For any financial company, it’s really important to verify the identity of their customer. Like for banks keeping the true information of their customers help them to prevent potential frauds and they can also identify money laundering. The ultimate aim of any Financial Institution 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, if you do business with a money launderer or terrorist. More importantly, KYC is a fundamental practice to protect your organization from fraud and losses resulting from illegal funds and transactions.
So it’s really important that they should have the information verified but as the number of people seeking to avail the facility of the bank, insurance companies, and any other financial company it’s becoming really difficult to fast track this verification process. KYC is useful to understand your customer’s real identity and their activities and access any money laundering risks associated with the customer. In a KYC process, the following information matters most
By verifying all information mentioned above helps the companies to identify who are the customers and in case of any fraudulent, they can easily identify the individual.
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.
Earlier in a KYC process financial institutions collect the information from millions of customers and pass through multiple layers of verification. The process of manual validation of customer data usually involves high-cost and is 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.
Using AI the data collection process makes it really easy and fast, which often requires days to months in a manual process. Starting from data collection to data extraction, verification, fraud detection everything can easily be done by using AI. We will show you how using AI we can build an automated KYC verification system.
We will follow the following steps to build our KYC verification system:
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 and we will use these images in further steps.
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.
The images we stored in the earlier stage 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 with respect to 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. Now we will run a verification method on those predicted images and re-annotate the data which were wrongly annotated. And in this loop, we will be annotating the whole dataset.
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. and we will also build a dataset where it will have the relationships between those entities.
This is the step where we have to pre-process the dataset that we prepared in our previous step. We will implement some of the Image augmentation and transformation techniques on the images to work in any light or outdoor or indoor environment and for the text datasets, we will be using some text pre-processing steps.
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 the location of each required data. And then on those fetched locations of the image, we will be running our OCR-based text recognition.
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.
And by creating another model to identify fraud and non-fraud by using the information we extract from the previous two models we are able to group customers into those two categories.
By using the above-trained models we will build our application. In our application, the input will be various documents of the customer, the form which needs to be submitted.
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