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What is credit decisioning?

Credit decisioning, also known as the credit/loan approval or decline process, is a critical step in the loan or credit process.

Business owners and credit professionals must consider multiple factors before extending payment terms to a new customer or increasing the credit limit of an existing customer.

The process requires an objective evaluation of the potential borrower's creditworthiness, taking into account the Four C's of Credit Granting – character, capacity, collateral, and capital. It is also essential to maintain impartiality. This can be a challenging task, but a crucial one to ensure that the credit granting process is fair and accurate.

How is automation redefining credit decisions?

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Photo by Kostiantyn Li / Unsplash

Conventional loan/credit processes involve both the credit applicant and the credit provider navigating numerous application forms and documents, which results in long approval or denial decision times.

Even if the loan or credit process has a digital front end, it entails many manual processes and piecemeal methods of collecting data at the time of credit evaluation. These labor-intensive models are costly for companies seeking to expand and often miss out on potential borrowers without a documented credit history.

Additionally, prolonged waiting times lead to higher abandonment rates, low customer satisfaction, and reduced overall business value for stakeholders.

With the ongoing advancements in big data, digital tools, and intelligent analytics, credit providers have new opportunities to enhance their credit decisioning models through automation.

The implementation of automated credit decisioning models offers numerous advantages for financial institutions. By automating routine tasks and streamlining the approval process, loan officers are freed from the administrative burden, and loan applications can be processed quickly and efficiently.

This results in a more objective, traceable, and transparent credit decision process. The automated high-performance models enable credit providers to define lending parameters and distinguish between creditworthy and non-creditworthy customers. The result is improved approval rates for qualified borrowers and reduced risk for the bank.

Benefits to automated credit decisioning

Many credit providers such as banks and other financial institutions have trouble updating their credit decisioning protocols due to a lack of data, simple analytical tools, reliance on personal opinions, and reluctance to change.

Manual labor-intensive decisioning processes are no longer useful, given the vast expanse of data required for the process. Furthermore, manual credit decisioning runs the risk of being subjective and not precise enough. For example, these methods might treat all restaurants in, say, New York as a high risk for default without considering numerous factors that are at play today, such as floating population, effects of pandemics, cultural connotations, etc.

The credit decisioning process has been revolutionized by digitization and automation, through the integration of new data sources. Automation also provides a better understanding of customer behavior, opens up access to new markets, and allows for a more agile response to shifts in the business environment. This, in turn, allows credit providers to deliver better customer service, expand their operations, and stay ahead of competition from fintech firms and new banks.

The rewards of implementing high-performance digital credit decisioning tools are substantial.

  1. Revenue Boost: The automation models can significantly increase revenue through higher acceptance rates, lower cost of acquisition, and improved customer experience. Automated credit decisioning can improve a credit provider’s ability to approve loans to trustworthy customers and cut costs by streamlining the assessment process, making it quicker and more efficient.
  2. Decreased Credit Loss Rates: Companies can see significant reductions in their credit losses by using models that more accurately determine customers’ likelihood to default, impacting the levels of provisions and capital a bank must hold.
  3. Improved Efficiency: The implementation of digital tools can increase efficiency through automation of bank data extraction, prioritizing cases, and improved model development.

Best practices for credit decisioning automation

The following four strategies can guarantee the efficient automation of the credit decision process: implementing a modular architecture, expanding the range of data sources, mining data to identify credit signals, and leveraging human expertise.

Implementing a modular architecture

A modular architecture is crucial for the effective automation of the credit decision process. It involves creating multiple submodels based on data coverage and industry differences, which are combined to form a single credit signal. This architecture offers flexibility to incorporate new data sources, react quickly to market changes, and identify new growth areas by providing a more comprehensive analysis of customer behavior. Implementing this approach requires coordination among the business, model-development team, and model-maintenance team to validate assumptions and avoid overlapping data. By combining data signals from all customer interactions, a higher-performance model can be achieved.

Expanding the range of data sources

Automated credit decisioning can leverage a combination of internal and external data sources to improve the accuracy of credit signals. This includes integrating traditional credit data, with other non-traditional external data sources such as social media information. The data can also be complemented with subjective insights from the underwriters. It can enable open banking and provide a more comprehensive view of the customer through the inclusion of transactional data from multiple banks. The incorporation of nontraditional external data sources such as social network information provides additional insight into individual behavior and financial status, all of which can help with the credit decisioning process.

Identifying credit signals

Machine learning and artificial intelligence models used in credit decision processes can identify specific variables from various data sources to derive credit signals. Various methods such as traditional transformations and ML techniques have excellent predictive power that can help with the credit decisioning process.

Leveraging human expertise

Automated credit decisioning utilizes advanced techniques such as machine learning (ML) and artificial intelligence (AI) to analyze and extract highly predictive credit signals from existing data sources. However, it is important to note that relying solely on statistical methods is not enough to achieve a robust and high-performing model. The involvement of internal business expertise is crucial to understand missing credit signals and identifying and validating new credit signals. For example, model designers should collaborate with underwriters and relationship managers to incorporate their insights and real-life experiences with customers into the model-development process. This can be achieved by incorporating qualitative questions that reflect the credit issues identified by these business experts. Additionally, these experts can validate credit signals based on their understanding of bank processes, compliance, and industry knowledge

How to automate credit decisioning?

Automated credit decisioning can be adopted by a wide range of companies, including financial institutions, lenders, and other organizations that need to make credit decisions for their clients.

This includes traditional banks, credit unions, alternative lenders, fintech companies, and other lending institutions. Additionally, any company that provides credit or loans, such as auto dealerships, furniture retailers, or mortgage companies, can also benefit from automated credit decisioning.

Automated credit decisioning can help these companies streamline their credit decision-making process, improve the accuracy of their assessments, and increase the speed and efficiency of the credit provision (or denial).

By following a five-stage, flexible approach, credit/loan providers can implement a new credit-decisioning model within a few months. This process involves the following stages:

  1. Credit Model Review: Examine the existing credit model, assessing its methodology, performance, and utilization to spot potential areas for improvement.
  2. Credit Scoring Model Assessment and Design: Assess the current state of data preparation, identify readily available data sources for modeling, and create a plan for incorporating them. Evaluate model performance across different segments and compare it to that of peers to identify areas of weakness.
  3. Data Preparation and Processing: Prepare the data for modeling by formatting, testing for completeness, and handling missing values and records.
  4. Development of Next-Generation Credit Scoring Models: Develop a production-ready, minimum viable product that typically requires three cycles of modeling, each lasting two weeks and incorporating feedback from experts and analysts.
  5. Integration of Credit Scoring in Lending Transformation: Automate the lending processes and update the credit decision-making model with the new credit scoring models.

The role of intelligent data extraction tools in credit decisioning

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Automated data extraction and intelligent OCR (Optical Character Recognition) tools play an important role in the automated credit decisioning process.

These tools are used to extract information from various sources such as financial statements, invoices, and other documents relevant to the credit decision. The extracted data is then fed into the credit decisioning system where it is processed and analyzed to determine the creditworthiness of the borrower.

Intelligent OCR tools are designed to recognize and extract information accurately, even from complex or unstructured documents, which helps to improve the efficiency and accuracy of the credit decisioning process. By automating the data extraction and OCR processes, businesses can reduce manual errors, speed up decision-making, and improve the overall efficiency of the credit decisioning system.

Nanonets, for example, is an intelligent OCR platform that can help with automated credit decisioning, Nanonets can be used to digitize a borrower's financial data, such as credit history and income, which can then be used to predict their creditworthiness.

Nanonets uses a variety of algorithms, including deep learning and computer vision, to analyze data from multiple sources and make predictions about a borrower's ability to repay a loan. The platform also allows for customization of the decisioning process, so that financial institutions can tailor their credit decisioning policies based on their specific requirements.

By automating the credit decisioning process, Nanonets can help credit providers and financial institutions make more accurate and efficient decisions, reducing the risk of loan defaults and improving overall loan performance. Additionally, the platform can help reduce the time and cost associated with manual credit decisioning, allowing for faster processing of loan applications.


Automated credit decisioning has the potential to revolutionize the way financial institutions approach loan applications. By using advanced algorithms and machine learning techniques to analyze vast amounts of data, the technology can provide more accurate assessments of a borrower's creditworthiness, reducing the risk of loan defaults and improving loan performance.

Automated credit decisioning also has the advantage of being faster and more cost-effective than manual methods, allowing financial institutions to process loan applications more efficiently and serve more customers. As technology continues to advance and the demand for faster, more reliable loan automation processing grows, it is likely that automated credit decisioning will become an increasingly important tool for financial institutions looking to stay ahead of the curve.