a surge in cases of document fraud. One of the many areas where Machine Learning algorithms have made advancements is in the field of document fraud detection. Consumers are concerned about flagging false positives and tamper-proof documents are becoming the Holy Grail for fraud prevention.
Document fraud is defined as the manufacturing, duplication, counterfeiting and forgery of official documents in an attempt to bypass legal authorities and checks. An illegal financial transaction using document records can end up in losing customers’ trust and increase follow-up costs. Rules-based fraud detection systems are proving to be ineffective since fraudsters are getting smarter with their forgeries.
Document fraud cases are rising in banks and NBFCs since these organizations deal with KYC and onboarding information. Documents are faked for various reasons and can cost global economies billions of dollars. Fraudsters use fake documents to apply for loans, purchase a property, make false insurance filings, and travel to different countries illegally. All industries support the use of official documents for financial transactions and identity verification which makes it imperative for organizations to prevent these cases before it’s too late.
Proportion of document fraud cases for various industries
New data from the Consumer Sentinel Network shows that the Federal Trade Commission received over 2.1 million fraud reports in 2020. Imposter scams was the most common type of document fraud and the stealing of online shopping credentials was reported to be second. Over $3.3 billion dollars was lost in fraudulent cases by consumers and the FTC received more than 4.7 million reports in 2020 about identity thefts. 406,365 people reported that their information was misused for applying for government documents and unemployment insurance. This was a huge jump from 2019 as cases doubled and FTC made this data available to the public on its data analysis website.
Scammers love going for real estate since these frauds take many forms. Victims face the consequences of false sale deed filings and literally fight to prevent getting evicted from homes. Fraudsters claim ownership of property and end up suing lawful owners which cost them exorbitant fees to resolve legal cases. Application fraud and identity theft make use of fake credentials and impact lives when they get leaked and shared online.
One of the risks faced by the maritime industry is sharing sensitive information across the supply chain level. Tampering of shipment documents redirects products to different locations and causes delays in customs clearance. False declarations can break procedural compliance and land customers in trouble without knowing why. The World Customs Organization uses the Harmonized System (HS) which is an international system of codes used for the classification of various goods. Miscoding is a popular type of document fraud in the logistics industry where information is incomplete or incorrect.
Accounts payable frauds include duplicate payments, fake invoices, check tampering, and incomplete vendor documentation. A news report published by American Express revealed that accounts payable fraud can impact anybody and cost a multi-billion dollar company huge losses. The reason was just one single email which looked legitimate and the company ended up depositing $8 million dollars to the fraudster’s account, until they realized something was amiss.
Types of document fraud
The document fraud industry is estimated to be valued at 3.2 trillion pounds and it's one of the largest targets for fraudsters. The most common types of document frauds are:
1. Forged Documents – Forged documents are files that have their details tampered with. Fraudsters can completely change information or partially alter it. Examples of forgeries in documents include adding timestamps or watermarks to files, inserting and removing pages, and digitally altering signatures. The integrity of documents gets affected when they’re forged.
2. Invoice frauds – This is when an employee impersonates a vendor and generates a false invoice. This is sent to the company who disburses funds directly to user accounts
3. Blank Documents – Blank documents can be used to insert falsified information and are leaked from the manufacturing supply chain. Blank fields have the ability to be tampered with since they’re empty and need to be verified for security.
4. Camouflage Documents – These are fake identities created by fraudsters who represent themselves as government officials or authorized entities. This is a rare type of fraud that can sometimes slips through the cracks if left unchecked.
5. Counterfeit Documents – These are unauthorized reproductions of official documents. The perpetrator’ can use these reproduced files to gain access to additional confidential information. An example of counterfeit document fraud is using a victim’s driving license to learn about their social security number and bank account details.
How to detect document fraud?
Manual checks on documents are still used to this day. But traditional methods of checking don’t suffice since there is a margin for human error. Obvious signs in fake documents can be spotted by checking for spellings and errors, inconsistencies in fonts and typefaces, and seeing blurred texts. Font formatting that is strange can be detected by the naked eye as well.
However, when you are dealing with huge volumes of documents this becomes impossible to keep up. Using document fraud detection software that uses automated workflows for validating and verifying the structure of information and visual details is integral to detecting fraudulent cases.
Red Flags in documentation checks
Document fraud detection technology has evolved over the years at unprecedented rates with the advent of Artificial Intelligence and Intelligent Document Processing (IDP).
Businesses are seeking automated software that saves hours in sorting through data and retrieving file records. Here are a few red flags to watch out for when reviewing the information presented in structured and unstructured documents:
1. Metadata Analysis – PDF files which show structural inconsistencies and have suspicious zones which are too different from original parts of documents
2. Financial transactions – Bank statements that cross-check monthly income with average balance and key financial APIs for verifying customer details
3. OCR and Barcode Data - Content obtained through OCR reading, biometric identification, and barcode scanning can be validated against official data repositories for verification and validation purposes
4. Duplicate Reports – These schemes involve using duplicate invoices or making double payments. Another red flag is when an employee raises a refund after purchasing an item using the company’s funds and transfers the money directly to their account
5. Other Variables – Reviewing metrics like total decline rates, cost per analysis, checkout abandonment rates, invoice numbers, and key fraud APIs
Ultimately, there is an element of human error involved which is why no document processing software is 100% accurate.
95 to 98% accuracy is ideal - The accuracy level of Document Fraud Detection processes is not 100% perfect but an accuracy of 95 to 98% is considered the ideal industry benchmark.
How Docsumo helps you to detect fraud in real-time
Document fraud is happening more so due to the latest advancements in technology. Fraudsters are learning how to manipulate information better and hide changes by using graphics processing and deep fake engineering. Image analysis helps in detecting signs of forgery which often go unnoticed. For example, Docsumo uses an automated procedure that scans for different fields and elements in a document. Analyzing the date of issue for document numbers, identifying low quality images, and differences in photos between the main document and fake document are ways in which the platform makes distinctions between forgeries and real files.
Optical Character Recognition (OCR) makes it possible to note the dynamics and changes in text elements. Fake document recognition systems map protection elements and cross-references with data reference models from authorized sources to confirm the validity and integrity of key information. If anything looks amiss, the platform raises red flags and alerts users to look into the matter.
Here's a list of the most popular features employed by Docsumo for state of the art document fraud detection:-
1. B & W Photos – Black and white photos are copies of original documents. Not all of these are fake but it’s helpful for users to learn when they get duplicate copies. In most cases, images of documents captured from secondary sources are fraudulent
2. Document Cropping – If your document has been cropped and details are cut out, Docsumo will flag and ask you to review those files
3. Scanned Images – If the document has been scanned and is not an original copy, the APIs will capture it using intelligent OCR and AI
4. Photo on Photo – Photo on photo cases refer to taking photos of existing documents from photos which have taken of them before. Docsumo intelligently captures the lighting, angle, and exposure of these photos and makes out using ML Algorithms whether you have a photo-on-photo event going on.
Besides these features, the platform automatically detects the formatting and presentation of official documents. You can extract relevant KPI-values and create automated workflows for cross-checking the structure of pages. If any pages are missing or if the subtotals in invoices don’t add up with taxes, the API will alert users.
Docsumo also reviews details related to document records using 2-way matching and validates captured information against company databases.
Docsumo uses smart, fast, and efficient processes which raise document analysis queries and ensure a swift resolution of fraudulent documents. Machine Learning algorithms and pre-trained APIs supercharge manual review processes and helps in the detection of fraudulent documents at faster rates.
Using advanced data extraction and OCR technology, it becomes convenient to scale operations and reduce processing times for bulk documents from hours to mere seconds. Human errors also get reduced as a result since the platform looks for common and uncommon patterns of tampering and immediately notifies users after fact- checking so that they can be stringent and not make mistakes.