Streamlining Your Finances with AI for Document Processing

What makes finances tough? 

Complex calculations or regulatory compliance? Neither of these options is correct because it's the financial document processing that makes finances the most tedious one. The finance industry generates massive data, approximately 2.5 quintillion bytes every day from loan applications, transfer requests, invoices, financial statements, and tons of other types of documents. 

All these documents form the foundation of everything that the financial industry entails. 

Now imagine your team is grappling to sort, store, and process all these financial documents because they don’t have the bandwidth to handle the volume. 

They fail to personalize the loan applications, flag invoice errors, and understand the hidden trends in the financial statements. Won’t it create a havoc? It will. This is why AI development for financial document processing is taking center stage. 

Using advanced algorithms such as machine learning and deep learning, AI sets core financial team freedom from the need for mundane document handling enabling them to focus on strategic initiatives. 

Must Read: How AI Agents in Finance Are Transforming The Sector? 

What is AI Financial Document Processing?

AI in financial document processing refers to using artificial intelligence and machine learning to not only automate extracting the documents but also analyze and manage the information stored in them. By doing so, businesses reduce the manual data entry loan from their financial team and improve the accuracy of data handling. 

As financial data is growing exponentially and demands constant accuracy, progressive businesses adopting AI in financial document processing and achieving up to 99% accuracy. 

The key aspects of AI financial document processing are mentioned below. 

1. Optical Character Recognition or OCR technology is used in AI tools for finances in document processing to convert scanned images of text into machine-readable text data.  It is crucial for the digitization of paper-based financial documents. 
2. Natural Language Processing or NLP is crucial to interpreting and understanding the context of text in the document.  It helps in extracting the relevant information from the unstructured data. 
3. Machine learning algorithms are used in AI for document processing to learn from historical data, identify patterns, and improve accuracy over time.  These algorithms can handle both structured and unstructured data, collected from various sources. 
4. Data extraction engines are used in combination with machine learning models to extract specific data fields from the documents. These engines are often configured using pre-defined rules and patterns. 
5. AI document classification system is used to maintain order and classify a wide range of documents. They use a certain set of label data and classify the rest data accordingly with the help of deep learning models. 
6. Data analysis technologies of AI document analysis to identify and extract key financial data points from documents. 

How AI Can Streamline and Simplify Financial Document Processing?

The effective integration of artificial intelligence document management can allow you to streamline and streamline financial document processing in a myriad of ways. 

Automated Data Extraction

If you’re expecting your human resources to extract every bit of financial data from spreadsheets, reports, emails, business documents, loan applications, and online forums then you’re making the biggest mistakes as they can’t. 

Only AI document processing has this bandwidth. Through the use of Optical Character Recognition (OCR) and Natural Language Processing (NLP), it can extract relevant information from various financial documents. Collected data can be as basic as invoice date to as crucial as total revenue.  AI in document processing is already helping businesses save up to 90% of their efforts. 

Intelligent Document Classification 

Traditional systems struggle with documents that have complex structures, like nested tables, charts, or mixed content (text and images). Extracting specific data points within these documents becomes difficult and classification is another level of headache.  

AI in document processing can automatically categorize documents based on their content& structure. Machine learning and deep learning can easily distinguish between invoices, recipes, bank statements, and tax forms. 

Data Validation and Error Detection 

It’s shockingly true that data entry errors in procurement, supply chain, and other areas cost businesses over $600 billion each year.  AI algorithms can cross-check data based on predefined rules and can flag any data discrepancies. Different modalities such as invoices, images of bills, a bank statement, and even transcription can be reviewed for data validation. 

Handling Unstructured Data 

Advanced AI algorithms such as deep learning can even understand the context and semantics of unstructured data, collected from emails, reports, spreadsheets, social media posts, and many more. They can extract the unstructured data that don’t follow a strict template with full accuracy. 

Enhanced Search and Retrieval

AI-powered systems can index documents and make them easily searchable. This holds great significance for bands and financial institutes as they can quickly find relent transaction details spread across thousands of documents, resulting in quick audits. 

Cloud-Based Processing

AI document processing is often cloud-based and offers immediate scalability. If there is a sudden spike in document volume then it’s easy for financial institutes to handle the increased documents without any change in on-premise infrastructure.  One can scale up and down easily. 

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AI Applications in Financial Document Processing

AI in financial document processing is transforming how documents are handled and transformed. If you have trouble finding the ideal use cases of AI in document processing, the below-mentioned list is of great help. 

Also Read: Key Applications and Use Cases of AI In Different Industries 

Information Extraction 

Ever wondered by your traditional document processing fails to extract key points like names, amounts, and account numbers from financial data? It is because of the lack of  Optical Character Recognition (OCR) and NLP in document processing. 

AI uses both these to convert scanned documents to text and extract every crucial key data point.  Through these means, it improves the efficiency of data capture while significantly reducing manual data entry errors. 

Example- JPMorgan Chase (JPMC) uses AI in financial document processing for automated invoice processing. The tool scans invoices automatically and scans crucial information with zero human intervention. 

Document Classification

Imagine your Finance Manager needs a key metric, say return over 10 years, about a specific stock. But, they can’t as you have only the overall stock performance with no classification. Traditional systems only do mass categorization. They fail to categorize at a micro level. 

AI for document processing has machine learning that learns from historical data and categorizes available data, improving classification accuracy. As a result, you start automating document routing, eliminate manual sorting, and make data available for faster handling. 

Example: HSBC, a leading bank, leverages AI in document classification. Through this, it manages to classify customer correspondence (emails, letters) based on content. 

Anomaly Detection

Do you know that 60% of fraudulent loan applications matched the patterns of first-party fraud? If you’re missing out on any suspicious patterns or inconsistencies in financial documents then you’re risking everything. 

AI document processing can flag those concerning patterns for you and help you predict future frauds.   

Example: American Express utilizes AI to detect unusual spending patterns on credit cards. 

Regulatory Compliance

Even though adhering to applicable regulatory compliance is a must for the financial industry, nearly 53% of organizations are dealing with skill shortages. The constant upgrades in the compliances are making things a little more difficult. 

Intelligent document processing by AI can automate the extraction and analysis of data from regulatory documents such as KYV, AML, and many more. Deep learning can even extract complex data from multiple modalities. 

It helps businesses review past, existing, and futuristic regulatory compliance-related needs and make desired changes. 

Example- Bank of America utilizes AI to analyze Know Your Customer (KYC) documents. 

Invoice Processing 

The average per invoice processing cost is within the range of $1 to $21, which goes into millions for combined operations. Despite spending so much, you’re likely to have errors in invoices, if invoice handling is manual. 

AI development for fintech cuts down the time, effort, and cost of invoicing processing by using NLP models. These models can capture structured and unstructured data with full accuracy.  

Going beyond, AI models can even authenticate the invoices by cross-checking the existing data from historical data, helping you unearth any discrepancies. 

Example- Kofax uses AI for invoice processing to capture data from invoices and validate it against the purchase orders. 

Online Form Processing 

It’s shocking to know that the finance industry, a domain that claims to streamline funding through online applications, has a 75.7% online form abandonment rate. And, the reasons behind this are security concerns, form length, and upselling.

If you don’t want to lose a potential customer due to any of these reasons, start using artificial intelligence document management.  It enables you to generate customized field, automate the processing and tracking, route the forms to the relevant department, and even extract key data. 

Example- CitiBank uses AI for online form processing, sorting, and category-based classification. 

Text Summarization and Insights Generation

If you don’t want to waste crucial work hours on analyzing financial documents and generating summaries of key information, try AI agents. They have machine learning and NLP capabilities that help them extract crucial data from a wide range of financial documents. 

Use them to find out due loan payments, summarize complex financial reports, create a report of outstanding loans, and even prepare a chart of different stock performances. 

Example- Morgan Stanley uses AI to analyze market research reports and generate summaries for investment analysts. 

Must Read: AI Agent Development Company | AI Agent Development

Credit Risk Assessment

Do you know that the personal loan delinquency rate in the U.S. was around 2.68% as of Q1 2024 for all commercial banks? Human agents fail to assess the creditworthiness of a person because a lot of data has to be consumed to reach an outcome. 

Intelligent document processing can analyze financial documents (tax returns, bank statements) and credit history data to assess the creditworthiness of loan applicants over a single click. It can review the loan application, find out the red flags, and allow loan professionals to do data-driver evaluation of credit risk effectively. 

Example- Upstart, a Fintech company, uses AI for loan application approval and asses creditworthiness. 

Contract Document Ingestion and Preprocessing 

If you don’t want to lose up to 9% of your total revenue due to poor contract management then start using  AI document analysis. AI agents can perform preprocessing steps such as eliminating noise convert contracts into a highly machine-readable format. 

Through the use of NLP techniques, these agents can also help you identify and classify key entities such as names of parties involved, dates, locations, and currency amounts within the contract with full accuracy. 

Not only this; AI can segment the contract text into clauses and help you understand the hidden relationships between them. In a nutshell, you can create accurate contracts, have a better understanding of contract terms, and keep contract frauds at bay. 

Example- Goldman Sachs utilizes AI to review loan agreements, expedite the review process, and highlight key clauses for the legal teams. 

Multi-Language Support 

Imagine a document management system where you don’t have to translate each document for different geographical regions and conduct operations without eliminating language barriers. 

Well, this is possible using AI document processing. Machine translation models are trained on the massive amounts of translated text data and can help you have documents auto-translate in multiple languages. 

On the other hand, AI-powered OCR can convert scanned images into consumable text regardless of the original language. AI agents for document processing use NLP to understand the context and meaning of text in different languages. 

Example- Adobe Acrobat Sign used AI-powered machine translation to facilitate e-signature workflows for international contracts and agreements.

Document Routing

Do you want to experience loan processing delay because the right manager is not receiving the right applications? Misrouted documents are a significant bottleneck in loan processing, causing delays and frustration.

AI-powered document routing systems lever pre-defined rules to categorize a given set of documents. Advanced AI models can go beyond keyword look-up. They can understand the overall context and content of the documents for accurate routing. 

Example- Bank of America uses AII to analyze incoming customer correspondence (emails, letters) and route them to the relevant department for quicker resolution.

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Benefits of AI Models in Financial Document Processing

Using intelligent document processing enables businesses to improve the quality and accuracy of data handling in a myriad of ways. 

Increased  Efficiency and Cost Savings 

AI in financial reporting can cut down the processing time by 60% while saving 25,000 reword hours. As AI models can learn from past mistakes and avoid making the same mistakes in the future, the account team doesn’t have to rework the same document. 

Better Accuracy 

Through the use of continuous learning, advanced OCR, and automated data extraction, AI in financial services can improve data accuracy and prevent bearing the extra burden of $878,000 per year because of human errors. 

In addition, ML algorithms can also enable customization based on specified needs, resulting in boosted efficiency. 

Intelligent Decision Making 

AI proffers data-driven insights and analytics by extracting structured data from unstructured documents while finding hidden patterns and correlations. With the use of preventive modeling, AI even assists in strategic palling by forecasting the outcomes. 

Improved Customer Experience

AI automates data extraction from documents such as loan applications, invoices, credit card payments, etc. Superior service providers of the finance industry don’t have to wait to acquire this information. As a result, the response time increases drastically and customer inquiries are answered without any delays. 

This high-level automation enables employees to focus on higher-level priorities, resolve customer queries quickly, and improve their overall experience. 

Reduced Risks 

The value of bank fraud has increased 10 times in the last 10 years. AI in financial document processing can reduce fraud risks by examining past transaction records and spotting any anomalies. 

Machine learning models can identify even a slight shift from the normal document process and flag fraudulent activity faster than humans. 

Fraud Detection 

A recent study just revealed that the cost of global banking fraud is likely to reach at USD 48 billion by 2029. The growing banking fraud number is a major concern for this industry and AI document processing enables players of this industry to combat this challenge. 

It can detect any anomalies in transaction records, customer data, financial documents, and financial reports using machine learning algorithms. They can identify any forged signatures or mismatched data in text and image data, enabling early fraud detection. 

Predictive Analytics

AI for document analysis can process different types of modalities to provide insights and forecasts. They can produce the cash flow based on the processed invoices and can help you predict the total revenue. This predictive aids in futuristic and full-proof financial planning and proactive decision-making. 

Easy Scalability 

AI models can be trained on a massive dataset of documents including invoices, contracts, reports, and many more with labeled and unballed data. They can identify patterns and relationships within the documents and refine their ability to extract information and categorize documents accurately.

AI models like machine learning and deep learning can continuously learn and improve. Hence, they can adapt quickly to changing document formats and data patterns. This ability of AI document processing is crucial for scalability as businesses will have a recent document processing system without any downtime. 

Higher Data Security 

AI-powered document processing can integrate added security features such as cutting-edge encryption, biometric verification, and voice-commend authentication in certain high-value and high-risk documents. Automated document processing also reduces the risk of unauthorized access and controls the incidences of data breaches, which are very common in manual document handling. 

Not only this, AI document processing tools can monitor unusual activities across the documents and flag potential security threats in real-time.  These systems will send immediate alerts to the team and even activate a prevention mechanism to avoid any hassle. 

Reduced Penalties 

AI document processing ensures that key documents adhere to applied regulatory compliance by all means possible. If a loan application or any other financial document is not adhering to regulatory requirements then AI document analysis can flag the error immediately, reducing compliance risk and associated fines. 

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How Does Ampcome Help in Building AI Models for Financial Document Processing?

Progressive businesses are using AI for financial document processing and are automating loan applications, KYC compliances, invoices, and financial report generation. If you wish to free your human capital to focus on high-value activity, it’s time to hire AI development services. 

Ampcome goes beyond the ordinary. Its AI development team has an unparalleled understanding of machine learning, deep learning, and Natural Language Processing (NLP) models and ensures that you don't settle for generic solutions. 

We design and build bespoke, intelligent document processing systems tailored to your unique workflow needs. Through our AI document processing systems, you can extract key data from complex financial documents, boost operational efficiency, and experience exceptional data accuracy. 

We provide AI agents with continuous learning and adapting abilities so that they can scale at any time, work with Nova data with ease, and continue automating new documents. 

So, don’t just process documents. Automate and revolutionize documents with the transformative power of AI in financial document processing with Ampcome. Contact us today for a free consultation and discover how AI can revolutionize the way you handle financial documents.