Loan application processing, one of the core processes in banking is complex and slow. Reason being, these services deal with large volumes of application documents on a day-to-day basis that need to be processed manually. This calls for the banks to look for process efficiencies. Fortunately, recent breakthroughs in artificial intelligence (AI) provide businesses with opportunities to automate manual processes particularly involving documents. AI-powered cognitive document understanding addresses major hurdles in the process.
What are the major hurdles in the loan application process?
Extended turnaround time
A customer applying for a loan usually expects quick sanctioning of the loan with minimum callbacks. The bank can enhance the customer’s experience by ensuring a quick turnaround of the loan application and disbursements, resulting in an increase in the number of loans processed and improved customer satisfaction as well.
No standardization of documents
Loan applications and supporting documents such as ID proof, address proof, age proof, bank statements, etc. are not necessarily standardized. Scanning and collating data from such documents to process a loan application can be tedious for a banker.
System integration
Integration of core banking systems and legacy systems can be intricate and challenging. Reason being, legacy systems were designed to handle structured transactions and not handle unstructured content.
Why the need for process efficiencies?
Lending business is competitive with steep business targets, bringing in process efficiencies using cognitive automation has a positive impact on the top line. Remodelling loan processing using digitization and automation techniques like machine learning, a sub-field of AI, will bring in process efficiencies improving top line and enhancing the customer experience.
How is Cognitive Document Understanding better than other approaches?
A Cognitive Document Understanding solution leverages machine learning to achieve Optical Character Recognition (OCR), Data Extraction, Document Classification, Content Classification, Relation Extraction, Data Validation and more. Unlike conventional rule-based systems or robotic process automation(RPA), machine learning involves learning from training examples that enable it to efficiently scale across different document formats.
Datoin’s Cognitive Document Understanding Suite.
Cognitive Document Understanding Suite utilizes Datoin Platform’s capability to build a robust system that solves most of the document related challenges in Loan Application Processing.
| • Supports most of the document formats and images formats |
| • Comprehensive document cleansing and preprocessing tools |
| • Adaptable Optical Character Recognition (OCR) |
| • Simple ML Model building tools for extraction, classification, and relationships. |
| • Pre-trained, ready to use models for common document formats |
| • Quick testing and data validation sandbox |
| • Easy incorporation of business validation rules |
| • Easy solution deployment, supports on-premise, and multiple clouds |
| • Faster training data creation tools |
| • An automated feedback loop for continuous learning |
Customers expect their loans to be sanctioned in the shortest period. A few progressive bankers have identified the possibility and have deployed machine learning to delight their customers. Lesser rejections of loan applications not only help in customer retention but also improve the revenue of the bank. The objective of machine learning applications is to speed up the process and not the complete automation of the system. With Datoin’s machine learning tools, one can optimize the loan application process and provide the organization with a competitive edge.