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Key Applications for Human-in-the-loop Machine Learning in Lending

12 Oct 2021
Human with ML 100

Automation and artificial intelligence (AI) give fintech companies the ability to compete at scale with the big legacy financial institutions. At the same time, the COVID-19 pandemic has accelerated the pace of disruption in the marketplace as consumers embrace online banking alternatives.

AI is transforming the entire workflow process for fintech companies, from loan processing to data analysis. Technology is being used to automate repetitive tasks and facilitate faster decision-making – with fewer errors. One of the most powerful fintech solutions is human-in-the-loop (HITL) machine learning. In this type of AI, human experts validate a machine learning model on the fly.

HITL Facilitates Digital Workflows

Fintech companies increasingly rely on AI for data processing and automation solutions. HITL is deployed as part of a full-stack software solution that automates the process of classifying, capturing, detecting, and analyzing financial documents. Automating the underwriting process gives digital-first lenders the ability to make fast, data-driven business decisions.

As author Robert Monarch notes in his book, Human-in-the-Loop Machine Learning, human involvement is an essential part of the process of labeling raw data so that it can be used to train machine learning algorithms. Monarch says that supervised learning models almost always become more accurate when human experts are involved in the data labeling process.

Robotic process automation (RPA) applications that integrate HITL technology offer a clear competitive advantage for fintechs. The combination of machine learning and trained data scientists can, for example, be used to refine credit and risk models that account for different industries, geographies, and professions. This enables lenders to offer more personalized services and more competitive products.

Automation Is Fast and Accurate

Digital transformation in the financial services sector now routinely includes machine learning to automate tedious financial analysis processes while integrating human oversight to ensure accuracy and efficiency. One of the key benefits of HITL is its ability to eliminate manual review of documents.

Document automation enables financial institutions to use machine learning to recognize and process documents with better than 99% accuracy. Machine learning algorithms can be trained to analyze everything from mortgage applications and credit reports to bank statements and tax documents. In addition to decreasing customer friction, automation can help reduce the likelihood of decisions that lead to bad loans.RPA can automate once labor-intensive manual tasks. That means software can be used for everything from data capture to cash flow analysis. AI also automates indexing and organizing and helps to detect fraud. Potential problems, such as document tampering or unusual financial activity, can be flagged during an automated review and routed to a human underwriter for additional scrutiny.

Combining Human Analysis with AI

In the highly regulated financial marketplace, human experts are needed to ensure the integrity of the automation process. Best-of-class AI solutions enable financial institutions to use a combination of machine learning and human review protocols. Automation facilitates the analysis and processing of massive amounts of consumer data. It also helps to ensure compliance with organizational and governmental requirements.

Innovative fintechs are using HITL to gain a competitive advantage over legacy firms by automating the underwriting processes to reduce overhead and improve customer experiences. While legacy banks and financial institutions have been slower to adopt machine learning, they are now embracing the use of AI.

According to a recent paper published in the journal Strategic Change, fintech disruptors and legacy banks share a common concern that big technology companies, like Google and Amazon, will use their own AI capabilities to expand into financial markets. To maintain a competitive edge, legacy incumbents are experimenting with AI for incremental innovations to existing products and services, while fintechs are using machine learning technologies to develop new solutions.

HITL is the key for giving fintechs and traditional financial institutions the automation tools they need to create and analyze digital data streams. Combining artificial intelligence and human-in-the-loop validation enables financial institutions to use machine learning to improve the efficiency of their entire workflow process, from document processing to risk analysis. That ultimately translates into smarter decisions and better customer experiences.

Ocrolus believes strongly in the future of AI within the lending industry. If you’re interested in learning more about how you could leverage human analysis with AI, feel free to book a free demo here.