Whenever someone thinks of a corporate job, most of the people think of suits, huge office spaces, coffee, and paper (lots of paper). This idea has been burned into our minds since we were young. Overall, the idea that a corporate job is boring and made up of the same paper-intensive manual tasks like document processing and data entry where critical thinking is the last thing on a worker's mind is still around. Studies show that on average, office workers spend about 8 hours per week searching paper documents and inputting data to their digital system (McKinsey, 2012). This is even more true when thinking about credit analysts in equipment finance, where they spend an average of 12-15 hours per week on correcting/inputting data instead of focusing on analyzing the “good” deals on the pipeline. Addressing this challenge is key to achieving efficiency in equipment finance credit analysis, where time saved directly impacts deal quality and decision-making speed.

While this might have been true for most of our lives, recent technological developments like artificial intelligence (AI) & machine learning (ML) have created a new reality for credit analysts, one where routine tedious tasks seem less daunting. Optical character recognition (OCR) technology paired with AI agents have revolutionized how these manual time-consuming tasks are carried out. Now, with the click of a button, these technologies are able to read, recognize and extract all the relevant information a credit analyst receives in their inbox and input it into their CRMs seamlessly.

So, why is this important for credit teams? The excess manual review of documents creates a bottleneck on the deals that move into the pipeline. Teams spend disproportionate amounts of time reading, correcting, and entering data from invoices, contracts, tax returns, and bank statements. This diverts attention away from the main objective where analysts usually enjoy and should spend most of their time: analyzing risk, structuring deals and advising decision makers.

Now, let’s think back about the technologies that are currently being used to address this issue: Optical Character Recognition (OCR), AI Agents, and Machine Learning (ML). Some might already be familiar with these terms as the AI boom has taken the world by storm, but for those that may not be as familiar, here is a quick summary.

OCR in credit analysis is the main responsible for digitalizing industries paper trail at unprecedented speed. It reads, identifies, and converts different formatted physical & digital documents like PDFs, PNGs, JPEGs, etc into machine readable text, eliminating the traditional “copy & paste”. It also ensures speed and accuracy by capturing vital information for the credit process like: business name, income figures, contract terms and asset information. This tool is not only reducing the time your analysts spend reviewing data, but also reducing human error and fatigue that comes from manual entry.

OCR sounds great right? But this is only one side of the coin. Extracting relevant information from different sources is only the first step in making your application processing efficiency more effective; the next step comes from AI agents for credit teams. This term is the most recent in the non-stop wave of AI innovation. These agents are software programs powered by artificial intelligence that can act autonomously to complete tasks by receiving relevant information (inputs) and then making a decision based on a desired outcome. AI agents for credit teams help by analyzing the relevant data extracted from documents (inputs), classifying the documents and then mapping out the most important fields into other systems (CRMs). What’s the coolest part of all this? Agents learn by themselves, so as they mature over time, they start making better and smarter decisions.

The pairing of OCR and AI agents is as synonymous as Tom Brady & Rob Gronkowski teaming up for another Super Bowl and like them, they need a great coach behind. In comes Machine Learning in application processing, the brains behind the decisions being made. It's a type of artificial intelligence that allows computing systems to learn from data instead of being programmed with a set of strict rules. How does this work? In short, you give the code lots of examples (historical data) to train with and learn from in order to find patterns and make decisions with new, unseen data. In the application intake process, ML powers the decision-making logic that supports automating credit application intake, ensuring both accuracy and scalability.

Now that we have the basics out of the way, let’s dive into the actual benefits from integrating these tools into your automating credit application intake process:

Efficiency Gains

• Time Saved: What used to take up hours of day time for analysts is reduced to minutes.
• Shift in Focus: Analysts spend time in value-adding tasks like evaluating risk, engaging with borrowers, and structuring deals.
• Scalability: Teams are able to process more deals without having to expand their teams.

Quality Benefits

• Consistency: Data entry fields are captured unchangingly from different application sources (portals, emails, etc)
• Error reduction: Reduced human error when capturing data that may lead to missed opportunities
• Customer Experience: quicker credit decisions with transparent information builds trust for future deals.

Employee Experience

• Reduced Burnout
• Critical Thinking: Employees spend more time in mentally stimulating work instead of tedious manual tasks.

Strategic Advantages

• Competitive Edge: Reduced turnaround time to capture more and better deals.
• Data Compilation: Data captured creates opportunities to analyze data in the future to optimize credit decisioning & portfolio performance.
• Future Readiness: Digitalizing all information leads to possible future enhancements (auto decisioning, stress-testing, fraud detection, etc)

The credit teams of tomorrow are set to be the champions of changing the idea of the traditional “corporate” banking job. Their responsibilities no longer have to be occupied by mountains of paperwork and manual tasks. The technologies I talked about are meant to empower analysts by letting them focus on critical thinking, strategy, and relationship building thus, moving the needle from paper to pipeline.

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