How can AI help financial institutions speed up customer onboarding?
Posted: Mon Dec 23, 2024 5:04 am
More than ever, the customer is king. Demands and expectations are very high and organizations in the financial sector are also looking for ways to differentiate themselves in various areas. This is difficult because products and services are rarely unique and price differences are minimal. Organizations that succeed in differentiating themselves usually win thanks to a seamless customer experience. At ProcessMaker IDP, we have therefore enriched our platform so that financial institutions can use AI technology to optimize one of the first experiences that customers have with them.
Before doing business with consumers or businesses, financial institutions are required to follow processes to identify and verify the customer. The documents and financial pakistan email address list free download records that are provided for this purpose are usually complex and unstructured data that must be processed manually. This results in long processing times and often delays in the process. Automation is pretty much the only option to make a difference here.
ProcessMaker IDP has achieved this by applying Intelligent Document Processing (IDP). Since the recently introduced version 2.1 of our platform, we offer the possibility to remove the bottleneck of manual customer onboarding. To do this, unstructured documents based on machine learning models are combined with logic and templates and converted into structured customer data.
How does intelligent document processing work?
Documents can be submitted for processing via API links or ProcessMaker IDP can retrieve documents from a variety of sources including file shares and emails. Image preprocessing optimizes images of scanned documents for OCR processing. For example, by optimizing resolution, contrast or straightening lines of text.
Using optical text recognition, ProcessMaker IDP converts scanned documents into editable text. Then, the documents are classified by document type and the information is checked for completeness. Relevant data is extracted from the documents and validated automatically. For extraction, we can use pre-trained machine learning models, regular expressions or a combination of both. A special approach is required to extract data from tables, this is called table extraction. Our solution also takes into account the requirements of the GDPR by automatically anonymizing personal data as part of the processing.
Before doing business with consumers or businesses, financial institutions are required to follow processes to identify and verify the customer. The documents and financial pakistan email address list free download records that are provided for this purpose are usually complex and unstructured data that must be processed manually. This results in long processing times and often delays in the process. Automation is pretty much the only option to make a difference here.
ProcessMaker IDP has achieved this by applying Intelligent Document Processing (IDP). Since the recently introduced version 2.1 of our platform, we offer the possibility to remove the bottleneck of manual customer onboarding. To do this, unstructured documents based on machine learning models are combined with logic and templates and converted into structured customer data.
How does intelligent document processing work?
Documents can be submitted for processing via API links or ProcessMaker IDP can retrieve documents from a variety of sources including file shares and emails. Image preprocessing optimizes images of scanned documents for OCR processing. For example, by optimizing resolution, contrast or straightening lines of text.
Using optical text recognition, ProcessMaker IDP converts scanned documents into editable text. Then, the documents are classified by document type and the information is checked for completeness. Relevant data is extracted from the documents and validated automatically. For extraction, we can use pre-trained machine learning models, regular expressions or a combination of both. A special approach is required to extract data from tables, this is called table extraction. Our solution also takes into account the requirements of the GDPR by automatically anonymizing personal data as part of the processing.