Intelligent Document Processing marks the ultimate convergence of OCR, Artificial Intelligence, and Machine Learning- a unified solution for advanced data processing. It involves data extraction from diverse sources, all managed on a unified platform. Decades ago, financial organizations couldn’t fathom such a technology easing their operations the way IDP does. In this blog, we will take you through the top six use cases of Intelligent Document Processing(IDP) in banking and financial services and understand why financial firms should transition from manual data entry and template-based OCR to advanced IDP solutions for document processing.
A deluge of documents and data
Globally, banks and finance revolve around astronomical volumes of data. Though hardcore financial documents with numerical data in debit, credit, profit, loss, and more are primarily structured, a monumental set of supporting documents is vital yet unstructured and perpetually changing. The diversity of sources, operational needs, and data types contribute to this lack of uniformity. The documents span earnings transcripts, filings, CRM data, emails, spreadsheets, texts, and presentations with images. These documents feature assorted data forms, such as texts, PDFs, images, graphs, and voice notes. Many key departments like accounts, sales, HR, and auditing across organizations critically depend on financial data for tasks like onboarding, customer management, insurance, and wealth management. The multiplicity of processes and document types further underlines the intricate interplay between structured numbers and adaptable documents in global financial operations.
Data repositories are growing with little yield.
A substantial volume of ambiguity and diverse data is present within the document repositories. Not just in content but also in format variations, all of which pose challenges in extracting valuable information and insights from them. As a result, numerous financial establishments possess inactive document archives holding unstructured or “dark data” due to inadequate tools and strategies for extracting, analyzing, and overseeing data.
Data from unstructured documents can be a rich source of knowledge. Businesses can use this data to get meaningful insights, reduce risk, increase user engagement, and optimize compliance. Extracting data manually from unstructured documents and processing and interpreting meaning from them requires tremendous time and resources and are immensely prone to errors. Moreover, neglecting unstructured data can result in knowledge gaps and many lost opportunities.
How can IDP benefit the banking and finance sector?
Effectively processing, managing, and integrating structured, unstructured, and semi-structured documents and data necessitates something much more than manual effort, surpassing the capabilities of traditional Optical Character Recognition (OCR) solutions. While legacy OCR, driving initial Intelligent Document Processing (IDP) systems, excel in processing structured documents, they rely on rigid templates or proprietary machine learning (ML) models that fall short in handling unstructured documents accurately.
The present AI-enabled IDP solutions, founded on Unstructured Data Processing (UDP) platforms, not only excel with unstructured documents but also encompass various unstructured data forms like audio, images, and video. Furthermore, these solutions often incorporate robust human-in-the-loop (HITL) functionality, seamlessly incorporating human involvement for model refinement and quality assurance.
6 key use cases of IDP in banking and financial services
Let us explore some use cases of IDP that are causing significant disruption in banking and financial services.
Processing bank forms
Banks are continuously processing a plethora of activities, such as withdrawals from retirement accounts, loan applications, and much more. Manual paper-based handling is time-consuming, very susceptible to errors, and demands substantial human input. Humans, as meticulous as they can be, face severe burnout when dealing with these applications. The result is a longer time to process and errors over time. In such processes, Intelligent Document Processing (IDP) technology is a whiz here as it can swiftly transform bank forms into digital documents that can be accessed within seconds. This software efficiently converts all types of data, whether in pure text or presented in tables/graphics, into machine-readable formats. As a result, it simplifies employees’ tasks, streamlining their work processes.
The lending process goes through 7 detailed steps. It starts with the pre-qualification process, loan application, and application processing, which involves checking for the accuracy, genuineness, completeness, and creditworthiness of the borrower. Then comes the underwriting process, credit decision by the firm, and quality check analyzing key variables and compliance regulations. Finally comes the loan funding after all the steps are checked off correctly. Firms take a minimum of 35-40 days typically to process and disburse loans, as every step is time-consuming and error-prone. However, by incorporating IDP and AI-based automated systems, game-changing fintech enterprises successfully fund loans within 10 – 30 minutes, shifting their focus on customer experience.
The entire credit application process, which involves painstaking manual labor and time-consuming document data extraction, is completely revolutionized in a whiz. Thus, with IDP taking over the automation of application forms, KYC processes, and more, it expedites the overall workflow and enhances data accuracy in banking services.
Processing mortgage documents
Processing mortgage documents involves a substantial amount of credit assessment. It necessitates a range of documentation, such as tax refunds, payslips, identification proofs, bank statements, purchase records, profit and loss statements, balance sheets, and cash flow statements. Again, IDP can consolidate all these diverse documents into a concise dataset that can be used for precise searching, extraction, and validation processes, all with a commitment to error-free results.
- Lease agreement: It is a similar case when it comes to processing lease agreements. Lease agreements involve a lot of data, legal fine print, complex terms, and unstructured documentation. IDP software can handle, extract, and process the data, recognizing interdependencies.
- Trust deeds: Trust deeds, similar to mortgage documents but with the presence of a neutral trustee, are important legal documents involving several details. Details of data such as trustor, beneficiary, trustee, individual details, property details, and complete payment specifications are present in the documents. IDP systems can extract and process the data accurately and expedite the process remarkably.
IDP in document processing automation
- Account initiation and closure– IDP software streamlines the account opening process by seamlessly integrating customer details into the core system. Additionally, banks employ robotic process automation (RPA) to modernize and automate account closure procedures, all within the same system.
- Data extraction from annual reports - IDP facilitates automated data capture from annual reports. Thus, it is critical to create insightful research reports to enable intelligent decision-making.
- Customer risk profiles and more– IDP also benefits post-loan termination document processing, generating customer risk profiles and KYC-based risk assessments. It also allows the transmission of paper-based data to AI-powered fraud detection algorithms, playing a pivotal role in detecting fraudulent financial activities. Additionally, IDP expedites the handling of financial product applications and billing forms, accelerating processing times and boosting productivity by up to 60%.
Tax return filing
Tax return filing and provisioning, including tax estimation for a business year, involve extensive data, ranging from structured ERP system records to unstructured physical documents like invoices, contracts, and memos. Even seemingly semi-structured spreadsheet data are considered unstructured because of the absence of inbuilt controls.
This work’s sheer data volume, evolving tax regulations, and time-sensitive nature make processing unstructured data in tax activities challenging. While tax math is straightforward, integrating structured and unstructured data from finance and tax systems complicates the process. Accuracy is vital in tax calculation to avoid financial loss, return errors, and reconciliation issues.
Intelligent Document Processing (IDP) solutions can extract and categorize data from diverse sources like W2 forms, invoices, receipts, and bank statements. Integrated with tax report forms, they auto-fill fields and compute tax estimates, filings, and returns, streamlining the entire process.
Banks factor invoices to offer working capital to businesses, minimizing risks compared to traditional loans. Factoring generates bank income while fostering connections with small to medium enterprises, potentially leading to future banking services engagement.
IDP benefits the processes of Accounts payable and receivable extensively. Invoices arrive in various formats requiring field identification matching with POs and receipts. IDP reduces costs by 80%, expedites payments tenfold, automates 3-way matching, detects exceptions, and prevents fraud, inefficiencies, and risks inherent in manual processing.
How can we help?
If you aim to unlock the latest power of unstructured data in your banking and financial processes, Saxon AI’s IDP solutions offer the perfect solution. With our cutting-edge data extraction solution, processing documents, harnessing unstructured data, and optimizing financial services can never be more seamless. You can now look after evolving customer needs and prioritize customer experience rather than fretting over document processing hassles. Try a demo today and drive your firm toward a future of innovation and excellence.
Follow us on LinkedIn and Medium to never miss an update.