Introduction
Financial organizations have to deal with a vast amount of documentation such as bank statements, loan applications, insurance claims, invoices, customer identification and contracts. Traditionally, this is done manually by staff reading, sorting, checking and adding the information to different financial systems. This can be effective however as the volume of documents increases, this can cause lengthy processing times and increased costs. By utilizing Intelligent Document Processing, financial organizations are able to extract the relevant information from documents with the use of Artificial Intelligence and document automation. This minimizes the manual input required and helps improve efficiency and customer experience.
Intelligent Document Processing within Finance
Intelligent Document Processing is a form of technology powered by Artificial Intelligence. It uses Optical Character Recognition (OCR), machine learning and Natural Language Processing to extract, understand and identify important information from structured, semi-structured and unstructured documents. OCR can convert written or hand-written text into computerized text. Machine learning helps identify patterns to improve the accuracy of extraction. Natural Language Processing can understand the meaning of words, phrases and sentences. This means financial organizations can process their documents with ease. Intelligent Document Processing systems can identify document types, extract relevant information, identify any inconsistencies and add the validated information to financial systems.
10 Ways Intelligent Document Processing Can Help Automate Financial Documents Using AI
1. Bank Statements
Banks and financial organizations will often look at bank statements to understand customer activity, ensure they are earning the correct amount of money and understand their financial history. This can be a lengthy process when there are many pages and different transaction types which can be difficult to identify. Intelligent Document Processing can help by identifying the account number, date of transaction, deposits, withdrawals, balance and other relevant information. OCR can extract the text whilst machine learning can identify patterns within the transactions. This can then be added to financial systems for analysis. This reduces the repetition of data entry and makes transactions easier to find. It can also help staff carry out financial analyses quicker and with greater consistency.
2. Loan Applications
Loan applications often include many documents, such as employment, income, identification, bank statements and credit. Financial organizations will need to look through these documents to ensure applicants meet the criteria. Intelligent Document Processing can help automate this process by identifying personal information, employment details, income and other financial indicators. It can also help identify any missing information by comparing information from different documents. For example, if an applicant has mentioned a different income in their bank statement and employment document it can highlight this. This reduces the need for staff to manually look at documents. It can help lenders organize the documents and speed up initial analysis.
3. Insurance Claims
Insurance companies will often receive claims along with many documents. These could include medical bills, estimates, photos, accident reports, receipts and policy agreements. This can cause delays if staffs have to manually look through these documents. Intelligent Document Processing can help automate the process. It can identify document types, extract relevant information and match the documents to policy information. Machine learning can help identify any unusual patterns and Natural Language Processing can help understand descriptions. This will help insure companies organize the evidence they have received. IDP can also help identify any incomplete or inconsistent applications. This will mean claims teams can spend less time on simple tasks and more time looking into complex claims.
4. Invoices
Invoicing can be a large part of financial organizations, especially if they have many suppliers and business partners. Traditionally staff would look at the invoices and identify payment information. They would then compare to purchase orders and add to an accounting system. Intelligent Document Processing can help automate this process. It can extract supplier names, invoice numbers, payment dates, tax, description and total cost. It can also compare the information to purchase orders and delivery information to identify any discrepancies. This can also help categorize the invoice into different departments, suppliers or expense types. When integrated with accounting software, this reduces repetition, speeds up the process and helps organizations keep track of their accounts payable more accurately.
5. Financial Contracts
Financial organizations will have many different contracts. These could include loan agreements, supplier agreements, investment agreements, insurance agreements and customer service agreements. These can often be long with important dates, payment terms, obligations and legal requirements. Intelligent Document Processing can help by using Natural Language Processing and Machine Learning to extract relevant information. It can also help identify important clauses and organize the information into easily searchable documents. For example, IDP can extract interest rates, repayment terms, renewal dates, termination terms and obligations from agreements. This can help staff find the information they need quickly. It can also help with managing records. However, complex clauses will need to be looked at by legal professionals.
6. Customer Identification
Customer identification is an important part of financial services. Financial organizations will need to identify who their customer is before offering them certain services. Traditionally this would be done by staff looking through information such as passports, national ID, driving licenses etc. Intelligent Document Processing can help automate this process. It can extract customer information such as name, date of birth, ID number and expiry date using OCR. Machine learning can help identify document types and find any unusual inconsistencies. Additional verification can be used to check the authenticity of the documents. This can help reduce the time spent gathering information about customers. However, care must be taken when designing systems to ensure personal information isn’t compromised, reduce the risk of identity fraud and ensure any suspicious or unclear documents are reviewed by a person.
7. Compliance and Records
Financial organizations will have many requirements to ensure they have accurate documentation. Compliance teams will have to manage documentation such as customer due diligence, anti-money laundering, transaction investigations and regulatory. Intelligent Document Processing can help organize these documents. It can help identify document types, extract important information and categorize documents. It can also help identify any missing documentation or inconsistent customer information. Intelligent Document Processing can help identify any issues quickly. The categorization can help find the right documents quickly during internal reviews. Whilst it doesn’t replace compliance professionals, it can help provide support. It can help increase visibility of documents, reduce administrative tasks and ensure information is managed consistently.
8. Reporting
Reporting often requires information from different sources. This could include invoices, receipts, account statements, expense reports and transaction information. This can cause delays when manually looking through documents. Intelligent Document Processing can help extract information. It can also help organize the information into standardized formats. For example, it can identify the transaction amount, payment date, supplier and expense type. This can be added to accounting systems. It can also help identify any discrepancies between the transactions and documentation. Unusual entries can be passed onto staff. This will help finance teams produce reports quicker, improve clarity of financial information and give them extra time to analyze business performance.
9. Searching For Information
Financial organizations will have a lot of documents to deal with. This can make it difficult for staff to find specific information quickly. Traditionally they may have to search through folders, databases or individual documents. Intelligent Document Processing can help by extracting information from the documents, categorizing them and creating searchable documents. Staff can search for information using relevant terms such as customer names, account reference, invoice numbers, contract dates and transaction information. Natural Language Processing can also help improve searching. It can help identify related terms. This will help staff find the right documents quicker and easier. This can be helpful when looking for information relating to customer queries, financial investigations, internal reviews and regulatory. Improved information availability can also help teamwork between departments.
10. Integration with Financial Systems
Intelligent Document Processing can provide many benefits when integrated with current financial systems. Banks and other financial organizations will already have accounting systems, enterprise resource planning, customer relationship management, loan management and document management. Intelligent Document Processing can integrate with these using APIs. This can help extract information and pass it between systems. It can also work well with robotic process automation. Robotic process automation uses software robots to complete repetitive rule based tasks. Intelligent Document Processing can work well with this as it can extract information from documents that can then be used by robotic process automation to update, move and start approval processes. This can help create a more integrated financial process and reduce unnecessary admin.
Key Benefits of Intelligent Document Processing In Finance
Intelligent Document Processing can provide many benefits to the processes within financial organizations. The first benefit is increased speed. Documents can be categorized, analyzed and organized much quicker than manually. This can help speed up processes such as onboarding new customers, loan assessments, invoice approvals and insurance claims. A second benefit is reducing data entry. This will help staff focus on tasks which require professional judgment. Intelligent Document Processing can also help ensure consistent information. This is due to information being extracted using standardized processes. Documents can also be searched as they are created as digital documents. Intelligent Document Processing can help reduce repetitive admin tasks and improve data availability. Overall it can help organizations manage increasing volumes of documents.
Challenges and Risks of Using AI in Financial Document Processes
There are many challenges to consider when using Intelligent Document Processing. The first is incorrect information. This can happen if documents are unclear, scanned badly, have unusual formatting or incomplete information. This can cause incorrect figures or customer information to be added into financial systems. It is important to have validation by staff, especially with high value transactions, sensitive customer information and important financial decisions. Another issue to consider is data security. Financial documents will often include confidential information. Care needs to be taken to ensure documents are encrypted, secured with the correct access, stored securely and monitored. Other factors to consider are regulation, compatibility with systems, costs and training.
Human Oversight and Ensuring Accurate Information
Whilst many processes can be automated, Intelligent Document Processing should be used to support staff. Intelligent systems may not always understand the information. Important details could be missed and documents could be incorrectly categorized. Financial organizations should put in place validation processes to ensure information extracted is accurate. This can be done by ensuring information is accurate by comparing to reliable sources before important documents are approved. Low confidence results should be reviewed by staff. Any discrepancies highlighted should be investigated. Complex situations should also be handled by staff. Regular checks can be carried out to improve the accuracy of the system. This can be done by improving templates, updating the system and training the machine learning with appropriate examples. By combining automation with human oversight, a more reliable process can be created. Repetitive tasks can be completed by the system whilst important decisions are made by staff.
Conclusion
Intelligent Document Processing is changing the way financial organizations work. It can help speed up processes such as gathering, extracting, categorizing, validating and storing documents. By using OCR, Machine Learning and Natural Language Processing, financial organizations can automate processes such as bank statements, loan applications, insurance claims, invoices, contracts, identification and compliance. This can help reduce data entry, speed up approvals, improve information availability and reduce errors when using validation processes. To ensure Intelligent Document Processing works well, good data security, reliable integration with software, regular monitoring and validation by staff should be put in place. As financial organizations continue to use digital systems, Intelligent Document Processing will become more important. It can help create an efficient, accurate and secure process. The biggest benefit is combining AI with human expertise to improve the way financial information is managed.
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