Artificial Intelligence and Loan Management Systems

Introduction 

Loan management systems are platforms that help to automate the process of loan application from collection to payment. The entire process of loan management, including verification of credentials from applicants, calculation of interest rates and monitoring the process of loan payment is used by banks, lenders and other financial institutions to facilitate loans. These multiple steps present various opportunities for automation and loan management systems offer less cumbersome and time consuming processes when compared to traditional practices. Lenders and borrowers have already begun to embrace automation and research showed that 61% of loan documents as far back as 2019 were digitally generated. To continue on this path, software to achieve further automation is in high and growing demand with a market expected to grow by a CAGR of 12.19% to $4.812bn by 2028. 

AI and Loan Management Systems

When employed by lenders, loan management systems have been found to reduce human error, shorten processing time and generally save costs to increase revenue. To further these strides, lenders are introducing AI empowered software to revolutionize the way that borrowers are accessed for loans. It is estimated that around 15% of AI solutions developed for banking are channeled towards lending services. These services are geared towards solving problems at various steps of the lending process:

Credit worthiness: Perhaps the most important step in deciding whether or not to grant a loan application is the determination of an individual or company’s ability to repay the loan. To predict this, lenders have commonly turned to reviewing documents provided by potential borrowers rigorously to assess how likely they are to default on payments. Unfortunately, this relies heavily on the accuracy of information provided and may not always be a true measure of risk. Recently, a startup called Lenddo developed an application which can be downloaded onto a potential borrower’s phone and is capable of analyzing multiple variables including social media activity, search history and other information. Although this information is not shared with lenders to protect user’s privacy, a credit score is generated from extensive analysis and can be used by lenders in the process of decision making. 

Fraud Monitoring: Apart from evaluating for credit worthiness, verification of identity, employment history and other information is integral to the loan process. Already, digital lending platforms are employing machine learning techniques to observe customers and identify cues in behavior that may point to fraudulent activity. This is especially important for customers with little credit history available for review and is carried out in line with regulatory policies and recommendations. 

Reduction of operational costs: As the size of the industry grows, it will become necessary for the system to be able to verify basic information such as identification to ensure authenticity. Human workers will then be able to focus on verifying more complex information such as references, employment and other important information. Doing this will reduce the amount of time spent during verification and will also reduce the cost of that aspect of loan servicing. 

Storage: Data collected during all these processes must be safely stored and at the same time easily accessible for retrieval and review when necessary. Storage  must be carried out while maintaining cognizance of the sensitivity of financial records. In line with this, systems must be safeguarded against theft and wrongful use of customer’s’ information.

General AI tools: Other general AI tools have been applied to improve the customer experience and encourage participation in the industry. Amazon, for instance, has developed programs to assess the activities of small business owners and advertise suitable loans to enable them to grow their businesses. Conversational AI agents are also used in chatbots to address questions and concerns made by customers. 

Conclusion 

Gradually, lending services are becoming less exclusive to banks and other large financial institutions and coming under the interest of small start ups and other businesses. Growth of the industry will therefore be dependent on advancements which will function to improve the ability of all establishments, regardless of manpower and size to carry out efficient processes while saving cost on operations. Continuous research and widespread implementation of existing solutions will go a long way to increasing revenue and creating sustainable growth in the industry. 


References

https://appinventiv.com/blog/build-loan-management-system/

https://www.finextra.com/blogposting/20688/use-of-artificial-intelligence-in-banking-world-today

https://appinventiv.com/blog/ai-in-banking/

https://www.visartech.com/blog/loan-application-development-guidelines/

https://www.alliedmarketresearch.com/loan-management-software-market

https://emerj.com/ai-sector-overviews/artificial-intelligence-applications-lending-loan-management/

https://www.birlasoft.com/articles/top-use-cases-of-ai-in-lending-for-banks-to-embrace

https://appinventiv.com/blog/technology-changing-digital-lending/



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