Data Extraction in Lending: Use Cases, Documents, Best Practices

Loan applicants must complete many forms and applications, which means that lending institutions must manage a lot of paperwork daily to process loan applications. 

Data extraction calls for automation to accurately extract data from loan documents and make the overall loan application process seamless and efficient. It also speeds up the financial institutions’ decision-making process, enabling a reduction in loan application backlogs. 

Optical character recognition (OCR) and robotic process automation (RPA) are the backbone of accurate data extraction from lending documents. In addition to loan applications, people applying for loans also need to submit documents such as bank statements, credit reports, declarations of assets, and so on. Data from these documents also needs to be extracted accurately.

Understanding data extraction in lending

Data extraction is the process of accurately capturing critical information from documents. Data extraction is important for lending institutions as it enables them to understand whether an applicant is eligible for a loan based on the documents they have provided.

Staff members or personnel enter data into the system in a manual data extraction process. This process can lead to clerical errors, such as entering the wrong name or date or adding information in the wrong data field. Such mistakes can have dire consequences for lending institutions.

The manual data extraction process is also time-consuming. A loan application requires plenty of documents, and all of this information needs to be read and keyed in manually, which can lead to backlogs in processing loan applications.

Using advanced data extraction technologies such as OCR, RPA, and machine learning (ML), end-to-end document automation is achievable, from data extraction to data storage at the backend. 

The automated data extraction process makes an impact on a lending institution in the following ways:

• It removes the need for human personnel, as zero to minimal human intervention is required while processing documents, enhancing accuracy and eliminating errors.
• Predictive analysis using statistical models, machine learning techniques, and artificial intelligence (AI) can determine loan eligibility and assess the lender’s risk.
• The customer satisfaction rate increases as loan applications are processed without interruptions or complications, enabling companies to personalize services and resolve customer queries more quickly.
• Customer retention rate increases, organically opening doors for new business opportunities and increasing revenue at zero marketing cost.

Key documents in lending for data extraction

Lending institutions require many documents before processing a loan application to assess whether the applicant can repay the principal amount on time. These documents also help determine the loan applicant’s net worth and provide collateral security to the institution in case of a default.

1. Loan application forms

A loan application form is the most important form a loan applicant must complete. It includes financial information such as loan repayment terms, total loan amount, source of income, etc. This form will help the lending institution determine the eligibility of a loan application.

2. Credit reports

Credit reports contain a credit score that helps determine the loan applicant’s financial behavior. If the credit score is high, the applicant pays their credit card and/or loan dues on time. If the credit score is low, the loan applicant does not have a positive track record of paying their credit card bills and/or loan dues on time. 

Credit rating companies such as Experian and CIBIL generate these credit score reports based on the applicant’s credit card and loan transaction history.

3. Bank statements

Lending companies typically request bank statement records from loan applicants as part of the application process. These statements enable the institutions to evaluate the applicant’s monthly income and expenses, which in turn helps determine their eligibility for the loan.

4. Income verification documents

Income verification documents declare the source of income and total income generated monthly by a loan applicant. For example, documents such as pay slips or income verification letters help understand the applicant’s employment history. 

The profit-loss statement, balance sheet, and cash flow statement provide a comprehensive overview of a company’s financial performance.

5. Legal and compliance documents

Legal and compliance documents protect the rights of lenders and loan applicants by clearly defining the terms of loans. Lending institutions require registration documents and other licenses/permits from businesses as proof.

For individuals applying for loans, identity proof such as a driver’s license or passport must be submitted to the lending institution.

Challenges in Data Extraction for Lending

Let’s explore some of the challenges that lending institutions face while extracting data from documents.

1. Data quality and consistency

Loan applicants may submit incomplete forms or outdated documents to lending institutions.
Inaccurate or ambiguous documents need to be flagged through quality checks and standardized document validation processes to manage the risk of approving a loan to an ineligible applicant

2. Regulatory compliance

Lending institutions must ensure that they follow privacy regulations and that the loan applicant’s data is safe. Any collaborations with third-party vendors must be declared in advance, and robust data network security frameworks should be implemented

3. Data security and protection

Lending institutions also need solid network security for internal and external data. Data hacks, internal data breaches, data access to an unauthorized department, and many such activities must be met with strict penalties by the institution. Failing to do so might reduce customer trust and lead to a lawsuit

4. Complex document formats

Loan applicants can submit documents in different formats, such as scanned PDFs, digital PDFs, Excel, or document formats. Therefore, it is essential to ensure that the documents are collected and organized standardized to avoid any foreseeable error while extracting data from them.

5. Legacy systems integration

Integrating new-age software with legacy software might pose a massive challenge for lending institutions. As datasets become increasingly complex, legacy systems may not be suitable for processing data extracted from documents. Synchronizing old systems with new ones is a time-consuming and costly affair

Essential Tools and Technologies for Data Extraction in Lending

Lending institutions can use these technologies and implement automated solutions to capture and store data from various documents.

1. Optical Character Recognition (OCR)

OCR helps convert text from scanned documents or photos to machine-readable text. It extracts data from loan applications, photo identity documents, bank statements, credit reports, etc. It automates the digitization process of data extraction, reducing the time it takes for a lending institution to process a loan application.

2. Artificial Intelligence (AI)

In the context of lending institutions, AI can be a powerful tool for pattern recognition and structuring unstructured data sets, particularly in the data extraction process. 

This allows for seamless loan application processing, with AI extracting only the most relevant information from the documents. Additionally, AI plays a significant role in fraud detection, analyzing past transactions of a loan application, and determining the authenticity of the documents submitted.

3. Robotic Process Automation (RPA)

RPA helps automate rule-based and repetitive tasks involving one or more digital applications. It executes these tasks using bots. Lending institutions can use RPA to extract data accurately from internal and external databases. 

For example, if a loan applicant does not submit a credit report, RPA can be used to log on to a credit rating website and generate the credit report of a loan applicant. It reduces manual effort and speeds up the loan application process for lending institutions.

4. Machine Learning (ML)

ML develops self-learning and self-evolving algorithms to make predictions based on past data. A subset of AI, ML can help lending institutions predict the creditworthiness of loan applicants. Relevant data from historical loan data and customer profiles can help create a predictive analysis solution that allows lending institutions to manage risk.

5. Document Management System (DMS)

DMS is used to manage and store documents digitally. Scanned PDFs, digital PDFs, and other physical documents are digitized and stored on DMS platforms. 

Lending institutions can use it to locate and retrieve loan-related documents easily. It works as a project management tool where physical documents are stored and organized appropriately.

It may consist of features such as indexing, document scanning, workflow automation, etc., which help streamline document processing and data extraction processes.

6. APIs for Document Classification

Document classification APIs can play an important role in automating and streamlining various processes, such as loan approval, risk assessment, customer onboarding, and compliance for lending institutions. They can help identify the document type, auto-categorize it into single-page or multi-page documents, mark missing pages or pages with errors, and so on.

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Best Practices for Data Extraction in Lending

Below are some best practices a lending institution should follow to improve its loan application process.

1. Quality standards

Data from documents with complete information and accurate details are discarded in the initial part of the data extraction process. Predefined benchmarks help ensure that only documents with complete information are processed further for loan applications 

2. Updation of extraction tools

Document formats and data sources evolve constantly, so data extraction tools must also be frequently updated. It is crucial to extract precise line items, capture relevant data, and ensure they meet the validation checks.

With outdated systems, chances of inaccuracies and errors might increase, so regular maintenance and updating these technologies is a must for a lending institution

3. Rigorous security measures

A lending institution must prioritize tightening its data security measures in the face of increasing data theft, cyberattacks, and internal breaches.

Access to sensitive data must be limited to concerned departments, and more than one layer of security should be put in place to access extracted data from documents

4. Implementation of automation

Lending institutions stand to gain significantly by implementing automation in their operations. It streamlines document processing for loan applications and automates tasks like data entry and validation checks.

This reduces the need for human intervention, thereby minimizing the risk of errors and enhancing overall efficiency

5. Staff training

Employees should receive detailed training on security protocols, data extraction technologies, creating validation criteria, etc. Institutions must also ensure appropriate training is provided as technologies upgrade.

Operational Improvements Through Effective Data Extraction

Accurately extracting data from documents quickly leads to faster decision-making, improved customer satisfaction, and business growth for the lending institution. 

1. Enhanced decision-making

By accessing multiple data sets of extracted data, these institutions can analyze the data quickly and decide on loan approvals accordingly. This way, the institution ensures that data backs its decisions and minimizes the risk of payment defaults

2. Improved customer service

An effective data extraction process also helps raise customer satisfaction levels. Lending institutions can process large volumes of data at a speed that increases their growth opportunities.
By understanding customer requirements, lenders can also personalize loan offers based on the applicant’s track record. This helps foster and develop a meaningful relationship with the customer, i.e., the loan applicant

3. Increased operational efficiency

Automating data extraction processes using a tool like Docsumo improves accuracy and reduces manual personnel effort. National Debt Relief, for example, achieved 99% accuracy while processing debt settlement letters with Docsumo.
Another way of achieving operational efficiency is through faster processing times. PayU, a multinational fintech company, optimized its customer onboarding procedures by leveraging Docsumo’s APIs to automate data extraction from bank statements and identity cards

4. Cost reduction

Lending institutions also save money by automating manual processes in the data extraction journey. This leads to a decrease in human personnel and minimizes errors as well. Analyzing data from documents can also mitigate financial risk, further reducing costs for the lending institution

5. Compliance and risk management

Effective data extraction ensures the lending institution can access accurate and updated regulatory compliance and risk assessment information.
Through constant monitoring of trends, lenders can identify risks they might face and proactively act on them to adhere to regulatory requirements. This can help lending institutions avoid unwanted penalties and negative brand perception

Conclusion: Transforming lending operations through advanced data extraction

Automated data extraction accurately captures data from documents, mitigates risks through customer profiling, and enables companies to implement solutions to process loan applications rapidly. By leveraging automated solutions like Docsumo, lending institutions can improve their operational efficiency and provide themselves with a competitive edge.

Docsumo uses advanced OCR and intelligent document processing (IDP) to process loan applications with complete automation and minimal human intervention with over 99% accuracy. 

Sign up for a free trial with Docsumo to learn about data extraction process for the lending industry.