How does the financial sector benefit from intelligent document processing?

Unstructured data is still one of the greatest untapped potentials in companies. A Gartner study (2021) shows that over 80% of company data is unstructured. Unstructured data refers to information that is not organised according to a structured database format. Most companies should be aware of this, but at the same time little is done to actually utilise this data. A study by Deloitte (2019) revealed that only 18% of all organisations are able to take advantage of the unstructured data available. What are the opportunities for companies to close this gap?

The use of intelligent document processing offers particularly great opportunities to utilise the potential of unused data. A high degree of automation minimises the additional work involved. The possible applications are extremely diverse and can be used across all industries. In the following article, we will take a closer look at specific examples of the use of intelligent document processing, focussing more on applications in the financial sector.

What is intelligent document processing?

Intelligent document processing (IDP) is a modern technology based on artificial intelligence (AI) and machine learning that aims to automate and optimise the process of document processing in companies. This includes extracting, analysing and processing information from various types of documents, including structured, semi-structured and unstructured documents such as invoices, contracts, forms and emails. The AI algorithms used in IDP systems are designed to understand documents, recognise relevant data and convert it into actionable information.

IDP therefore offers numerous benefits for organisations, including a significant increase in efficiency in business processes that previously required manual document processing. This helps to reduce human error and improve the accuracy of data extraction. In specific use cases, it is possible to save 500 man-days per year thanks to IDP.

What are the benefits of using intelligent document processing?

Overall, intelligent document processing enables the automation of document processes, which leads to increased efficiency, cost reductions and better compliance with regulations. Specifically, five key aspects can be emphasised:

Cost savings: IDP automates repetitive tasks, resulting in significant cost savings by reducing the need for manual data entry and error correction.
Increased efficiency: The automation of processes leads to faster workflows and shorter processing times.
Accuracy: IDP minimises human error that can occur during manual data processing.
Compliance: IDP helps to ensure that all data is accurate, complete and transparent.
Analytical insights: IDP can provide data for analysis to gain better insights into trends and performance indicators.

How does the financial sector benefit from intelligent document processing?

The financial sector can benefit in many ways from the use of intelligent document processing. In accounting and bookkeeping, invoices, receipts and other financial documents can be analysed and relevant information such as amounts, dates and transaction details can be extracted. This significantly speeds up accounting processes and minimises errors.

Accounts payable accounting also benefits by automatically matching invoices with the corresponding orders and deliveries to ensure that no duplicate payments or errors occur.

The benefits of IDP can also be utilised in the compliance area. Thanks to intelligent algorithms and automation, it is possible to monitor documents and ensure that financial reports and compliance requirements are met.

What specific applications does intelligent document processing offer for the financial sector?

The following options are available for automatically extracting data from unstructured documents such as invoices, contracts or forms:

Optical Character Recognition (OCR): OCR technologies are used to recognise printed or handwritten text from documents and convert it into machine-readable text. This enables data to be extracted from different fonts and document types. OCR can be used in combination with other methods such as text recognition and layout analysis to extract data accurately.

Machine learning and artificial intelligence (AI): Machine learning models and AI algorithms are used in IDP systems to identify and extract data. These models can be trained to recognise specific information such as invoice numbers, amounts or supplier information from documents. Over time, they improve their ability to adapt to different document layouts and structures. This method offers high accuracy and scalability in data extraction.

To summarise, intelligent document processing can combine OCR and machine learning to extract data from unstructured documents. This enables organisations to extract information more efficiently and accurately from a variety of document sources. In finance, this can be used to analyse transactions or create comprehensive financial reports. By using these technologies, banks and financial institutions can search through huge amounts of data even faster and gain precise insights that would be almost impossible to gather manually.

Data classification plays a central role in intelligent document processing, as it ensures that the extracted information is assigned to the correct categories. There are various approaches to data classification.

Rule-based classification: In this approach, predefined rules and patterns are used to categorise documents. For example, certain keywords or layout features can be used to identify documents. This approach is relatively easy to implement, but requires regular updates as it is sensitive to changes in document structures.

Machine learning-based classification: Here, machine learning algorithms are used to recognise patterns and correlations in the documents. The system learns from a set of training data how to classify documents automatically. This approach can be more flexible and accurate as it can adapt to changes without the need for constant manual updates.

Hybrid classification: This approach combines rule-based and machine learning methods to utilise the benefits of both approaches. Rule-based rules can be used to initialise and improve classification, while machine learning models have the ability to adapt to changing document patterns. These hybrid solutions often offer a balance of performance and flexibility.

Overall, choosing the right method of data classification in IDP enables more efficient and accurate processing of documents, which can offer huge benefits across a range of industries (from finance to healthcare). The financial sector in particular benefits from faster and more accurate analysis of large amounts of data, allowing users to benefit especially in risk assessment and decision making. Automated classification and processing of documents also facilitates compliance with regulatory requirements and reduces manual effort. This leads to cost savings and allows financial institutions to focus on strategic and customer-centred initiatives.

Multi-lingual extraction in intelligent document processing is particularly important as documents are often written in different languages.
There are various ways to implement multi-lingual extraction:

Language detection: A basic method for multi-lingual extraction is to recognise the language of the document. This can be done using linguistic models and machine learning. After language recognition, the system can specifically access language patterns and dictionaries in the recognised language to extract the information.

Translation models: Another option is to translate documents into their primary language and then perform the extraction in the target language. This requires powerful machine translation models that can reliably and accurately translate the texts into the target language. After translation, extraction is performed in the target language, which ensures the consistency and accuracy of the data.

Multilingual models: Some advanced IDP systems use specialised multilingual models for extraction. These models are designed to process texts in different languages simultaneously and extract information. They are particularly useful when organisations work with a large number of documents in different languages and require high accuracy in extraction.

The choice of method depends on the specific requirements of the organisation, including the document inventory, the variety of languages and the desired extraction accuracy. In practice, a combination of these approaches may also be required to ensure efficient and accurate multi-lingual extraction in IDP systems. Financial users benefit from more accurate identification and classification of data from multilingual documents, enabling more reliable risk analysis and therefore more informed decision making. By combining different methods, they can both ensure regulatory compliance and effectively detect fraud attempts.

The following options are available for automatically extracting data from unstructured documents such as invoices, contracts or forms:

Optical Character Recognition (OCR): OCR technologies are used to recognise printed or handwritten text from documents and convert it into machine-readable text. This enables data to be extracted from different fonts and document types. OCR can be used in combination with other methods such as text recognition and layout analysis to extract data accurately.

Machine learning and artificial intelligence (AI): Machine learning models and AI algorithms are used in IDP systems to identify and extract data. These models can be trained to recognise specific information such as invoice numbers, amounts or supplier information from documents. Over time, they improve their ability to adapt to different document layouts and structures. This method offers high accuracy and scalability in data extraction.

To summarise, intelligent document processing can combine OCR and machine learning to extract data from unstructured documents. This enables organisations to extract information more efficiently and accurately from a variety of document sources. In finance, this can be used to analyse transactions or create comprehensive financial reports. By using these technologies, banks and financial institutions can search through huge amounts of data even faster and gain precise insights that would be almost impossible to gather manually.

What standard solutions are available?

There are a large number of standard solutions for intelligent document processing in the financial sector. These solutions are designed to extract, analyse and process documents such as invoices, contracts, account statements and other finance-related documents. Here are some of the standard solutions commonly used in the financial sector:

AvidXchange:
AvidXchange offers an IDP solution that specialises in the automation of invoice handling processes. It enables the extraction of data from invoices, authorisation of payments and seamless integration with accounting software.

Kofax:
Kofax offers a wide range of document management and financial document extraction solutions. Their platforms enable the processing of invoices, bank statements, tax forms and other documents to optimise the financial process.

ABBYY FlexiCapture:
ABBYY is known for its OCR and IDP solutions. FlexiCapture specialises in extracting data from a wide range of documents, including financial documents. It offers high accuracy in data capture.

Ephesoft:
Ephesoft is a company that specialises in document extraction and processing. Their solutions are capable of processing a wide range of financial documents and converting the data into usable information.

ReadSoft from Lexmark:
ReadSoft is an IDP solution from Lexmark that focuses on the automated capture of data from invoices and other financial documents. It offers data validation and compliance features.

Individual standard solutions can usually map part of the requirements of financial organisations. The advantages of standard solutions are the quick and cost-efficient implementation thanks to a prefabricated infrastructure.

However, standard solutions are usually not perfectly tailored to the requirements of companies, which often leads to compromises in extraction accuracy and processing speed. In addition, financial documents often contain sensitive information and special care must be taken when using standard software to ensure that the relevant data protection and security requirements are met.

Why is it worth using a customised solution for intelligent document processing?

As already described, standard solutions are often unable to fulfil all the criteria required by financial companies. Customised individual solutions can be adapted to all the requirements of the respective user group and offer significant advantages:

Customisation to specific requirements: Financial organisations often have very specific document processing requirements – be it invoice processing, credit checking or compliance monitoring. These requirements can be customised with individual solutions. The software can be developed to precisely match the processes and data formats required in the financial sector.

Greater accuracy and efficiency: By adapting to the specific needs of finance, customised IDP software can provide greater accuracy and efficiency in document processing. It can use special rules and algorithms to extract financial data more accurately and minimise processing times. This helps to reduce errors and increase productivity.

Integration into existing systems: Financial organisations often have complex IT infrastructures with existing systems for accounting, ERP, CRM and other processes. Customised IDP software can be seamlessly integrated into these existing systems to ensure smooth data exchange and consistent data management. This facilitates the automation of workflows and the integration of IDP into the existing IT infrastructure.

Improved compliance and security: The financial sector is subject to strict regulations and security requirements. Customised IDP software can be tailored specifically to comply with these regulations. This includes security mechanisms to ensure the confidentiality and integrity of financial data and the implementation of audit trails to facilitate proof of compliance.

Scalability and future customisation: Customised software solutions provide the flexibility to accommodate future requirements and changes in the financial sector. Depending on requirements, these can be expanded and adapted at any time to meet new challenges – be it by integrating new document types, adapting to changes in regulations or scaling to meet increasing document volumes.

Overall, customised IDP software solutions in the financial sector enable a higher degree of precision, efficiency and control over document processing. This in turn helps to improve financial processes, minimise risk and increase competitiveness.

If you have any further questions about the use of intelligent document processing, please do not hesitate to contact us.

New B2B commerce study

New B2B commerce study

New B2B commerce study

New B2B commerce study

How does the financial sector benefit from intelligent document processing?

What is intelligent document processing?

What are the benefits of using intelligent document processing?

How does the financial sector benefit from intelligent document processing?

What specific applications does intelligent document processing offer for the financial sector?

What standard solutions are available?

Why is it worth using a customised solution for intelligent document processing?

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