Making KYC Verification Easy with Artificial Intelligence Techniques

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Automate KYC (Know Your Customer) Process

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Why KYC?

All You Need To Know About The Relevance Of KYC

 For any financial company, it’s essential to verify the identity of their customers. For instance, banks keeping the true information of their customers helps them to prevent potential frauds. Based on customers’ information, companies can identify money laundering. In addition, any Financial Institution’s ultimate aim is to earn the confidence and faith of their customers. But equally important to verify the information customers provide back to them. If you’re a financial institution, you could face possible fines, sanctions, and reputational damage. More importantly, KYC is a fundamental practice to protect your organization from significant illegal funds and transaction losses. 

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 It becomes crucial to have the verified information as the number of people seeking to avail the bank, insurance companies, and any other financial company increases. Customers’ documents verification process becomes challenging to fast track this verification process. KYC is useful to understand your customer’s real identity and their activities. And also, it is helpful to know any money laundering risks associated with the customer. In a KYC process, the following information matters most 

      1. Name
      2. Phone Number
      3. Address
    1. Unique Identity number/Verification ID Number

 By verifying all information mentioned above, those sections will help the companies to identify fraud customers. And then, they can quickly identify in case of any fraudulent activity occurring by any individual client. And as per RBI (Reserve Bank of India) norms, it is strictly mentioned that any financial institution has operations related to money. So now it’s becoming an essential part of any financial institution to have KYC, and it’s equally important to have a faster process for KYC verification.

The Old Method of KYC Verification

Know Your Customer - Document ID Verification

 Earlier in the KYC process, financial institutions collect information from millions of customers and pass through multiple verification layers. The method of manual validation of customer data usually involves high-cost and time-consuming. When there is a large number of customer data available, and the verification process itself becomes a tedious process, this creates a large backlog of verification, which results in customer dissatisfaction, financial crimes, fraud. 

AI-Based KYC Verification

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Till now, we discuss the disadvantages of the old process of KYC. In this section, we will focus on Artificial Intelligence technology’s impacts on KYC. Using AI, the data collection process makes it easy and fast, which often requires days to months in a manual process. Starting from data collection to data extraction, verification, and fraud detection, everything can easily be done using AI. We will show you how using AI; we can build an automated KYC verification system. And then follow the following steps to build our KYC verification system: 

    • Data Extraction

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 This is the step where we need to use the digitally stored documents. By collecting all the images, we can build a data storage system that will keep these images. We will use these images in further steps. 

    • Annotation Process

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For our KYC-based system, we will have multiple Deep learning-based models to be in use, so for those models to get trained, we need to build well-annotated datasets. 

Text Recognizer OCR Annotation

 The images we stored in the earlier stage we will use those images for annotation for an OCR based text recognition system. Here we will randomly select 100s of the images and send them to our annotation system. In return, we will get the annotated images and text concerning their corresponding images. Now we will use this annotated data to build a machine learning model, which we will be using to make predictions on the next set of images. We will then run a verification method on those predicted images and re-annotate the wrongly annotated data. And in this loop, we will be annotating the whole dataset. 

Entity Recognizer and Relation Extraction

 Apart from the OCR Text recognizer, we will also need a dataset where it will recognize key entities and the relationships between those entities. For that type of dataset, we will use our NLP-based annotation tool to annotate a NER dataset with required entities like name, Aadhaar Number, PAN Number, Address, etc. We will also build a dataset where it will have the relationships between those entities. 

    • Data Pre-Processing

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 This is the step where we have to pre-process our dataset that we prepared in our previous step. We will implement some Image augmentation and transformation techniques on the images to work in any light or outdoor or indoor environment. For the text datasets, we will be using some text pre-processing steps. 

    • Model Development

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      1. Document Data Recognition

        This model will help us to recognize the characters in the images. Before running the recognizer, we will have to run an object detection model to identify each required data location. And then, on those fetched locations of the image, we will be running our OCR based text recognition.

      2. Data Verification

        By using models like Named entity recognition, we will identify the required entities. And With the help of the dataset we prepared in the Data Annotation step, we can train a model to identify the entities. Then similarly, we will use the other dataset to extract relationships between the entities. In that way, we will use two models to extract this information. We can group customers into fraud and non-fraud categories by using another classification model.

    • Inference and Deployment of Model

 By using the above-trained models, we will build our application. In our application, the input will be various customer documents, the form that needs to be submitted. 

    • Once we get the input data, we will pass them through the recognizer model, which will extract the text’s required information.
    • These textual data will then be sent to the NER and Relation extraction based models on understanding the information.
    • The output from the previous step will be sent to our verification system. Here system will be verifying the submitted form input with the other documents; it will also try to find the patterns from past data.
    • After the verification is completed, it will group the customer data as fraud or non-fraud. And then send the data to the corresponding database and response mail to the individual.