Loan Automation and AI: Streamlining lending and borrowing with advanced technology
By Raghu Madiraju
Loan processing automation is an increasingly popular approach among today’s lenders and borrowers. As digital technology becomes more sophisticated and integrated into all aspects of life and business, the benefits of loan automation are now more apparent. The use of advanced technologies, such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and optical character recognition (OCR), have quickened the process while decreasing common errors associated with manual lending practices. But there continues to be a period of adjustment for all parties involved as this once manual task evolves into an entirely new dynamic.
From document classification and information extraction to rules stipulations such as credit scoring and loan-to-value ratios, advanced technology is used to expedite the application phase and underwriting of this traditionally drawn-out process. This process can be streamlined to its maximum potential when best practices are implemented appropriately and technology is integrated effectively.
Loan processing automation
Although it was hardly a household name at the time, the introduction of AI for loan processing was announced by the Federal Home Loan Mortgage Corporation (Freddie Mac) in 1993 as a test program that would seek to predict the likelihood of a customer’s loan’s defaulting. At the same time, the Federal National Mortgage Association (Fannie Mae) had been developing an automated underwriting system. Today, RPA and other automation tools are being employed throughout the mortgage processing phase of lending transactions. Companies such as Upstart, Better Mortgage, Lending Club, and Rocket Mortgage are examples of those that are successfully leveraging processing automation for their loan disbursements.
Various types of advanced technologies are becoming more involved in the automated loan process, perhaps most notably AI and ML. When utilized collaboratively, AI and ML algorithms help to analyze vast amounts of borrower data more quickly to determine creditworthiness, reduce the need for manual underwriting reviews, detect fraudulent activity, and analyze loan data to identify trends and patterns that can optimize loan portfolios and improve their performance. These algorithms are also used to analyze historical loan data, borrower information, credit scores, and other relevant factors to develop predictive models for assessing credit risk. In addition, AI techniques help to detect anomalies among loan applications and transaction data to identify potential fraud cases, thus reducing the risk of approving fraudulent applications. This helps to ensure fairness of the loan process and assists lenders in making more informed decisions. AI-powered chatbots manage customer inquiries through natural language processing and natural language generation to improve responses based on customer interactions for a more personalized experience. Automated extraction of relevant information from loan application documents such as income statements through OCR and other techniques can more accurately and efficiently impact the loan underwriting and decision-making processes.
RPA is now more commonly used for automating repetitive manual tasks and data entry responsibilities, which are more likely to be housed in cloud computing applications. It was announced recently that Lentra, a fintech company, will introduce a software-as-a-service (SaaS)-based loan management system on the Google Cloud Platform. The platform reportedly utilizes a microsystems architecture that brings various services together in an application that is independently deployable as opposed to a monolithic architecture.
Examining direct and indirect benefits of loan automation
Lenders and borrowers alike experience unique benefits when working with loan automation from both a direct and indirect perspective. Direct benefits for the banks include improved productivity and costs connected to better efficiency within all aspects of processing. Not coincidentally, enhanced data security with digitalized document processing reduces the risk of lost or stolen data, and better risk management using automation to reduce the risks of defaults is expected.
Indirect benefits to lenders (and thus direct consumer benefits) include improved transparency and customer experiences such as faster approval and funding wait times that more often result in better rates and terms. Lenders can also offer indirect benefits to their customers with more likelihood of improved credit scores as defaulting becomes less common and loan applications presumably become fewer. Moreover, customers assume greater financial security through quicker access to funds and the avoidance of late payments to creditors and other debts.
Identifying best practices to implement automation
Effective automation begins with properly assessing and evaluating the best available technologies today. There are several key factors that organizations can consider before selecting a technology:
- Objectives and requirements. Clearly define business objectives and requirements, including identifying pain points, desired results, and overall goals.
- Scalability and flexibility. Chosen technologies should be able to address expected workloads and adapt to changing business needs over time, such as increased transaction volumes and support for future enhancements.
- User experience. It’s important to evaluate the usability and intuitiveness of technology solutions to keep customers at the center of decision-making.
- Integration capabilities. Integration with existing systems and data sources, such as customer relationship management systems, will be necessary.
- Security and compliance. Customer data must be kept sensitive, private, and protected in accordance to regulatory requirements related to encryption and audit trails.
- Associated costs. Upfront costs such as licensing fees and ongoing maintenance expenses will be incurred.
Once technologies are chosen, creating a roadmap that outlines the required steps to implement the automation, including system integration, data migration, and employee training, will be an important next step. Likewise, it’s essential to establish metrics for success, such as processing time, error rates, and customer satisfaction scores, and track progress toward goals over time. Maintaining strong data privacy and security mechanisms is a constant focal point as well. Implementing encryption methods to protect sensitive data in transit and at rest along with robust multifactor authentication to verify the identity of users accessing any loan system is advised. Internally, role-based access will help control which employees can access which files based on job description and regular security audits can be conducted to identify potential vulnerabilities.
Incident response and data breach protocols should also be established components of any employee training program to enhance security. Helpful in-house training methods include mentorship, coaching, and training sessions with product vendors who can provide specialized teaching on specific protocols. Knowledge-sharing sessions that invite employees to present what they learned, and their experiences and best practices are also effective practices for improving internal communication. Regularly occurring systems backups and disaster recovery plans also extend the reliability of automated processing while ensuring business continuity in the event of system failures or cyberattacks.
Automation and the human element
Advanced technology continues to prove that it can effectively be integrated into all aspects of life and business to add convenience, security, and reliability. In the lender-consumer relationship, however, there is evidence supporting the need for the presence of human interaction and oversight. For example, automation might be unable to consider all nuances of a borrower’s situation or external circumstances such as natural calamities that could cause a lender to deviate from regular parameters.
It’s essential for today’s banks to ensure certain manual tasks will continue to complement their automation processes to provide customers with personalization, proper guidance, and follow-through. These ongoing manual tasks and human judgment calls include complex or unique loan scenarios for non-standard applications and the handling of extremely unexpected situations such as power outages that can render automation ineffective. Yet, the future of banking will most likely include the combination of automation and human involvement to meet customer needs.
About the Author:
Raghuram Madiraju is a seasoned professional with a diverse background in technology and business. Mr. Madiraju has more than 20 years of experience in building applications using both front-end and back-end technologies and is currently building solutions to business-critical problems using AI and ML technologies. With a passion for innovation and a keen understanding of market dynamics, Mr. Madiraju has successfully spearheaded numerous projects and delivered exceptional results throughout his career. Mr. Madiraju holds a master’s degree in computer science from Georgia Tech University. Connect on LinkedIn.
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